University of Southern California Dissertations and Theses

GM TV: sports television and the managerial turn

(USC Thesis Other)

GM TV: sports television and the managerial turn

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content

GM TV:
SPORTS TELEVISION AND THE MANAGERIAL TURN

By
Branden Buehler

Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
SCHOOL OF CINEMATIC ARTS
in partial fulfillment of the requirements of the
DOCTOR OF PHILOSOPHY
IN CRITICAL STUDIES

August 2016

Copyright 2016 Branden Buehler

ii

Acknowledgements

As this dissertation represents the culmination of innumerable conversations and
experiences, there are many people to thank for their assistance along the way. To begin, I need
to express my sincerest gratitude to my advisor, Tara McPherson, for being an absolutely
wonderful mentor. Always a source of support and guidance, Tara has provided a model of
wisdom, generosity, and positivity that I will strive to emulate going forward.
The other members of my committee, too, have all been inspiring mentors. Anikó Imre
has provided crucial feedback throughout the course of this project and continually asked
incisive questions that I will continue to chew on long after this dissertation is submitted. I
cannot thank Vicky Johnson enough for serving on this committee across county lines. It was
her scholarship that first made me realize that a project like this was possible, and I have been so
lucky to be able to turn to her for advice over the last few years. Throughout my time at USC, I
have deeply appreciated my many conversations with Virginia Kuhn. She, too, has provided an
invaluable model of instruction that I will take with me in the coming years. Ellen Seiter has
generously offered both encouragement and advice both before and during the dissertation
process. It is hard to imagine my USC experience without her guidance.
My time at USC has also been deeply shaped by my fellow Ph.D. students, particularly
Roxanne Samer and Raffi Sarkissian. I am also very appreciative of my Critical Studies cohort
for their continued friendship, especially Nik Bars, Emma Bernstein, Nicholas Emme, Rosie
Levy, Charlie Porter, Brett Siegel, Jeremy Tipton, Jason Voss, and Katie Walsh.
I also want to acknowledge my parents, Charles Buehler and Barbara Wells, as I could
not have made it to this point without their love and support. And, finally, I want to express my
iii

thanks to Laura Kohler for her love and constant encouragement. I am so incredibly fortunate to
have you by my side.

iv

Table of Contents

Acknowledgements ii

Introduction 1

Part I 23
Chapter One: Managerial Television 23

Part II: Datavisuality 82
Chapter Two: The Rise of Datavisuality 87
Chapter Three: Critiquing Datavisuality 165

Conclusion 202

Bibliography 225

1

Introduction

“The Cult of the General Manager”
In 2011, the film Moneyball became something of an unexpected hit – pulling in a
worldwide box office of $110 million on a budget of $50 million.
1
This success was not only a
surprise because sports movies have recently failed to do well at the box office, but also because
of the exact sports subject the film was dealing with – baseball analytics. Indeed, the project had
long met skepticism as it slowly lurched through the development process. Based on a 2003
Michael Lewis book of the same name, the story centers around the Oakland Athletics general
manager, Billy Beane, and his attempts to field a competitive team on a bare-bones
budget. Unable to rely on traditional mechanisms for success, like the acquisition of high-priced
free agents, Beane turns to new statistical measures to find under-valued players. While the
book was a best-seller, as Noel Murray of The A.V. Club opined in 2009, “Very little about its
true story of stat-heads transforming the business of baseball screams ‘movie.’”
2
Similarly,
Vulture’s Lane Brown expressed extreme skepticism a film studio would actually spend $50
million “on a film about baseball statistics.”
3
Nonetheless, the film eventually made it to the
screen – with analytics still included. And, as mentioned, the film became the rare hit sports
movie, not only raking in box office dollars, but also receiving an “A” grade from CinemaScore
and garnering six Academy Award nominations.
4

While the success of Moneyball may have come as a surprise to the many film industry
skeptics who had assumed asset valuation did not make for a compelling plot device,
Moneyball’s ability to reverberate with American audiences had a certain amount of
predictability. Over the past few decades, sports media has become increasingly fixated on front
office maneuverings. Indeed, it is because of this trend that audiences likely embraced Brad Pitt
2

playing a general manager rather than a fading athlete looking for one last chance at glory. Team
owners and general managers (GMs) – essentially operating as the owners’ proxies – are, in a
sense, the new glamor positions of the sports media world. Sports fans no longer just yearn to
make millions soaring up down a football field, but also by putting together the team that gets
there. Indeed, 2014 saw Kevin Costner also play a general manager, anchoring Draft Day as the
leader of the Cleveland Browns’ front office. Later that same year, Jon Hamm starred in Million
Dollar Arm – again, not as a player or a coach, but rather as a smooth-talking agent scouring
India for raw baseball talent. As critic Willie Osterweil summarized, “There’s a new breed of
sports movie in town.” He continued, “These films celebrate the real heroes of sports, the real
heroes of any workplace: the bosses.”
5

Sports movies, too, have hardly been the only example of sports media texts shifting their
focus from the playing field to the front office. Turning to videogames, for instance, it might be
observed that one of the most routinely popular sports games franchises across the world is
Football Manager, which places its players in charge of a soccer team, and in the process,
eschews action and instead tasks players with duties ranging from scouting to roster management
to payroll accounting. Other massively popular franchises, including FIFA, Madden and NBA
2K, also now include managerial modes. Madden, for example, has included an owner mode
that asks players to consider issues like finances and marketing. In fact, the game mode has even
asked players to set the price of team merchandise like hats and sweatshirts. Perhaps even more
significant than the introduction of managerial modes to sports videogames, though, has been the
ever-increasing popularity of fantasy sports, which place participants in control of virtual rosters
of athletes. According to Fantasy Sports Trade Association data compiled by STATS, an
3

estimated 56.8 million people now play fantasy sports in the USA and Canada – up from 12.6
million in 2005.
6

Looking to other forms of sports media, it can also be observed that sports radio and
sports podcasts are turning their interest to off-field maneuverings. Examining the rankings of
the most popular sports podcasts, one can find numerous podcasts devoted to fantasy sports. As
the NFL draft approaches every year, draft-focused podcasts also soar up the rankings.
Moreover, on several of the top-ranking podcasts, such as “The Lowe Post” and “The Vertical
Podcast with Woj,” front office executives are regular guests. Perhaps even more notable,
though, is how frequently these audio texts integrate managerial tasks into their regular
programming. For example, one of the most consistently dominant podcasts over the last decade
has been “The Bill Simmons Podcast,” which previously ran as “The BS Report” on the ESPN
podcast network. In his podcast, Simmons often discusses NBA free agency, NFL and NBA
drafts and, even more frequently, the NBA trade deadline. Indeed, Simmons has even dubbed
himself as the “Picasso of the Trade Machine,” referring to an ESPN web tool that allows fans to
simulate NBA trades.
Print and web journalism, too, has become preoccupied with the work of the front office.
Minor transactional events that were once relegated to the back of the sports section, like the
NFL and NBA drafts, now generate copious coverage. This trend is particularly notable on the
web. The NBA’s free agency period and the NBA’s trade deadline, for instance, are now the
source for countless stories on websites like ESPN.com, Yahoo! Sports and CBS Sports. As
Bryan Curtis, who previously wrote about the sports media industry for ESPN’s Grantland and
now covers the industry for Bill Simmons’s The Ringer, argues in a 2014 article entitled “The
Trade Rumor Era,” “The ‘trade rumor’ – shorthand here for any offseason transaction news – has
4

become the dominant form of NBA journalism.”
7
He quotes a veteran NBA reporter, ESPN’s
March Stein, who comments, “People love transactions.” Curtis points out this “love” of
transactions has even given rise to an entirely new category of sports journalists – the “insiders,”
or to use another Curtis descriptor, the “rumormongers” – who traffic almost solely in
transactional news and speculation.
8
These “insiders,” like ESPN NFL reporter Adam Schefter
and Yahoo! NBA reporter Adrian Wojnarowski, have been able to attract massive online
followings. To that point, Schefter has 4.92 million Twitter followers, while Wojnarowski has
1.27 million.
All of these examples lend credence to Neal Pollack’s argument on Slate that we are
increasingly living in “the age of the general manager.”
9
As he writes, “If you don't care about
buying, selling, and trading, the sports world has much less to offer you these days.”
10
What has
been largely excluded in this introduction thus far, though, is television – the medium that
continues to dominate the sports media landscape. However, as the next section will show,
television has definitely not been left out of the rush towards management.

GM TV
The many examples mentioned above were meant to establish just how widespread sports
media’s interest in management has been. It should come as no surprise, then, to learn that
sports television has also moved towards management – thus birthing a new mode of sports TV
that might be termed “GM TV.” For one, GM TV entails the increasing importance of
managerial figures. The performance of GMs, for example, has become a common point of
discussion during sports TV talk shows and, as in podcasts, front office executives have become
frequent guests on these programs. Furthermore, former executives are now commonly
5

positioned as “experts” throughout sports TV programming. ESPN, for example, regularly
features figures such as Tom Penn, formerly an Assistant General Manager for the Portland Trail
Blazers and Memphis Grizzlies; Louis Riddick, formerly the Director of Pro Personnel for the
Washington Redskins and Philadelphia Eagles; and Bill Polian, formerly the General Manager
for the Buffalo Bills, Carolina Panthers and Indianapolis Colts. For another, GM TV entails the
increasing coverage of transactions. Most noticeably, this means the ever-expanding coverage of
events like scouting combines, drafts, free agency periods, and trade deadlines. It also means a
growing role for the “insider” figures mentioned above, for they are the ones privy to managerial
maneuvers. Schefter, ESPN’s seemingly all-knowing NFL reporter, can be found on ESPN
throughout the year, providing constant updates on contract negotiations, trade terms, roster
moves, etc. Finally, GM TV entails the increasing interest in player evaluation – a natural
accompaniment to the increased interest in transactions. This is not just evidenced by way of the
expanded coverage of events like scouting combines and drafts, as well the expanded role for
evaluation “experts” like ESPN’s Todd McShay – who can be found roving the sidelines during
college football broadcasts, intermittently discussing players’ NFL draft prospects – but also by
way of the increasing role of statistical data throughout sports TV.
GM TV, then, means an interest both in GM figures and the work performed by GM
figures – e.g. scouting and drafting players, negotiating contracts, weighing trades, etc. GM TV,
moreover, is a mode that encompasses both studio programming and live game broadcasts. That
is to say, the interest in GMs, transactions and scouting is liable to rear its head in news shows
like SportsCenter, talk shows like Pardon the Interruption, pre-game and post-game shows, as
well as routine live game coverage. GM TV, it should also be noted, is a phenomenon that is not
solely limited to American television. In the UK, for instance, broadcasters devote great
6

attention to football league transfer deadlines, which function much like the trade deadlines for
major sports in the US. Foreign broadcasts, too, are increasingly data-driven.
There are a large number of potential reasons for GM TV’s rise – some rooted in the
sports TV industry, some in the sports industry, and some in audiences. The second chapter of
this project will address the specific reasons why sports TV has become increasingly data-driven,
but here we can consider the possible origins for the GM TV phenomenon, as a whole. To
begin, we might start with the sports TV industry. The rise of GM TV is undoubtedly closely
related to networks’ interest in turning major sports into topics of year-round coverage.
Unfortunately for sports networks, the massively popular NFL only plays games for four months
of the year. The NBA, NHL and MLB seasons, meanwhile, each only extend over seven months
of the year. Thus, by focusing on transactions – many of which occur during leagues’ offseasons
– networks are able to continue filling their programming schedules with NFL, NBA, NHL, and
MLB content even while players and coaches have retreated to the golf course. As Curtis
comments during a recent phone interview, “If you can populate as much of your schedule with
NFL programming, no matter whether NFL games are happening at the same time or not, good
for you.”
11
On a related note, GM TV provides a way for networks to cover popular leagues
even if they do not hold rights to cover a league’s games. This, arguably, is how GM TV began
its rise, as the NFL draft’s introduction to TV emerged largely out of necessity.
When ESPN launched in 1979 it declared a bold intention to be the first cable network
devoted entirely to sports. However, it was initially hampered by a relative lack of both
subscribers and money. Thus, the channel was hard-pressed to find many sports to actually
televise when it launched. Indeed, an early promotional brochure touted ESPN’s ability to bring
viewers sports like “Professional Karate,” “Horse Show Jumping” and “Pro Celebrity Golf.”
12

7

The channel, then, was eager to get a piece of the major sports leagues however it could. To that
end, then, it looked for events that had long been considered undesirable by the major networks.
Or, as the promotional brochure put it, live events that “used to be considered avant-garde in the
industry,” like the “Baseball Hall of Fame inductions.”
13
Alongside the Hall of Fame
inductions, ESPN also listed the “NFL player draft.” Indeed, the draft was considered such an
undesirable (or “avant-garde”) property that anecdotes have the league’s then-commissioner,
Pete Rozelle, responding to ESPN’s initial offers to cover the draft by asking, “Why would you
want to do that?”
14
Furthermore, the league’s owners unanimously voted against the network’s
initial offer to broadcast the draft, not eager to place any of its proceedings on the fledgling
network. As Fred Gaudelli, a former ESPN producer reflected, “Nobody knew what ESPN was
about. Why would [the NFL want] to put its brand on their air?”
15
Moreover, getting the draft
television-ready seemed more trouble than it was worth. Jim Steeg, the NFL’s vice president of
special events at the time, later commented, “It seemed like a pain in the neck to set all this up
for something that didn’t seem to be a very popular event.”
16
However, ESPN’s then-president,
Chet Simmons, was eventually able to convince Rozelle that the draft had major television
potential – Simmons drawing upon a pre-existing relationship with Rozelle that had developed
while Simmons was head of NBC Sports.
17
Rozelle then sold the league’s owners and in April
1980, ESPN covered its first NFL draft.
Several decades later, similar dilemmas continue to occur. Recent years have seen the
launch of many new national cable sports networks, including NBCSN, FS1/FS2, CBS Sports
Network, Gol TV, beIN sports, the Golf Channel, and the Tennis Channel. In addition, there are
now also several league-owned networks, such as the NFL Network, the MLB Network, NBA
TV, and NHLTV, as well as an increasing number of college-focused channels, including the
8

Big Ten Network, the SEC Network, the Pac-12 Network, and the Longhorn Network.
Significantly, too, there are a growing number of regional channels, some of them team-owned,
like the Yankees Entertainment and Sports Network (YES) and the Mid-Atlantic Sports Network
(MASN). But while the number of outlets has continually increased, the number of live sporting
events available to broadcast has remained relatively static. While some channels have dealt
with this situation by turning toward content that has often been disregarded by major sports
networks – see, for example, NBCSN’s embrace of hunting, fishing and racing programming –
another solution has been to zealously cover managerial maneuvers. This, then, invites another
sort of niche audience: devoted fans of major sports who are fascinated by off-field mechanics.
NBCSN, for instance, has not just relied on hunting and fishing programming to fill its schedule,
but also the daily talk show Pro Football Talk, which runs throughout most of the year – even
during the league’s lengthy offseason. To provide a brief example of what sort of minor NFL
content can be mined several months before a new season begins, a recent May episode led off
with a discussion of the Jacksonville Jaguars’ decision not to extend the contract of offensive
lineman Luke Joeckel.
GM TV programming, too, often has the added benefit of being relatively cheap. As
more and more networks have vied for the rights to live sporting events – events that have
generally attracted large audiences that are also said to be “DVR-proof” – the fees to these live
events have increased dramatically. Because of these escalating fees, a frequent question within
the television industry has been whether or not there is a “sports media bubble,” with rights fees
perhaps outpacing the actual value of these properties. This is a question that has been given
added urgency over the last few years, as more and more consumers have decided to cut their
cable subscriptions. ESPN, for instance, could be found in 99 million homes in 2013, but was
9

down to 89.5 million homes in June 2016.
18
John Ourand, who covers the sports television
industry for SportsBusiness Journal, has recently suggested there are indeed “leaks” in this
bubble. After talking to a number of league officials and network executives, Ourand asserts that
networks “are becoming more judicious about where they spend their money, sometimes opting
to produce cheaper studio programming over carrying live sporting events.”
19
Thus, studio
programming concerned with managerial minutiae, as in a talk show like Pro Football Talk,
would seem to hold more and more appeal. This situation, of course, recalls the rise of reality
TV programming. As Ted Magder documents, “the emergence of reality TV” came as part of a
“general effort to reduce production costs and financial risk.”
20
And, as Chad Raphael points
out, such an effort was partially precipitated by the explosion in the number of television outlets
– an explosion that diluted audiences and advertising revenues and, in turn, led to pressures to
cut programming costs.
21

GM TV, too, appears to be particularly well-suited to an era of media convergence. In
effect, GM TV becomes part of a feedback loop between web outlets and television outlets.
ESPN’s reporters, for example, will break transaction news on Twitter, elaborate on a television
show like NFL Insiders or SportsCenter, write a brief story for the ESPN website, and then
follow up again on Twitter. “Insiders” like Schefter will even ostentatiously read text messages
and use social media platforms while on TV – signaling that important information is currently
being exchanged or that major news may be breaking online. As Curtis jokes, “Reading a
blackberry on air is the new power in media … If you’re allowed to do that, that means you’re
really powerful.”
22
Many of the concerns of GM TV – information about contract terms, trade
negotiations, etc. – are, to repeat, not beholden to rights agreements. As such, this information
can be endlessly circulated and amplified between platforms. Moreover, Curtis implies that
10

programming focused on transactions can serve to increase interest in other programming,
including live game broadcasts. More specifically, he suggests that transactions – and rumors
regarding future transactions – serve as fodder for second screen discussions.
23
During recent
Oklahoma City Thunder games, for instance, fans have used Twitter to discuss the impending
free agency of star forward Kevin Durant – a frequent topic on studio shows like NBA
Countdown.
Potential origins for GM TV can also be found in the sports industry beyond sport
television. For one, it is in the interest of sports leagues to make their sports subjects of
fascination throughout the course of the year. Tyler, a producer for a sports talk show that runs
on a national cable sports network, looks at the recent interest in NFL free agency and
comments, “I’m sure the league is happy in mid-March, they are one of the biggest stories – free
agency owns the day.”
24
But as he and Curtis both tell me, this is not serendipity – the league
has intentionally positioned itself to have news-worthy “events” scattered throughout the year:
the Super Bowl in January, the Scouting Combine in February, free agency and the annual
owners meeting in March, the draft in late April/early May, and then mini-camps and preseason
training sessions (OTAs) later in May. “You see what the NFL is trying to do … every month,
there’s something big that happens,” Tyler comments.
25
As evidence that the NFL is
“orchestrating” this sequence, Curtis notes that in recent years, the league has moved the draft
later in the year. This, Curtis suggests, was done so that there would a smaller “dead period” in
the league’s offseason.
26

It is not just in leagues’ interest to keep attention on a sport throughout the year, but also
in players’ interest. Tyler says, “Don’t discount the players’ role,” before suggesting that they
want to strengthen their “brands” during the offseason.
27
As Curtis explains, it is “better for
11

marketing opportunities and fame … if you don’t disappear for six months of the year.”
28
Curtis
also suggests players want to author their own narratives. “They want to be their own
storytellers,” he comments, before pointing to the 2014 launch of The Players’ Tribune, a web
outlet that is meant to serve as a direct pipeline between players and fans and which has featured
the bylines of star players like Kobe Bryant and Kevin Durant.
29
Players, then, might be eager to
attract extra attention to offseason transactions and, in so doing, intensify offseason coverage.
To highlight how exactly players might do this, both Tyler and Curtis bring up the 2015 free
agency of Los Angeles Clippers center DeAndre Jordan. Early in the free agency period, Jordan
came to initial agreement with the Dallas Mavericks. After re-thinking this agreement, Jordan
wavered and contacted the Clippers about the possibility of staying in Los Angeles. This in-
decision set off an “emoji war” that became a major sports media news item. In brief, Dallas
Mavericks player Chandler Parsons tweeted an emoji of a plane – appearing to signal he was
traveling to meet with Jordan so as to convince him to commit to the Mavericks. Next, Clippers
guard J.J. Redick tweeted an emoji of a car – indicating he was also on the way to see Jordan –
which was then followed by Clippers forward Blake Griffin tweeting emojis of a plane,
helicopter and car – an intimation that he was ending his vacation in Hawaii and heading to
Jordan’s home. From there, more and more players – and teams – joined the emoji “battle,”
which also eventually took to players’ Instagram accounts. As Tyler and Curtis suggest, the
players’ use of social media during this period made Jordan’s free agency a much bigger story
than it otherwise would have been. And, as Tyler concludes, “I don’t think all of that’s an
accident.”
30

Leagues and players also have another role in the rise of GM TV – this one, though, less
intentional. Curtis, looking at the rise of the NBA “trade rumor era,” points out that the coverage
12

of transactions relies, of course, on the existence of transactions.
31
And, as he explains, there are
now many more transactions than there were just a few decades prior. This increase, though, is
not random chance, but rather a product of recent changes in labor relations. Significantly, free
agency is a relatively new development for the major sports leagues, as players were once
closely bound to their original teams. While drafts and trades may have been familiar league
features, players did not frequently change teams during offseasons. Baseball, though,
introduced unrestricted free agency in 1976, followed by football in 1992, hockey in 1995 and
basketball in 1996. As a result of these new labor situations, there was much more player
movement and, accordingly, media outlets were granted a new angle with which to cover the
world of sports.
GM TV’s ascent might also be traced to audience demand. What might cause fans to be
interested in GMs and the relatively mundane tasks that they perform, though? Curtis speculates
that the appeal of player transactions and player evaluations largely resides in their ability to
exercise the imagination. Writing on the NBA “trade rumor era,” he comments, “It’s about the
possibility of what might happen on a court in the future … Oddly, that’s what makes it fun.”
32

He quotes ESPN writer Henry Abbott, who notes that the NBA finals are only able to host but
two teams featuring just a few dozen players. A player’s free agency, though, “is about
everybody’s imagination … You can project your dreams into it.”
33
The mention of “dreams” is
key because, as Curtis elaborates, transactions and evaluations invite hope.
34
If your favorite
team signs a new player or drafts a college star, you can momentarily believe that your team will
reach new heights. Again, anything is possible. Implicit here, too, is that the coverage of
transactions is significantly about every team – not just successful ones. That is to say, even fans
of teams consistently mired in mediocrity, like the Philadelphia 76ers or the Cleveland Browns,
13

can get excited by transactions – after all, every team needs to fill a roster and, as such, every
team is involved in transactions and player evaluations. Curtis points out that certain offseason
“events” are even oriented toward these fans. As a case in point, he mentions the NFL
Network’s emphasis on “Black Monday” – the day after the NFL regular season ends and many
unsuccessful teams fire their coaches. “That’s something that if you’re a fan of, say, the
Cleveland Browns or the Tampa Bay Buccaneers or any of these teams that don’t get any
attention during the regular season after week six because they’re completely irrelevant, that
absolutely brings you in,” he opines. “All of a sudden you’re the story – you’re a big part of the
story.”
35

Another reason fans might be interested in front office executives and their work is that it
is relatively easy to picture doing that work. After all, the work of a front office executive is the
sort of work commonly associated with the “information economy” – a point that will be further
explored in the conclusion. As Curtis suggests, “We’re not really going to play centerfield for
the Yankees or quarterback for the Patriots, but we can imagine sitting in an office and making
phone calls.”
36
In other words, the GM role seems attainable. This perception has only been
bolstered, Curtis speculates, by a new wave of young GMs.
37
Theo Epstein, for example, was
just 28 when he became GM of the Boston Red Sox in 2002. Three years later, the Texas
Rangers hired John Daniels – also 28 – to be their GM. Just a few weeks ago, the NHL’s
Phoenix Coyotes generated headlines by going even younger – hiring 26-year-old John Chayka
to serve as their GM. It is notable, too, that many of these new GMs have arrived to their roles
without having playing the sport professionally. Again, this would seem to make the role of GM
appear more attainable for the average sports fan. According to Curtis, the GM role also seems
increasingly attainable because fans have ever more information about GMs. GMs, he explains,
14

used to operate rather invisibly. Now, though, because “insiders” relentlessly cover managerial
maneuvers and because executives regularly appear on podcasts, TV shows and other media,
“we tend to just know a lot more about these guys.”
38
As such, it is easier for fans to imagine
taking these jobs. Moreover, the job seems increasingly appealing not just because GMs have
become public figures, but also because of the way the media frames them. As Curtis argues,
Moneyball was not a neutral portrait. Rather, it made front office executives “into heroes” – at
once clever, powerful, and self-effacing. But it is not just Hollywood that has made GMs into
heroes.
39
Curtis maintains that this is the dominant depiction of front office executives
throughout sports media. “Insiders” like Adrian Wojnarowski, Curtis asserts, do not just cover
GMs and assistant GMs, “but they really make heroic portraits out of them” – thus making them
into “role models.”
40
Curtis implies that this framing is perhaps inevitable. It is the front office
executives, after all, who feed these “insiders” their information.
41

In his piece on the NBA “trade rumor era,” Curtis connects sports media “insiders” like
Adrian Wojnarowski to film media “insiders” like Nikki Finke.
42
Such a connection suggests
another theory for fans’ interest in GM TV: the universal appeal of gossip. In his 1989 book
Media, Sports, and Society, Lawrence Wenner argues that the “sports press provides a socially
sanctioned gossip sheet for men in America, a place where a great deal of conjecture is placed
upon ‘heroes’ and events of little worldly import.”
43
Wenner was writing about sports
journalism, generally, but this sentiment seems particularly true of sports media focused on
transactions – including much of GM TV. As Curtis explains, transaction-focused journalism is
heavy on rumor and hearsay. Asked about Wenner’s work, he immediately expresses agreement.
“We like to gossip,” he declares. “An Adrian Wojnarowski rumors column is the People
magazine that we otherwise wouldn’t allow ourselves to be seen with.”
44

15

Gossip segues into another potential reason why fans might clamor for GM TV:
narrative. Tyler mentions that that the host of his show often comments, “The NFL is the
ultimate reality show – crazy stuff tends to happen, which you don’t expect.”
45
The problem
with this “reality show,” though, is that it lasts but four months a year. To repeat from above,
lengthy offseasons are a similar problem for all of the major sports leagues. The focus on
transactions, though, allows these leagues’ “characters” to stay in audiences’ lives for the entire
year. Moreover, it allows links between seasons – providing sports a sense of seriality. Curtis
even argues that transactions can produce more intriguing “narratives” than those found in the
course of game action. He returns to the DeAndre Jordan example. Jordan, Curtis argues, is
probably not the most fascinating NBA player for most basketball fans. His free agency period,
though, was full of twists and turns, including the aforementioned “emoji war.” Curtis argues
that sports “don’t have naturally occurring soap operas,” but implies that episodes like Jordan’s
free agency can come relatively close.
46

An undeniable factor behind the rise of GM TV is the increasing popularity of fantasy
sports. Tyler, for instance, says that viewers of his show consistently demand to know how
transactions are going to affect players’ fantasy values.
47
Curtis, meanwhile, argues that fantasy
has made sports fans more and more concerned with the minutiae of transactions and has fueled
the increasing desire of fans to become GMs. “We’re all drafting teams and making trades and
doing the work of the general manager in our daily lives,” he says.
48
From this perspective, it
would only make sense for fans to want sports TV that reflects this work. The growth of fantasy
sports triggers a follow-up question, though: did fantasy sports create the demand for managerial
sports media texts (e.g. ESPN’s coverage of the NFL draft), or did managerial sports media texts
16

(like the draft) create the demand for fantasy sports? As Curtis asserts, it may be impossible to
disentangle them.
49
Rather, it would seem they have fed into each other.
This question regarding the intertwined relationship between GM TV and fantasy sports
speaks to a similar question that we can use to wrap up this section: is GM TV more of a product
of industrial trends, like the increasing number of cable sports outlets, or more of a product of
audience demand? Or, to put it in other words, is the interest in GMs and their work amongst
sports fans an independent phenomenon, or a media creation? Perhaps tellingly, Tyler and Curtis
disagree on this point. In Tyler’s view, the ability of a sports talk show like his own to run
throughout the year is a direct result of fans clamoring for content related to the major sports
leagues, even during offseasons when the show’s major talking points involve minutiae
surrounding topics like contract disputes. “I think there is a desire and a market for it,” he says,
before continuing, “That's why media companies continue to put shows on and they're fine
putting on an NFL show year-round – because people just eat it up.”
50
Curtis, though, suggests
that this “desire” has largely been produced by media companies, commenting that “writers and
broadcasters largely created or helped to lead people to” what he terms “transaction culture.”
51

As an example, he points to the NBA trade deadline. Journalists at major media companies like
ESPN and Yahoo, he observes, “have done a really good job of conditioning people to think that
the trade deadline is a climactic event” – a “conditioning” evident in the countless articles,
podcasts, and TV features leading up to the deadline. Despite the hype for the most recent trade
deadline, though, Curtis recently looked back and noticed that “basically nothing happened that
appreciably changed the trajectory of the NBA season or postseason.”
52
For Curtis, this
discrepancy between hype and reality highlights just how constructed managerial “events” can
be and how successfully fan interest can be fabricated. But as Curtis admits, trying to fully
17

separate industrial maneuvering from audience interest is bound to be a fruitless exercise. Again,
it is more than likely that the two have fed into each other.

Project Scope
It can be gathered from the sections above that several critics, like Curtis and Pollack,
have taken a strong interest in sports media’s managerial turn. It might not be surprising to
learn, then, that a few scholars of sport have also taken heed of this development. Perhaps most
notable here is the work of Thomas Oates. In his 2009 article “New Media and the Repackaging
of NFL Fandom,” Oates surveys the NFL media landscape and develops the idea of “vicarious
management,” which he defines as “the presentation of athletes as commodities to be consumed
selectively and self-consciously by sports fans.”
53
He comments, for example, how the Madden
owner mode “offers up fantasies where the skills of a tycoon merge with control over elite
athletes.”
54
Another scholar who has dealt with the managerial phenomenon, if less directly, is
Andrew Baerg. In several pieces, Baerg details how digital sports texts, namely sports
videogames and fantasy sports websites, have emphasized the quantification of athletes.
55

Videogame scholar Garry Crawford, meanwhile, has explored the specific appeal of managerial
games like Football Manager by way of ethnographic analysis.
56

This previous scholarship is analyzed with more depth in the conclusion, but in the
meantime, it can be stated that this project takes a significantly different approach than any of
this prior work. For one, this project subjects sports media’s managerial turn to sustained
analysis – thus allowing it to dig deeper than Oates’s article-length work and more directly than
Baerg and Crawford’s scholarship. Even more significantly, though, this project is specifically
grounded in one medium: television. As was evidenced in the previous section, the rise of GM
18

TV is very much rooted in certain industrial trends particular to television, like the explosion in
cable outlets searching for content. GM TV’s fit within the particular contours of the television
industry is detailed throughout the project, further illustrating that any attempt to understand
sports media’s managerial turn must carefully consider the specific medium being affected.
Because this project is solely concerned with television, too, it is able to consider sports media’s
managerial turn within the context of a variety of scholarship that has yet to be tapped by
scholars of sport, including work around reality television, television style, the critical study of
big data, and digital humanities.
As suggested in the previous section, GM TV is a sweeping phenomenon,
noticeably affecting almost every realm of sports television. That in mind, it would be difficult –
and likely fruitless – for this project to analyze the phenomenon as a whole. In approaching GM
TV, then, this project is divided into two parts that each address the phenomenon from a
relatively narrow angle. The first part/chapter, “Managerial Television,” is particularly
concerned with placing the rise of GM TV within the context of the wider television landscape
beyond sports. More specifically, the chapter argues that GM TV programming – in
emphasizing managerial figures and managerial lessons – closely resembles a sub-genre of
reality TV, termed “Boss TV,” that celebrates entrepreneurs and their managerial “wisdom.”
Both GM TV and Boss TV, it is further suggested, offer managerial lessons that align with recent
economic shifts. More specifically, the chapter links GM TV and Boss TV to the rise of the
“managing consumer.” As the chapter details, companies have increasingly offloaded
managerial responsibilities, including the evaluation of employees, onto consumers – a trend
exemplified by Uber’s driver ratings system. By teaching viewers how to conduct themselves as
managers, GM TV and Boss TV serve as a natural accompaniment to this shift.
19

The second part of the dissertation, “Datavisuality,” spans the second and third chapters.
The second chapter, drawing on John Caldwell’s concept of “televisuality,” asserts sports TV –
in line with GM TV’s emphasis on evaluation – has become increasingly data-driven, thus
birthing “datavisuality.” The second chapter does not just summarize what it means for sports
TV to have become data-driven, but also uses a number of interviews with industrial sources to
explain what factors have contributed to this shift. Moving forward, the third chapter
specifically focuses on the implications of datavisuality’s ascent. Because datavisuality has
involved a rapidly growing overlap between big data and sports TV, much of this chapter relies
on the burgeoning field that is the critical study of big data. Pulling from this recent scholarship,
the chapter argues that datavisuality both reinforces “big data mythology” and reifies “big data
divides.”
By subjecting sports TV’s turn toward management to sustained critique, this project is
able to make significant contributions to the field of television studies. For one, television
studies has thus far largely ignored sports TV – a gap that is particularly noticeable given the
continuing popularity and financial import of the genre. This gap in mind, the project first
establishes new connections between sports TV and the wider television landscape by making
links to reality TV. Through this comparison, the project is able to establish that the ideological
workings of sports TV are not too dissimilar from other realms of televisions. On a similar note,
the project next provides an infrequent examination of sports TV’s form – an examination that
allows a productive parallel to be drawn between sports TV and the seminal concept of
televisuality. Significantly, too, this project offers a rare industrial analysis of sports TV. As
detailed in the second chapter, the sports TV industry is a complex landscape that only loosely
20

resembles the rest of the television industry. This project provides a unique window into how
this opaque industry operates and how sports broadcasting decisions get made.
Furthermore, television studies is only beginning to grapple with the increasing role of
big data in the television industry – a trend seen everywhere from Hulu’s personalized
advertising to Netflix’s recommendation algorithms. By studying big data’s growing place
within sports television, this project is not only able to offer a concrete case study of what
exactly this overlap looks like, but is also able to explore the potential consequences of this
overlap. As such, this project begins to put television studies in dialogue with the critical study
of big data. As big data continues to become more and more influential across society, including
within media industries, this is a dialogue that will only grow more important – both for media
studies and for the critical study of big data.

1
“Moneyball (2011) - Box Office,” Box Office Mojo, accessed May 31, 2016,
http://www.boxofficemojo.com/movies/?id=moneyball.htm.
2
Noel Murray, “No Money for Moneyball,” A.V. Club, June 22, 2009, http://www.avclub.com/article/no-
money-for-emmoneyballem-29476.
3
Lane Brown, “So How Bad Was Soderbergh’s Revised Moneyball Script?,” Vulture, June 23, 2009,
http://www.vulture.com/2009/06/so_how_bad_was_soderberghs_rev.html.
4
Pamela McClintock, “Box Office Report: ‘Moneyball’ Wins Friday, But ‘Lion King,’ ‘Dolphin Tale’ Still
Contenders,” The Hollywood Reporter, September 24, 2011, http://www.hollywoodreporter.com/news/box-
office-moneyball-dolphin-tale-239865.
5
Willie Osterweil, “The Rise of the Sports Management Movie,” Al Jazeera America, April 27, 2014,
http://america.aljazeera.com/opinions/2014/4/sports-managementmoviemilliondollararmmoneyball.html.
6
“Industry Demographic Analysis with FSTA” (STATS, n.d.).
7
Bryan Curtis, “The Trade Rumor Era,” Grantland, July 7, 2014, http://grantland.com/features/nba-trade-
rumors-espn-yahoo-new-york-post-lebron-james-jason-kidd-offseason-trade-signing/.
8
Ibid.
9
Neal Pollack, “The Cult of the General Manager,” Slate, August 29, 2005,
http://www.slate.com/articles/sports/sports_nut/2005/08/the_cult_of_the_general_manager.html.
10
Ibid.
11
Bryan Curtis, interview by Branden Buehler, Phone, May 9, 2016.
21

12
“ESPN Promotional Material,” 1981.
13
Ibid.
14
Michael Freeman, ESPN: The Uncensored History (Rowman & Littlefield, 2001), 102.
15
Rob Reischel, 100 Things Packers Fans Should Know & Do Before They Die (Triumph Books, 2013), 100.
16
Bill King, “Feel the Power — Feel the NFL Draft,” SportsBusiness Journal, accessed May 31, 2016,
http://www.sportsbusinessdaily.com/Journal/Issues/2000/04/20000424/No-Topic-Name/Feel-The-
Power-151-Feel-The-NFL-Draft.aspx.
17
Freeman, ESPN: The Uncensored History, 102.
18
Andrew Bucholtz, “ESPN down 1.5 Million Subscribers as Cable Sports Networks Keep Shedding Viewers,”
Awful Announcing, June 1, 2016, http://awfulannouncing.com/2016/espn-down-1-5-million-subscribers-as-
cable-sports-networks-keep-shedding-viewers.html.
19
John Ourand, “Does Media Rights Bubble Have a Leak?,” SportsBusiness Journal, May 2, 2016,
http://www.sportsbusinessdaily.com/Journal/Issues/2016/05/02/In-Depth/Media-rights.aspx.
20
Ted Magder, “Television 2.0: The Business of American Television in Transition,” in Reality TV: Remaking
Television Culture, ed. Susan Murray and Laurie Ouellette (NYU Press, 2009), 164.
21
Chad Raphael, “The Political Economic Origins of Reali-TV,” in Reality TV: Remaking Television Culture, ed.
Susan Murray and Laurie Ouellette (NYU Press, 2009), 123–40.
22
Curtis, Phone.
23
Ibid.
24
Sports talk show producer “Tyler,” interview by Branden Buehler, Phone, May 9, 2016.
25
Ibid.
26
Curtis, Phone.
27
Tyler, Phone.
28
Curtis, Phone.
29
Ibid.
30
Tyler, Phone.
31
Curtis, “The Trade Rumor Era.”
32
Ibid.
33
Ibid.
34
Curtis, Phone.
35
Ibid.
36
Ibid.
37
Ibid.
38
Ibid.
39
Ibid.
40
Ibid.
22

41
Ibid.
42
Curtis, “The Trade Rumor Era.”
43
Lawrence A. Wenner, “Media, Sports, and Society: The Research Agenda,” in Media, Sports, and Society, ed.
Lawrence A. Wenner (Newbury Park, Calif: SAGE Publications, Inc, 1989), 15.
44
Curtis, Phone.
45
Tyler, Phone.
46
Curtis, Phone.
47
Tyler, Phone.
48
Curtis, Phone.
49
Ibid.
50
Tyler, Phone.
51
Curtis, Phone.
52
Ibid.
53
Thomas Oates, “New Media and the Repackaging of NFL Fandom,” Sociology of Sport Journal 26, no. 1
(2009), 31.
54
Ibid., 40.
55
See, for example Andrew Baerg, “Classifying the Digital Athletic Body: Assessing the Implications of the
Player-Attribute-Rating System in Sports Video Games,” International Journal of Sport Communication, no. 4
(2011): 133–47; and Andrew Baerg, “Neoliberalism, Risk, and Uncertainty in the Video Game,” in Capital at
the Brink: Overcoming the Destructive Legacies of Neoliberalism, ed. Jeffrey R. Di Leo (Open Humanities Press,
2014).
56
See Garry Crawford, “The Cult of Champ Man: The Culture and Pleasures of Championship
Manager/Football Manager Gamers,” Information, Communication & Society 9, no. 4 (August 1, 2006): 496–
514; and Garry Crawford, “Is It in the Game? Reconsidering Play Spaces, Game Definitions, Theming, and
Sports Videogames,” Games and Culture 10, no. 6 (November 1, 2015): 571–92.
23

Part I

Chapter One: Managerial Television

From 1998 through 2008, Jon Gruden put together a fairly illustrious resume as an NFL
head coach – a resume highlighted by a Super Bowl win with the Tampa Bay Buccaneers. As
effective as Gruden was as a coach, though, he has arguably been even more successful in his
next job: working for ESPN as a football commentator. Notably, Gruden has become a fixture
on Monday Night Football, one of American sports television’s signature properties, and
accordingly, he is reported to be ESPN’s highest-paid broadcaster. Gruden, though, has not just
made his mark on television by serving as a commentator during live game telecasts, but also by
hosting Gruden’s QB Camp – a program that, for the last seven years, has aired in the weeks
leading up the NFL draft. In each episode of the show, Gruden spends time with a top college
athlete whom is likely to be selected in the upcoming draft. For all intents and purposes, these
exchanges operate like televised job interviews. In his role as host, Gruden essentially fills the
role of an NFL personnel executive – asking the prospects about their personal lives, quizzing
them about football strategy, and even running them through drills at a Disney-owned sports
complex. Throughout, Gruden volunteers his opinion of the prospects – offering, in sum, an
assessment of whether or not they are fit for an NFL role.
On the surface, Gruden’s QB Camp, in combining the traditional sit-down interview with
detailed player analysis, might seem a bit unusual for many sports television viewers. Deadspin,
for example, called the show “totally bizarre,” while Sports Illustrated noted Gruden’s role as
media member-cum-talent evaluator made for an “unconventional arrangement.” However,
Gruden’s QB Camp is not as atypical as it may seem at first. As documented in the introduction,
sports television has become increasingly fixated with front office figures and the work that they
24

perform. Thus, Gruden’s QB Camp, by spotlighting a former head coach and, moreover, by
having him offer detailed player evaluations, is very much aligned with this trend. This chapter,
though, examines the place of a show like Gruden’s QB Camp within the television landscape –
and society at-large – with even more specificity. As is argued, GM TV programs like Gruden’s
QB Camp closely resemble shows within a vibrant sub-genre of reality television that this
chapter dubs “Boss TV.” Like Boss TV, GM TV showcases the work of management and, in the
process, provides lessons in managerial conduct. These lessons, it is further argued, fit alongside
broader economic shifts. More specifically, it is suggested that by training viewers how to act as
managers, both Boss TV and GM TV align with a trend that has seen corporations pass on more
and more managerial responsibilities to consumers – a trend that has produced a new figure
termed “the managing consumer.”

Studying Reality
Few things have influenced the shape of the American television landscape over the past
few decades like the emergence of “reality” programming – a trend that began to pick up steam
in the late 1980s with the debut of shows like Cops and America’s Most Wanted and that fully
solidified in the late 1990s and early 2000s with the success of primetime programs such as Who
Wants to be a Millionaire, Survivor and Big Brother. As might be expected, the ascendance of
reality TV eventually attracted the attention of television scholars, many of whom sought to
understand the ideological workings of this new genre. One major strain of this scholarship
narrowed in on the genre’s links to theories of governmentality – theories that originate with the
work of Michel Foucault and refer “to the processes through which individuals shape and guide
their own conduct – and that of others – with certain aims and objectives in mind.”
1
As Laurie
25

Ouellette and James Hay clarify, though, understanding such processes and objectives requires a
firm grounding in specific social, political, and economic contexts.
2
They, along with most
other scholars of governmentality – television scholars included – thus focus on the specifics of
the “neoliberal present,” explaining both what this neoliberal moment entails and how television
fits within.
Turning our attention first to the broad outlines of neoliberalism, the term generally
encompasses a set of ideas meant to push back against the development of the welfare state –
both in the United States and elsewhere – over the course of the 20
th
century. Although
neoliberalism can broadly be understood as a response to the creation and entrenchment of the
welfare state, it can also be explained with more specificity. Perhaps most significant here is
neoliberalism’s reliance on the logic of the marketplace. Not only do neoliberal reformers turn
to both regulation and deregulation in attempts to construct a private sector fully adhering “to
competition and market-based practices,” but they also seek to transform the public sector by re-
shaping it according to market principles.
3
For example, recent decades have seen a massive
push to privatize public education by way of mechanisms like vouchers and charter schools –
devices intended to make public education subject to market principles like “competition” and
“consumer choice” and, in turn, to make public education more “efficient” and “accountable.”
As the welfare state morphs and recedes under such logic, citizens find themselves entering a
new relationship to the state. In essence, citizens are largely left to their own devices to weather
the ebbs and flows of capitalism. Citizens, then, are meant to take responsibility for their own
fate and to become “active” and “enterprising” – phrases that signify how neoliberal logic even
begins to insert the marketplace into everyday life. As Brenda Weber explains, a citizen must
26

become an “entrepreneur of the self”, maximizing themself so that they can become “competitive
within a larger global marketplace.”
4

Because one of the primary tenants of neoliberalism is dismantling the welfare state and,
in turn, empowering “active citizens,” neoliberal governance becomes a matter of “governing at
a distance” – that is to say, dispersing rule throughout society. As sociologist Nikolas Rose
writes, neoliberalism asks “whether it is possible … to govern through the regulated and
accountable choices of autonomous agents – citizens, consumers, parents, employees, managers,
investors.”
5
As such, the study of neoliberal governance requires a focus that extends beyond the
state. “Scholars of governmentality,” Ouellette and Hay explain, “look beyond the formal
institutions of official government to also emphasize the proliferation and diffusion of the
everyday techniques through which individuals and populations are expected to reflect upon,
work on and organize their lives and themselves as an implicit condition of their citizenship.”
6

For Ouellette and Hay, television – particularly reality television – represents a key site for the
circulation of such “techniques” – an argument, Ouellette notes, that aligns with the work of
other scholars of governmentality. Rose, for instance, describes how “the mass media” operates
as one of many “technologies that install and support the civilizing project by shaping and
governing the capacities, competencies and wills of subjects, yet are outside the formal control of
public powers.”
7
He continues, “They have provided a plethora of indirect mechanisms that can
translate the goals of political, social and economic authorities into the choices and commitments
of individuals, locating them into actual or virtual networks of identification through which they
may be governed.”
8
Television scholars, then, have sought to build on such work to explain how
exactly “mass media” has performed such “translation.”
27

Perhaps the most thorough accounts of television’s role within neoliberal governance
have come by way of Ouellette and Hay. They initially laid out their thoughts independently,
with Hay first contending in 2000 that television is used to “govern at a distance” by molding
“self-disciplining subjects,” and Ouellette later citing Hay in 2004 while explaining that reality
television is particularly well-suited to such an “indirect, diffuse mode of cultural
governmentality.”
9
As a primary example, Ouellette points to the popular courtroom show
Judge Judy, arguing that it “draws from and diffuses neoliberal currents,” training “TV viewers
to function without state assistance or supervision as self-disciplining, self-sufficient,
responsible, and risk-averting individuals.”
10
In closing this argument, Ouellette argues that
Judge Judy is far from alone, asserting, “We can see variations of the neoliberal currents
examined here in makeover programs, gamedocs, and other reality formats that ‘govern at a
distance’ by instilling the importance of self-discipline.”
11

Following through on this claim, Ouellette and Hay would eventually return in 2008 with
a lengthy examination of such varied reality formats in their joint-authored Better Living
Through Reality TV. In the work, the scholars suggest that reality TV’s relationship to neoliberal
governance is more complicated than a mere espousal of neoliberal logic. For one, they argue
that reality TV provides resources to help viewers become “active and productive citizens within
the current governmental rationality that values self- reliance and enterprise.”
12
They point, for
instance, to makeover shows like Honey We’re Killing the Kids, which help “govern at a distance
by shaping and guiding behavior toward ‘rational’ choices and outcomes.”
13
In the case of
Honey We’re Killing the Kids, that means “guiding” viewers toward healthy eating habits. For
another, Ouellette and Hay explain that reality TV even goes so far as to enact the very sorts of
practices that have become lynchpins of neoliberal agendas. As an example, they single out a
28

show like Extreme Makeover: Home Edition. In seeking out families in need of home
renovations and then partnering with various corporate sponsors to execute those renovations,
the scholars argue, the show offers a private sector alternative to social services that might
otherwise be provided by the welfare state.
As mentioned, though, Ouellette and Hay have not been the only scholars to link
neoliberalism and reality TV. For instance, the same year that Ouellette and Hay released Better
Living Through Reality TV, Nick Couldry argued in multiple locations that reality shows like The
Apprentice and Big Brother reflect the workings of the neoliberal workplace, spotlighting
dynamics like the need for workers/contestants to submit to an external authority and to do so
while expressing “passion.”
14
For several other scholars, the makeover genre has been of
particular interest. Alison Hearn, for example, connects the precariousness that has accompanied
the neoliberal shift toward “flexible” labor to the genre’s emphasis on self-branding, with self-
branding being offered as a way to “compete and gain power in the volatile work world of
flexible capital.”
15
John McMurria, meanwhile, argues that “Good Samaritan” makeover shows,
like the aforementioned Extreme Makeover: Home Edition, circulate “neoliberal narratives of
privatization and personal responsibility.”
16
Extreme Makeover: Home Edition, for instance,
presents the recipients of its makeovers as conforming “to the ideals of neoliberal citizenship” –
laboring in precarious conditions with little complaint and turning to the private sphere, rather
than the public sector, when assistance is required.
17
Neoliberalism also plays a major part in
Brenda Weber’s Makeover TV. The genre, Weber writes, “imports neoliberal ideologies, which
position the subject as an entrepreneur of the self, who does and, indeed, must engage in care of
the body and its symbolic referents in order to be competitive within a larger global
marketplace.”
18
Weber explains that this is not just a matter of circulating and promoting
29

neoliberal discourses, but also taking on the role as “an agent of care” – an argument that recalls
Ouellette and Hay’s contention that reality TV enacts neoliberal logic by way of shows like
Extreme Makeover: Home Edition.
19

A Changing Terrain
All of this scholarship in mind, it would probably be fair to say that reality TV’s
relationship to neoliberalism is well-trodden ground for the field of television studies. Moreover,
all of the aforementioned scholarship is relatively new – mostly having been published within the
last decade. It is not just well-trodden ground, then, but recently-trodden ground; the footprints
are still very much fresh. The world, then, does not appear to be in dire need of more work
linking neoliberalism to reality TV. Nonetheless, it is terrain already worth re-visiting. As
Ouellette and Hay write, “Governmental practices and rationalities are never stable.” Thus,
“Attempts to map and analyse them must be contextual, geographic and historical.”
20
Although
the work mentioned above may be relatively recent, the social, political, and economic
landscapes in which these pieces were embedded have already undergone significant shifts –
shifts that invite a re-visiting of the reality TV scholarship.

The End of Neoliberalism?
To begin, the pieces mentioned above were primarily authored ahead of the worst days of
the financial crisis of 2007-2008 and the ensuing recession – a chaotic episode whose fallout
continues to be felt today. The crisis marked one of the most turbulent times for global
capitalism and averting an even worse economic disaster required unprecedented actions from
world governments – including the US government, as both the Federal Reserve and the
30

Department of Treasury scrambled to prop up an increasingly illiquid financial sector. In the
wake of the financial crisis, governments across the world attempted to ensure such a calamity
would not happen again. Within the US, the reform agenda was packed, with the government
considering a wide variety of issues ranging from executive pay schemes to mortgage
securitization to derivative regulation. Ultimately, this reform agenda culminated in the Dodd-
Frank Wall Street Reform and Consumer Protection Act, which attempted to prevent future
calamity by way of provisions like higher capital requirements for banks.
After the tumult of the crisis, faith in the global economic system was weakened.
Furthermore, financial reforms like the Dodd-Frank Act meant that economies had a slightly
different look to them. The question emerged, then, whether neoliberalism had lost its sway.
Toby Miller, for instance, remarked that the economic quagmire had marked the end for
neoliberalism. “Ultimately,” he opined, “neoliberalism sank under the weight of
contradiction.”
21
Such a declaration appears to be premature, though. To that point, any number
of anecdotes could be used to illustrate the fact that the faith in market logic remains incredibly
strong amongst the political and economic elite. Take, for example, the continued prominence
within American politics of Charles and David Koch, brothers whose massive fortunes have
shaped a number of political races and policy debates in accordance with their free-market
ideology.
22
Moving beyond anecdote, one continues to find it difficult to declare neoliberalism
“dead.” For example, the reform undertaken in the wake of US financial crisis may have
tweaked the country’s economic system, but it was far from revolutionary – the more radical
solutions, like a re-instatement of the Glass-Steagall Act, were left behind early in the reform
process.
23
Moreover, it remains to be seen how exactly Dodd-Frank will be implemented – and
what long term effects it might have – given significant resistance from both the financial
31

industry and politicians.
24
The reform process, then, was nothing like that undertaken during the
Great Depression, which saw a thorough re-thinking and subsequent re-working of the financial
system.
Suffice it to say, then, neoliberalism appears to be far from finished. Indeed, one might
even assert that neoliberalism has not merely avoided defeat in the wake of the financial crisis,
but has actually grown more entrenched. For example, the economist Guy Standing contends
that the financial crisis ultimately served to accelerate the enactment of certain neoliberal
principles. Most significantly, Standing argues that the financial crisis has been used as
justification in the unrelenting march to make workforces ever more flexible. As Standing
explains, one of the major emphases of neoliberalism has been a call for “labour market
flexibility” – a condition that has entailed not just wage flexibility and employment flexibility,
but also job flexibility (the ability to easily move employees within a company) and skill
flexibility (the ability to easily adjust employees’ skills).
25
The shift toward labor market
flexibility, Standing continues, has meant a disturbing loss of labor-related security for many
workers – ranging from labor market security (the ability to find a job that pays an adequate
wage) to employment security (such as the protection against wrongful termination) to
representation security (the ability to collectively bargain).
26
According to Standing, the
financial crisis “accelerated the growth” of labor flexibility and, accordingly, a loss of labor-
related security.
27
Not only did firms turn to “flexibility measures” as a way to cut costs during
the recession, but government policies also promoted such moves. Moreover, the recession has
provided companies a continued “excuse” to shift towards temporary workforces or to outsource
their labor. Standing summarizes the effects of these shifts in stating, “Traditionally, major
recessions lead to reductions in inequality, but this time income differentials went on
32

widening.”
28
Economists Lawrence Katz and Alan Krueger have recently come to similar
conclusions, making a preliminary link between the Great Recession and a massive rise in
“alternative work arrangements” since 2005.
29
Again, then, it would seem clear neoliberalism
has avoided any “sinking” in the years since the financial crisis.

Neoliberalism Evolved
However, to say that neoliberalism continues to hold sway is not particularly helpful in
establishing why the work on reality TV necessitates re-visiting. What is more important is
noting how neoliberalism has changed in character over the past several years. To that point, one
trend to emerge in the wake of the financial crisis was a temporary loss of prestige for the
financial sector. As Kevin Roose documents throughout his examination of the lives of young
Wall Street analysts, Young Money, major banking firms like Goldman Sachs and JP Morgan
Chase had once loomed large in the minds of many young Americans – seemingly representing a
smooth ticket to a cushy life complete with dinners at Per Se and summer weekends in the
Hamptons. After the tumult of the financial crisis, though – and especially after the controversial
government actions to revive the financial sector – the reputations of the banking firms were in
tatters. Not only did working on Wall Street no longer come with quite the same perks or the
same chances of finding wealth, but it also became an unseemly life decision. Whereas working
for a bank may have once seemed relatively innocuous, if unimaginative, it now seemed an
especially immoral act – working all day and all night to serve the reckless gamblers who had
destroyed the economy and had then been bailed out anyway. Roose reflects, “Many of the
young people who came to Wall Street expecting champagne and caviar got dirty looks and
ignominy instead.”
30

33

While Wall Street began to dim in the popular imagination, another location-cum-symbol
began to shine even brighter: Silicon Valley. As Roose writes, the technology sector gained ever
more allure in the wake of the financial crisis. Amongst the young bankers he profiled, fantasies
of hedge fund riches were gradually being replaced by fantasies of startup riches. One of these
bankers, a private equity analyst from Wisconsin, tells Roose that it is tech founders like
“Zuckerberg, Steve Jobs, the guy who built Instagram” who now “do shit in the world.”
31
While
Roose’s subjects undoubtedly offer but a narrow view of the world, there are reasons to think
that this shift in the cultural imaginary from finance to tech extends beyond the young financial
analysts dreaming of life beyond UBS valuation spreadsheets. According to a 2013 Reuters poll
of 1,400 Americans, a strong resentment toward Wall Street has lingered in the wake of the
financial crisis – a resentment, it might be added, that surpasses anything seen since the
ascendance of neoliberalism.
32
The tech sector has proven more popular. 51 percent of the
Reuters respondents indicated that the tech sector was better than the financial sector “at creating
jobs and helping the U.S. economy,” while just 10 percent said the reverse.
33
And beyond public
opinion, there are a variety of other indications that the tech sector and Silicon Valley are having
a particularly pronounced impact on the economy. Although the tech sector continually exists
alongside the specter of the “bubble,” it is also true that venture capital has flowed into Silicon
Valley over the last several years and that an increasing number of Silicon Valley companies
have gone public.
34
In 2013, 20 of the 222 US Initial Public Offerings (IPOs) were by Silicon
Valley companies.
35
In 2014, it was 23 of 275. More established firms have thrived, too. The
two most valuable public companies in the world are – as of February 2, 2016 – Alphabet (the
parent company of Google) and Apple. Amazon and Facebook are but a few spots behind.
36

34

The shift in the popular imaginary from finance to tech, of course, cannot be captured by
economic facts and figures – it is largely a cultural shift. Naturally, this begins with
technology’s presence in everyday life. Approximately 94 million iPhones are in use in the
US.
37
Facebook has 1.5 billion active users across the world, WhatsApp 900 million, Instagram
400 million, Twitter 320 million, and Snapchat 200 million.
38
Airbnb claims 50 million users,
while Uber generates hundreds of thousands of trips per day in large markets like New York, Los
Angeles, and San Francisco.
39
Tinder counts 50 million users and, according to the company,
the average user logs into the app 11 times per day.
40
Billions of dollars now flow through the
money exchange app Venmo.
41
All of this to say, experiences with the world are increasingly
mediated through apps and devices.
The cultural impact of Silicon Valley extends past the apps and devices themselves,
though. As the young banker’s quote indicates, tech founders are now cultural icons. These
moguls do not just beget biopics, such as The Social Network and Steve Jobs, but also larger-
than-life comic book characters, exemplified by Robert Downey Jr.’s decision to partially model
his depiction of Iron Man on Tesla/SpaceX founder Elon Musk, as well as the recent
transformation of Superman adversary Lex Luthor from a besuited corporate executive to a
shaggy tech tycoon.
42
It is perhaps unsurprising, then, that tech moguls have become part of
celebrity culture, popping up in locations largely reserved for actors, musicians, and politicians.
When Stephen Colbert launched his new version of CBS’s Late Night in 2015, many of his first
guests were tech executives, including the CEOs of Apple, Uber, Snapchat, Airbnb, and Netflix,
as well as Tesla/SpaceX’s aforementioned Elon Musk. The show’s co-executive producer
commented, “These innovators are as powerful right now as any other person who you would
have on a late-night talk show.” She continued, “We’re just going where the heat is.”
43

35

This brings us back to neoliberalism. The increasing visibility of the Silicon Valley elite
has meant ever more opportunities for them to publicize their unique worldview. Indeed, the
Silicon Valley elite has realized this. FWD.us, a political lobbying group founded by Mark
Zuckerberg with the intention of representing tech titans like himself, put together a prospectus
that suggested, “Our voice carries a lot of weight because we are broadly popular with
Americans.”
44
What is that worldview, though? As Richard Barbrook and Andy Cameron
explain in their prescient 1996 essay “The Californian Ideology,” the prevailing Silicon Valley
mindset is a distinct combination of philosophies rooted in both the left and the right, emerging
out of the “bizarre fusion of the cultural bohemianism of San Francisco with the hi-tech
industries of Silicon Valley.”
45
This ideology, the authors continue, “promiscuously combines
the freewheeling spirit of the hippies and the entrepreneurial zeal of the yuppies” – a
combination held together “through the profound faith in the emancipatory potential of the new
information technologies.”
46
As this description indicates, the emergence of the “Californian
Ideology” is very much related to the ascendance of neoliberalism, evidenced by the unwavering
faith in market logic and, in particular, the unwavering faith in the figure of the entrepreneur.
Evidence of this faith is not hard to find. Take, for instance, the comments of one
anonymous startup founder recently surveyed as part of an exploration into the politics of Silicon
Valley. This founder opines that the “problem with government [organizations] is they don’t
really have an incentive to innovate or improve processes, services, customer experience and are
run very efficiently.” The founder continues, “If they were run in more of a private market
environment like startups they could have better [Return on Investment] and deliver better
service for all. Competition is a healthy way to encourage that.”
47
Not all tech founders need
anonymity to promote such unfettered market logic, though. For instance, a recent New Yorker
36

profile of LinkedIn co-founder and chairman Reid Hoffman details his belief in the company’s
ability to fix inequality by helping workers embrace labor flexibility and, in the process, helping
them become more entrepreneurial. As one of Hoffman’s associates reflects, Hoffman’s
“religion is entrepreneurship.”
48
The author of the piece, Nicholas Lemann, goes on to
summarize, “The master construct in Hoffman’s world is allocating capital to other
entrepreneurs.”
49

As Lemann notes, one of Hoffman’s favorite “entrepreneurs” to support is New Jersey
Senator Cory Booker. The mention of Booker is apt because Booker’s time as mayor of Newark
provides key evidence of how the Californian Ideology has leaked out from Silicon Valley and
into other realms of American society. While mayor, Booker famously enlisted Facebook
founder Mark Zuckerberg, as well as a number of other corporate titans, to contribute hundreds
of millions of dollars to the Newark school system (an experiment that has, suffice it to say, not
worked as intended). As reporter Dale Russakoff reflects in her postmortem of the Newark
experiment, The Prize, Booker’s plea to Zuckerberg reflects a larger shift in education
philanthropy that has accompanied the rise of Silicon Valley. Major philanthropists like Bill
Gates and Michael Dell, Russakoff writes, “became known as ‘venture philanthropists’ and
called themselves investors rather than donors.” She continues, “Employing management
consultants and the kinds of analytics tools that fueled the rise of their companies, they pressed
for data-driven accountability systems to measure the effectiveness of teachers and schools.”
50

Eventually, Russakoff explains, this sort of market logic even worked its way into federal policy,
as major initiatives like Race to the Top reinforced ideas like tying teacher pay to student test
scores. Education is hardly the only policy realm where Silicon Valley has tried to insert its
ideology, either. For instance, FWD, the aforementioned tech lobbying group, has advocated for
37

immigration reform – putting particular emphasis on the figure of the entrepreneur. “The United
States needs an immigration system,” the group claims, “that allows the world’s most talented
entrepreneurs to start the next generation of innovative companies here.”
51
Their solution? A
new program that would single out foreign entrepreneurs for either temporary visas or permanent
residency status.
As mentioned above, Barbrook and Cameron’s essay on the Californian Ideology was a
farsighted piece of criticism – identifying many of the ideas that have continued to circulate
amongst the tech elite in the ensuing decades. That said, it would be a mistake to assume that the
Californian Ideology has remained entirely static over the last twenty years. Significantly, the
technology that has undergirded Silicon Valley has evolved in those twenty years. While scores
of companies that were fixtures in the mid-1990s have remained powerful, including behemoths
like Apple and Microsoft, many new sectors of the tech industry have either matured or emerged
in the years since, exemplified by the rise of internet search engines like Google (founded in
1998), media distribution services like Netflix (1997) and Spotify (2006), media outlets like
BuzzFeed (2006), e-commerce sites like Amazon (1994), cloud storage services like Dropbox
(2007) and, of course, social networking platforms like Facebook (2004) and Twitter (2006). In
recent years, one of the most lucrative new sectors of the tech economy has been the so-called
“Sharing Economy,” composed of companies that were founded on the idea of facilitating
exchanges of resources between individuals – whether that might mean “sharing” a car, as in
companies like Lyft and Uber; a home, as in companies like Airbnb; funds, as in companies like
Lending Club; or a person’s labor, as in companies like TaskRabbit or Handy.
52
And these
services are not just lucrative – see Uber’s $62.5 billion valuation – but also popular.
53

38

According to a Time survey published in early 2016, 44% of adult Americans have now
participated in the Sharing Economy.
54

As new technologies have come to dominate Silicon Valley, they have re-shaped some
the specific contours of the Californian Ideology. For instance, the rise of internet content
companies has created a notable divide over the issue of net neutrality, with content companies
like Netflix pitted against hardware companies like Cisco – both sides claiming to represent the
true logic of free markets. Perhaps no new technologies have had a bigger role in re-shaping the
outlines of the Californian Ideology, though, then the apps that now constitute the Sharing
Economy. For example, deregulation has long been a tenant of the Californian Ideology, as it
aligns with the general libertarian streak of Silicon Valley. That in mind, the Sharing Economy
makes deregulation an emphasis like never before. Companies like Uber and Airbnb, for
instance, are built on the very idea of gaining a competitive advantage by skirting existing – and
financially onerous – regulations. In the case of Uber, that means going around the many
regulations placed on the taxi industry, while in the case of Airbnb, that means going around the
many regulations placed on the lodging industry. As Tom Slee writes in his critique of the
Sharing Economy, What’s Yours is Mine, the Sharing Economy is hardly, as the “Sharing” label
implies, “about building an alternative to a corporate-driven market economy.” Rather, “it’s
about extending the deregulated free market into new areas of our lives.”
55

Of course, too, the Sharing Economy also puts a particular emphasis on labor flexibility –
another tenant of the Californian Ideology amplified by the Sharing Economy. Companies like
Uber and Airbnb, as well as a score of others, ranging from Instacart to Postmates to Washio,
strongly promote the idea of turning individuals into “micro-entrepreneurs” able to work
wherever and whenever they want. As Slee explains, though, this pitch hardly an act of
39

beneficence. By classifying workers as independent contractors, rather than full employees,
companies are relieved “from having to pay employment insurance premiums, sick leave, and
from having to abide by employment standards.”
56
Slee summarizes, “Risk is pushed entirely
onto the sub-contractor.”
57
If there is any question whether this “micro-entrepreneur” discourse
aligns with neoliberalism of the Californian Ideology, one only has to look at a recent proposal
by two former Congressional staffers to use services like Uber and Lyft to reinvigorate the idea
of workfare:
“The government should expect that able-bodied safety net beneficiaries be willing to engage in the gig
economy before collecting benefits … As long as Uber and Lyft will hire anyone who can pass a
background check, we can’t just give away a free check to anyone who chooses not to work … As
Republicans turn their attention to the next round of welfare reform, they should look to the gig economy to
lead the way.”
58

As mentioned, though, deregulation and labor flexibility are pre-existing tenants of the
Californian Ideology. The Sharing Economy may have amplified their importance within that
ideology, but this does not constitute a fundamental shake-up for the ideology. A more
foundational change, then, comes by way of a relatively novel feature of the Sharing Economy:
ratings. The claim for “novelty” needs some defense, though. Ratings, of course, have been a
major part of the Silicon Valley landscape for decades. For instance, e-commerce sites like eBay
and Amazon have long relied on user ratings both to help customers evaluate products and to
foster trust within the platforms. Other sites, like Yelp and TripAdvisor, have even made user
ratings their core feature – subjecting everything from hotels to restaurants to natural landmarks
to user reviews. That said, rating systems within the Sharing Economy are distinctly different
from their predecessors. To begin, ratings are practically obligatory on Sharing Economy
platforms. At the conclusion of an Uber ride, for example, users are immediately prompted to
40

rate their drivers. Similarly, Airbnb regularly prompts users to summarily leave reviews of their
hosts. More significant, though, is what is being rated. On traditional e-commerce sites,
including eBay and Amazon, users generally rate large businesses, small businesses, or
anonymous user accounts (see, for example, my recent eBay purchase of a videogame from one
SimplyGil20). In the case of the Sharing Economy, though, the subjects of ratings are almost
always individuals. Moreover, they are not anonymous. After a passenger uses Uber, they do
not rate Uber, nor do they rate the anonymous DriverGil20. Instead, they rate a person – a
person using their actual identity. Deeply significant, too, are the stakes involved in these
ratings. As a case in point, rating a restaurant poorly on Yelp can have negative consequences
for that business, but not necessarily. Business owners are free to publicly respond to a negative
review and, moreover, other users, having read the content of a review, may choose to dismiss it
(“what a crank!”). In the case of platforms like Uber and Airbnb, though, negative reviews can
be disastrous for those being reviewed. In the case of Uber, an average rating that falls below
4.6 can get drivers removed from the platform.
59
Airbnb, meanwhile, threatens to remove hosts
that “receive 4 consecutive less than 4 star reviews.”
60
Other Sharing Economy services have
similar cutoffs or, alternatively, penalize workers with low ratings by making it more difficult for
them to access work.
The combination of these factors has ensured that users have taken on the roles of middle
managers within the Sharing Economy. Put in the position of evaluating workers – and even
being able to effectively dismiss the ones they deem subpar – a user becomes a boss every time
they step into an Uber, stay in an Airbnb, or order dinner through Postmates. This is such a new
development that, as of the writing of this chapter, is has yet to make its way into much
sociological literature. That said, it is still possible to trace the offloading of managerial
41

responsibilities to earlier work in the field. For one, we can see the beginnings of managerial
dispersion in Linda Fuller and Vicki Smith’s 1991 article on the increasing “utilization of
consumer feedback to manage employees.”
61
As Fuller and Smith document, the growing
emphasis on “quality of service” within the service sector eventually led to a similarly increased
emphasis on using customer feedback to manage both employees and middle managers. The
authors here point out that in their survey of 15 companies, every single one collected, analyzed
and used customer feedback, whether that meant employing telephone surveys, focus groups,
mystery shoppers, or other methods. Fuller and Smith dub this process “consumer control or
management by customers.”
62

Another thread of sociological literature worth noting involves the increasing amount of
scholarship concerned with the growing intersection between production and consumption. For
example, Kerstin Rieder and G. Günter Voss have responded to this overlap by coining the term
“working consumer.” As Rieder and Voss explain, the notion that “customers are active is not a
new idea.” Rather, they detail, customers have “always had to inform themselves about the
products and services on offer, they had to get to the point of sale, transport the goods, prepare
them at home and to dispose of the wrapping and packaging.”
63
However, what is new, they
argue, is the increasing transfer of “functions from employees to customers, clients and patients”
– a trend they find in everything from IKEA furniture assembly to subway ticket dispensers to
online banking sites.
64
“Customers,” Rieder and Voss summarize, “are no longer the classic
kings to be waited upon, but are more like co-workers, who assume specific elements of a
production process that remains ultimately under control of a commercial enterprise.” They
continue, “We interpret this development as the emergence of a new consumer type: the working
42

customer.”
65
As Yiannis Gabriel et al. note, other scholars have coined similar terms, including
the “prosumer,” the “digital prosumer,” and the “working consumer.”
66

According to Gabriel and Tim Lang, the “rise of the consumer-worker” represents a
major moment in the development of capitalism, essentially turning “the Fordist Deal on its
head.” They argue, “Something qualitatively new is indeed unfolding.”
67
On the one hand, we
can read the Sharing Economy as a simple continuation of this ascendance of the consumer-
worker, with the customer simply taking on more tasks – tasks that align with Fuller and Smith’s
observation regarding the increased importance of customer feedback. It is perhaps more
tempting, though, to read the Sharing Economy as begetting yet another major moment for
capitalism: the creation of the “managing consumer.” No longer are consumers acting, in the
words of Rieder and Voss, “as co-workers,” but rather as managers.
68
The rising importance of
consumer feedback began to give customers a roundabout way of exerting power over
employees, but now customers are being granted direct control over employees. As Alex
Rosenblat and Luke Stark observe in the case of Uber, we are witnessing a “redistribution of
managerial oversight and power away from formalized middle management.”
69
Consumers are
granted authority that they have never had before.
Lending credence to the idea that the “managing consumer” represents “something
qualitatively new” is that the Sharing Economy is likely to be just a starting point for consumer
management. Writing for The Verge, Josh Dzieza comments, “Rating systems are too efficient a
model not to spread” – an argument, he writes, that is echoed by all of the economists and
investors he spoke to for his article.
70
He elaborates, arguing that Sharing Economy customers
are “more efficient than any boss a company could hope to hire.” He continues, “They’re always
there, working for free, hypersensitive to the smallest error.”
71
Indeed, Dzieza points out that
43

ratings systems have already begun to spread beyond the Sharing Economy. As an example, he
points to the growing use of tableside tablets by restaurant chains like Olive Garden that allow
customers to rate their service. The manufacturer of these tablets, Ziosk, brags that their clients
see a dramatic (“>30x”) growth in the number of customer surveys.
72
The Finnish company
HappyOrNot attempts to make such ratings even more portable, manufacturing compact
machines that allow customers to rate services by way of a spectrum of smiley/sad faces.
According to the company, a thousand such machines are now in use within the US at locations
like banks and health care facilities.
73
If you have stopped by an airport restroom recently, there
are good odds you may have already seen one.
If one is to concede that the “managing consumer” represents a significant economic and
social development, it becomes imperative to further grapple with the consequences of consumer
management. As sociologists have noted in the scholarship surrounding customer feedback and
consumer work, there are complications involved in relying on consumers. Fuller and Smith, for
instance, point out that the incorporation of consumer feedback into a company’s management
process can breed resentment and resistance amongst employees as they grapple with “negative
reports on their work.”
74
In a review of marketing texts, meanwhile, Marie-Anne Dujarier finds
that marketing professionals have identified “the many advantages … of putting consumers to
work,” but have also had to worry about ensuring “consumers act correctly,” having discovered
that customers can be “capricious and emotional,” can come with “varying levels of competence
and skill,” and, perhaps most importantly, are unlikely to be concerned with a company’s
primary objective, “which is profit.”
75
And so, as might also be expected, the creation of the
“managing consumer” has also produced its fair share of issues for companies and – even more
significantly – their employees. First and foremost, consumers should not be expected to be
44

neutral, fair-minded managers operating with a consistent set of standards. Rather, as Slee
writes, they should be expected to produce “erratic and inconsistent ratings.”
76
Dzeiza attempts
to imagine why such inconsistency abounds, writing, “What do we rate for? We rate for the
routes drivers take, for price fluctuations beyond their control, for slow traffic, for refusing to
speed, for talking too much or too little, for failing to perform large tasks unrealistically quickly,
for the food being cold when they delivered it, for telling us that, No, we can’t bring beer in the
car and put our friend in the trunk – really, for any reason at all.” And, as Dzieza continues, such
reasons are bound to include race and gender, for “ratings also give power to whatever biases
customers have.”
77

Both Slee and Dzieza argue that the arbitrariness of customer ratings has had the effect of
making the lives of Sharing Economy workers ever more precarious. Slee argues, “A reputation
system is the boss from hell: an erratic, bad-tempered and unaccountable manager that may fire
you at any time, on a whim, with no appeal.”
78
Dzieza, meanwhile, quotes Harvard Law
professor Benjamin Sachs, who explains just how troubling it is for these services to be
substituting “customer reviews for management.”
79
As Sachs explains, unions long fought
“against arbitrary decision-making.” Sachs elaborates, “You don’t want to allow management to
fire someone because they don’t like them, because they looked at them funny.” When
customers become managers, though, these hard-fought labor victories become irrelevant.
“There’s no check on what customers can do,” Sachs suggests, “and it’s almost impossible to
imagine how you’d impose such a check.”
80
And, as both Slee and Dzieza argue, the lack of
such checks ensures Sharing Economy workers operate with great anxiety. Slee writes, “Uber
drivers’ discussion boards show a consistent fear over occasional bad ratings – how did they
come about? Can they be appealed? (no), how can they be avoided?” He continues, “Drivers
45

must continually worry about that one-in-ten customer who will complain that you didn’t offer
them bottled water, or that you were overly friendly, or not friendly enough.”
81
Talking to a
dozen Uber drivers, Dzieza notes a similar “extreme wariness about the customers who wield the
ratings.”
82
He quotes one driver who says, “Only bad things can happen to you. We’re
scurrying like rats.” Another driver notes that they have to be cautious when picking up riders.
“Once you start the ride, you’re screwed, you put all the power in the customer’s hands.”
83

As Slee, Dzieza, Rosenblat and Stark all point out, the culture of fear created by user
ratings begets significant demands for emotional labor. Ratings, Slee writes, are “a way to
enforce ‘emotional labor’; service providers are compelled to manage their feelings and present
the face that the platforms demands.” He continues, “It’s the fast food worker’s ‘Have a nice
day’ taken to the next step.’”
84
Rosenblat and Stark, meanwhile, also note that the “behaviors on
the part of Uber drivers are classic example” of emotional labor.
85
Dzieza makes a similar
argument. He writes, “Ratings result in a sort of coerced friendliness, emotional labor markedly
different from unrated taxi drivers.”
86
Temple Law professor Brishen Rogers comments to
Dzieza, “Ratings create strong incentives for drivers to be subservient, to smile, to be happy even
when they’re not.” Suffice it to say, then, that the rise of the “managing consumer” is
accompanied by a rise of headaches for workers.
To summarize, then, neoliberalism is far from dead. Indeed, it would seem to be thriving
– particularly as we consider the steady deterioration of labor security and the growing
prevalence of the free market logic embedded within the Californian Ideology. It would be too
simple, though, to merely say that neoliberalism continues to hold influence in the wake of the
financial crisis and the Great Recession. Rather, it must be acknowledged how neoliberalism is
continually altering its shape as the economy shifts and, in particular, as the technology sector
46

cements its hold over the economic, cultural, and social landscapes. As mentioned above, the
rise of the Sharing Economy has meant an intensified interest in deregulation and labor
flexibility. More novel, though, has been the scattering of managerial responsibilities across
customer bases. Within the Sharing Economy, middle managers are largely eliminated, their
tasks now passed on to consumers who – minus any training or standards – become responsible
for evaluating and penalizing employees. As this process spreads beyond the Sharing Economy
and into other sectors of the economy, neoliberalism is inevitably altered. No longer is the ideal
to push basic production tasks onto costumers, but also management.

Boss TV
As mentioned above, “governmental practices and rationalities” should not be expected
to remain static. That in mind, the previous section has worked to establish how neoliberalism
has evolved over the last several years in response to significant economic, social, and cultural
shifts. For our purposes, what is important here is that the neoliberalism that undergirded the
bulk of the reality TV scholarship is not the same as the neoliberalism of today. As reality TV
scholars have suggested, reality TV and neoliberalism appear to exist in parallel, sharing
something akin to a symbiotic relationship. More specifically, these scholars have alternatively
described reality TV as circulating neoliberal discourses, enacting neoliberal principles,
“training” viewers in neoliberal logic, and providing resources to help viewers navigate a society
beholden to a neoliberal consensus. Given neoliberalism’s evolution in recent years, the
question becomes whether or not this multi-faceted relationship has survived neoliberalism’s
changes.
47

To repeat from the previous section, recent years have seen neoliberalism successfully
weather the tumult of the financial crisis and Great Recession. Indeed, certain core tenants of
neoliberalism, like deregulation and labor flexibility, may have even more influence than ever
before thanks to both the crisis and the continuing strength of the technology sector. As argued
above, though, the rise of the Sharing Economy has meant one rather novel adjustment for
neoliberalism: consumers are increasingly serving as middle managers – a shift exemplified by
the expectation that consumers rate Sharing Economy workers and, in the process, filter out any
“subpar” individuals. This is a notable development in the history of neoliberalism because – as
the reality TV scholarship makes clear again and again – one of the tenants of neoliberalism has
been self-governance. In other words, according to neoliberal logic, citizens are meant to fend
for themselves, rather than rely on government assistance. Part of neoliberalism, then, has been
the expectation that citizens learn to manage themselves properly. As Rose writes, the goal is to
“create individuals who do not need to be governed by others, but will govern themselves,
master themselves, care for themselves.”
87
However, the rise of the Sharing Economy and the
birth of the “managing worker” now means that citizens are no longer just expected to properly
manage themselves, but also others. Self-governance, then, begins to extend beyond self-
management.
Given the emerging expectation that citizens now manage each other – in addition to
themselves – we can ask whether reality TV has kept up. Following largely in the wake of
Ouellette and Hay, we might – to be more precise – ask whether reality TV has provided
additional resources that might assist viewers with this change. In order to answer this question,
the rest of this section will turn to a sub-genre of reality TV almost entirely concerned with
management – a sub-genre that this paper will term “Boss TV.” As the rest of the section will
48

detail, Boss TV has indeed provided managerial resources to viewers – training them how to
interact with employees and, in turn, succeed as managers.

Defining “Boss TV”
As other scholars have documented, one of the sub-genres of reality TV to emerge over
the last decade and half has involved shows focused squarely on the world of business.
Raymond Boyle and Maggie Magor scan the reality TV landscape in the UK and observe, for
instance, “the explosion in television programmes relating to business or entrepreneurship in one
form or another.”
88
Similarly, Nick Couldry notes how “the growth of Reality TV in Britain and
many other countries since the early 1990s has involved many portrayals of the contemporary
workplace.”
89
These scholars, focusing largely on the UK, mention a wide range of shows
touching on business, ranging from The Apprentice (BBC, 2005-present) to Mary Queen of
Shops (BBC, 2007-2010), but this section will have a slightly different – and narrower – scope.
For one, this section will largely focus on American television, though it will naturally touch on
other countries, too. Reality formats can, of course, span the globe. For another, it will focus on
shows that have aired over the past few years – a focus that aligns with the previous section’s
suggestion that neoliberalism has evolved in recent years. Most significantly, though, it will
narrow its focus to those reality TV programs that especially emphasize the work of
management.
To be more specific, Boss TV concerns itself with how to manage – a task generally
accomplished by showcasing seasoned businessmen and businesswomen able to offer both
implicit and explicit lessons in how to be a successful administrator. In sketching out what Boss
TV looks like with some more precision, though, it is useful to employ a simple taxonomy
49

outlined by Boyle, along with Lisa W. Kelly, in the article, “The celebrity entrepreneur on
television” – one of several pieces in which Boyle and co-authors examine the overlap between
business and television. In this piece, Boyle and Kelly analyze the place of the “celebrity
entrepreneur” (e.g. The Apprentice’s U.K. centerpiece Alan Sugar) on reality TV and argue that,
generally, these entrepreneurs appear either as a “presenter/expert” or as a “judge/investor.”
90

Although not all of the entrepreneurs at the heart of Boss TV are necessarily celebrities in the
mold of Sugar, it is still possible to largely divide Boss TV into these two categories.
To begin with the first category, in shows in which the managerial figure is a
“presenter/expert,” the primary focus tends to be on fixing struggling businesses. As Boyle and
Kelly note, this focus on “troubleshooting” ailing companies has deep roots in the UK, as
Troubleshooter – which ran from 1990 to 1993 on BBC – eventually beget later shows like
Ramsay’s Kitchen Nightmares (Channel 4, 2004-2014). In the US, the history is perhaps not as
strong, but the troubleshooting focus has nonetheless found success by way of shows like Food
Network’s Restaurant: Impossible (2011-present) and Restaurant Stakeout (2012-present), Fox’s
Kitchen Nightmares (2007-2014, adapted from Ramsay’s Kitchen Nightmares) and Hotel Hell
(2012-present, also starring Gordon Ramsay), Spike’s Bar Rescue (2011-present), and Travel
Channel’s Hotel Impossible (2012-present). Perhaps the quintessential example of this
“troubleshooting” format in the US has been Kitchen Nightmares, which ran for seven seasons
between 2007 to 2014. In a typical episode, things open with Ramsay arriving to a floundering
restaurant and sampling the menu. Soon after, Ramsay begins to diagnose the restaurant’s issues
– a diagnosis that often centers on unwieldy menus, unsanitary food preparation, and improper
management. Ramsay then sets about fixing these issues, whether that might mean streamlining
a menu, scrubbing a kitchen, or lecturing managers about their failures. After a week of
50

problem-solving, Ramsay departs, only to return again several weeks or several months later to
see if the fixes have stuck. As Boyle and Kelly note, troubleshooter shows often involve family
tensions and, indeed, Kitchen Nightmares is no different, with Ramsay often serving as a
counselor for families whose relationships have been strained as a result of their failing
businesses.
One of the most consistently popular examples of Boss TV, CBS’s Undercover Boss
(2010-present), has entailed a slight variation on the troubleshooter formula. In Undercover
Boss, the managerial “presenter/expert” is not an outsider parachuted in to fix a struggling
company, as in the aforementioned troubleshooter shows, but rather a high-ranking executive
tasked with investigating their own company by going “undercover” as an incognito jobseeker.
91

In the usual Undercover Boss episode, we begin with a short profile of the executive about to go
undercover, often visiting them at their home and watching them interact with their family.
Following this brief profile, the show introduces the executive’s disguise – frequently involving
hairpieces and fake eyewear – and then begins placing the executive in various roles across the
company, each of which typically comes with a mentor. For example, in a season one episode
featuring the CEO of 7-11, Joseph DePinto, DePinto is first tasked with preparing coffee during
a popular store’s morning rush – a role that pairs DePinto with a long-tenured 7-11 employee
who teaches DePinto the ins and outs of coffee and customer service. After several of these roles
– DePinto, for example, also yawns his way through a night shift and later rides aboard one the
company’s delivery trucks – the executive then returns to the company’s headquarters, where
they proceed to debrief other executives about what they have learned during their time
undercover and, most significantly, what problems need to be addressed. Finally, each episode
wraps up with the executive calling in the various employees who have served as mentors and
51

rewarding them for their dedication to the company. In the 7-11 episode, for instance, DePinto
gifts his upbeat delivery truck mentor with a 7-11 store of his own. Occasionally, too, the
executive metes out corrective action to employees who have been observed performing below
par.
Returning to Boyle and Kelly’s schema, the rest of Boss TV puts managerial figures into
“judge/investor” roles. In The Apprentice, for example, businessman Donald Trump presides
over a series of challenges that pit young, aspiring tycoons against each other – the winner
receiving the chance to work for Trump’s company, and the losers each being told, “You’re
fired.” In recent years, a more popular strain of the “judge/investor” variety of Boss TV has
derived from ABC’s Shark Tank – itself a derivation, having descended from Japan’s Money
Tigers. In Shark Tank, fledgling entrepreneurs appear before a panel of successful businessmen
and businesswomen – termed “sharks.” After the “sharks” listen to the entrepreneurs pitch their
businesses, the sharks then mull whether to invest and, if so, at what terms. In the wake of Shark
Tank’s continued success, CNBC, which airs Shark Tank reruns, has launched multiple shows
that use a similar formula. In Restaurant Startup, aspiring restaurateurs pitch their culinary ideas
to two potential investors. West Texas Investors Club, meanwhile, largely follows the Shark
Tank formula, but moves the location to West Texas.
While most Boss TV shows neatly fall into one of the two categories mentioned above,
one show has notably combined the “presenter/expert” role with the “judge/investor” role. The
show, CNBC’s The Profit, hands both of these roles to Marcus Lemonis, CEO of Camping
World and Good Sam Enterprises. For the most part, Lemonis operates in the typical
troubleshooter role – first arriving to struggling businesses, then diagnosing their ills, and finally
offering advice for turning things around. As with the typical troubleshooter show, too, there are
52

often family tensions to sort out. What separates The Profit from the typical troubleshooter
formula, though, is that Lemonis eventually makes investment offers to the companies. In the
first episode of season three, for example, Lemonis heads to SJC Drums – a drum manufacturer
based out of Massachusetts. After identifying a number of issues with the company – ranging
from their manufacturing process to their profit margins – Lemonis offers $400,000 for 33% of
the company. The company, having faced large losses in recent years, agrees to the deal and
Lemonis comes on board as a partner. The combination of these two roles does have certain
complications, though – after only three seasons, Lemonis had built up a sizable investment
portfolio. This in mind, CNBC and Lemonis are teaming up again for The Partner, in which
Lemonis will search – in the style of The Apprentice – for someone to manage his growing
portfolio of investments.
The above paragraphs have established how Boss TV structures itself – generally by
placing an experienced entrepreneur into the role of a “presenter/expert” troubleshooting
struggling businesses or into the role of a “judge/investor” assessing potential business
opportunities – and have also laid out many of the shows that make up the Boss TV category.
What is left unclear, though, is how exactly Boss TV provides its managerial lessons. To better
understand this aspect of Boss TV, we need to explore the perspectives offered by the Boss TV
texts. As mentioned above, scholars interested in reality TV have already scrutinized several of
the shows that make up Boss TV, particularly earlier shows like The Apprentice. And, typically,
these scholars have focused on the workers/contestants depicted in these shows – arguing that the
shows provide lessons for these workers/contestants in becoming exemplary managers of the
self. Ouellette and Hay, for instance, argue that The Apprentice “casts the game as a learning
experience and casts the contestants … as there to learn – to prove that they possess qualities
53

which Trump would and will reward” – namely, self-actualization, self-discipline, and the ability
to be “energetic makers of their own fortunes and financial futures.”
92
The scholars continue,
“In this respect, the series is not only a vehicle for displaying Trump’s business ventures and
brands, but a lab for teaching/producing/inventing an ideal corporate citizen.” Nick Couldry and
Jo Littler make comparable comments about the show, suggesting that “the cultures of work”
within The Apprentice encourage contestants to “fashion themselves into successful working
agents” and, furthermore, note how the show “enshrines the individualized atomized self as the
privileged or meaningful site of work.”
93
Writing on Undercover Boss, meanwhile, Toby Miller
argues the show “emphasizes the responsibility of each person to master their drives and harness
their energies in order to secure better jobs, homes, looks, and lovers.”
94
Similarly, Barton St.
John argues Undercover Boss shows “the importance of self-government in the increasingly
privatized American sphere.”
95

There is an implicit assumption in much of this scholarship that the main entry point into
Boss TV shows like The Apprentice and Undercover Boss is by way of the workers/contestants,
rather than the managers. While all of these scholars would doubtless assert that viewers might
choose to read these programs any number of ways, their own readings are fairly uniform in this
shared focus on workers/contestants. On the surface, such uniformity makes a lot of sense.
Significantly, in certain Boss TV shows, including The Apprentice, the workers receive the bulk
of the screen time. Moreover, for the majority of viewers, the workers/contestants may be easier
to relate to than the managers. The Apprentice, for example, goes out of its way to establish
Trump as an extraordinary figure – not just because he is positioned as a masterful businessman
whom the workers would be privileged to have as a boss, but also because he is shown living a
luxurious life fit for a king (or president?). All of that said, it is worth considering the other side
54

of the equation, too, for viewers do not necessarily have to watch these shows solely from the
perspective of the workers/contestants. Indeed, Boyle and Kelly analyze Dragon’s Den and
comment, “The viewer is regularly aligned with the viewpoints of … the ‘dragons’” rather than
the fledgling entrepreneurs on the other side of the stage.
96

Similar statements could be made for every other Boss TV program. In The Apprentice,
for instance, the camerawork often aligns viewers with the workers/contestants and places them
under the vicarious scrutiny of Trump’s furrowed brows, but they are just as often aligned with
Trump. In Kitchen Nightmares, meanwhile, viewers might empathize with the restaurant owners
and chefs that Gordon Ramsay visits, but the show strongly encourages viewers to primarily
align with Ramsay by way of televisual devices like staged confessionals with Ramsay. Boyle
and Kelly explain how these devices work. They write, “By addressing the camera directly and,
by extension, the audience at home, the television viewer is encouraged to identify with the
presenter/expert in these programmes, a position that is supported due to their way in which their
credibility is validated by their offscreen success within their respective restaurant, retail and
property businesses.”
97
The fact that Boss TV leaves plenty of room for viewers to identify with
both managers and workers/contestants has significant ramifications for how the shows operate.
As mentioned above, a number of scholars have identified the ways in which Boss TV programs
provide lessons in how to become the “ideal corporate citizen.” If, though, we think further
about the managers of Boss TV, we can also see how the shows provide lessons in how to
become the ideal managers of these ideal corporate citizens. Boyle and Kelly make such an
argument. Writing on Dragon’s Den, they argue that because viewers are aligned with the
viewpoints of the ‘dragons,’ viewers are “encouraged to draw upon the skills, knowledge and
55

expertise put forward by these entrepreneurs before going on to judge the contestants and
participants accordingly.”
98

Dragon’s Den, though, is hardly the only show in which viewers are meant to take in the
“skills, knowledge and expertise” of the managers at the center. In The Profit, for instance,
viewers are not only encouraged to heed the show’s insistence that workers be self-motivated
and self-disciplined, but also to absorb Lemonis’s broader lessons in managing a successful
business – lessons he condenses into the mantra “people, process, product.” In terms of
“process,” for example, Lemonis again and again urges business owners to re-evaluate their
manufacturing processes and cut out inefficiencies. Similarly, in Kitchen Nightmares, viewers
are exposed both to the show’s continual insistence that lower-level restaurant employees be
passionate and flexible, and to Ramsay’s repeated refrains about what it takes to be a profitable
restaurant owner. As with The Profit, there are frequent lectures about managing people, process
(e.g. streamlining a menu in order to make cooking easier), and product (e.g. improving the
quality of a restaurant’s produce). As Boyle and Kelly’s Dragon’s Den example implies,
though, such managerial lessons are not always so explicit. In Kitchen Nightmares, for instance,
audiences are not only offered the chance to take in the many pieces of advice that Ramsay
offers to restaurant employees, but also to observe how Ramsay identifies these issues and passes
along his advice – in short, to see how Ramsay conducts himself. The audience learns, then,
more than the importance of constructing a coherent menu – they also learn how to carry
themselves as a manager. From Ramsay, for example, one might learn the importance of being
confident, detail-oriented, and straightforward with feedback. To repeat, though, these lessons
are implicit – it is rare that Ramsay calls attention to any of these attributes. Rather, the audience
56

is left to conclude for themselves that it is traits like these that helped Ramsay become such a
successful manager.
To summarize, then, Boss TV is defined both by its structure and its content. In terms of
structure, Boss TV places “bosses” at the center – typically either as “presenters/experts” or as
“judges/investors.” In terms of content, meanwhile, Boss TV revolves around its lessons. These
are not just the aforementioned lessons of self-management that other scholars have identified,
but also lessons in managing a business and, accordingly, other workers. The previous
paragraph has referred to these managerial lessons in basic terms – mentioning, for instance, The
Profit’s focus on “people, process, product” – but what has not yet been established are the
specifics of these managerial lessons, particularly as they pertain to managing other people. The
next section will delve into these lessons more thoroughly and, in the process, explain how they
tie back into neoliberalism’s recent evolution.

Boss TV in the Neoliberal Context
As has been mentioned throughout this chapter, a number of scholars have discussed the
relationship between reality TV and neoliberalism. As has been mentioned, too, a major
argument to emerge out of that literature has been that reality TV provides resources to help
viewers navigate neoliberalism – as Ouellete and Hay write, “TV is being reinvented to make
itself more useful as a resource for expanding one’s capabilities, fashioning a ‘better’ life, and
developing strategies for addressing problems or threats pertaining to one’s body, household,
work, property, and family.”
99
Neoliberalism, though, is not an entirely static phenomenon, so
the question that has been posed above is whether reality TV has kept pace with neoliberalism’s
changes, or, to be more specific, whether it has continued to make itself “useful as a resource” to
57

viewers. To answer this question, we might begin by asking what sorts of problems might arise
as a result of neoliberalism’s evolution and, accordingly, what resources might help alleviate
these problems.
Returning, then, to neoliberalism’s evolutions, the above sections have argued that one of
the most significant modifications to neoliberalism over the last several years has involved the
increased expectation that consumers act as middle managers. In short, then, the neoliberal
directive of “applying, conducting, and cultivating oneself in the best way possible” now also
includes managing others.
100
Such a change has a number of obvious benefits for companies,
most notably the ability to shrink their payrolls, but the expectation that consumers stand in for
middle managers has also presented certain aforementioned complications. To repeat,
consumers can be fickle. Moreover, they lack a shared set of standards. One Uber customer
might rate a driver down for being too talkative, while another customer might rate the same
driver down for being too quiet. Significantly, too, customers come ingrained with all sorts of
biases – biases that they bring with them into the Sharing Economy. A study of Airbnb hosts
found, for instance, that “non-black hosts are able to charge approximately 12% more than black
hosts.”
101
It is easy to imagine, then, Sharing Economy workers being rated down – consciously
or not – due to factors that are normally protected under employment discrimination laws. As
Rosenblat argues, “Through the rating system, consumers can directly assert their preferences
and their biases in ways that companies are prohibited from doing.”
102

A range of actors would seem to have an interest in solving these problems. Sharing
Economy workers, of course, want to be evaluated fairly. On that note, Josh Dzieza, author of
The Verge article on rating systems, comments, “Despite all their complaints about ratings —
and there were many — almost none of the workers I spoke with wanted them to go away.”
58

Rather, he writes, workers wanted improvements – including “better education for customers
about what ratings meant.”
103
Similarly, Rosenblat and Stark note that a “common complaint”
they heard while studying Uber drivers involved “the lack of passenger education on how the
company utilizes ratings.”
104
Sharing Economy companies, meanwhile, want the rating systems
to be reliable so that, on the one hand, consumers trust them and so that, on the other hand, they
do not provoke major backlashes from workers – backlashes that, in their most feared scenarios,
might lead to the need for actual managers earning actual salaries. And, finally, customers have
an interest in improving the situation because they may eventually find themselves being
evaluated through similar ratings systems – as mentioned above, rating systems appear to be
expanding their reach beyond the Sharing Economy.
As the quote from Dzeiza indicates, many of the problems presented by the rise of the
consumer-cum-manager can be linked directly to the lack of education surrounding ratings. Of
course, many of these issues are ones of basic knowledge. Uber passengers, for example, are
never told what becomes of their ratings and, as such, are able to remain blissfully unaware that a
four star rating might cause a driver to lose their job. Better informing customers about the
implications of the ratings, then, could go a long way toward standardizing the behavior of
customers. Some of the issues go deeper, though – convincing customers to be less capricious
cannot be accomplished solely by telling customers more about how the ratings systems work.
Rather, it requires them to change their approach to rating – becoming more consistent, more
open-minded and, perhaps most important of all, more empathetic. In short, then, “better
education” is not merely a matter of educating customers about the purpose of rating systems,
but also of educating customers how to be raters – a matter not just of passing along the right
information, but also of instilling proper conduct.
59

As mentioned in the previous section, Boss TV is very much concerned with imparting
lessons in how to conduct oneself as a manager. It was briefly noted, for instance, that Gordon
Ramsay of Kitchen Nightmares models traits like confidence and candidness. However, what
remains to be done is to more thoroughly delve into the managerial lessons provided by Boss
TV, particularly those lessons that appear across the genre with some consistency. To begin, a
number of Boss TV programs emphasize the importance of accountability – showing the need
for managers to be responsible and thorough in their decision-making. Perhaps the quintessential
model for accountability comes by way of Shark Tank. Each segment of the program begins
with a pitch to the “sharks.” It is these portions of the program that are liable to feature nervous,
sweaty inventors; cute children; adorable animals; smug, over-confident wannabe moguls; and
narratives about overcoming unforeseen financial hurdles and medical issues. However, the real
drama of the show begins once the pitches wrap up and the focus shifts to the sharks. As the
sharks consider whether to invest, they grill the entrepreneurs about their business models, their
cash flows, and their plans for the future – in particular, what they would plan to do with a
potential investment. If the sharks like what they hear, they then move on to discussing
investment terms, going back and forth with the entrepreneurs on items like equity versus debt
financing, equity percentages, and total valuation numbers. If multiple sharks are interested in
the business, negotiations are liable to become slightly more prolonged and, at times,
contentious.
As one might surmise from the Shark Tank title, the show’s grillings are its signature
feature. From the grillings, one might make any number of conclusions about the sharks and
their personalities – perhaps that they are too cruel, too kind, too exploitative, too trusting, too
risk-averse, too risk-prone, etc. – but it is clear that one of the show’s preferred readings is that
60

the sharks are consummate professionals, acting consistently and methodically. The show
spotlights, for instance, the ability of the sharks to talk about financials with some level of
thoroughness and never skips an opportunity to show the sharks doing math in their notebooks.
Moreover, the show highlights the sharks’ ability to approach the “tank” with unbiased, open
minds – on the one hand, carefully listening to the pitches that appear superficially absurd and,
on the other hand, also rigorously scrutinizing (if more delicately) the pitches that come attached
with pets or children. The implicit message is that part of what has made these sharks so
successful – and the opening credits featuring yachts, sports cars, and mansion make no secret of
their wealth – is that, at least when it comes to their business dealings, they are accountable.
Shark Tank is hardly the only instance of Boss TV positing that accountability breeds
success. CNBC’s The Profit is another example. As detailed above, The Profit finds
businessman Marcus Lemonis both troubleshooting and investing in a wide range of businesses.
In every one of these visits, Lemonis is shown being thorough and, just as importantly, being
cool-headed. As is usual in troubleshooting shows, Lemonis is often able to find problems that
have escaped the attention of those within the company. In the first episode of season two, for
example, Lemonis heads to a used car dealership in Illinois. After deliberately examining the
dealership and its inventory, Lemonis determines that the dealer should be selling a wider range
of vehicles. This in mind, Lemonis begins the process of liquidating the current inventory. But,
as mentioned, Lemonis is not just positioned as being meticulous, but also cool-headed. In this
episode, as is often the case in the show, the business owner resists Lemonis’s suggestions.
Lemonis, though, is not flustered by the owner’s anxiety. Rather, Lemonis calmly talks the
owner through his decision-making processes. As with Shark Tank, success is equated with
accountability.
61

One more example worth citing is the oft-analyzed Apprentice. Although Donald Trump
has long cultivated a reputation that screams anything but “responsible,” particularly in light of
his turbulent presidential run, the version of Trump that shows up in The Apprentice is far more
composed. As is the case with the bosses at the heart of Shark Tank and The Profit, Trump is
depicted as prizing caution. This is particularly evident in each episode’s closing boardroom
scenes in which Trump decides which worker/contestant will be “fired.” The show makes clear
that these “firings” are far from impulsive. Rather, Trump slowly interviews the
workers/contestants about the week’s challenge and, as he proceeds, remains calm even as
emotions flare amongst the workers/contestants. Trump, then, appears a model of composed
decision-making. Re-visiting the show’s first season, Matt Thomas makes similar observations.
Trump, Thomas notes, is consistently equanimous and “never gets defensive.” Moreover, Trump
“seems to listen to and take into account others’ views when making decisions.”
105
Referring to
the Trump’s interaction with his fellow judges, Thomas describes how Trump “relies on their
observations, asks them questions, and takes their opinions seriously. There are a surprising
number of shots of Trump just listening.” Thomas continues, “Even after he’s fired someone, he
invariably checks in again … to ask them if they think he’s made the right decision.”
106
Again,
we see Trump modeling management as requiring deliberation and an open mind.
Across the board, then, we see Boss TV providing a clear picture of how managers
should conduct themselves. Its bosses, the shows imply, found massive success by being both
thorough and open-minded. However, these are not the only traits to prevail across Boss TV.
Behind the importance of accountability, perhaps the most consistent message within Boss TV is
the importance of empathy. The strongest evidence of this comes by way of Undercover Boss.
As mentioned, Undercover Boss disguises corporate executives as down-on-their-luck job
62

seekers and places them in the metaphorical trenches of their companies. While in these roles,
the executives get to know some of their lower-level employees, particularly those who have
been asked to provide guidance to the executives. As the executives become acquainted with
these employees, they often discover that the lower-level employees have complex backstories.
In the aforementioned 7-11 episode, for instance, CEO DePinto finds out that his coffee mentor,
Dolores Bisagni, is undergoing dialysis twice a week. Later, he learns that his trainer on the
delivery truck, Igor Finkler, emigrated from Kazakhstan with just $50 in hand. Invariably, the
executives react with astonishment – and, occasionally, with tears of sorrow – upon learning
these stories. The repeated emphasis on these revelations across the many seasons of
Undercover Boss suggests that managers do not just need to carefully monitor their companies’
bottom lines, but also to pay attention to the humanity of their employees.
While Undercover Boss stresses empathy most insistently, it is far from the only show
within Boss TV where empathy is a prized managerial asset. We might return, for example, to
The Profit. As mentioned above, Marcus Lemonis’s managerial mantra is “people, process,
product.” As part of that first term, “people,” Lemonis speaks regularly about both prizing and
empowering employees. This emphasis appears both on the show and in the press. “The
customer is not number one,” Lemonis says in one interview. “They're number two, right behind
the employee.”
107
In multiple episodes, he insists on giving employees equity and, even more
frequently, he enlists the help of lower-level employees in both identifying and solving the
problems that are ailing their companies. Part of the focus on “people,” too, involves empathy.
Again, Lemonis emphasizes this both on the show and in the press. He comments in an
interview, “You have to be empathetic and sympathetic to people, and you have to be humble
enough to think it’s important to do that.”
108
To cite a representative example, as noted above,
63

troubleshooter shows are prone to cross over into the realm of family melodrama, as many of the
small businesses featured on these programs are family-run. The Profit is no different and, as
such, it is not uncommon to find Lemonis taking on the role of family therapist. In the season
three episode mentioned above, in which Lemonis visits a small drum manufacturer in
Massachusetts, Lemonis ends up mediating between the two brothers who co-founded the
company but, after a series of disputes and a contentious buyout process, have since become
estranged. In this mediation process, Lemonis counsels each of the brothers and seeks to
understand what happened between them. As was the case with Undercover Boss, The Profit
regularly attempts to understand how lives extend beyond the workplace.
We see, then, that a few managerial lessons appear across Boss TV. Rather consistently,
these shows – through the behaviors of the celebrated businessmen and businesswomen that
anchor them – posit that successful managers are both methodical and empathetic. These lessons
in mind, we can now return to the question that opened this section: has reality TV kept up with
the changing terrain of neoliberalism? Or, to be more precise, has it continued to align viewers
with resources for learning how to manage daily life within neoliberalism? As mentioned above,
the offloading of managerial responsibilities onto ordinary consumers has been one of the more
notable recent changes to neoliberalism. And, to repeat, this change has come with certain
issues. Significantly, consumers are fickle, inconsistent, and prone to bias – all problems that
stem, in part, from a lack of education about how to conduct oneself as a manager. Boss TV,
though, provides such an education. By stressing thoroughness, it teaches that management
should be considered and consistent. By stressing empathy, meanwhile, it teaches that
management should consider the circumstances of employees’ lives. These managerial lessons
in mind, Boss TV can be considered – like the reality TV that preceded it – a “resource for
64

acquiring and applying practical knowledge and skills.” As the ideal of self-management has
expanded to include the management of others, reality TV has continued to provide guidance in
maximizing the self.

A Recuperation?
Before moving on from Boss TV, it is worth lingering on the genre just slightly longer –
trying to make some additional sense of these aforementioned lessons. As readers may be aware,
the shows within Boss TV have been some of the more reviled members of the reality TV genre.
For example, The New York Times greeted the premiere of Kitchen Nightmares by writing, “The
subtext of Kitchen Nightmares is that ordinary middle-class business owners need brash and
brilliant moguls to save them from a sad reliance on their own mediocrity. It is an ugly
message.”
109
The Boston Globe, meanwhile, remarked of Shark Tank, “The show is like
watching fish getting shot in a barrel. It offers up poor souls with harebrained schemes and
makes merry sport of eviscerating them.”
110
Particular scorn has been reserved for Undercover
Boss. The Washington Post’s review opined that the show is “drizzled with the feel-good syrup
of corporate bunk” and that it offers but a “hollow catharsis for a nation already strung out on the
futility of resenting those who occupy CEO suites.”
111
Salon, meanwhile, called it an “elaborate
P.R. experiment.”
112
And Entertainment Weekly, just as witheringly, argued, “Instead of being
uplifting, Boss feels opportunistic and condescending.”
113
At a recent media studies conference,
the mere mention of the show elicited a loud chorus of groans.
The derision heaped upon a show like Undercover Boss is not undeserved. Of course, it
is upsetting that the show advocates individual solutions to systemic problems. That is to say,
the show normally ends with a handful of stellar employees receiving cash “rewards” that are
65

meant to alleviate the effects of low wages and a deteriorating social safety net – issues that
undoubtedly affect large swaths of these companies’ workforces and that would be better
addressed by way of improved wages and benefits. As St. John implies, it is also troubling that
Undercover Boss shares a framework with classic mythology that places powerful figures (e.g.
gods and kings) in the role of commoners so that they might learn lessons like the importance of
humility. Drawing upon several television critics, he also observes how discomforting it is that
every week the show allows a new executive to promote themselves as a “‘compassionate
corporate master’ who has the workers’ ‘best interests at heart.’”
114
And, as The Washington
Post review implies (see the “hollow catharsis” comment), such depictions might even have
worrisome effects in the workplace. That is to say, by offering executives as empathetic,
relatable everymen (a vast, vast majority of the executives featured on the show are male), the
show may deflate the anger and resentment that workers might have toward highly-paid
corporate leaders – anger and resentment that could potentially fuel collective action and effect
meaningful change. These criticisms are hard to deny. Indeed, as companies that have
participated in the show readily admit, the program essentially works as free advertising, offering
both exposure and positive brand associations. To quote Gawker, The AV Club, and so many
others, Undercover Boss is – rather transparently – capitalist propaganda.
115

But while Undercover Boss may be incredibly deserving of critique, it is perhaps too
simple to entirely dismiss the show as capitalist dreck. To explain why, we need to move from
thinking about Boss TV as offering specific instantiations of capitalist benevolence to instead
thinking about Boss TV as more broadly suggesting a utopic model of capitalism. As mentioned
above, a core managerial lesson from Boss TV is the value of empathy. Undercover Boss, for
instance, highlights the need for executives to consider how the lives of their employees extend
66

beyond the workplace and to manage with such considerations in mind. While the bosses that
star on the show may just be using the program as free promotion and may not intend to change
their managerial approach after returning from the proverbial “factory floors,” the underlying
message remains: this is how management should work. Managers, that is, should care about
their employees and should value their thoughts and feelings. The workplace, the show strongly
suggests, would ultimately be a better place if managers saw their employees not as replaceable
cogs, but instead as humans with real wants, real needs, and real desires.
As consumers become workers and, in turn, managers, we might wonder whether there is
value to Boss TV emphasizing such messages. To repeat from above, consumers are being
placed in managerial roles without training – a lack that can have severe consequences for
workers they are now supervising. Obviously, ideal solutions to this dilemma would involve
larger changes to ratings systems (e.g. Uber increasing ratings transparency) or, thinking even
bigger, to the very nature of alternative labor arrangements (e.g. Uber drivers uniting behind
driver-owned platforms, as in Trebor Scholz’s call for “platform cooperativism”), but in the
meantime, there may be some value in media offering resources that take aim at issues like
consumer fickleness.
116
So, yes, Undercover Boss may be, to quote Salon, a “capitalist fairy
tale,” but that tale can – to some degree – speak to real concerns.

Boss TV vs. GM TV
To repeat from the introduction, GM TV can be loosely defined as a sub-set of sports TV
primarily concerned not with game action, as in traditional game telecasts or highlight shows like
SportsCenter, but rather with the more mundane duties of a front office executive. NFL and
NBA draft coverage, for instance, emphasizes the evaluation and ranking of college athletes.
67

Similarly, Premier League deadline day programming is solely interested in the buying and
selling of professional soccer players. To repeat, too, there is a significant amount of overlap
between GM TV and reality TV. Like reality TV, for example, many instances of GM TV have
their origins in their relatively cheap production costs. Given such links, it is a natural leap to
begin connecting the ideological work of reality TV to the ideological work of GM TV. More
specifically, we might begin connecting GM TV to Boss TV.
Like Boss TV, GM TV heavily features celebrated managers, in this case front office
executives. Gruden’s QB Camp, for instance, revolves around Jon Gruden, who won a Super
Bowl as the head coach of the Tampa Bay Buccaneers. ESPN’s NFL draft coverage, meanwhile,
is full of one-time executives, like Louis Riddick, formerly the director of pro personnel for the
Philadelphia Eagles, and Bill Polian, longtime general manager of the Indianapolis Colts. As in
the case of Boss TV, these experts are the bearers of managerial lessons. In ESPN’s recent
coverage of the NBA draft and NBA free agency, for instance, former NBA executive Tom Penn
lays out the basics of salary cap and rostering rules. The structural parallels between Boss TV
and GM TV occasionally go much deeper, too. Both Shark Tank and The Apprentice are, in
essence, job interviews, with the workers/contestants pining for investment and employment,
respectively. Similarly, in Gruden’s QB Camp, prospective NFL quarterbacks try to prove their
worth to Gruden, showing that they are worthy of a high draft pick. While Gruden does not hold
the final say on whether the quarterbacks will make their way onto an NFL roster, he nonetheless
operates in a similar “judge” role as the bosses in Boss TV – grilling them about their football
aptitude and offering his opinions on their potential.
117
Prospective NFL players also attempt to
prove their capability in Hard Knocks, this time auditioning for actual front office executives
68

operating as the “judges” who will determine whether or not these players will, as the weeks roll
on, avoid roster cuts and eventually make their way onto a team.
Again, as was the case with Boss TV, the lessons of GM TV are not always explicit.
Yes, viewers do have the opportunity to learn from the experts as they lecture about minutiae
like the financial realities of the NBA salary cap or what skills to look for in a starting offensive
tackle, but once again, viewers might also notice the implicit lessons about the conduct of
management – learning not just the what of sports management, but also the how. To a large
degree, these lessons closely resemble those offered by Boss TV. To begin, one of the prevailing
lessons of GM TV is the value of due diligence, which is nearly identical to Boss TV’s emphasis
on accountability. As an illustration, we might point to the NBA trade deadline show produced
by Yahoo’s NBA section, The Vertical, which was anchored by figures like reporter/”insider”
Adrian Wojnarowski and former Nets executive Bobby Marks. During the show, these experts
slowly worked their way through the players that did, or just as frequently, did not get traded
during the deadline – parsing why certain moves happened and, again, just as frequently, did not
happen. Throughout, the dominant attitude was skepticism. The show led off, for instance, with
a discussion of Houston Rockets star Dwight Howard, whose name had been lauded around as a
potential trade target, but who had not ended up changing teams. In the discussion, each of the
show’s four speakers confidently declared why Howard, despite being a star, was a less than
desirable player – touching on his performance, his injury history, his personality, his salary, and
his contract status. At one point, the show’s host, NBA reporter Chris Mannix, even asked
Marks to “put his general manager hat back on” and discuss whether he would go after Howard
if he was in charge of a team, which elicited a detailed response from Marks about the NBA’s
evolving salary cap situation and why Howard was a risky bet. Quite clearly, then, the show
69

suggests that to be a front office executive, one must be savvy and, in particular, thorough – able
to cut through popular wisdom by being detail-oriented and methodical.
The Vertical trade deadline show is just one example of GM TV’s emphasis on
accountability. As another case study, we might turn to the NFL Network’s sprawling coverage
of the 2016 NFL draft and, in particular, to a show like Path to the Draft, which airs every
weekday afternoon in the months leading up to the league’s annual draft. Like most other
examples of GM TV, Path to the Draft puts managerial experts at the center, as it does not just
feature former NFL players like Charles Davis and Maurice Jones-Drew, but also a former NFL
scout, Daniel Jeremiah, and a former NFL general manager, Charley Casserly. Throughout each
edition of the show, these experts focus on previewing the upcoming draft. An episode from
March 22, 2016, for instance, finds the broadcast team first diving into the needs of the New
Orleans Saints and projecting which college players the team might look to draft come April.
Later, the show turns its attention to scrutinizing Dak Prescott, a quarterback from Mississippi
State University – an examination that, in turn, produces a broader debate about the difficulty of
scouting college players given changes in the college game. Throughout these discussions, the
show’s experts model the same sort of fastidiousness maintained throughout the Vertical show.
The discussion on college scouting, for example, features back and forth exchanges regarding
details like running formations as well as frequent references to specific players that exemplify
the difficulties of scouting college players. Casserly, for instance, is prompted by the show’s
host to talk about how, “as a GM,” he would evaluate players based upon changes in the college
game, to which Casserly responds by detailing the rise of the “two point stance” amongst college
linemen and the related struggles of the previous year’s top draft pick, Jacksonville Jaguars
70

tackle Luke Joeckel. Again, then, the emphasis is on management as detail-oriented and
methodical.
In continuing to draw parallels between Boss TV and GM TV, we can also observe that
GM TV teaches that sports management requires a certain amount of empathy. Of course, one of
the more problematic aspects of the rise of GM TV has been its eagerness to borrow the
industry’s language and treat athletes as faceless “assets” to be drafted, traded, and released – or,
in the case of soccer, to be bought and sold. Indeed, this sort of language is so prevalent and so
obviously dehumanizing that it has caused certain journalists, like popular ESPN basketball
writer Zach Lowe, to publicly express their discomfort with the “asset” terminology. That in
mind, though, much of GM TV suggests how important it is to consider the backstories of
athletes. This suggestion is both direct and indirect. Beginning with the direct, several examples
of GM TV explicitly state that an athlete’s life outside of sport helps define who they are as a
person. Gruden’s QB Camp, for instance, is primarily focused on film study and drills, but
Gruden, in interviewing these college quarterbacks, also intersperses personal questions. As a
case in point, a 2014 episode focusing on Fresno State quarterback Derek Carr opens with a
question about Carr’s relationship with his brother, who was also a highly ranked quarterback
prospect. Soon after, Gruden invites Carr to talk about his personality, an exchange that leads to
a discussion about the serious medical issues encountered by Carr’s infant son. Carr’s episode is
not an isolated example, either. Another 2014 episode, focusing on University of Louisville
quarterback Teddy Bridgewater, begins with a discussion of Bridgewater’s recruitment process
and his experiences on the Louisville campus. Similarly, a 2014 episode on University of
Georgia quarterback Aaron Murray features a conversation about Murray’s experience as a
Georgia student, during which Gruden asks Murray about his time as a psychology major and
71

what he learned in his Ph.D.-level statistics courses. “That’s awesome that you would take
advantage of the academic side of Georgia,” Gruden eventually declares.
Gruden, then, displays an eagerness to know about athletes as people, exploring how their
lives extend beyond the field. The suggestion, then, is that a front office executive should not
only care about matters like a player’s “football IQ” or their accuracy throwing a ball, but also
about their character and their emotions. Not all of GM TV is quite so direct in making this
suggestion, though. Rather, much of GM TV implicitly suggests this by deliberately depicting
athletes’ lives off the field. Perhaps the strongest example of this tendency comes by way of
HBO’s long-running Hard Knocks. As detailed in the introduction, each season of Hard Knocks
brings the viewer into a different NFL team’s training camp, with the primary drama revolving
around which players will make the team and which players will be cut. Although the majority
of the show is spent in the team’s practice facilities, whether on the fields, in the film room, or in
the front offices, a large portion of the show also explores the lives of the players, coaches, and
executives outside of football. For instance, in the 2013 edition of the show, which follows the
Cincinnati Bengals, the third episode begins inside the art studio of linebacker Aaron Maybin,
where he is shown working on an elaborate painting of a tiger’s eye and musing about the nature
of his art. Later, the episode goes inside the homes of safety Taylor Mays, who is shown playing
cards with his girlfriend and modeling his favorite Teenage Mutant Ninja Turtles backpack, and
running back Giovani Bernard, who is shown talking future furniture purchases with his
girlfriend. The suggestion here, as in Gruden’s QB Camp, is that that athletes are not just
identical cogs in a system – rather, they have lives outside of football that are worth exploring.
There may not be a celebrated manager explicitly asking about these lives and modeling an
attention to an athlete as a well-rounded person, as in Gruden’s QB Camp, but the show’s
72

cameras perform a similar role – implicitly communicating that we should want to know about
the athletes in a wider context.
Hard Knocks is not alone in performing such work. As another example, we might look
to ESPN’s extensive build up to the NFL draft. One show that has been part of this coverage the
last two years has been Draft Academy, which follows several prospects in the months preceding
the draft. In following these athletes, the show focuses not just on their preparations for the
draft, as in their workouts and their interviews with teams, but also their personal lives. In one
2015 episode, for instance, the show heads to the childhood home of North Dakota State running
back John Crockett, delving into the mental health issues that affected his mother during his
youth and the large role his football coaches had in his upbringing. As in the case of Hard
Knocks, the suggestion here is that athletes’ lives off the field merit examination, too – that
athletes, in short, contain multitudes.
Such lessons in mind, GM TV’s place within the wider television landscape begins to
come into sharper focus. GM TV, like Boss TV, celebrates the manager. Moreover, GM TV,
much like Boss TV, offers explicit managerial lessons – in this case, involving instruction in
particulars like salary cap structure and football formations. More significantly, though, GM TV
also offers implicit lessons in how to conduct oneself as a manager – offering not just the what of
management (e.g. the two point stance vs. the three point stance), but also the how of
management (e.g. whimsy vs. savvy). And, also significantly, these lessons are quite similar. As
detailed, both GM TV and Boss TV suggest that management requires one to be detail-oriented
and methodical. Good managers, it is implied, cannot be erratic. Moreover, both GM TV and
Boss TV suggest that management requires one to be empathetic. To manage, these genres
argue, is to care about workers as more than the product of their work.
73

By positioning GM TV against Boss TV, we then also have a way of explaining GM
TV’s role within neoliberal society. As mentioned in the previous sections of this project, the
first waves of reality TV consistently provided resources to viewers in accordance with the
demands of neoliberalism. Makeover competition shows like Next Top Model, for instance,
present the lesson of “work on the self as a prerequisite for personal and professional success,”
while group-based game shows like Survivor, educate “about the proper forms of belonging, of
managing differences, and of participation by citizens in various spheres of life.”
118
As
neoliberalism has evolved, so too have these resources. Again, as previously detailed, the reality
genre of Boss TV imparts lessons in how to conduct oneself as a manager – an increasingly
necessary requirement within a post-welfare society in which both work and management are
being pushed onto consumers. As the managerial lessons of GM TV closely resemble those of
Boss TV, it is not too difficult to suggest that GM TV is doing much the same. That is to say, by
providing instruction in how to conduct oneself as a manager, it is providing valuable resources
for consumers now expected to become “managing consumers.”
However, in emphasizing the similarities between GM TV and Boss TV, we should also
remain mindful of the important ways in which these genres diverge – divergences that also
speak to the ideologies at work in these genres. The source of the accountability and empathy in
these genres, for instance, is worth considering with more length. To some degree, there are
parallels in the origins of these lessons. Take, for example, the shared emphasis on empathy. As
explained above, Lemonis views empathy, on the one hand, as a moral imperative and, on the
other hand, as a way to maximize productivity. That is to say, considering employees’ emotions
is not just ethical, but also ensures that employees are working as hard as possible. Within the
context of GM TV, there are similar motivations at work. Gruden, for instance, is ultimately
74

interested in a player’s experiences off the field because those experiences may speak to the
player’s athletic potential. In other words, if they have shown traits like leadership and grit off
the field, those traits may also carry over onto the field. ESPN even pitches Draft Academy with
such language. Matthew Volk, the network’s director of programming and acquisitions, touts the
program as highlighting “the grit, determination and character of some of the biggest names” in
the draft.
119

The accountability and empathy of GM TV, though, also serve to resolve one of the most
pressing tensions at work within both the genre and sports TV at-large – a tension, significantly,
that is unique to sports TV. Professional athletes, of course, are generally compensated fairly
well. Given the rising income inequality endemic to neoliberalism, this has the potential to be an
uncomfortable fact for television viewers. What GM TV serves to do, then, is suggest that
athletes have fully earned the fortunes that come their way. GM TV’s emphasis on savvy, for
instance, showcases the need for sports executives to be shrewd, but it also highlights just how
hard top athletes must work in order to be successful. As an illustration, Gruden’s QB Camp
challenges quarterback prospects to come ready to talk details regarding matters like formations
and play calls. As mentioned above, this provides a clear managerial lesson: managers should be
detail-oriented and have consistent standards – basing their evaluations on deliberate study,
rather than gut emotions. However, the interrogation of the quarterback prospects also serves to
showcase how prepared athletes must be in order to succeed. To get drafted, the show makes
clear, is not a matter of sheer luck. Rather, it requires that athletes study and display a hard-
earned aptitude for the sport. Similarly, GM TV’s emphasis on empathy may highlight the need
for sports executives to consider the lives of athletes beyond the field, but it also serves to call
attention to the challenges that athletes may have overcome in order to become professional
75

athletes. As noted above, shows like Gruden’s QB Camp and Path to the Draft are prone to
delve into hardship, while Hard Knocks illustrates that the line between unemployment and
professional stardom can be incredibly thin. Again, then, the underlying message is that athletes
are not simply handed their wealth and their fame. Instead, they have to work for their success
and, quite often, navigate precariousness along the way.
In closing, it is now possible to make sense of GM TV with an account that extends
beyond the industrial. As this chapter has illustrated, GM TV is a natural fit alongside the reality
TV sub-genre of Boss TV, as it shares with Boss TV many of the same managerial lessons, such
as accountability and empathy. The affinity between GM TV and Boss TV, in turn, reflects how
GM TV is a natural fit alongside the post-welfare state, for the managerial lessons of GM TV,
like those of Boss TV, can serve as resources for “managing consumers.” And, as mentioned in
the previous paragraph, GM TV does not just provide resources for navigating neoliberal society,
as in its managerial lessons, but also circulates neoliberal logic in depicting athletes as working
their way out of precarious conditions.

1
Laurie Ouellette, and James Hay. Better Living through Reality TV: Television and Post-Welfare Citizenship.
Wiley, 2008, 3.
2
Ibid.
3
Guy Standing. The Precariat: The New Dangerous Class. A&C Black, 2011, 66.
4
Brenda R. Weber. Makeover TV: Selfhood, Citizenship, and Celebrity. Duke University Press, 2009, 39.
5
Nikolas Rose. “Governing ‘Advanced’ Liberal Democracies.” In Foucault And Political Reason: Liberalism, Neo-
Liberalism And The Rationalities Of Government, edited by Andrew Barry, Thomas Osborne, and Nikolas Rose,
37–64. Routledge, 1996, 61.
6
Ouellette and Hay, Better Living through Reality TV, 3.
7
Rose, “Governing ‘Advanced’ Liberal Democracies,” 58.
8
Ibid.
9
James Hay. “Unaided Virtues: The (Neo-)Liberalization of the Domestic Sphere.” Television & New Media 1,
no. 1 (February 2000), 54; and Laurie Ouellette, “Take Responsibility for Yourself: Judge Judy and the
76

Neoliberal Citizen,” in Reality TV: Remaking Television Culture, ed. Susan Murray and Laurie Ouellette (NYU
Press, 2004), 234.
10
Ouellette, “Take Responsibility for Yourself,” 232.
11
Ibid., 247.
12
Ouellette and Hay, Better Living through Reality TV, 140.
13
Ibid., 90.
14
Nick Couldry, “Reality TV, or the Secret Theater of Neoliberalism,” Review of Education, Pedagogy, and
Cultural Studies 30, no. 1 (2008): 3–13; and Nick Couldry and Jo Littler, “The Work of Work: Reality TV and
the Negotiation of Neoliberal Labour in The Apprentice,” in Rethinking Documentary: New Perspectives and
Practices, ed. Thomas Austin and Wilma de Jong (Maidenhead: Open University Press, 2008), 258–67.
15
Alison Hearn, “Insecure: Narratives and Economies of the Branded Self in Transformation Television,”
Continuum 22, no. 4 (2008), 498.
16
John McMurria, “Desperate Citizens and Good Samaritans Neoliberalism and Makeover Reality TV,”
Television & New Media 9, no. 4 (July 1, 2008), 320.
17
Ibid.
18
Weber, Makeover TV, 39.
19
Ibid., 66.
20
Ouellette and Hay, Better Living through Reality TV, 3.
21
Toby Miller, “Foucault, Marx, Neoliberalism: Unveiling Undercover Boss,” in Foucault Now, ed. James
Faubion (John Wiley & Sons, 2014), 194.
22
As a recent New Yorker profile makes clear, the Kochs’ agenda is thoroughly anchored in free-market
ideology – one they are attempting to re-brand as “a movement for well-being” given the country’s lingering
tendency to associate major corporations with greed. The piece summarizes the advice of the Kochs’ primary
political advisor, the economist Richard Fink, to convince the country that “free markets forged a path to
happiness, whereas big government led to tyranny, Fascism, and even Nazism.” See Jane Mayer, “New Koch,”
The New Yorker, January 25, 2016.
23
Alan S. Blinder, After the Music Stopped: The Financial Crisis, the Response, and the Work Ahead (Penguin,
2013).
24
Ibid.
25
Standing, The Precariat, 6.
26
Ibid., 10.
27
Ibid., 49.
28
Ibid., 50.
29
Lawrence F. Katz, “The Rise of Alternative Work Arrangements & the ‘Gig’ Economy,” March 14, 2016,
https://www.scribd.com/doc/306279776/Katz-and-Krueger-Alt-Work-Deck.
77

30
Kevin Roose, Young Money: Inside the Hidden World of Wall Street’s Post-Crash Recruits (Grand Central
Publishing, 2014), xii.
31
Ibid., 183.
32
Michael Erman, “Five Years after Lehman, Americans Still Angry at Wall Street: Reuters/Ipsos Poll,” Reuters,
September 15, 2013, http://www.reuters.com/article/us-wallstreet-crisis-idUSBRE98E06Q20130915.
33
Natasha Lennard, “Poll: We Still Hate Wall Street,” Salon, accessed March 28, 2016,
http://www.salon.com/2013/09/15/poll_we_still_hate_wall_street/.
34
On the topic of the recent bubble talk, see, for example, Rachel Feintzeig, “Tech Workers Get Choosy About
Changing Jobs,” Wall Street Journal, March 17, 2016, sec. Tech, http://www.wsj.com/articles/tech-workers-
get-choosy-about-changing-jobs-1458086282?mod=LS1; Maya Kosoff, “Silicon Valley Shaken as 19 Start-Ups
See Their Valuations Slashed,” Vanity Fair, February 29, 2016,
http://www.vanityfair.com/news/2016/02/silicon-valley-shaken-as-19-start-ups-see-their-valuations-
slashed; and Selina Wang, “Hedge Funds Pumped Up Silicon Valley. Now They’re Pulling Out,” Bloomberg
Business, March 24, 2016, http://www.bloomberg.com/news/articles/2016-03-24/hedge-funds-pull-back-
in-silicon-valley-as-ipo-market-atrophies;
35
“2015 Silicon Valley Index” (Joint Venture Silicon Valley, February 3, 2015),
http://www.jointventure.org/images/stories/pdf/index2015.pdf.
36
Ari Levy, “Alphabet Tops Apple as Most Valuable Firm,” CNBC, February 2, 2016,
http://www.cnbc.com/2016/02/01/google-passes-apple-as-most-valuable-company.html.
37
Don Reisinger, “iPhones in Use in the US Rise to 94M, New Study Suggests,” CNET, accessed March 28, 2016,
http://www.cnet.com/news/nearly-100m-iphones-in-use-in-the-us-new-study-shows/.
38
“Leading Global Social Networks 2016,” Statista, accessed March 28, 2016,
http://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/.
39
Sarah Buhr, “Airbnb Hosted Nearly 17 Million Guests This Summer,” TechCrunch, accessed March 28, 2016,
http://social.techcrunch.com/2015/09/07/airbnb-hosted-nearly-17-million-guests-this-summer/; and
Alyson Shontell, “LEAKED: Internal Uber Deck Reveals Staggering Revenue And Growth Metrics,” Business
Insider, accessed March 28, 2016, http://www.businessinsider.com/uber-revenue-rides-drivers-and-fares-
2014-11.
40
Nick Bilton, “Tinder, the Fast-Growing Dating App, Taps an Age-Old Truth,” The New York Times, October
29, 2014, http://www.nytimes.com/2014/10/30/fashion/tinder-the-fast-growing-dating-app-taps-an-age-
old-truth.html.
41
Leena Rao, “PayPal’s Payments App Venmo Has Its Biggest Month Ever - Fortune,” Fortune, February 16,
2016, http://fortune.com/2016/02/16/venmo-billion/.
42
Ashlee Vance, “Elon Musk’s Space Dream Almost Killed Tesla,” Bloomberg Business, May 14, 2015,
http://www.bloomberg.com/graphics/2015-elon-musk-spacex/.
78

43
Georgia Wells, “Stephen Colbert Gets Last Laugh With Tech Execs,” The Wall Street Journal, September 28,
2015, http://www.bloomberg.com/graphics/2015-elon-musk-spacex/.
44
George Packer, “Change the World,” The New Yorker, May 27, 2013,
http://www.newyorker.com/magazine/2013/05/27/change-the-world.
45
Richard Barbrook and Andy Cameron, “The Californian Ideology,” Science as Culture 6, no. 1 (January 1,
1996), 44.
46
Ibid., 45.
47
Greg Ferenstein, “An Attempt To Measure What Silicon Valley Really Thinks About Politics And The World,”
Medium, November 6, 2015, https://medium.com/the-ferenstein-wire/what-silicon-valley-really-thinks-
about-politics-an-attempted-measurement-d37ed96a9251#.b4atrcuhj.
48
Nicholas Lemann, “The Network Man,” The New Yorker, October 12, 2015,
http://www.newyorker.com/magazine/2015/10/12/the-network-man.
49
Ibid.
50
Dale Russakoff, The Prize: Who’s in Charge of America’s Schools? (Houghton Mifflin Harcourt, 2015), 9.
51
Shanna Gong et al., “Recommendations from the Tech Community: Entrepreneur Pathways Brief” (FWD.us),
accessed March 28, 2016, http://www.fwd.us/pathways.
52
As a number of critics have pointed out, the phrase “Sharing Economy” has become a rather significant
misnomer. As Tom Slee writes, “sharing” implies “a non-commercial, person-to-person, social interaction.”
Within the “Sharing Economy,” though, resources are generally exchanged for money. This fact in mind, a
number of alternative labels have been proposed in place of the “Sharing Economy,” including the “On-
Demand Economy” and the “Gig Economy.” Following Slee’s lead, though, this paper will keep the Sharing
Economy terminology so as to match much of the mainstream coverage of the phenomenon. Like Slee, too,
Sharing Economy will be capitalized rather than persistently placed in “scare quotes.”
53
Mike Isaac and Leslie Picker, “Uber Valuation Put at $62.5 Billion After a New Investment Round,” The New
York Times, December 3, 2015, http://www.nytimes.com/2015/12/04/business/dealbook/uber-nears-
investment-at-a-62-5-billion-valuation.html.
54
Katy Steinmetz, “Exclusive: See How Big the Gig Economy Really Is,” Time, January 6, 2016,
http://time.com/4169532/sharing-economy-poll/.
55
Tom Slee, What’s Yours Is Mine: Against the Sharing Economy (OR Books, 2016), 27.
56
Ibid., 73.
57
Ibid.
58
Cesar Conda and Derek Khanna, “Uber for Welfare,” Politico, January 27, 2016,
http://www.politico.com/agenda/story/2016/1/uber-welfare-sharing-gig-economy-000031.
59
James Cook, “Uber’s Internal Charts Show How Its Driver-Rating System Actually Works,” Business Insider,
February 11, 2015, http://www.businessinsider.com/leaked-charts-show-how-ubers-driver-rating-system-
works-2015-2.
79

60
“Airbnb ‘Threatens’ Host over Poor Star Ratings,” GlobalHosting Forum & Blogs, accessed March 28, 2016,
http://globalhosting.freeforums.net/thread/46/airbnb-threatens-host-over-ratings.
61
Linda Fuller and Vicki Smith, “Consumers’ Reports: Management by Customers in a Changing Economy,”
Work, Employment & Society 5, no. 1 (March 1, 1991), 2.
62
Ibid., 3.
63
Kerstin Rieder and G. Gunter Voss, “The Working Customer – an Emerging New Type of Consumer,”
Psychology of Everyday Activity 5, no. 2 (2010), 2.
64
Ibid.
65
Ibid., 4.
66
Yiannis Gabriel, Marek Korczynski, and Kerstin Rieder, “Organizations and Their Consumers: Bridging
Work and Consumption,” Organization 22, no. 5 (September 1, 2015, 637.
67
Yiannis Gabriel and Tim Lang, The Unmanageable Consumer (SAGE, 2015), 224.
68
Rieder and Voss, “The Working Customer,” 4.
69
Alex Rosenblat and Luke Stark, “Uber’s Drivers: Information Asymmetries and Control in Dynamic Work,”
SSRN Scholarly Paper (Rochester, NY: Social Science Research Network, October 15, 2015), 11.
70
Dzieza, “The Rating Game.”
71
Ibid.
72
“Ziosk - Industry Leading Tabletop Ordering, Entertainment and Payment Solutions,” accessed March 28,
2016, http://www.ziosk.com/.
73
Cheryl V. Jackson, “HappyOrNot Lets Customers Give Smiley or Frowny Feedback on the Spot,” Chicago
Tribune, May 26, 2015, http://www.chicagotribune.com/bluesky/originals/ct-happyornot-bsi-20150526-
story.html.
74
Fuller and Smith, “Consumers’ Reports,” 12.
75
Marie-Anne Dujarier, “The Three Sociological Types of Consumer Work,” Journal of Consumer Culture, April
8, 2014, 5-6.
76
Slee, What’s Yours Is Mine, 108.
77
Josh Dzieza, “The Rating Game: How Uber and Its Peers Turned Us into Horrible Bosses,” The Verge,
October 28, 2015, http://www.theverge.com/2015/10/28/9625968/rating-system-on-demand-economy-
uber-olive-garden.
78
Slee, What’s Yours is Mine, 101.
79
Dzieza, “The Rating Game.”
80
Ibid.
81
Slee, What’s Yours is Mine, 106.
82
Dzieza, “The Rating Game.”
83
Ibid.
84
Ibid.
80

85
Rosenblat and Stark, “Uber’s Drivers,” 12.
86
Dzieza, “The Rating Game.”
87
Rose, “Governing ‘Advanced’ Liberal Democracies,” 45.
88
Raymond Boyle and Maggie Magor, “A Nation of Entrepreneurs? Television, Social Change and the Rise of
the Entrepreneur,” International Journal of Media & Cultural Politics 4, no. 2 (June 13, 2008), 134.
89
Couldry, “Reality TV, or the secret theater of neoliberalism,” 9.
90
Raymond Boyle and Lisa W. Kelly, “The Celebrity Entrepreneur on Television: Profile, Politics and Power,”
Celebrity Studies 1, no. 3 (October 27, 2010), 336-338.
91
As Boyle and Kelly explain in The Television Entrepreneurs, the Undercover Boss format, which debuted in
the UK in 2009 before making its way to the US the next year, also has deeper roots in UK television, having
followed the lead of BBC Two’s Back to the Floor, which ran between 1997 and 2002 with a similar premise.
See Raymond Boyle and Lisa W. Kelly, The Television Entrepreneurs: Social Change and Public Understanding
of Business (Surrey: Ashgate, 2012).
92
Ouellette and Hay, Better Living through Reality TV, 176.
93
Nick Couldry and Jo Littler, “Work, Power and Performance: Analysing the ‘Reality’ Game of The
Apprentice,” Cultural Sociology 5, no. 2 (July 1, 2011): 269.
94
Miller, “Foucault, Marx, Neoliberalism,” 200.
95
Burton St. John, “The Top Executive on Undercover Boss: The Embodied Corporate Persona and the
Valorization of Self-Government,” Journal of Communication Inquiry 39, no. 3 (July 1, 2015), 284.
96
Boyle and Kelly, “The celebrity entrepreneur on television,” 337.
97
Ibid.
98
Ibid.
99
Ouellette and Hay, Better Living through Reality TV, 21.
100
Ibid., 20.
101
Benjamin G. Edelman and Michael Luca, “Digital Discrimination: The Case of Airbnb.com,” Harvard
Business School NOM Unit Working Paper 14–054 (January 10, 2014),
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2377353.
102
Alex Rosenblat, “Uber’s Pax: Hidden Bias in Rating Systems,” in Algorithms at Work, 2015, 2.
103
Dzieza, “The Rating Game.”
104
Rosenblat and Stark, “Uber’s Drivers,” 12.
105
Matt Thomas, “What I Learned About Donald Trump From Binge-Watching The Apprentice,” Fusion,
January 28, 2016, http://fusion.net/story/261299/donald-trump-2016-the-apprentice/.
106
Ibid.
107
“‘The Customer Is Not No. 1’: Marcus Lemonis,” CNBC, June 24, 2015,
http://www.cnbc.com/2015/06/24/the-customer-is-not-no-1-marcus-lemonis.html.
81

108
Judy Magness, “The Profit’s Marcus Lemonis: A Miracle Worker,” The Suit Magazine, July 15, 2014,
http://www.thesuitmagazine.com/top-stories/22356-the-profits-marcus-lemonis-a-miracle-worker.html.
109
Ginia Bellafante, “Kitchen Nightmares - TV - Review,” The New York Times, September 19, 2007,
http://www.nytimes.com/2007/09/19/arts/television/19kitc.html.
110
Laura Bennett, “Beware: Captitalist Sharks in New Reality Show,” The Boston Globe, August 8, 2009,
http://archive.boston.com/ae/tv/articles/2009/08/08/beware_captitalist_sharks_in_new_reality_show/.
111
Hank Stuever, “TV Preview of ‘Undercover Boss’ on CBS,” The Washington Post, February 7, 2010, sec. Arts
& Living, http://www.washingtonpost.com/wp-dyn/content/article/2010/02/04/AR2010020405103.html.
112
Heather Havrilesky, “‘Undercover Boss’: Capitalist Fairy Tale,” Salon, February 3, 2010,
http://www.salon.com/2010/02/04/cbs_undercover_boss_fairy_tale/.
113
“Undercover Boss: Season 1,” Metacritic, accessed April 15, 2016,
http://www.metacritic.com/tv/undercover-boss/critic-reviews.
114
St. John, “The Top Executive on Undercover Boss,” 227.
115
Hamilton Nolan, “Undercover Boss: Now Simply Evil,” Gawker, March 22, 2010,
http://gawker.com/5499057/undercover-boss-now-simply-evil; and Todd VanDerWerff, “Undercover Boss,”
A.V. Club, February 7, 2010, http://www.avclub.com/tvclub/iundercover-bossi-37961.
116
Trebor Scholz, “Platform Cooperativism vs. the Sharing Economy,” Medium, December 5, 2014,
https://medium.com/@trebors/platform-cooperativism-vs-the-sharing-economy-
2ea737f1b5ad#.qm1xfeho3.
117
NFL Network’s own version, Game Changers, featuring former coach Steve Mariucci, works much the same
way.
118
Ouellette and Hay, Better Living through Reality TV, 99, 164.
119
Allie Stoneberg, “ESPN’s Draft Academy Series Returns April 14,” ESPN MediaZone, March 26, 2015,
http://espnmediazone.com/us/press-releases/2015/03/espns-draft-academy-series-returns-april-14/.
82

Part II: Datavisuality

As explained in the introduction and first chapter, sports television has been increasingly
fixated on GMs and the work that they perform. Largely left unsaid in the previous chapters,
though, was that the nature of being a GM has undergone significant changes over the last
several years. More specifically, the role of the GM has been progressively influenced by sports
analytics. While “analytics” has become a buzzword with a slippery definition that varies widely
depending on whom you ask, this project will largely hue toward the definition offered by
Benjamin Alamar and Vijay Mehrotra. Sports analytics, they write, encompasses “the
management of structured historical data, the application of predictive analytic models that
utilize that data, and the use of information systems to inform decision makers and enable them
to help their organizations in gaining a competitive advantage on the field of play.”
1
As this
definition makes clear, analytics can most usefully be thought of as a process. More specifically,
it can be thought of as a process that prioritizes the systematic gathering of data – often
quantitative – and using that data throughout an organization’s decision-making, impacting
everything from roster building to in-game strategizing. To be a GM, then, increasingly means
employing data-driven approaches to team building.
The growing importance of analytics within front offices has meant that the sports
industry has recently begun to integrate the types of tools and techniques that fall under the big
data label – tools and techniques, in other words, that rely on extremely large data sets, often
automatically gathered and processed.
2
Most NBA GMs, for example, are no longer working
solely with anecdotal scouting reports and basic statistics like field goal percentages and
turnover/assist ratios. Rather, they are now complementing these traditional tools with advanced
statistics based on vast reams of historical data as well as new forms of analysis based on
83

complex data gathering mechanisms like STATS’s SportVU tracking system, which is derived
from Israeli missile defense technology and uses a series of cameras to record the positions of all
objects on a court 25 times per second – thus producing a flood of data unlike anything the sport
has seen before.
3
Similarly, every baseball GM now has access to Sportvision’s PITCHf/x
service, which records and tracks the full trajectory of every single pitch thrown over the course
of a season, while the NHL and NFL are also in the process of rolling out their own data-
intensive tracking systems.
4
Moreover, many teams have begun using biometric tracking devices
that generate massive amounts of real-time performance and health data. The intersection of
sports and big data is a global phenomenon, too. Soccer teams across Europe, for example, are
making use of their own tracking systems, while Australian Rules Football teams have already
begun using biometric data to shape in-game adjustments.
5

The rapidly emerging intersection between the sports industry and big data means that the
sports world is very much relevant to the growing academic conversation around the rise of big
data. As scholars like Mark Andrejevic and Kate Crawford have argued, big data has become a
dominant cultural and economic idea over the past several years. Crawford, writing alongside
Kate Miltner and Mary L. Gray, comments, “Big data as a term has spread like kudzu in a few
short years, ranging across a vast terrain that spans health care, astronomy, policing, city
planning, and advertising.”
6
In accordance with the mounting ubiquity of the term, such scholars
have begun to critically interrogate big data – questioning, for example, the power relations
involved in the deployment of big data systems and the epistemological foundations of big data
methodology. Given the massive cultural and economic import of sports, it is natural to extend
these interrogations to the sports world. In fact, Brett Hutchins has taken these initial steps in a
piece in which he traces the rise of big data in the sports industry and – following the work of
84

scholars like Andrejevic and Crawford – subsequently questions the growing divide between
teams, as well as sports, able to implement big data approaches – a divide that has steep
economic ramifications.
7
Women’s and semi-professional sports, for example, lack the
resources to gather large data sets and, therefore, are unable to draw attention from data-hungry
fans. As a result, these sports also struggle to attract corporate sponsors looking to capitalize on
fan data. Major men’s sports, Hutchins concludes, will only grow more dominant as the
fascination with big data in the sports world continues.
Hutchins’s article does significant work in beginning to tie sports into the scholarly
discussion around big data, but what is largely left out of his account is the role of sports media
in the growing intersection of sports and big data. As the next two chapters will explain, sports
television has very deliberately responded to the growth of sports analytics by incorporating
elements of sports analytics directly into sports broadcasts. More specifically, the next chapter
will argue that the incorporation of sports analytics into broadcasts has created a new mode of
broadcasting that I call “datavisuality” – a mode of broadcasting that attempts to quantify and
visualize player contributions, often leaning on the very same big data tools that teams and
leagues use in their internal decision-making processes. As a result, sports media offers a unique
way of approaching the relationship between big data and media. Thus far, the relationship
between big data and media has largely, out of necessity, been approached indirectly – big data,
after all, normally operates behind the scenes, as in the case of Netflix’s recommendation
algorithms or Hulu’s personalized advertising.
8
In the case of sports media, though, big data is
made highly visible, thus uniquely connecting media scholarship to big data scholarship.
In order to develop and critique the idea of datavisuality, this section of the dissertation
will be divided into two chapters. To begin, the second chapter will define the idea of
85

datavisuality, drawing on several specific examples to trace the general contours of this new
mode of broadcasting. Next, the second chapter will establish why sports television has arrived
to datavisuality, first by providing an overview of the rise of sports analytics and then by
explaining how the sports television industry has responded to this rise. Drawing on statements
from a variety of industry sources, this industrial analysis will detail how sports media
executives have gradually – and occasionally tepidly – embraced analytics, responding to a
perceived audience desire for data-driven coverage spurred on by the sports industry’s move
towards big data. The third chapter will then discuss the implications of this new mode of
broadcasting. Drawing heavily on the big data scholarship mentioned above, this critique will
follow several threads, considering sports television’s role not just in reinforcing the mythology
of big data, but also in creating unique big data divides and furthering the “surveillance creep”
normalized by big data.

1
Benjamin Alamar and Vijay Mehrotra, “Beyond ‘Moneyball’:The Rapidly Evolving World of Sports Analytics,”
Analytics, October 2011.
2
The definition of “big data” is a slippery one due to the relative nature of the terms. As Mark Andrejevic
writes in “Big Data, Big Questions: The Big Data Divide,” International Journal of Communication 8 (2014),
1675, “In the sense of standing for more information than any individual human or group of humans can
comprehend, the notion of big data has existed since the dawn of consciousness.” Such relatively in mind, it
necessarily follows that what seems “big” at this present moment is very likely to appear more manageable in
the future. How do we define “big data” as it is currently being used, then, given this challenge? Andrejevic
pins the current usage of the “big data” terminology to “the emergence of the prospect of making sense of an
incomprehensibly large trove of recorded data – the promise of being able to put it to meaningful use even
though no individual or group of individuals can comprehend it.” Continuing, he adds, “Big data denotes the
moment when automated forms of pattern recognition known as data analytics can catch up with automated
forms of data collection and storage. Such data analytics are distinct from simple searching and querying of
large data sources, a practice with a much longer legacy.” It is essentially this definition that the project cites
here.
3
Baxter Holmes, “New Age of NBA Analytics: Advantage or Overload?” Boston Globe, March 30, 2014.
86

4
Clint Boulton and Michael Hickins, “Billy Beane Expects Big Things from MLB’s Big Data Play,” March 5,
2014, http://blogs.wsj.com/cio/2014/03/05/billy-beane-expects-big-things-from-mlbs-big-data-play/.
5
Noah Davis, “Today’s Soccer Gear: Shorts, Cleats, Shinguards and a GPS Unit,” The New York Times, July 27,
2015; and Kevin Seifert, “GPS Technology Could Be NFL Game-Changer,” ESPN.com NFL Nation, March 26,
2014, http://espn.go.com/nfl/story/_/page/hotread140326.
6
Kate Crawford, Kate Miltner, and Mary L. Gray, “Critiquing Big Data: Politics, Ethics, Epistemology,”
International Journal of Communication 8 (2014), 1663.
7
Brett Hutchins, “Tales of the Digital Sublime: Tracing the Relationship between Big Data and Professional
Sport,” Convergence: The International Journal of Research into New Media Technologies, May 2015.
8
See, for example, Nick Couldry and Joseph Turow, “Advertising, Big Data, and the Clearance of the Public
Realm: Marketers’ New Approaches to the Content Subsidy,” International Journal of Communication 8
(2014); and Jeremy Wade Morris, “Curation by Code: Infomediaries and the Data Mining of Taste,” European
Journal of Cultural Studies 18, no. 4–5 (August 2015).
87

Chapter Two: The Rise of Datavisuality

Defining Datavisuality
“Starting in the 1980s,” John Caldwell writes in Televisuality, “American mass-market
television underwent an uneven shift in the conceptual and ideological paradigms that governed
its look and presentational demeanor.” He continues, “In several important programming and
institutional areas, television moved from a framework that approached broadcasting primarily as
a form of word-based rhetoric and transmission … to a visually based mythology, framework,
and aesthetic based on an extreme self-consciousness of style.”
1
Caldwell terms this shift
“televisuality,” and, over the course of the book, develops both its parameters and its sources.
Pointing to examples ranging from Moonlighting to Northern Exposure to Pee-Wee’s Playhouse,
Caldwell shows how the “stylistic emphasis” that came to fore in the 1980s and early 1990s
could manifest in various ways and, moreover, had its origins in numerous locations, most of
them related to a bevy of industrial “tendencies and changes.”
2
“Televisuality,” he thus
concludes, “is less a defining aesthetic than a kind of corporate behavior and succession of
guises.”
3

Televisuality undoubtedly had a dramatic effect on sports television whilst it was also
affecting the rest of “mass-market television,” participating in the transformation of studio shows
and live game broadcasts into visually complex affairs.
4
In recent years, though, sports
television has arguably been undergoing another shift with effects just as profound. Starting
with the increasing use of informational graphics in the mid-1990s and accelerating during the
subsequent decades, sports television – both in the United States and elsewhere across the globe
– has, to borrow Caldwell’s words, undergone a “shift in the conceptual and ideological
paradigms that [govern] its look and presentational demeanor.”
5
Sports television, to be more
88

precise, has increasingly privileged the place of data across its various forms. An on-going
process more than a static shift, datavisuality has entailed not only ever increasing amounts of
data, but also ever more complex data – a complexity intended to remove any ambiguity from
sport. As with televisuality before, this process has sprung from a variety of sources, many of
them located in industrial practices. The sections below will trace these sources in detail.
Before moving into the sources and consequences of datavisuality, though, a clearer
definition of phenomenon is required. In the introduction to Televisuality, Caldwell sketches out
several principles that “further define and delimit the extent of televisuality.”
6
Following his
lead and drawing from these principles, it is possible to more precisely explicate the parameters
of datavisuality:
1. As with televisuality before, datavisuality has “many different looks,” or alternatively,
“many variant guises.”
7
Not only do the specific manifestations of datavisuality vary, but as
examples below will illustrate, datavisuality also affects far more than traditional game
broadcasts. Rather, its influence can be found everywhere from national news shows like
SportsCenter to shoulder programming like the pre-game and post-game shows that fill the
schedules of the many regional sports networks (RSNs) across the country.
2. Caldwell argues that “televisuality represented a structural inversion” – style was now
prioritized over subject, reversing the traditional televisual hierarchy. In other words, “What had
always been relegated to the background now frequently became the foreground.”
8
Similarly,
while data has long been part of sports, it has traditionally been a relatively minor part of sports
television. Datavisuality, though, brings data closer to the foreground.
3. “Televisuality was an industrial product,” Caldwell declares – emphasizing the need to
“recognize that television is manufactured.” Continuing, he notes that industrial factors like
89

technology and labor practices were “important components in the formation of a televisual
mode of production.”
9
New production tools, for example, enabled certain looks and, in doing
so, “helped comprise an array of conditions, and a context, that allowed for exhibitionism.”
10
In
much the same way, datavisuality is an industrial product deliberately manufactured by scores of
figures hailing from the wide variety of sub-industries that make up sports television. To that
end, much of this chapter will explore datavisuality’s status as a manufactured product, checking
in with both the graphics industry, which provides much of the technology that “allows” for
datavisuality, as well as one of the most prominent sports broadcasters, ESPN.
4. The previous point requires a brief corollary. Datavisuality is, like televisuality, an
industrial product produced by the television industry. Unlike televisuality, though, datavisuality
also has strong roots in another industry: the sports industry. As the following section will show,
many of what Caldwell might term the “ideational resources” required by datavisuality have
little to do with television. Rather, they arise out of the operations of various professional sports
teams working across various leagues. While this makes the roots of datavisuality quite different
from televisuality, this industrial intermingling is perhaps to be expected. In brief, separating the
sports television industry from the larger sports industry is difficult. The sports television
industry, after all, largely exists to televise sporting events put on by the sports industry. This
makes discussing textuality relatively challenging. A televised game, after all, has a vague
status. Is it a distinct televisual text? Or might it instead be considered a paratext of the sporting
event itself, which is produced by the sports industry and nominally meant to serve a different
audience? Similarly, is a pre-game show a distinct text, or a paratext of the game? Or a paratext
of a paratext? Such questions are fairly unique to sports television. Thus, it should not be
90

surprising to learn that large changes within the sports industry would reverberate outwards and
eventually affect the sports television industry.
5. Caldwell argues, “Televisuality was a function of the audience,” for “the cultural abilities
of audiences had … apparently changed by the 1980s.”
11
For example, networks during this
period began to take advantage of the yuppie demographic, which seemed to want “programs
that made additional aesthetic and conceptual demands not evident in earlier programming.”
12

Similar statements could be made of datavisuality, with studio shows and live game broadcasts
beginning to suppose a certain level of sophistication amongst audiences, now seemingly able to
digest increasingly complex statistical information and, moreover, engage with these statistics
beyond television. Caldwell notes, though, that televisuality was “not singularly tied to either
low- or high-culture pretense.”
13
In much the same way, datavisuality may often cater to
mathematically adept viewers, but it is not solely aimed at such audiences. Rather, as will be
explained in later sections, datavisuality is meant to attract both diehard statheads and more
casual fans. Additionally, the rise of datavisuality reflects the broader infusion of digital data
across myriad forms of employment and daily life
6. Televisuality, Caldwell explains, was “a product of economic crisis.”
14
More
specifically, it was tied to the rise of cable television and the subsequent loss of market share by
the major networks. As Caldwell suggests, stylishness was then formulated as tactic that could
be deployed by these networks to stop the loss of viewers. There is a marked parallel in the birth
of datavisuality. Over the past several years, more and more networks have entered the battle for
sports television dominance, with Fox Sports 1 (FS1) and NBCSN both undergoing major re-
launches and with leagues also staying committed to their own channels, such as the NFL
Network and NBATV. The most significant result of this battle for sports viewers is that there
91

have been increasingly dogged fights by networks – both broadcast and cable – to win viewers
and to win the rights to cover live sporting events, mainly the result of FS1 and NBCSN joining
the traditional power players, including the broadcast networks, as well as ESPN and Turner, in
bidding for these properties. Escalating rights fees have meant that even the most successful
networks – like ESPN, long thought to be an infinite fount of profit – are facing increasingly
tight budgets, all whilst cable subscriptions continue to decline.
15
Datavisuality is also very
much a product of this situation. As will be discussed below, broadcasters see data-driven
programming as a way to distinguish themselves from their competitors and woo viewers.
7. As mentioned above, datavisuality is very much a global phenomenon. In fact,
companies outside of America have taken the lead in developing many of the technologies that
are facilitating datavisuality. Moreover, many of the first adopters of this technology have been
located outside of America. That said, while this chapter will occasionally invoke this global
scope, particularly in detailing the contours of the graphics industry, the focus will largely be on
the particularities of American television.
This list of principles has sketched the outlines of datavisuality. The question remains,
though, how exactly datavisuality manifests itself. As mentioned, datavisuality operates in many
guises. No one example, then, can fully capture the on-going process of datafication in which
sports television is enmeshed. Nonetheless, a few representative examples can help illustrate the
current extent of datavisuality. To begin, it could be helpful to see how datavisuality is playing
out in even the most ordinary of sports television programming. To that end, the following two
examples are deliberately pulled from a relatively unremarkable midseason baseball game. The
game in question took place on July 5, 2015 and saw the Washington Nationals hosting the San
Francisco Giants on a muggy night in Washington, DC. A Sunday night game, it was airing on
92

national TV as part of ESPN’s Sunday Night Baseball property, but would attract a
comparatively modest audience of 1.4 million viewers.
16

Not long after ESPN’s broadcast began – during the bottom of the first inning –
Washington’s backup first baseman, Clint Robinson, came to the plate to face off against San
Francisco starting pitcher Ryan Vogelsong. Following Vogelsong’s first pitch, a graphic
containing Robinson’s season statistics was displayed across the bottom of the screen:

2.1 Use of OPS during ESPN Sunday Night Baseball.
A few moments later, Robinson took a called strike on the outside corner. Immediately after, a
graphic appeared to show where the ball had been thrown in relation to the strike zone:

2.2 Use of ball tracking technology during ESPN Sunday Night Baseball.
93

As mentioned above, the primary attribute of datavisuality is the foregrounding of data.
Starting with the first graphic, then, it is obviously significant that so many numerical figures are
being displayed. Not only is the viewer given information about the game’s progress – by way
of the score box in the lower right corner that provides both the score and situational
information, including the inning and score – but they are also given a wealth of data about the
player’s performance, both for the season as a whole and for this particular series against the
Giants. This large quantity of information is now familiar to most sports fans, as graphics like
these are have become a routine feature during live game telecasts, but this ubiquity has been a
relatively recent development. As will be further detailed, graphics were rare just twenty years
ago. Even during major events, viewers were provided with just brief glimpses of text
containing only the most basic information about the game in progress. Familiar features like the
score box only debuted in the mid-1990s. For viewers to now be routinely given such a wealth
of statistics, then, represents a recent sea change in broadcasting.
The sheer quantity of information is not the only piece of import in this graphic, though.
Attention must also be paid to what sorts of data are being highlighted. To that end, it can be
observed that most of the numbers on display are simple counting statistics that are familiar even
to non-sports fans. A home run, for instance, is both easy to measure and easy to work into
everyday metaphors. What stands out, then, is the second column, which displays a statistic
called OPS – short for “on-base plus slugging.” Written out, the formula for the statistic is as
follows:
𝑂𝑃 𝑆 =
𝐴𝐵 ∗ ( 𝐻 + 𝐵𝐵 + 𝐻𝐵 𝑃 ) + 𝑇𝐵 ∗ ( 𝐴𝐵 + 𝐵𝐵 + 𝑆𝐹 + 𝐻𝐵 𝑃 )
𝐴𝐵 ∗ ( 𝐴𝐵 + 𝐵𝐵 + 𝑆𝐹 + 𝐻𝐵 𝑃 )

OPS, of course, is not only a less familiar statistic than the others, but also a more complex
statistic than a counting statistic like home runs or a simple rate statistic like batting average.
94

This was by design. As Alan Schwarz documents, OPS was created by John Thorn and Pete
Palmer for their 1984 book The Hidden Game of Baseball, which attempted both to re-evaluate
the traditional baseball statistics, including home runs and batting average, and to look for
potential alternatives – alternatives that would not shy away from long, complicated formulas.
17

The first screenshot, then, shows how datavisuality has entailed both increasing amounts
of statistical information and increasingly complex statistical information. That is to say,
viewers are not only getting more and more data, but also more complex data – complexity
meant to help viewers better evaluate player performance, as in the case of OPS. However, the
statistical information in the first screenshot represents perhaps the simplest manifestation of
datavisuality: the basic presentation of numbers. Complexity is evident, but that complexity
comes by way of a dense display of numbers, as well as the use of a more sophisticated metric.
The second screen shot, though, demonstrates how the process of datafication has expanded well
beyond the simple display of numerical data. To understand how this is so, one must understand
how the graphic was generated. In brief, every MLB stadium is now outfitted with tracking
systems that can precisely and continuously track the locations both of the players and of the
ball. Networks, then, have the ability to instantly show a wide variety of data that was once
beyond easy measure, including pitch speed and pitch trajectory. In this case, ESPN relies on
Sportvision’s K-Zone Live pitch-tracking graphics – a technology based on the company’s
aforementioned PITCHf/x system, which uses three tracking cameras to record the trajectory of
every pitch thrown over the course of the season. ESPN first began using the K-Zone graphics
intermittently 2011, but in 2015 committed to using them for every single pitch in every game it
broadcast.
18

95

The rise of tracking data – a phenomenon now stretching across all the major sports – has
opened many new doors for sports broadcasters. In attempting to evaluate player performance,
networks are no longer limited to traditional statistics, but can now also unleash new measures
that rely on real-time positioning information – a transition that fully moves sports broadcasting
into the realm of big data. Soccer broadcasters, for instance, can easily display the distance
traveled by players over the course of a game, while golf broadcasters can show where a ball will
land while it is still in flight. Again, this is an ongoing process, with broadcasters rolling out
more and information with ever increasing complexity. Baseball broadcasters, for example, first
made use of tracking data by displaying simple statistics like pitch speed. Now, though, they
regularly show pitch trajectory and pitch location, as in the screenshot above. Going forward,
league officials hope to regularly display even more player information, as in new graphics that
show the routes players take in fielding the ball and that also display the “route efficiency” – a
development to be discussed below. Undoubtedly, other experiments relying on big data
methodology will follow these new efforts.
The above examples are broadly representative of the ongoing datafication of sports
television, but the greatest limitation of these examples is that sports television is much more
than live game telecasts. Rather, sports television encompasses everything from these telecasts
to national news shows to local call-in shows. As mentioned above, datavisuality has affected
this entire spectrum of programming. Take, for example, this screenshot pulled from a 2014
episode of SportsCenter:
96

2.3 Use of OPS during SportsCenter.
Here, again, viewers are provided not just with a large number of statistics, but also the new,
complex statistic that is OPS.
While the above examples may represent the more typical guises of datavisuality,
perhaps the most conspicuous displays of datavisuality have come in less traditional venues. As
Victoria E. Johnson has documented, sports television has recently evolved into a multi-platform
affair. Audiences, she observes, are now frequently expected to watch games on their televisions
while simultaneously using other devices to check their fantasy teams or social media feeds.
19

However, this model of “personalized TV” is no longer the only one being pursued by the sports
television industry. While second screen content continues to proliferate, the past few years have
seen networks launch experiments focused not on integrating second screens, but rather on
creating primary screen alternatives – alternatives that the industry tentatively terms
“microcasts.” These “microcasts” have – rather quickly – become sites for pure expressions of
datavisuality. In October 2014, for example, FOX broadcast the NL Championship Series
between the St. Louis Cardinals and the San Francisco Giants on its flagship broadcast network.
However, for one game – the first of the series – the company decided to use one of its sports
cable channels, FS1, to air a secondary broadcast devoted to statistical analysis. Hosted by a
97

panel of statistical experts, the broadcast was intended to be, in the words of panelist Rob Neyer,
a “grand experiment” driven by numbers and graphics. “Perched atop a bedrock of data,” he
wrote, “we’ll analyze batter/pitcher match-ups, umpiring tendencies, defensive shifts,
controversial calls, and the debatable managerial moves that seem to be the talk of nearly every
game in October.”
20
In its final form, the broadcast was a data-heavy presentation:

2.4 JABO broadcast on FS1.
While the broadcast ended up attracting a relatively small rating, it received fairly strong reviews
– particularly from statistically-oriented fans. For example, writing on SB Nation, Neil
Weinberg praised the broadcast for disregarding “useless” stats like home runs, and instead
emphasizing advanced metrics like wOBA, wRC+ and FIP. The broadcast, Weinberg
concluded, “showed how easy it is for quality baseball minds to blend in advanced stats.”
21

Several months after Fox’s baseball experiment, ESPN conducted a data-driven
“microcast” experiment of its own. In 2014, the network had decided to cover the college
football championship game by debuting what it called a “Megacast.” While a traditional
telecast of the game would air on ESPN, the company’s other outlets, ranging from cable
channel ESPNU to the streaming service ESPN3, would be filled with simulcasts each with a
98

slightly different focus. ESPNews, for example, featured film analysis from several college
coaches, while ESPN2 featured discussion from a large group of comedians and celebrities.
While the initial 2014 Megacast featured eight separate feeds, ESPN expanded to 12 for the 2015
edition. One of the additions to the coverage was a feed that ESPN titled the Data Center, shown
here:

2.5 ESPN Megacast "Data Center."
If Fox’s baseball experiment showcased datavisuality’s tendency toward ever more
complex data, as seen in the emphasis on statistics even more intricate than OPS, then ESPN’s
experiment draws attention to datavisuality’s push towards ever growing quantities of data.
Indeed, there is so much data squeezed into this frame that it can be difficult to read even on the
largest of televisions. But if these two experimental broadcasts show off different aspects of the
twin inclinations of datavisuality – quantity and complexity – then they are united by an
underlying sense that the process of datafication is proceeding so forcefully that it is not always
easy to contain within the space of a primary broadcast. Thus, one final tendency of
datavisuality might be that of overflow, with data primed to spill into new, relatively uncharted
spaces.
99

While the above section has allowed for an overview of the recent phenomenon that is
datavisuality, a number of questions remain. Most pressingly, the question lingers as to how
datavisuality has taken hold so firmly and so quickly. What forces, in other words, have led to
the influx of complex data into sports television? Moreover, what has caused this flood of data
to spill into alternate broadcasts? These questions in mind, the following sections will further
explore the context from which datavisuality emerged. As has been mentioned, datavisuality has
its roots both in the sports industry at-large and the sports television industry. Next, then, this
chapter will explain how they converged as datavisuality.

The Ascendancy of Sports Analytics
As mentioned above, datavisuality is very much an industrial product. In later sections,
then, this chapter will explore the technologies and corporate decision-making processes that
have precipitated the rise of datavisuality. Underlying all of the new technology and industrial
maneuvering, though, is an assumption that audiences increasingly want data to be a part of
sports television. Thus, technological shifts and industrial practices are necessary, but not
sufficient, explanations for datavisuality. Again, a parallel idea emerges out of Televisuality. As
Caldwell explains, if technological and industrial developments enabled the creation of
televisuality, it was “landmark” programs like Miami Vice that offered the “ideational resource”
required to effect such a change.
22
In other words, such programming “provided the conceptual
framework – that is, the audience expectation and the cultural capital – needed to effect a shift in
the televisual discourse.”
23
As will be detailed throughout this chapter, the “conceptual
framework” necessary for datavisuality has emerged out of the rising place of sports analytics
100

within front offices and within popular culture. Much as Miami Vice paved the way for
televisuality, Moneyball has cleared a path for datavisuality.
Before getting to the technology and industrial practices that have facilitated the rise of
datavisuality, then, this chapter will first visit the world of sports analytics. Such a visit is
necessary because to fully understand how the sports television industry has responded to the rise
of analytics and come to imagine a demand for data-driven content, more context is required.
More specifically, what is required is an overview of how sports analytics rather quickly
transformed from a curiosity to a dominant paradigm all but impossible for the sports television
industry to ignore. The rest of this section, then, will chart the rapid ascendance of sports
analytics, in the process introducing many of the key texts and figures that have driven the
popular narratives surrounding analytics.

From Moneyball to Moreyball
Before proceeding into the details regarding the sports analytics “movement,” it must be
clarified that the use of data in sports is not exactly a new development. Rather, modern sport
has long been data-driven. As Brett Hutchins writes, “A quantitative imperative infuses the
pursuit of excellence in sport, with times, distances, heights, hits, throws, runs, goals and scores
used to define and measure performance over time.”
24
Figures within the industry make broadly
similar claims, noting that executives have long made use of analytical reasoning informed by
data. In a recent talk at the MIT Sloan Sports Analytics Conference, for example, Harvard
geographer-turned-basketball analyst Kirk Goldsberry explained that even the simple fact of
whether a player is right or left-handed is very much a piece of data.
25
Teams, of course, have
101

long used such straightforward information – as well as statistics like “times, distances, heights,
hits, throws, runs, goals and scores” – in putting together their rosters.
But even though front offices have long relied on data – whether that might mean
something as basic as a baseball player’s weight or as complicated as his BABIP (batting
average on balls in play) – to make their decisions, the recent fascination with sports analytics
has nonetheless represented a sea change in thinking. Not only has quantitative data become
more and more important to the process of roster building, but the gathering and implementation
of such data has also become rigorously systemized, as explained in the “sports analytics”
definition that began this chapter. These trends have culminated, then, in the aforementioned use
of big data methodology, with teams beginning to make use of extremely large data sets and
predictive analytics. This trajectory, of course, generally parallels that of other industries. As
Cornelius Puschmann and Jean Burgess explain, the use of data mining and analytics in the
business world stretches back decades.
26
Data-driven approaches, in other words, are far from
novel in most industries. However, as is the case with sports industry, the incorporation of big
data methodology throughout the business world over the course of the past several years
represents a distinct break from what came before. As Mark Andrejevic comments, this is a
unique moment “in that it marks the emergence of the prospect of making sense of an
incomprehensibly large trove of recorded data—the promise of being able to put it to meaningful
use even though no individual or group of individuals can comprehend it.”
27
While the sports
industry may not have fully arrived at the moment where automated pattern recognition outstrips
human comprehension, many analysts may imagine this as the logical endpoint for increasingly
complex tracking systems like SportVU.
102

The sports industry’s transition away from simple statistics and toward the rigor and
complexity of analytics is most commonly traced back to Michael Lewis’s Moneyball – a work
that continues to hold an enormous place in the sports analytics mythology. As mentioned in the
dissertation’s introduction, Lewis’s book tells the story of how Billy Beane, the general manager
of the Oakland Athletics, put together a roster of misfits and castaways that had been
“undervalued” by other teams – a unique strategy necessitated by Oakland’s relatively small
budget. Lewis details with some depth how exactly the team identified these players that had
been overlooked by the rest of the league. According to Lewis, Beane and his assistants placed a
major emphasis on statistics, but not the sorts of statistics that had traditionally been valued
within the sport, like hits and RBIs. Instead, Oakland bucked conventional wisdom and relied on
other quantitative measures that seemed to more closely align with on-field success – including
relatively complex metrics like VORP (Value Over Replacement Player). Continually drawing
parallels to the financial industry and bankers’ perpetual search for market efficiencies, Lewis
explains how an attention to metrics like VORP played a significant role in guiding Oakland
toward the proverbial diamonds in the rough.
The primary conflict within the film version of Moneyball – and also a continuing subplot
in Lewis’s book – revolves around just how novel it was to emphasize these new, occasionally
baroque statistics. To repeat, though, data was not new to sports, and neither was statistical
analysis. This was particularly true in the case of baseball. As Schwarz details, there have been
waves after waves of fans who have approached the game’s statistics with the utmost rigor.
Schwarz writes, “The serious study of baseball statistics … has consumed people from chemists
to crackpots for more than a century,” dating back to scrutiny over the first versions of the box
score that began to appear in the 1840s and 1850s.
28
Schwarz writes, for instance, about the
103

scores of diehard fans – many of them serious scientists and mathematicians in their normal lives
– who experimented with crunching baseball statistics on early generations of mainframe
computers and who tackled ambitious projects like the research-intensive Baseball
Encyclopedia, a massive volume first published in 1969. Thorn and Palmer’s work on OPS,
Schwarz explains, came as part of a particularly dense “explosion” of work in the 1970s and
1980s spearheaded by writer Bill James, whose annual, statistically dense guides to the sport
gradually grew more and more popular between 1977 and 1988.
29

However, the work of writers like James remained largely confined to small pockets of
diehard fans. While there was a growing audience for these writings, the work remained largely
unknown to the vast majority of the public, not to mention baseball front offices. The innovation
of Beane’s Oakland Athletics, then, was not to invent advanced statistical analysis, but rather to
be the first team to take this long line of existing work seriously and to incorporate such
experimentation into their decision-making processes. As Lewis details, the majority of baseball
scouts and executives – many of whom had played the game professionally and, accordingly,
carried with them certain biases based on their playing careers – clung to a limited set of beliefs
about what made for a successful baseball player. Significantly, these beliefs often relied on
intangibles, even including assessments based on players’ appearances. In fact, Lewis writes that
some scouts “still believed they could tell by the structure of a young man’s face not only his
character but his future in pro ball.” Lewis continues, “They had a phrase they used: ‘the good
face.’”
30
Indeed, Lewis explains how Beane himself had once been a high draft pick in part
because he fit many scouts’ perceptions of what a professional baseball player should look like.
While it is doubtless physiognomy has likely been more the exception than the rule amongst
104

baseball scouts, Lewis’s point is clear: for most baseball insiders, evaluating talent has long been
an imprecise exercise – and, more often than not, an exercise in futility.
Given this entrenched set of beliefs, in which a player’s appearance could become an
acceptable predictive metric, it is not surprising that Beane’s fascination with unconventional
numerical equations encountered some skepticism from both his executive peers and others
within the sport. This, then, developed into the film’s main conflict – Beane and his
mathematical equations taking on the sport’s traditionalists, a metaphorical battle of the brain
versus the gut. It was a narrative apparently so compelling that is has lingered within the sports
culture over a decade since the book’s release in 2003. In early 2015, for example, TNT’s star
NBA analyst, the former power forward Charles Barkley, went on an extended on-air rant
decrying the rising popularity of statistical measures within NBA front offices – in the process
seeming to re-enact the film’s storyline. “Analytics is crap,” Barkley said, before adding,
“Analytics don’t work at all. It’s just some crap that people who were really smart made up to
try to get in the game because they had no talent.” Continuing, he took on the many
mathematically-inclined NBA general managers that have come to power in the wake of
Moneyball. “All these guys who run these organizations who talk about analytics – they have
one thing in common: they are a bunch of guys who never played the game and they never got
the girls in high school and they just want to get in the game.”
31
Within hours, Barkley’s diatribe
was all over sports newswires.
While Barkley’s invective attracted attention across the sports world, the hoopla obscured
just how rare such attacks are within the media – a fact that points to the now-widespread use of
analytics across the sports world. Beane, for example, may have once been considered an
oddity, but he is now one of the most recognized executives in sports – as well as a sought-after
105

corporate speaker – and his approach has become the norm within baseball. As The Wall Street
Journal noted upon the film’s release in 2011, “What's the legacy of Moneyball? One thing it's
not anymore: a raging culture war. Mr. Lewis deftly framed it that way when he wrote the book.
Now, both sides acknowledge that statistical analysis has a place in baseball.”
32
The path of
Keith Woolner, who created the complex VORP statistic, is instructive. A 1990 graduate of
MIT, Woolner subsequently worked in Silicon Valley as a software developer.
33
In his free time,
though, he wrote articles for Baseball Prospectus – a small, niche publication that focused on
statistical analysis of the sport. As Baseball Prospectus co-founder Rany Jazayerli recollected,
writers like him and Woolner grew accustomed to working in relative obscurity. “Not only was
statistical analysis something that wasn't sort of mainstream, but it was almost mocked. It was
really degraded both by baseball teams and also by the media,” Jazayerli recalled years later.
34

However, not long after Moneyball was released in 2003, both mainstream media and baseball
teams had begun taking notice of the type of work done by Baseball Prospectus. In 2007,
Woolner was finally able to turn his analytical skills into a position with the Cleveland Indians –
the type of hiring that exemplifies the growth of analytics. Woolner comments, “In the early
days of [Baseball Prospectus], we were very much the outsiders. By the time I joined
Cleveland, I came into an organization that was data-driven and had buy-in towards analytics.”
35

Lewis sums it up in the Wall Street Journal article when he says, “The geeks have definitely
been let off the leash.”
36

For a number of reasons, baseball was the ground zero of the “analytics revolution” – the
sport, in other words, that has had the most vibrant history of statistical experimentation, as
exemplified by the development of OPS, and the one that first saw professional teams embrace
such experimentation (if belatedly). The most significant reason for this fact is that baseball is,
106

in Lewis’s words, “an individual sport masquerading as a team one.”
37
That is to say, a baseball
game can largely be boiled down to a series of discrete matchups between batters and pitchers.
This means, then, that it is relatively easy to quantify each player’s contribution to a victory (or a
loss). But even though baseball most easily lent itself to measurement, the fascination with
analytics has not confined itself solely to that one sport – a fact made clear in Barkley’s rant. As
Lewis would write several years after publishing Moneyball, “The virus that infected
professional baseball in the 1990s, the use of statistics to value players and strategies, has found
its way into every major sport.” Backing up, Lewis explains that each major sport, whether it be
baseball or tennis or rugby, now hosts “a subculture of smart people who view it not just as a
game to be played but as a problem to be solved” – a problem, essentially, of odds. “Modern
thinkers,” he adds, “want to play to play the odds as efficiently as they can … hence the new
statistics, and the quest to acquire new data, and the intense interest in measuring the impact of
every little thing a player does on his team’s chances of winning.”
38
“Moneyball,” then, is not
just the new normal for baseball, but also every other major sport.
Besides baseball, the sport that has perhaps most consistently embraced analytics has
been basketball. Indeed, as an example for the spread of analytics beyond baseball, Lewis points
to the rapid ascension of Daryl Morey, who was hired as the general manager of the Houston
Rockets in 2007 at the uncommonly young age of 35. Lewis recounts how Morey, like Beane,
was heavily influenced by the work of the aforementioned baseball statistician Bill James. But
while Morey shared with Beane a deep respect for James, Morey's path to the general manager
role more closely resembles that of Beane's mathematically-inclined assistant, Paul DePodesta –
a Harvard grad who would eventually take over the Los Angeles Dodgers at the age of 31. As
Lewis details, Morey's athletic career peaked in high school – perhaps not surprising given what
107

Lewis describes as Morey's "quizzical, geeky aura" – and so Morey subsequently followed "a
fairly conventional academic career" that took him from Northwestern to an MBA program at
MIT's Sloan School of Management.
39
From there, Morey landed with a consulting firm and
soon after, following a fortuitous turn of events, ended up in a business role with the Boston
Celtics – a role in which he was able to catch the attention of Houston's owner, who heard that
Morey “was out in front of those trying to rethink the game.”
40
Houston’s owner subsequently
hired Morey as the team’s assistant general manager in 2006 before promoting him to the lead
role in 2007.
Once in power in the Houston front office, Morey went about pursuing a team-building
plan reminiscent of the “Moneyball” strategies that Lewis had documented in Oakland – a
product not only of Morey and Billy Beane’s shared outlooks on their respective sports, but also
of similar circumstances. Much like Beane, Morey was faced with the dilemma of improving the
team in the face of a relatively limited budget – in this case a result of a salary cap instead of the
limitations of a small market. Morey is quoted by Lewis, then, as saying that he had to go
“looking for nonsuperstars that we thought were undervalued,” which he calls the “scarce
resource” of the NBA – thus recalling the type of asset-driven language scattered throughout
Moneyball.
41
In light of this need for “undervalued” players, Morey had to find novel ways of
identifying which players throughout the league were underpaid and, thus, like Beane, Morey
had to rethink how players were measured and evaluated. Again recalling Moneyball, Lewis
points out that basketball had long lacked “meaningful statistics,” instead relying on “what is
easy to measure,” like points, assists and rebounds. “Someone created the box score,” Lewis
quotes Morey as saying, “and he should be shot.”
42
This quest to go beyond the traditional
statistics and into more complex calculations of efficiencies helped lead Morey to otherwise
108

disregarded players like Shane Battier – an athletically-limited but defensively-gifted forward
who Lewis dubs the “no-stats all-star.” Before long, Morey’s strategies had generated their own
title: “Moreyball.” Baseball, clearly, was no longer alone in the sports analytics universe.

From “Moreyball” to Status Quo
At this point, the story of how analytics became a regular part of baseball is likely to be
vaguely familiar to many casual sports fans, particularly thanks to the success of both the book
and film versions of Moneyball. In fact, they have both been such successes that Moneyball
imitators continue to appear over a decade since the book topped bestseller lists. 2015, for
example, saw the release of books such as Big Data Baseball, which is like Moneyball but with
the Pittsburgh Pirates and more data, and The Best Team Money Can Buy, which is like
Moneyball but with the Los Angeles Dodgers and more money. However, the path of analytics
into basketball has not been as well documented, even though, as mentioned above, basketball
has been just behind baseball in the rush towards analytics. In continuing to summarize the rise
of analytics, then, it might be worth sticking with basketball, for its path may foreshadow the
paths that other sports are likely to take going forward. We return, then, to Daryl Morey.
Although Morey has yet to lead Houston to a championship in the decade since his hiring, his
analytical approach to the game has perhaps become the primary model within the league. In
fact, almost every NBA team now features an analytics department and, moreover, many teams
have recently hired analytically-oriented general managers who may lack professional playing
experience, but instead come ready to dissect quantitative data – a trend partially spurred on by
the recent purchases of many NBA teams by businessmen with backgrounds in technology and
finance who embrace “the dispassionate language of expected value and probability.”
43
For
109

example, a group of private equity investors purchased the Philadelphia 76ers in 2011 and, not
long after, installed Morey’s closest assistant, Sam Hinkie, as the team’s new general manager.
Like Morey, Hinkie had earned an MBA and worked as a consultant before finding his
way to the NBA. Hinkie, though, may be even more extreme than Morey in his approach to the
sport – a fact that has helped make him one of the more recognizable names within sports
analytics. As Pablo Torre noted in a notorious 2015 profile in ESPN The Magazine, Hinkie “has
a hyperactive brain that runs cost-benefit analysis as relentlessly as a normal human breathes
oxygen.” In Houston, for example, Hinkie was known for “measuring essentiality everything,”
in addition to “constructing financial models and running Monte Carlo simulations of the NBA
season, investigating, among many other things, how certain players impact thousands of
possible outcomes.”
44
Accordingly, Hinkie quickly gutted the Philadelphia roster that he
inherited upon his hiring, attempting to completely rebuild the team by way of his efficiency-
oriented worldview – an effort that has included an unprecedentedly drastic desire to lose games
in order to garner a stockpile of valuable draft picks. “Hinkie is the analytical gunslinger and the
draft his Wild West,” as Grantland’s Danny Chau wrote in 2015.
45
The team, purposely terrible
year after year, has subsequently been, according to Torre, “derided for being not just
uncompetitive but singularly anti-competitive.”
46

While shedding well-known players like Michael Carter-Williams, Hinkie has
simultaneously attempted to remake the front office in his image – hiring young, well-educated
analytics experts who, like Hinkie, have come to the sport with little playing experience.
“Alongside their boss,” Torre observes, “they must make up the shortest front office in the
league.”
47
Meanwhile, Hinkie’s fixation with measurement has carried over from Houston to
Philadelphia. For example, Torre describes how Hinkie “tracks each shot his players take, not
110

just in games but also in shootarounds and practices” – which the team’s coach then uses to
determine which players are, in his words, “investing time into development” so that he can
reward them with playing time and other privileges. As veteran shooting guard Jason
Richardson tells Torre, “You can’t hide.”
48
In what would appear to be a significant sign for the
growing acceptance of analytics, many of the team’s fans have accepted Hinkie’s drastic path
forward. Torre notes here that Philadelphia fans are notoriously demanding – known, for
instance, for “whipping batteries at subpar infielders” – but that they have “not been
proportionally vicious” when it comes to the 76ers’s unparalleled awfulness.
49
As revealed in
Torre’s piece, Philadelphia’s players are repeatedly told to “trust the process” – a message that
has extended to the fans and become a mantra for the team.
Given the above account, it should be not surprising to learn that Morey and Hinkie have
been two of the most aggressive pursuers of big data tools. According to Hinkie, Houston was
“customer zero” for STATS’s SportVU – the aforementioned optical tracking system – while he
was Morey’s deputy with the Rockets.
50
Naturally, Hinkie has also enthusiastically made use of
the technology after being hired in Philadelphia. As former STATS executive Brian Kopp
explained, this pattern of adoption was common in the first few years of the technology, with
analytics experts being poached from teams known for their analytics proficiency, like the San
Antonio Spurs, and then bringing their interest in the technology with them to their new teams.
51

Similarly, Morey and Hinkie have also been major proponents of wearable technology.
Philadelphia, for example, uses a device developed by Australian company Catapult which tracks
player positioning during practices. “Every player has worn it in our gym every day since I’ve
been here,” Hinkie has boasted.
52
Unsurprisingly, Morey has also made use of Catapult tracking
devices in Houston.
53

111

Morey, Hinkie and other early devotees to analytics, including RC Buford of the San
Antonio Spurs and Mike Zarren of the Boston Celtics, may have helped usher sports analytics –
and, eventually, big data methodology – into the sport of basketball, but as was the case with
baseball in the post-Moneyball era, all indications point to the data-driven approach being the
new status quo. Teams like the Rockets, then, are far from outliers at this point, with most other
teams having taken their approaches, if not their personnel. “It’s like a lot of competitive
environments,” Hinkie told ESPN. “There’s an advantage, and then it goes away quickly.”
54

SportVU data, for example, is now available to all teams. Furthermore, much of the league is
currently rushing gleefully towards the relatively unexplored terrain of biometric monitoring –
perhaps another site for GMs like Hinkie to find an “advantage.” As Pablo Torre and Tom
Haberstroh detail, teams like the Golden State Warriors and Dallas Mavericks have implemented
sleep programs in which players can elect to have their rest monitored by wearable devices.
55

Five unnamed teams, meanwhile, “convinced players to wear a skin-adhesive, torso-mounted
sensor that is colloquially known within front offices as ‘the patch,’” which can measure fatigue
both during practices and at home. “We need to be able to have impact on these players in their
private time," Kings then-general manager Pete D'Alessandro is quoted as saying.
56
The
Mavericks have even started emphasizing blood data. “I think the smartest thing we do for
health from a data perspective,” Dallas owner Mark Cuban has said, "is take ongoing
assessments and even blood tests so we have a baseline for each individual that we can monitor
for any abnormalities.”
57
Of course, ambitions go even further than blood tests. “The holy
grail," D'Alessandro tells Torre and Haberstroh, "is sequencing and understanding the genome.
And how that relates to pro athletes on an injury basis and who's naturally good at certain
sports.”
58
That dream of genetic determination is not here quite yet, though.
112

Baseball and basketball may have been the focus of this section, but as was explained
above, every major sport is now host to analytics enthusiasts who have been pushing these sports
toward their own versions of “Moneyball” and “Moreyball.” The NHL, for example, now
prominently displays advanced statistics on its website, while a number of NFL fans – including
mainstream figures like Bill Simmons – can be found discussing relatively intricate metrics like
DVOA (defense-adjusted value over average). Gradually, then, these other major sports have
become host to big data methodology. As mentioned in the introduction, many European soccer
teams make use of optical tracking technology not too dissimilar from the NBA’s SportVU
system. The NFL, meanwhile, has begun implementing a tracking system based on RFID chips
embedded in players’ shoulder pads. Several NFL teams, too, have been aggressively pursuing
biometric monitoring programs. The Philadelphia Eagles, for instance, collect sleep reports, use
Catapult devices during practices to track player performance information like speed and agility,
and also ask their players to take wellness questionnaires every morning.
59

Across the major sports, then, there has been a significant change in thinking over the
past decade. Traditional GM tasks – detailed film study, scouting trips to closely observe college
prospects, etc. – remain important, but they are now accompanied by analysis that relies on vast
new collections of data, including the sorts of predictive analytics that have become mainstays
across a variety of other industries. Increasingly, these new approaches cross over into the realm
of big data methodology, with extremely large data sets being automatically collected and
processed. In the course of the shift, a new type of GM has been valorized – an efficiency-
minded executive as comfortable with complex data models as they are with anecdotal scouting
reports gathered from a far-flung scouting network. To repeat Lewis’s summation, “The geeks
have definitely been let off the leash.” The question lingers, though, as to how this rapid
113

transformation affected sports media. The next two parts of this section, then, will explore how
multiple sectors of the television industry have reacted to sport’s transition to a data-driven world
and, in the process, spurred the growth of datavisuality.

Graphic Changes
The previous part of this chapter detailed how the sports industry has become smitten
with analytics. This change, then, undergirds the imagined audience demand and cultural capital
– “the ideational resources” – required for the emergence of datavisuality. However,
datavisuality does not spring directly from sports analytics. Rather, it is very much a television
phenomenon and, as such, is rooted in the workings of the television industry. This in mind, the
rest of this chapter will dig into the sports television industry – a relatively unexplored area
within media scholarship. This lack of scholarship, though, is not altogether surprising. First
and foremost, scholarship on sports media, in general, remains rather sparse despite sports
media’s continued financial and cultural import. Thus, research into the sports television
industry is bound to be similarly limited. Perhaps just as important, though, is the fact that sports
television industry is not as straightforward as it might initially appear and, moreover, operates
very differently from other parts of the television industry. Comprising a hazy milieu that
includes a smorgasbord of broadcasters, programming that defies easy categorization, as well as
ever-shifting and ever-multiplying rights, it can be a difficult environment to pin down.
Moreover, it has grown more complicated in the post-network era. If, for instance, I wanted to
explain how I was able to watch ultimate frisbee on my TV this past weekend, I would have to
tell you who was broadcasting the event (ESPN), which specific network I was watching
(ESPN3, available only on streaming devices), who paid for the rights (in contrast to most major
114

events, the ultimate league likely paid ESPN to broadcast the game), who hired the announcers
(again, the ultimate league), who shot and edited the footage (Fulcrum Media, a video production
company hired by the ultimate league), who provided the graphics package (ESPN), who
received the advertising money (the ultimate league), etc. For any given sports television
property – whether it is broadcast on a major cable network, a regional sports network, or a
streaming-only service like ESPN3 – there is a dense web to untangle.
It is important to begin with the understanding that sports television industry is a dense
network that escapes easy comprehension because the integration of sports analytics into sports
television is not just a matter of swapping in new widgets able to display a larger set numbers.
Rather, sports analytics, it will be explained, touches on many different parts of the industry. In
fact, sports analytics has helped bolster entirely new sectors of the industry. This complexity in
mind, the next two parts of this chapter will focus on two different strands of the elaborate
ecosystem that is the sports television industry, explaining how each of them has played a role in
the emergence of datavisuality. The first of these sections will delve into the graphics industry –
detailing how and why this industry has produced many of the technological innovations that
have allowed for datavisuality to flourish. The next section will analyze the role of broadcasters
in fueling datavisuality – specifically focusing on one of the most aggressive purveyors of
datavisuality, ESPN.
The graphics industry is a useful starting point because graphics technology has provided
a key pre-cursor to datavisuality. Indeed, the clearest instances of datavisuality, as in the Sunday
Night Baseball examples cited above, are all built on platforms engineered by the graphics
industry. As such, datavisuality again parallels televisuality. As Caldwell notes, tools like Rank-
Cintel played a key role in creating “televisuality” by providing “the ‘technical competence’
115

needed for a change in the television industry.”
60
Similarly, technology has played a significant
role in “datavisuality” by creating “an array of conditions, and a context” that have allowed
sports media to keep up with the sports industry’s growing fixation with data – whether that
might mean the development of graphics systems able to display advanced metrics like OPS or
the creation of even more advanced tools able to render real-time tracking data.
61
In explaining
the rise of datavisuality, then, it is necessary to pay heed to the graphics industry and, more
specifically, to explain how the industry has arrived to these technologies. To that end, this
section will first delve into the particulars of the sports graphics industry, detailing not just how
the industry operates, but also how it has changed in recent years in response to shifting
marketplace conditions. Next, the section will explain how an eruption of sports data is affecting
the industry, both in its present state and beyond. Finally, the section will draw on statements
from a number of industry figures to examine why the graphics industry has approached the
explosion of data with a mixture of excitement and apprehension.

A Brief Introduction to Sports Graphics
Before delving further into how the graphics industry has precipitated the rise of
datavisuality, it would be beneficial to provide further explanation of how exactly the graphics
industry operates. As mentioned above, the sports television industry is not as straightforward as
one might initially assume. When it comes to graphics, then, it should not be surprising to learn
that that there is a relatively complex network of connections lying behind something as
seemingly simple as a score ticker. Indeed, even the ESPN analytics employees I interviewed
had just a vague sense of how their work eventually ended up in graphic form. In breaking down
graphics, then, it might be helpful to begin with a simplified taxonomy – classifying graphics
116

based on when and where they appear, as well as their purpose – accompanied by brief
explanations of how these graphics are designed and implemented.
The flashiest and most intricate – and, thus, most noticeable – sports graphics are the full-
frame graphics that are typically employed either during studio shows or outside of live game
action, sometimes referred to as animations. These include opening logo reveals, replay wipes
and bumpers, amongst others. Often, these graphics are produced far in advance by creative
agencies that have been hired by broadcasters to deliver either comprehensive network branding
or project-specific branding. One prolific example of such an agency is the Los Angeles-based
firm Troika, which provides branding and marketing services not just for the sports industry, but
also for a variety of other entertainment clients, including the Sundance Institute, Hulu and
Entertainment Tonight. Sports, though, is one of Troika’s specialty areas and they have been
involved in the branding of many familiar sports properties, from the Golf Channel to HBO
Boxing to ESPN’s Monday Night Football. For example, Time Warner Cable SportsNet, a Los
Angeles-area RSN primarily known as the home to the Los Angeles Lakers, hired Troika to
provide branding for its initial launch – an effort that included, amongst a number of other
services, logo design, music composition, and graphics packaging to accompany game
broadcasts and studio shows.
62
Thus, Troika, rather than the network itself, is the original source
for the network’s many reveals, bumpers and various other full-frame graphics. These intricate
interstitial graphics, though, are not always produced by outside firms. National broadcasters, in
particular, are likely to be home to large graphics departments able to design and execute major
projects. Turner, for example, will often work with agencies like Troika, but will also
occasionally create graphics packages independently – a feat made easier by the company’s in-
house staff of motion graphics designers. For instance, TNT’s current NBA packaging –
117

featuring distinctive time-lapse sequences – was designed and implemented by its own design
team.
63

The graphics mentioned in the above paragraph – bumpers, logo reveals and the like –
have been referred to as interstitials because they fill gaps in programming or in gameplay. As
John Ellis has written, interstitials play a “key role” for television viewers. They are, he
suggests, “a series of distillations of television, and an internal meta-commentary on ordinary
TV.”
64
Moreover, Ellis argues that their significance has been too often ignored by academics.
“Their importance in everyday TV is increasing,” he writes, yet “their importance to scholarship
has hardly begun to be acknowledged."
65
This project, though, will primarily focus on television
elements that have perhaps been even less acknowledged than interstitials: informational
graphics. As the name implies, they add information, contextualizing what is on screen. Unlike
interstitials, informational graphics usually appear in the course of programming or gameplay.
Unlike typical interstitials, too, most informational graphics are heavily customizable. Indeed,
informational graphics are often referred to as “inserts” within the industry because new data is
meant to be inserted into pre-existing templates. Viewers can find them across the full range of
sports programming, including all varieties of studio shows and game broadcasts. They come in
many shapes and sizes, too, with some filling the whole screen – as in the case of the
aforementioned interstitial graphics – and some filling just small portions of the screen.
Informational sports graphics may take a variety of forms, but there are a few standard
types. The most familiar type might be the score box – also referred to as the score bug – that is
normally found in the upper left of the screen during live game broadcasts. The score box is now
ubiquitous, but it has a relatively short history. As Richard Sandomir details, Sky Sports debuted
the first version of a score box in 1992, using it to display both the score and the time remaining
118

during English Premier League games.
66
In 1994, America was introduced to its first score
boxes when ABC and ESPN deployed them for the 1994 World Cup. That August, the score
box moved beyond soccer when Fox premiered the “Fox Box” during an NFL preseason game.
Soon after, ESPN incorporated a score box for its own NFL games and, the following year, the
score box had also moved to baseball. Of course, the score box is by now a regular part of all
sports broadcasts, whether they are found on national networks or on web-only streaming
services. Moreover, score boxes have evolved to take several shapes and sizes, and they will
often display much more than the score and time remaining. Baseball score boxes, for instance,
now typically provide a game’s score, the inning, whether it is the top or bottom of that inning,
the number of outs left, the pitch count and which bases have runners. Each network puts their
own stamp on the score box, too – offering distinct designs and, quite often, distinct information.
FOX, for instance, complements all of the usual score box information with additional
information about the pitcher and batter, including pitch speed and pitch count.
In addition to the score box normally found in the upper left of the screen, sports
television viewers will also have become quite familiar with graphics in the lower third of the
screen. As in news broadcasts, lower third graphics are often used to deliver basic information.
During a studio show like SportsCenter, for example, that could entail displaying who is
speaking and the topic of conversation. During a game broadcast, meanwhile, that could mean
displaying information such as which player is currently at the free throw line and their free
throw percentage. Moreover, the lower third is usually the location of a scrolling ticker – again a
common sight during news broadcasts. In the case of sports broadcasts, though, the ticker is
known just as much for displaying current game scores and other incidental sports information,
like college rankings, as it is for displaying breaking news. As was the case with score box, the
119

history of the sports ticker is relatively short. According to Michael Hiestand, ESPN first
premiered on-screen news graphics in 1985, but it was not until 1995 that the ticker became a 24-
hour presence.
67
In the twenty years since, tickers have expanded across the industry and have
now become commonplace across the vast majority of sports networks – both national and
regional. Their presence is hard to miss. As Deadspin has documented, networks regularly
devote a large portion of their screen space to tickers. For example, SNY, an RSN home to the
New York Mets, devotes 12.4% of the screen to its ticker, while the NFL Network devotes
10.8%.
68

Beyond score boxes and lower third graphics, informational graphics become less
standardized. Many networks, for example, frequently employ graphics that line up two athletes
for a head-to-head comparison. However, such comparison graphics can appear in many shapes
and sizes and have no shared label. Similarly, it has now become a common sight for networks
to display lineups ahead of games, but these graphics can take many different forms – some
appearing as detailed formations and others as simple horizontal lines of headshots or basic lists
of names. What unites these graphics, as well as a host of other information graphics, beyond
the vague “informational graphic” title, then, is a shared workflow. Like the first graphics
mentioned above – interstitial animations – informational graphics often begin with specialist
firms like Troika. Commissioned by broadcasters, these agencies will typically develop a full
“insert graphics package” that encompasses everything from score bugs to lower thirds to
comparison graphics. When FOX Sports launched the new channel FS1 in 2013, for example,
they called on another Los Angeles-area studio, |drive|, to design and animate a new insert
package – including the information-packed baseball score box.
69
Similarly, when NBC re-
branded the Versus Network as the NBC Sports Network in 2012, they hired Troika not just to
120

make a full set of interstitial graphics, but also to create an insert package that covered
everything from score boxes to lower thirds to line ups.
70

When companies like Troika design and animate interstitial graphics, like a network ID
or a replay wipe, the graphic is essentially complete. All that is left is for a broadcaster to trigger
it at the appropriate moment. The workflow for informational graphics can be a bit more
complicated. As mentioned above, informational graphics are often referred to as inserts
because information is inserted into pre-existing templates. Creative agencies like Troika, then,
are responsible just for that first step – designing and animating the templates. Work remains to
fill the graphic with information. Traditionally, graphics operators have done this work
manually. For example, they might type a player’s statistics into a lower third graphic using
specialty software, like ChyronHego’s Lyric system, that would then interface with the
previously designed templates. That statistical data, meanwhile, was likely pulled either from an
external data provider, such as STATS or Opta, or an internal source, as major broadcasters like
ESPN may also maintain their own departments dedicated to gathering and distributing sports
data – data that can supplement what is provided by external sources. Thus, the final product
that appeared on screen was the result of several layers of interaction.
Although the sports graphic industry is a relatively new segment of the sports media
ecosystem, the taxonomy described above has operated with relative stability for much of the
past few decades. If one were to compare, for example, an NBA game from 1998 and one from
2008, it would be hard to find many noticeable differences in the use of graphics. While the
graphics in 2008 may have more motion effects and a greater affinity for curved lines, they still
follow similar rules as the ones that appeared a decade prior. That is to say, score boxes remain
anchored in space and statistical information is both limited and largely confined to lower third
121

graphics or full-frame graphics that appear during breaks in play. However, as the next section
will describe, this system has begun to change in recent years. These changes, it will be argued,
have disrupted the industry and, in the process, facilitated datavisuality’s ascent.

An Industry in Transition
As mentioned, the basic system described above – split between interstitial graphics like
bumpers and informational graphics like score boxes and lower thirds – has been relatively stable
for much of the last two decades. Over the last several years, though, new technology has
brought changes to this system and, in turn, facilitated the rise of datavisuality. The first major
change has been the increased use of a new type of graphic: the virtual graphic. In brief, it is
now possible for broadcasters to insert digitally rendered visual elements anywhere and
everywhere. During an NFL pregame show, for example, a dancing robot, or, more subtly, a
virtual banner displaying team logos, might appear next to an anchor desk. More significant is
the fact that virtual graphics have become commonplace during live game telecasts. Some of
these virtual graphics operate much like traditional informational graphics – displaying
information that otherwise would have been placed in a static box. It has become a regular sight,
for example, to see NFL broadcasters placing virtual billboards on top of stadiums – these
billboards displaying either sponsor messages or player statistics. Similarly, NBA and NHL
broadcasters will often “hang” virtual signs from arena rafters.
Other virtual graphics, though, are more thoroughly integrated into game action. An
early and prominent example would be the yellow first down line that appears during football
games. Invented by graphics company Sportvision, the virtual first down line debuted in 1998
and has since become a regular part of both college and professional football broadcasts.
71
In the
122

years since, that system has evolved, too. Sportvision, for instance, now offers the option of
joining the first down line with other on-field graphics that display the down, distance and game
clock. Virtual graphics are not just limited to football, though, as major graphics technology
providers like Sportvision, ChyronHego and Vizrt have developed systems that allow any sport
to feature a bevy of virtual graphics. Soccer games, for example, now regularly feature virtual
offside lines, on-field numbers displaying the distance from a free kick position to the goal, and
many other enhancements.
With the rise of the score box and the increasing usage of lower third and full-frame
graphics in the 1990s, graphics became a regular part of the sports television experience. At any
given moment in a game, graphics were now sure to be present – a major change from just a
decade before, when graphics were largely limited to occasional flashes of text that might
indicate the score or the name of an announcer. Virtual graphics, though, seem to represent
another major moment in the history of sports graphics. With graphics no longer confined to
certain areas of the screen or structured by breaks in play, entire games become subject to
graphic “enhancement.” The ramifications for the graphics industry, then, are significant.
According to Tomas Robertsson, an executive at graphics technology company deltatre, the
sports graphic technology industry was until recently a commodity service, meaning that there
were many companies offering essentially the same service. Thus, Robertsson explains, the key
question for companies was “how far down you can drive the price.” That status, though, has
changed “in the last couple of years with the advent of virtual technology.”
72
Broadcasters, he
argues, are no longer mainly looking at price when choosing graphics providers, but rather at
innovative technologies that will help set them apart from their competitors. Press releases from
broadcasters bear this statement out. FOX, for example, has promoted its new golf coverage as
123

making use of a “high-tech arsenal” that continues the network’s “leadership role in the use of
production technology” – an arsenal very much heavy on virtual graphics.
73
Similarly, ESPN
has touted its use of new ChyronHego virtual graphics technology in its soccer coverage.
74

Virtual graphics, then, have not just affected the look of sports broadcasts – turning them,
to use industry terminology, into augmented realities – but also how the industry operates. Now
charged with setting broadcasters apart, technology providers have raced to develop a rash of
new virtual capabilities. Such a race, of course, provides the backdrop for datavisuality. In
brief, virtual graphics allow data to enter parts of telecasts where data was once unwelcome. As
mentioned, informational graphics were once confined to lower thirds or to full-frame graphics
that appeared during breaks in play. However, the introduction of virtual graphics, means that
these traditional rules no longer apply. The “high-tech” arsenal promoted by FOX, for example,
includes virtual graphics that trace colorful ball trails as well as floating signs displaying the
distance to course features like bunkers and water hazards. The technology that ESPN has
touted, meanwhile, includes virtual graphics that allow the network to highlight formations and
single out notable players. In both cases, the playing surface becomes covered in graphics. Data
now knows no boundaries, as in the graphic shown here:
124

2.6 Virtual graphics technology developed by SMT for NBC and the Golf
Network.
Undergirding this major change – in which the entirety of a game broadcast becomes
subject to data overlays – has been another change just as significant: a massive influx of data.
FOX’s new golf graphics, for instance, draw on data-intensive technology that constantly
measures a ball’s flight and maps course layouts. ESPN’s soccer graphics, similarly, rely on
optical systems that track players’ positions throughout the game – thus recalling the SportVU
system mentioned above. Virtual graphics, then, are not just significant in their own right, but
also serve as the most explicit visual manifestations of massive data operations going on behind-
the-scenes. Indeed, this influx of data may have been the primary theme when more than 150
figures from the sports graphics industry gathered at HBO’s headquarters in Manhattan in
February 2015 for the Sports Video Group’s first ever Sports Graphics Forum – an industry
event meant to address the current and future states of the sports graphics industry. Across a
number of panels, ranging from those focused on technology to creative, data came up again and
again. While the word “virtual” may have been in the titles of multiple panels, it was data that
often became the focus.
125

According to the figures at the SVG event, the influx of data has affected the graphics
industry in a number of ways beyond what is immediately visible on screen. Most pressingly,
companies are struggling to figure out what exactly to do with the massive amounts of data that
are being generated. As mentioned, graphics technology providers have moved aggressively into
player tracking – a pursuit that generates large troves of data. ChyronHego’s optical tracking
system, for example, resembles STATS’s SportVU system in that it gathers 3D positioning data
for all objects on a playing surface 25 times per second – thus creating a flood of X, Y, Z data for
every game.
75
Other companies, meanwhile, are working with RFID, GPS and radar systems,
amongst other tracking technologies. Perhaps having just as strong of an effect as these player
tracking efforts, though, is the data being gathered by external data providers. Lying in between
graphics technology providers and broadcasters, these data providers, such as STATS and Opta,
collect and disseminate much of the information that eventually winds its way into graphics.
Traditionally, data providers would provide broadcasters with basic statistics as well as notes that
could be highlighted during a broadcast. The Elias Sports Bureau, for example, might provide
ESPN’s Monday Night Football crew with notes indicating that an NFL quarterback had thrown
more than 300 yards five of the last seven games. Graphics operators, then, could manually
work such information into graphics. However, advances in technology mean that external data
providers can now pass on more and more information. Opta, for instance, advertises its abilities
to provide broadcasters with XML feeds containing live stats for players and teams, text
commentaries, positional information, season to date cumulative stats, historical data sets, and
more.
76
Graphics, then, are drawing from a much larger pool of information than before.
On a basic level, the influx of data has meant that the graphics industry – all the way
from the technology developers to the data providers to the broadcasters – has had to re-think
126

how this torrent of data makes it way to the screen. Steve Hart, co-founder of RT software,
comments, “It’s the most important thing we work on at the moment – trying to solve the data
problem.”
77
Similarly, David Melfi, senior designer at NBC Sports, says, “Data is a constant – I
won’t say worry, but something we’re dealing with.”
78
“Workflow,” then, becomes a frequent
topic of conversation, as industry figures attempt to develop a process by which the massive
amounts of data should be integrated into broadcasts. For instance, David Jorba, a senior vice
president at Vizrt, states, “I think the main focus probably for us, and it has been for the past few
years, is workflow and reading all this data.”
79
Along the same lines, Soren Kjellin, CTO of
ChyronHego, comments, “Everybody is about workflow” – specifically, integrating data into
broadcasts.
80
While graphics technology providers, such as ChyronHego and deltatre, have been
able to develop interfaces that read in data from external providers and the external providers
have also worked to seamlessly integrate with these graphics providers, these technical
partnerships still leave additional questions. Namely, how to quickly filter and display the
appropriate information.
As mentioned above, graphics have long been operated manually. Designers developed
standard templates, and then graphics operators entered the appropriate information using
graphics systems created by the aforementioned technology providers. This process has largely
continued even as data providers have begun offering XML data feeds that can be easily
integrated into the graphics systems, as manual operators are needed to pull in the appropriate
data and display the graphics at the right moments. With the amount of data continuing to
increase, though, automation has become a touted solution to the workflow questions mentioned
above. “I think that various kinds of automation is where things are going to go,” says Kjellin,
of ChyronHego.
81
Indeed, automation has already begun, to a degree. Graphics provider
127

wTVision, for example, uses RFID technology to recognize playing cards during poker
tournaments, thus allowing the company’s software to automatically produce graphics.
“Everything is automated,” comments wTVision’s CEO Mario Sousa, “driven by the data.”
82
To
provide another example rooted in a more popular American sport, the graphics company Reality
Check Systems recently produced a number of partially automated graphics for the NFL
Network’s coverage of the NFL draft combine in February 2015 – graphics meant to illustrate
the similarities between established NFL stars and the draft hopefuls being tested at the combine.
For these combine graphics, Reality Check set up databases containing historical NFL data and
live combine data, and also worked with an analytics company, analy7ics, to devise an algorithm
that would “analyze, filter and compare information across both databases; generate the NFL
player with stats that best match the combine player; and then display the results as graphics as
desired throughout coverage.”
83

2.7 NFL Combine graphics created by Reality Check Systems for the NFL
Network.
Such efforts, though, are seen as only the tip of the iceberg. Kjellin offers the possibility
of combining camera position with player tracking data and game scores to automatically trigger
graphics. “That’s where I think a lot of things will be going forward,” he says. “It’s going to be
128

much more automation that is planned in beforehand, but also automation that can respond to a
context.”
84
Sousa agrees, also mentioning the potential of combining camera tracking with
player tracking – providing, as an example, the possibility of automatically displaying player
statistics in basketball games based on who has just shot the ball.
85
“Context” here is a much-
repeated word, for more data allows graphics software to properly interpret game scenarios. As
deltatre’s Tomas Robertsson writes in a blog post summarizing the conference’s highlights, “As
the data becomes more sophisticated, the more intelligent the graphics can become.”
86
Of
course, too, reduction of labor costs is part of the appeal of automation – if one that was
underplayed at the conference, with figures like Jorba stressing “efficiency” and maintaining
graphics departments would continue to grow in the coming years as graphics continued to take
up “more and more of the screen.”
87
Robertsson, though, is more explicit in his web post. He
writes, “As with so many other things, human operations and logistics is a major cost component
of a graphics service. Naturally, automation and remote operation becomes attractive.”
Continuing, he adds, “There is definitely a desire among graphics product and services
companies to simplify the workflow and reduce the reliance on human operations.”
88

Eventually, then, the influx of data offers the promise of reduced labor costs.
In summary, the graphics industry has recently witnessed two major technological
developments – the introduction of virtual graphics and the influx of massive amounts of data –
that have not only changed how the industry functions, but also facilitated the rise of
datavisuality. As recounted above, it is now possible for data to be anywhere and everywhere
during sports television programming. While this does much to explain the “technical
competence” that lies behind many of the guises of datavisuality, some related questions remain.
129

Not only is it still unclear how exactly the industry seeks to deploy the technology, but it is also
unclear where the demand for this technology is coming from.

“Don’t give me a math lesson”: The Industry Takes Stock
If we now have some answers as to how the graphics technology has recently evolved –
changes that have partially enabled datavisuality – a few larger questions remain as to how data
is being incorporated into broadcasts and, going even deeper, why exactly the industry has been
open to this data flood. Tackling the how first, the graphics industry is not just faced with the
logistical dilemma of handling vast new supplies of data, but also with the even more pressing
dilemma of successfully integrating all of that data into broadcasts. As Jason Cohen, Vice
President of Production at HBO Sports, comments, “There’s so much data and so much
information but only so much time to display it on screen.”
89
And it is not just a question of
fitting all of this data on screen, but also of fitting it all on screen in a comprehensible fashion.
Along those lines, Gerard Hall, CEO of technology provider SMT, asks, “It’s a fire hose of
information – how do you get that reduced down to something that’s really salient and then come
up with a way to visualize it quickly?”
90
Robertsson, meanwhile, writes online, “With so much
data available, it is very easy to overload the viewer with information without providing real
relevance or context.”
91
Again and again, the question looms: how do graphics companies and
broadcasters make use of all of this new data? Or, to be more specific, how do graphics
companies and broadcasters make use of all of this new data without overwhelming viewers?
Melfi, of NBC Sports, offers a thorough breakdown of the situation:
It’s not just taking in the data – we’ve got to make that data work in a useful way, as well … We
can parse the data – how much a soccer player has run during a match is great – but then how do
we present that information to the viewers in a meaningful way? … A lot of the more complicated
130

data is a lot more complicated for the average viewer to understand. So, if you’re talking about
maybe the speed of a puck on a slapshot or the force of that puck, you can tell them it’s going 100
mph, but [they] don’t really know if that’s fast or slow. So, it’s parsing the data and automating it
in a meaningful way.
92

In figuring out this growing quandary, practically all of the speakers at the graphics
forum turn to the language of “storytelling.” As they explain, a game broadcast can be seen as a
thicket of interweaving narratives. Will a team continue their undefeated streak? Will a coach
be fired if a team loses again? Will a player successfully return after a long injury? Etc. Data-
rich graphics, according to the speakers, need to either create or supplement such narratives.
Melfi, for instance, argues, that it is the job of the graphics industry to allow “the producers and
the directors to really tell the stories that they want to tell.” He continues, “It’s not just throwing
information at the viewer now. It’s presenting it in a meaningful way.”
93
Similarly, Robertsson
comments, “A lot of work needs to be done to contextualize the data and make sure it enhances
the storytelling rather than confuses it, while also doing it in a way that is simple and easily
comprehensible.”
94
Data-rich graphics, then, cannot just exist for their own sake – novelty is not
enough. Rather, they must be incorporated into narratives that stretch across entire broadcasts.
Given this emphasis on “storytelling,” it might not be surprising to learn that many
speakers at the graphics conference try to explain just how well their novel, data-driven graphics
will fit into larger narratives. During a panel on new baseball graphics able to display advanced
statistics like route efficiency and perceived pitch velocity, Joe Inzerillo and Dirk Van Dall,
executives at MLB Advanced Media, which runs the league’s interactive services (and also
provides the streaming infrastructure for HBO and Sony, amongst other clients), hone in on the
ability of these graphics to combine “data points together into a narrative.”
95
By looking at
metrics like route efficiency over time, for example, they explain that fans can start noticing that
131

“this guy was better than this guy” or discerning “why something is an exemplary play.”
Inzerillo observes, “Part of the thing about professional sports is … making the impossible look
easy and the easy look impossible. You can now actually figure out which of the two a player
is.”
96
Data, then, is not intended simply to impress viewers with novelty, but rather to add
context about a play or player – thus fitting into the larger stories being circulated by the
announcers and other elements of the broadcast, not to mention studio shows like SportsCenter
or Baseball Tonight, as well as a host of other extratexts found on television, online and
elsewhere.
That an all-important goal is to avoid novelty for novelty’s sake is made clear by several
other speakers at the conference. HBO’s Cohen, for example, asks, “Are we at the point now
where we’re providing all of this data and all this analysis because we can or is it because there’s
a hunger for it and an appetite for it from not just the broadcasters but ultimately our
audiences?”
97
Taking a similar approach, SMT’s Hall reflects, “I think one of the themes
certainly here today has been the gratuitous use of graphics versus does it actually help tell a
story.”
98
These fears in mind, graphics providers are occasionally left to vigorously defend the
necessity of their innovations. Thus, an executive like Mike Jakob, COO of Sportvision, frames
the company’s new hockey technology, which can display which players are on the ice and
provide insight into shift times, as being both intuitive and necessary to understanding the
game’s nuances. He comments, “To me, I think those are kind of the graphics that are going …
to be the most relevant for the fan. They don’t require a lot of explanation.”
99
Jakob then
contrasts these efforts with other graphics technologies the company’s engineers have previously
worked on, like the infamous glowing hockey pucks that were developed for FOX in the 1990s –
a project that Slate referred to as “one of sports broadcasting’s most-ridiculed experiments.”
100

132

Jakob says, “Some of the other stuff … it’s certainly interesting, but after a while, you can easily
overwhelm the fan. So we have to be careful that we don’t put too much on the screen at
once.”
101
In other words, restraint is paramount.
Given these calls for restraint, it should be no surprise that speakers at the graphics forum
focus much of their attention on second screen applications – explaining that they do not want to
overwhelm casual viewers on primary screens and that it is on second screens that they can fully
make use of their new data streams and, in the process, cater to diehards. For example, Bill
Squadron, Executive Vice President at data provider STATS, talks of getting as much of the
“firehose of information” as possible in the primary broadcast, but then locating other data on
“other screens” for “those fans who want additional information.”
102
Squadron here refers to the
FOX’s aforementioned JABO broadcast. He comments, “It obviously was geared toward people
who wanted to get another level of detail, analytics and data. But for that audience it was
phenomenal and because they had that other channel, they could satisfy an audience for that kind
of information.” He summarizes, “Certainly you can overdo it, but we have a lot more ability to
give different kinds of fans different kinds of content and product.” Robertsson expresses many
of the same views, arguing, “One size doesn’t fit all.” He continues, “It’s impossible to get the
statistics on air that everybody wants at that point in time.”
103
Second screens, then, offer a
potential outlet for new technologies – a place where broadcasters might not need to worry about
overwhelming casual viewers with data-heavy presentations.
Across the board, then, the speakers cite the need to deploy graphics only when necessary
– preferably, when graphics can facilitate, in Hall’s words, “a better storytelling opportunity.”
104

The question remains, though, where the demand for these graphics is coming from, particularly
if innovations are met with calls for restraint. In order to understand this situation, in which the
133

graphics industry pushes forward with new technology, including virtual graphics and tracking
systems, while also keeping these developments in check, it is helpful to understand that the
industry figures, particularly the technology providers, speak of pleasing an audience of
broadcasters, rather than audiences at home. This is a sensible position, for the technology
providers, of course, are hired by broadcasters, not by viewers. The NFL Network, for example,
might hire a company like SMT to implement new virtual graphics techniques during their NFL
combine coverage. Similarly, networks will buy particular graphics systems, like ChryonHego’s
Lyric system, for their graphics workflows.
Graphics technology providers, then, are primarily concerned with pleasing these
networks. And, according to the graphics executives, it is from the networks that graphics
providers are getting both the demand as well as the overarching message of restraint. Deltatre’s
Robertsson explains, “The broadcast industry has traditionally been quite conservative of what
they put on air.” As an example, he details how soccer broadcasters have, for several years, been
offered the chance to work with player tracking data, but have been reluctant to experiment. He
elaborates, “We really struggled to get the broadcasters to really adapt it and put it on screen
because the way you cover a soccer match is very formulated and there wasn’t really room to
add in the graphics or there wasn’t the willingness.”
105
Jakob, of Sportvision, recounts similar
frustrations. “When we talk to our clients and look at what they want to put on air, it’s got to be
simple. I’ve heard again and again - don’t give me a math lesson. I don’t have time, I’m trying to
tell a story, don’t make it too complex.” In light of these experiences, he expresses skepticism
regarding several of the new innovations on display at the conference, including the player
tracking graphics touted by MLB Advanced Media. Many broadcasters, he predicts, will say,
“It’s too complex, it takes too long, there’s too much information there.”
106

134

While Jakob remains a staunch skeptic, doubting broadcasters’ willingness to experiment
with complex graphics featuring new metrics, others see broadcasters beginning to alter their
conservative stances. Robertsson, for example, observes a major shift of late. He argues,
“We’ve seen an appetite change in the last few years,” with broadcasters increasingly eager to
make use of new data. This, then, helps explain why the graphics industry has rushed toward the
new technology mentioned above. Robertsson partially links this desire to industrial pressures –
including the aforementioned need for networks to differentiate themselves – but, like others,
also connects this “appetite change” to an evolving audience.
107
One particularly vocal
proponent of this view is Squadron, who argues, “Increasingly, you will get people who are just
more and more comfortable with using data.”
108
Hall, too, speaks of a coming “cultural
landscape” in which audiences will have grown more comfortable discussing advanced statistics
derived from tracking data.
109
Presumably, then, this will lead broadcasters to further lean on the
graphics industry, increasing the demand for data-driven graphics.
The above examination of the graphics industry has offered a look at some of the
technology that has pre-figured the rise of datavisuality, including virtual graphics and
increasingly robust data feeds. Moreover, it has offered an industrial perspective on this new
technology – showing that the graphics industry has approached the rapid development of data-
driven graphics with a mixture of excitement and hesitation, as broadcasters’ demand for new,
distinctive technology has been accompanied by a simultaneous call for restraint. The obvious
missing piece in this puzzle, then, is a more complete perspective from a broadcaster. That is to
say, only from a broadcaster’s point of view will be able to fully understand this balance of
enthusiasm and hesitancy for data-driven broadcasting. To that end, the next part of this chapter
will engage with a broadcaster that has been particularly aggressive in shifting towards
135

datavisuality: ESPN. In the process, it will be asked both what is fueling this aggression and,
referring back to the graphics executives’ repeated mentions of restraint, whether the network is
facing any pushback as it moves towards data-driven broadcasting.

A Broadcaster’s Perspective
As the previous part of this chapter detailed, the sports graphics industry has developed
new technologies that have facilitated the rise of datavisuality. Thanks to virtual graphics and
increasingly large data sets, it is now possible for sports television to be constantly blanketed by
data. The graphics industry, though, does not operate in isolation. Rather, it works in
accordance with the desires of the broadcasters who eventually make use of the graphics
technology. To get a fuller sense of the emergence of datavisuality, then, requires a closer look
at a broadcaster. The rest of this section, then, will analyze perhaps the most notable broadcaster
within the sports television landscape, ESPN. As will be explained below, ESPN has been at the
forefront of data-driven broadcasting. Indeed, it is no coincidence that several of the examples
of datavisuality cited above come from ESPN programming. This eagerness for data-driven
broadcasting, it will be detailed, fits with the company’s long-standing interest in sports data.
However, the push towards datavisuality over the last few years results from more recent trends.
More specifically, it will be observed that ESPN’s interest in data-driven content is a result both
of the company’s desire to keep up with industry’s shift towards data-driven decision-making as
well as the company’s attempts to distinguish itself from an increasing number of competitors.
Underlying both of these factors is the premise that audiences are increasingly interested in data-
driven programming.

136

A Brief History of ESPN, Data, and Sports Analytics
To understand ESPN’s push towards datavisuality, one must first understand how it came
to embrace sports analytics. As ESPN employees explain, this embrace did not happen
overnight. Rather, ESPN’s involvement with sports analytics represents the culmination of a
longer history with sports data. As mentioned in the introduction, ESPN launched in 1979. For
most of its early history, the company – like other sports television broadcasters, not to mention
newspapers and radio stations – relied on external data providers, like the aforementioned
STATS LLC, for the majority of its sports data. As the company grew, though, it had an ever
increasing demand for data – needing to provide information not only to its mainstay programs
like SportsCenter, but also to new platforms like the ESPNet online service and ESPN2, a
secondary cable outlet that premiered in 1993. In light of this growing need, in 1994 ESPN
purchased 80% of SportsTicker, a sports-wire news service that had previously been owned by
Dow Jones & Company.
110
Based out of New Jersey, the company provided “scores, statistics,
news and features” to a wide range of clients, including radio and television broadcasters,
newspapers, wire services, and professional sports teams. Bringing such a company into ESPN’s
corporate control thus removed the need to outsource all of its data needs. As Richard Glover,
an ESPN vice president, said at the time of purchase, “We had audio, video and graphics, but we
didn't have a text-based data service.”
111

For 12 years, SportsTicker served as ESPN’s primary source of data. In addition, it
continued to work with the many clients it had served both before ESPN’s purchase and after,
including major companies like Yahoo, Microsoft and FOX.
112
Complementing SportsTicker,
meanwhile, were two separate groups within ESPN that also worked with sports data, including
the Production Research department, which directly assisted game and studio broadcasts, and the
137

BottomLine team, which oversaw the scrolling tickers that appeared on the bottom of the
company’s television networks. In 2006, though, the company decided to consolidate its
information services and, in the process, get out of the syndication business to instead focus
entirely on serving its own needs. To that end, ESPN sold SportsTicker to PA Sport, a European
sports information provider.
113
In its place, the company created a new Stats & Analysis Team
tasked with supplying information across ESPN’s many platforms. That group was then placed
alongside the Production Research department and the BottomLine under a new umbrella
organization dubbed the Stats & Information Group (SIG) – an all-encompassing group meant to,
in the words of current employee Clark, act like “a total sports consciousness – knowing
everything that’s going on around the sports world, having every stat, every score, from every
little minor league soccer game in Venezeula, all the way to the NFL.”
114

According to Roger, formerly an employee within the Stats & Analysis Team and now a
SIG executive, a primary reason that ESPN had been looking to form its own information
gathering division was that it was eager for the freedom to fully pursue its own initiatives and to
“get out on the cutting edge” – not something it could necessarily do when it also had to fulfill
the needs of so many other corporate clients.
115
As he explains, the growth of sports analytics
was partially fueling this desire for data independence – starting at the top of the company.
According to both Roger and Alan, then part of the Production Research department and now
also a SIG executive, John Walsh, a powerful ESPN executive who served as the chairman of the
company’s editorial board and who had been a guiding force behind many of the company’s
signature properties, including SportsCenter and ESPN The Magazine, had become a strong
proponent of sports analytics.
116
As Alan explains, “One of the things [Walsh] really cared about
was sabermetrics and Moneyball.”
117
Similarly, Roger comments that Walsh “was a firm
138

believer in Moneyball and analytics as a storytelling device and what the potential of” the Stats
& Analysis Team might be.
118
Thus, both Alan and Roger explain that Walsh pushed the
company to more concertedly embrace sports analytics. Walsh, too, was hardly the only ESPN
employee eager to embrace analytics. Alan, for example, describes how the Production Research
department had long been full of sabermetrics devotees looking to go “way deeper” than basic
statistics. Certain analysts and anchors, he adds, were also eager to discuss advanced
statistics.
119

But as Roger details, the incorporation of sports analytics into ESPN’s everyday
operations was a slow process – even after the formation of the Stats & Analysis Team and even
with the strong support of a legendary figure like Walsh, not to mention the many other
employees scattered throughout the company. When the Stats & Analysis Team was formed, he
recalls, “The bar was pretty low” for cooperation.
120
Members of the team, he notes, traveled
across the company’s many divisions trying to raise awareness of what they could offer, whether
that might mean helping a show like SportsCenter or contributing to sites within ESPN.com.
“We were out there campaigning, doing road shows, doing educational series, trying to build
relationships one-on-one with people – fighting the good fight about it,” he says. However, these
conversations were difficult. As Roger explains, the culture of the company had not yet shifted –
many employees were either unaware of sports analytics or resistant to new methodologies.
121

Although the Stats & Analysis Team initially struggled to gain traction in its attempts to
sell the value of sports analytics, there were signs that analytics was slowly on its way to
becoming a bigger part of the company’s efforts. Alan, for example, points to the
aforementioned addition of OPS to baseball telecasts – a change that he recalls happening in
either 2008 or 2009. As Alan explains, it was an addition that was a long time coming, as
139

production researchers had for years been encouraging the company to de-emphasize basic stats
like batting average and RBIs. “It was a long haul,” he says. “We were finally like, ‘alright,
we’re going to that this year.’ It’s OK, the analysts don’t have to have mention it, we’ll just put
it up there and take it off the screen.” According to Alan, this was definitely a noteworthy
change. “People who came up in the TV industry, you just did batting average, homers, and
RBIs – and that’s just what you did.”
122
Adding OPS, then, represented a significant change of
thinking. Roger, meanwhile, mentions ESPN’s growing involvement with the MIT Sloan Sports
Analytics Conference during this time period.
123
MIT’s conference – commonly referred to as
the Sloan conference or, thanks to the efforts of former ESPN commentator Bill Simmons,
“Dorkapalooza” – has become a major gathering for academics, industry figures, and media
members interested in sports analytics.
124
Co-founded by the aforementioned Daryl Morey (an
MIT Sloan alum) in 2007, the Sloan conference has grown rapidly in the ensuing years, from 175
attendees traveling between a few MIT classrooms in that first year to 3,200 attendees sprawling
out across the massive Boston Convention & Exhibition Center during the 2015 edition.
125

Moreover, it is now a gathering that routinely includes not just the commissioners of most of the
major sports leagues, but also representatives from professional teams across the sports world.
The Boston Celtics and Sacramento Kings, for example, each brought 12 employees to the 2015
event, while the Dallas Cowboys brought 16.
126
Such numbers in mind, it is quite easy to argue
that the Sloan Conference is not just, in the words of Forbes, “The Super Bowl of Analytics,” but
rather, as Paul Flannery suggested in Boston Magazine, “One of the signature events on the
sports calendar.”
127

It took several years for the Sloan Conference to make its presence felt across the sports
industry, but ESPN was involved with the event from the very beginning – even sending
140

representatives to that first conference held on a Saturday afternoon in MIT classrooms. As
Roger explains, the conference solidified the desire of many within ESPN to further commit to
analytics. “We came back from that first year and starting talking internally, like, ‘Wow! That
was awesome!’”
128
As the conference grew in scale, then, so did ESPN’s involvement with the
event. In 2009, for example, the conference’s 12 panels were full of ESPN employees – a list
that included names like John Hollinger, Marc Stein, Bill Simmons, Jeff Van Gundy, Ric
Bucher, and John Walsh.
129
By 2010, ESPN had become the conference’s “presenting sponsor”
– a status it still holds today and one that ensures the company’s name is plastered on as many
conference items as possible, including on the program covers and on the stage. By 2015, ESPN
employees had become as ubiquitous at the conference as the company’s logo – accounting for
over 200 of the conference’s 3,200 attendees.
130

While ESPN’s growing involvement with the Sloan conference may represent the most
visible manifestation of the company’s increasing engagement with sports analytics, especially in
the moments when the conference has worked its way into the columns of popular writers like
Simmons or into the segments of programs like SportsCenter, even more significant moves were
taking place behind the scenes. Most notably, toward the end of 2010, SIG began laying the
groundwork for a new division devoted entirely toward sports analytics. As Alan and Roger
explain, Walsh had given SIG the support to “move around some headcount,” clearing the way
for an analytics team.
131
Keeping with the broader role of SIG, this new division was meant to
serve the larger needs of ESPN – looking for and responding to opportunities to create stat-
infused content, whether that might mean developing projects for the company’s website,
generating numbers for the company’s many television programs, or any of a host of other
possibilities.
132

141

Labeled the Production Analytics Team, the division had modest beginnings. As Greg,
who has been with the Analytics Team since its creation, recalls, he and the team’s other initial
hires had math and programming skills, but little applied experience with sports analytics. Soon
after, though, Alan recruited Dean Oliver to the team.
133
Unlike the team’s other employees,
Oliver came with plenty of industry experience, having joined ESPN directly from the Denver
Nuggets, where he had been Director of Quantitative Analysis.
134
As Greg explains, Oliver got
the team “off the ground,” but its mission remained hazy. “We had to figure out what the best
projects were to work on and how to approach them so they would work for ESPN, which is
different from making it work for a team, which is different from making it work for an
academic paper.” He continues, “It wasn’t fully formed.”
135
Moreover, the group had to work to
sell itself. Alan explains, “We did a lot of evangelizing and education around our own campus –
‘here’s who we are, this is what we do, and here’s how we can help you.’” The group did not
have the easiest time explaining itself to other departments, either. Alan says, “We were the
‘Moneyball/sabermetrics group at ESPN’ and that’s how we’d have to describe ourselves to
people – even at ESPN – because we’re this niche, little department.” He elaborates, “Luckily,
people knew about Moneyball, either because of the book or, most likely, because of the movie.
And Sabermetrics was a term sometimes used derogatorily, but at least they’re aware –
tangentially – what it implied. And then we would use that as an opening to say we do this for
all sports.”
136

Although the Analytics Team was initially uncertain of what type of work they would be
doing and what parts of the company they would be serving, these questions soon came into
focus as the team began developing its first projects. Alan explains that they looked for
segments of the sports analytics landscape that were underexplored, which eventually led to a
142

focus on professional leagues that were particularly secretive about their analytics efforts, like
the NBA, as well as sports where data was limited and public discussion of analytics relatively
scarce. Baseball, then, may be the starting point for sports analytics, but it has attracted little
attention from ESPN’s Analytics Team. Alan comments, “We’ve barely ever done anything
with baseball … we’ve barely touched it, after four-plus years, just because there’s so much
publicly available baseball analytics out there.” Easier focus areas have been sports like college
football and college basketball, where discussion of analytics has been sparse. Indeed, Alan says
that some of the first demand for the Analytics Team came from ESPN’s college basketball
analysts, who had been looking for a better way to rank teams throughout the season.
137

In addition to focusing on specific parts of the sports landscape, the Analytics Team also
decided to narrow in on certain types of projects. Most notably, they have developed several
“power indices” that rank teams based on novel mathematical formulas. The NFL Football
Power Index, for instance, is a prediction system that factors in performance in previous games,
rest, altitude, and injuries, amongst a host of other determinants. Similar indices have also been
created for college basketball, college football, the NBA, and international and club soccer. In
addition, the Analytics Team has also developed a rating system for quarterbacks – “Total QBR”
– that was first applied to the NFL, and later adapted to college football. According to Alan,
these sorts of projects have to come to represent “brands” for the Analytics Teams. As he
explains, there is significant value in consistency. While some of the group’s initial efforts,
including the first power indices, were met with curiosity and necessitated education efforts –
both within the company and amongst the public – these types of projects have now become
known quantities.
138
Nick, currently a leader within the Analytics Team, says, “Those things are
getting adopted much, much quicker than [before], just because people are so much more
143

familiar and comfortable with it.” He continues, “We’re getting to a point now where with these
very specific kinds of metrics – these power index kind of things, projection tools – that we can
roll these out and there is a base of understanding.”
139

Although two of the Analytics Team’s original members have since left ESPN, Oliver
having returned to the NBA to work for the Sacramento Kings and Albert Larcada having
departed for a sports data company, the Analytics Team has grown in size over the past few
years. As Greg reveals, the growth in size has meant that the group can now further specialize.
Whereas the Analytics Team’s first members all had to be multi-taskers, switching between data
management, programming, and communications, there are now specialists fully devoted, for
instance, to writing and to database operations.
140
The growth reflects what Nick refers to as a
recently developed “momentum.” According to every one of the ESPN employees I
interviewed, there has been increasing demand within the company for the Analytics Team and
its work. Nick, for instance, points out that the company’s digital products team specifically
requested the NFL Football Power Index. “People are actually starting to ask us for stuff like
that – these big structural things.” Meanwhile, the team is also generating requests for smaller
projects, too, like a recent inquiry from ESPN’s soccer group, ESPN FC, asking whether the
team could project Lionel Messi’s goal scoring for the 2015-2016 season. “These kinds of
requests are starting to come in now in a way that they didn’t really come in before,” Nick says.
He adds, “I can’t think of a platform we have that hasn’t asked us for something in the last
year.”
141

As Nick’s comment reveals, the Analytics Team has continued to embrace its
aforementioned duty of serving all of ESPN’s many platforms, not just the flagship cable
channel. Greg says, “We want to be across platforms.” Similarly, Roger notes, “We are
144

platform-agnostic.” Of particular interest, though, have been second screen applications and the
emerging phenomenon that is microcasting. As mentioned above, ESPN has produced a college
football championship “megacast” in recent years that features different telecasts across the
company’s many outlets, including an ESPN3 “data center” microcast that included a variety of
statistics and information generated by SIG. As an even more recent case in point, a major focus
for the Analytics Team over much of 2015 has been developing a live, in-game win probability
system for the NFL – a system meant to be featured on ESPN’s website and in mobile
applications.
142
If, to use a hypothetical example, a team had a punt blocked in the third quarter
while trailing by 12 points, the system might show that team’s chance of winning drop from 20%
to 14%.
According to Nick, the Analytics Team’s decision to focus on microcasts and second
screen applications like the win probability system has been very deliberate. “That’s a growing
part of our industry,” he comments. “Everybody recognizes that fans watch with their phones or
with their computers now. That’s understood. And so we want to make sure we have that
experience, that we have those eyes and not somebody else.”
143
Alan makes similar remarks.
“Second screen is important. Everyone’s got a phone in their pocket.” Going forward, then,
figures like Nick, Alan and Roger see microcasts and second screen applications as continuing to
be significant for the Analytics Team – particularly because analytics content seems to be so
well-suited for these relatively new terrains. As Alan comments, for example, “Our stuff lends
itself to a second screen.” To that point, he suggests that analytics allows for “deeper dives” into
sports debates than traditionally allowed by television broadcasts and, even more importantly
going forward, mentions that analytics content can be produced cheaply and – potentially –
automatically. As an example, he points to the NFL’s recent deal with Microsoft to produce
145

automated content based on player tracking data.
144
Extra content, in other words, with little cost
and little effort.
As briefly mentioned above, sports television is an increasingly competitive market.
With the entrance of cable networks like NBCSports and FS1 into an already crowded landscape
the escalation of rights fees has only intensified. As Matt Yoder has documented, this is a
phenomenon that has affected all corners of the sports landscape.
145
Networks are not just
bidding larger and larger sums for the rights to major properties like the NFL and the Olympics,
but also for smaller properties like MLS. Compounding the budgetary havoc presented by the
increasing rights fees has been the fact that sports networks are also being affected by the
ongoing loss of cable subscribers – a loss that has meant an accompanying decline in carriage
fees. Indeed, Disney stock fell nearly 10% in August 2015 following news that ESPN has lost
3.2 million subscribers during the previous year and Disney CEO Bob Iger commented, ESPN’s
“business model may face some challenges over the next few years.”
146
In the face of such
pressures, sports media companies – including ESPN – have actively searched for opportunities
to cut costs. Yoder, for instance, notes how the network has begun experimenting with
announcing some games from its studios in Connecticut, rather than traveling to all of the games
it covers. He mentions, too, that the network has been willing to part with several of its highly
paid personalities, including Bill Simmons and Keith Olbermann.
147
In September 2015, news
appeared that the company would also be cutting a large number of employees in response to
Disney’s request that the company trim $100 million from its budget in 2016 and another $250
million in 2017.
148
In mid-October, those initial stories were confirmed as ESPN laid off
approximately 300 of its workers – roughly 4% of its workforce.
149

146

Despite ESPN’s growing budget woes and the looming specter of staff cuts, the SIG
employees I spoke to all expressed an unwavering optimism about the future of the Analytics
Team and its place within ESPN. Alan says, “Buy-in is there.” He continues, “People are
coming to us all the time. We are very well positioned right now.” Alan, in particular, is bullish
about the possibilities presented by the growth of tracking data. “That’s a huge game changer
for us.” He elaborates, “We can really do some metrics – even deeper – around player and team
performance, and projecting future games and future performances.”
150
Roger, like Alan, points
to a change in culture at the company – contrasting the current demand for the Analytics Team
against the earlier skepticism that had greeted the Stats & Analysis Team. Now, he says, the
group no longer has to explain the basics of what they do or summarize what analytics means to
sports. Instead, other parts of ESPN are trying to understand how they can actively work
analytics into their content. “It’s much less about, ‘Hey, this is what WAR is and it’s got real
value,’” he explains. “Now the conversation is definitely more about, ‘Hey, what’s a really neat
way to do something with this in the digital space or on the phone – something that works on the
phone?’ ‘Could you ever use WAR in a quick, little animation on Instagram?’ ‘How could we do
that so it’s understandable to people?’” He summarizes, “The conversation has definitely
changed. The culture is shifting.”
151

The Case for Analytics
The previous section documented ESPN’s gradual embrace of analytics, most clearly
manifested through the creation and growth of the Production Analytics Team. What has yet to
be clearly established, though, is why ESPN would bother. That is to say, how exactly does
ESPN benefit from the Analytics Team? What does a shift in “culture” offer to the network,
147

other than a flood of numbers? As the rest of this section will explore, ESPN executives see the
purpose of the Analytics Team as twofold: allowing the company to keep up with a changing
industry and presenting it with a way to distinguish it from its many competitors. As will be
further explained, both of these reasons are underlined by a conviction that fans are increasingly
interested in analytics content – although perhaps not quite interested enough to yet cause
massive changes to the company’s live game broadcasts.
To begin, it has been mentioned repeatedly throughout this chapter that the sports
industry is moving steadily towards analytics. The data-driven approaches made famous by
Moneyball are, in brief, the new normal everywhere from baseball to hockey to soccer. As the
ESPN figures tell me, it is the company’s responsibility to adjust to this new world so that they
can adequately cover the sports industry – a pressing concern if ESPN is going to continue bill
itself as the leader in sports media, a brand reputation that the company cherishes and is
expressed in the ubiquitous company tagline “The Worldwide Leader in Sports.” Talking about
the increased demand for the Analytics Team, Nick mentions advanced metrics are “becoming
more and more a part of sports” and then says, “Because we are ‘the worldwide leader in sports,’
we are ESPN, we have to be on board with that, we have to understand that trend.” Nick, too,
claims the Analytics Team is not alone within ESPN in perceiving this shift. “There’s more and
more understanding of [the rise of analytics] across the talent, across the producers,” he explains.
“I think it’s hard not to see it.”
152
Clark makes similar claims. “There’s a movement in sports
towards analytics-based decision-making and analysis,” he begins. “ESPN wants a share of that.
They want credibility. They see this as a movement and they don’t want to be caught behind.”
He adds, “It’s more than a growth market. It’s a spiraling in all different kinds of unpredictable,
serendipitous directions.” Because ESPN is “all sports to all people,” then, it only makes sense
148

for the company to “want to make sure they’re central to that market.” Concluding, he says,
“They’re really just trying to stay ahead of the curve.”
153

Roger is even more specific about why it is important for ESPN to not be “caught
behind.” After discussing the rise of analytics within the sports industry, he says, “If we’re
going to be an organization that’s legitimate, in terms of our coverage, in terms of our content …
we have to be looking through the same prism.” As a point of comparison, he recalls his
experience working at newspapers in the early 2000s, when beat reporters were reluctant to
investigate how teams were using analytics, even as teams were clearly beginning to move in
that direction. Reporters “never asked the right, meaningful questions,” Roger explains. As
such, they were unable to offer a satisfying picture of the teams they were covering. ESPN, he
suggests, wants to avoid such mistakes by paying closer attention to how teams are conducting
their decision-making. He explains, then, “Teams are using analytics, so we should be using
analytics.” Getting more precise, he adds:
“If the teams are quantifying performance, the teams are projecting outcomes or performance –
around their athletes, or their games, or their strategies – we have to do that. So we need to hire
people who are capable of doing that, we need to acquire the right kind of data, we need to build
the right kind of tools, and work with the right third party vendors that are leaders in this space –
to do those things, too. Otherwise, we’re going to be left behind.”
154

As the ESPN employees explain the importance of keeping pace with the industry’s
move towards analytics, they all bring up a related line of argument – the need to stay in front of
the company’s many rivals. Roger, for instance, brings up the fact that both leagues and teams
have become much more aggressive about covering themselves – offering a form of vertical
integration that recalls Studio Era Hollywood. Not only do the four major American sports
leagues each own their own television networks, but they are also aggressive about providing
149

heaps of online coverage. Many teams, meanwhile, now have stakes in their own RSNs.
Leagues and teams, then, have become increasingly prominent sources of information about
themselves and, in the process, have become yet another rival for ESPN. According to Roger,
the teams and the leagues are eager to use analytics as they seek to fill their new outlets with
contents. After all, they already have access to all of the data and many employees able to work
with that data. ESPN, then, has to use analytics in order to keep pace:
“We have to be able to tell the most unique and differentiating stories. And if we’re not using
analytics to tell those stories, we’re going to be left behind. We’re not going to tell stories as well
as the teams themselves can tell stories or the leagues themselves. [They] are in competition with
us, digitally and on television and in print and on mobile. Why would we cede that ground to
them and say, ‘Oh yeah, let the leagues tell all the best stories because they have all the best data
and they already have analytics experts working for their teams’?”
155

The ESPN employees I talked to were generally reluctant to refer to specific competitors,
whether that might mean a league-owned network like the NFL Network or other rivals like
NBCSN and FS1, but in talking about the demand for analytics at ESPN, others used the same
language of “differentiation” employed by Roger – emphasizing how the Analytics Team helps
set ESPN apart from other outlets within the sports media landscape. Greg, for instance, notes
that before the creation of the Analytics Team “there wasn’t a huge history of serving the
average fan with analytics.”
156
The Analytics Team, then, was able to fill a gap in the market.
Alan, meanwhile, mentions that the Analytics Team currently has “tremendous support at high
levels – not just from [the head of SIG], but his peers as well – decision makers. They see this as
differentiating content.” He continues, “They want us to keep pushing to figure out how to be
production leaders on executions that resonate and differentiate.”
157
The same sports media
congestion that has brought layoffs to ESPN, then, is also a source for the Analytics Team’s
150

confidence; such congestion provides justification for the Analytics Team’s expertise and the
content it produces. Much as FOX touts its data-rich golf graphics as differentiating visuals that
put the network in a production “leadership role”, ESPN sees the Analytics Team’s efforts as
leading to unique content that makes the company “production leaders.”
Both of these stated justifications for ESPN’s growing use of analytics – being able to
mirror developments in the sports industry at-large, as well as helping to set the company apart
from a raft of competitors – rely on a key assumption: that ESPN audiences are interested in
analytics. Or, at least, can be made to be interested in analytics. According to each of the ESPN
employees I interviewed, this is increasingly the case. As several of them suggest, there already
exists a subset of fans strongly interested in analytics – a group that provides a built-in audience
for data-driven content. Clark, for example, comments, “There’s this niche of fans who is really
interested in analytics,” and notes that, thanks to its recent efforts, ESPN is now “serving that
niche.”
158
Nick, meanwhile, echoes a few of the comments from the graphics forum by saying,
“Younger fans are more comfortable with these typical advanced metrics … because they’ve
grown up with them, and they know them, they expect them, they believe that they are better
than scoring per game averages.”
159
As Roger indicates, it is the more dedicated, diehard fans
that are being served by several of the company’s data-driven efforts, such as the college football
championship “Data Center.” He says, “I think we’re targeting a niche audience, ultimately”
with such products. Echoing comments from speakers at the graphics forum, Roger suggests that
second screens and microcasts are an ideal way to specifically cater to viewers with a strong,
pre-existing interest in analytics.
160

However, ESPN employees are noticeably reluctant to describe data-driven content as
purely niche content. Rather, as might expected, they repeatedly emphasize the ability of
151

analytics to appeal to mass audiences. The party line, it seems, is that analytics is increasingly
for everyone. Nick, for instance, states that many sports fans have a developing, natural
curiosity about analytics given the wider industry swing. “They read the same news articles –
they understand, they see that teams are hiring all these analytical people.” He continues,
“There’s a segment that really wants to understand that. Like, what’s going on there? Why is
that useful? Why is my team hiring this guy who was a professor of electrical engineering? …
They want to know this stuff and I think that interest is growing constantly.”
161
Similarly, Alan
suggests that fans have gradually become more attuned to analytics – just like the ESPN
personnel who are now asking the Analytics Team for their help. He comments, “People are
more comfortable with that kind of information – it's in video games, on the back of Tops
baseball cards.” He argues, then, that fans are familiar with advanced metrics “even if they don't
understand all the math and the concepts behind it.”
162

More generally, it is a common refrain amongst the ESPN employees that analytics
assists storytelling – both creating new narratives and adding fresh layers to existing ones; a line
of thought, of course, that recalls the graphics executives’ insistence that data-rich graphics could
benefit live game broadcasts. All fans, according to this argument, can find themselves engaged
by analytics – not just the diehard fans who might debate the merits of OPS versus wOBA
(weighted On-Base Average). Alan, for instance, says, “I think what we're talking about,
generally, are things that should appeal to a very wide audience of fans. And that's who we're
ultimately serving is. The hard math, leave that to us. Just, what's the story?” He mentions, for
example, using advanced metrics to drive debates about the best teams and players in college
football – conversations, he argues, in which most college football fans are already invested. He
argues, “It's just putting context on the feelings that people already have, whether the verbal
152

predictions they're making or the way they're talking about water cooler things.” He then goes
on to draw comparisons between the Analytics Team and the many ex-players that ESPN
employs as analysts. Both, he argues, provide unique perspectives that fuel larger
conversations.
163
Clark agrees, commenting that the Analytics Team is like “one more facet to
the jewel that is ESPN,” while Roger shares a parallel metaphor of analytics and metrics being
“just tools in a toolbox” that the company can use when necessary.
164
Nick offers similar
thoughts, but also refers to specific projects in defending the wide appeal of the storytelling that
springs from analytics. First, he brings up the Football Power Index (FPI) and its ability to run
predictive analytics. Football fans, he argues, “care about what are the odds that [his or her]
team is going to win. That's an FPI question. That's exactly what it is.” He continues, “There
aren't many fans who wouldn't want to know that.” Second, he refers to win probability.
“Imagine a page on an NFL Sunday that has every single game's live win probability going on.
That's like the RedZone Channel on hyperspeed.” He elaborates, “Fans will be crazy for that –
they love that kind of stuff. It's not just the stat, nerdy folks that want to know that – it's anybody
who is a fan, who is dedicated to watching football on Sunday.”
165

While the ESPN employees are uniformly adamant any fan can benefit from data-driven
storytelling and tout it with zeal, there is certainly a point to the comments made by the figures
from the graphics industry in the previous section regarding broadcasters’ conservatism. That is
to say, ESPN’s television programming does not necessarily reflect all of the efforts being made
by SIG and, even more specifically, the Analytics Team to advance analytics within ESPN.
Mentions of power indices like FPI, for instance, are relatively limited within ESPN’s television
programming. Game broadcasts may be slowly incorporating more advanced metrics like OPS,
but basic statistics, whether that might mean home runs or touchdowns or goals, remain far more
153

common. And, as the graphics executives would likely emphasize, ESPN could go much further
with its graphics, even though it has begun to incorporate complex graphics like the K-Zone
pitch-tracking system mentioned above. Datavisuality may be ascendant, in other words, but
with limits.
When asked about these limits, the ESPN employees occasionally bristle. Nick, Alan and
Roger all acknowledge that the ESPN’s television broadcasts could be making further use of
advanced metrics and data-driven graphics, but they all also insist that the graphics executives’
depiction of broadcasters as unadventurous to be both outdated and unfair. For one, the ESPN
staffers insist that game broadcasts are composed of too many moving parts to be subject to rapid
changes. Talking about adding more data-driven elements to broadcasts, Alan comments, “It's
not like a big light switch.” He continues, “TV's hard. It's hard to tell a complex story in live
TV. Maybe it's easier in scripted dramas on cable networks. But in the live broadcast window,
in play-by-play and all the live variables, an unscripted sporting event, to jam in something is
math-y or difficult to comprehend at first grasp, takes some nuance.” He continues, “It's just a
hard medium.”
166
Similarly, Nick says, “You don't have a lot of time to produce stuff.” He
elaborates, “You really have to have a comfort level with the kinds of tools that exist in that
truck to get things live on TV because you have 30 seconds to turn something around.” Because
of the intricacy of sports television, the ESPN executives argue that television producers need
extra time to grow more comfortable with both advanced metrics and new graphics technology.
Nick says, “Just because some cool graphics company says 'hey, look, we can do this' – that's not
a reason for them to change … and same with advanced metrics.” He expands, “You have to
show them that the information that's being generated is going to be there, it's going to be
accurate, it's going to be verifiable and is going to be useful – to make for a better story during
154

the game broadcast. And once you can do that, I think people are open to figuring out the best
way to do that.”
167
Roger, taking a similar tack, comments, “It takes a while to figure out how to
get that content onto television in a meaningful way that tells real stories.” Like the graphics
executives, he explains the importance of “context.” Data, in other words, should only be
integrated into broadcasts once producers can use it to enhance storytelling; it cannot be
incorporated purely for its own sake. He says, “We don't want to put graphics up on television
tomorrow that just have a bunch squiggly lines and dots and that kind of stuff, or shows that
someone runs from home to first base at 19 miles per hour. That tells us nothing. You need
context around it.”
168

According to the ESPN executives, it is only a matter of time before ESPN’s television
programming becomes even more data-driven – practically a given, they suggest, based on the
company’s larger cultural shift towards analytics, as represented by the growing demand for the
Analytics Team. As they explain, once the comfort level with analytics increases amongst
broadcast crews, more data with more complexity will make its way on television screens, just as
it has in other ESPN outlets. Indeed, Alan and Roger suggest this process is already underway.
Roger, for example, mentions that the company is starting experiments with NFL and NBA
tracking data.
169
Alan, too, mentions ongoing experiments – experiments that the public is not
necessarily aware of. He says, “We're trying new things all the time, with new cameras, new
graphics, new statistics. We do a lot more than the public recognizes.” He continues, “We’re
looking at, all the time, what we can do to spotlight players, to show trails of the ball – maybe in
soccer – or the way we use data visualizations in static graphics that can be shareable, that tell a
story and are easily.” Summarizing, he says, “I think we're more open minded than people
think.”
170

155

Pouring out of ESPN’s headquarters in Connecticut, then, is a confidence that sports
analytics has a large role to play in both the present and the future of the company. It is a
confidence that anchors the sense that the company’s move towards datavisuality was neither
accidental nor representative of a short-term flight of fancy. Instead, an embrace of data-driven
content appears as a deliberate economic choice rooted-in long-term thinking. For one, an
investment in data-driven content allows the company to maintain its image as operating at the
forefront of the sports landscape – embracing the very same logic that the sports industry at-large
has also adopted. For another, data-driven content gives ESPN another way to distinguish itself
from a growing field of competitors – an increasing concern reflected in the company’s budget
crunch. As the ESPN employees maintain, such content has the ability to engage both diehard
and casual fans, so being at the forefront of sports analytics has the potential to draw in a wide
range of audiences.
At the beginning of this chapter, it was argued that sports television is increasingly
displaying the marks of a new, data-driven mode of broadcasting that this project has termed
“datavisuality.” A phenomenon that has seen broadcasters increase both the quantity and
complexity of data within their programming, datavisuality has affected not just live game
broadcasts, but also a wide range of studio programming. As the rest of this chapter has
explained, datavisuality has resulted from a confluence of factors. Technologically, datavisuality
can be heavily linked to two major developments within the graphics industry: the introduction
of virtual graphics and a massive influx of data. These developments have allowed data to be
everywhere and anywhere on the television screen. Just as important, though, has been the
conscious decision by broadcasters to move toward data-driven content. In studying ESPN, this
chapter has observed how this broadcaster has viewed data-driven programming as a way to
156

strengthen its brand and distinguish itself from a growing field of competitors while drawing in
both diehard and casual sports fans. Underlying these technological and industrial factors has
been the movement of the sports industry at-large toward data-driven decision-making. A shift
that has had major consequences for the industry and registered in the public consciousness by
way of texts like Moneyball, it has been hard for sports media to ignore.
While this chapter has been able to offer an introduction to datavisuality and been able to
establish its origins, what has remained elusive thus far is the significance of this phenomenon.
Sports television, it is clear, is undergoing changes – but to what end? What are the
consequences, in other words, of sports television becoming blanketed in increasingly complex
data? Why does it matter that a baseball game now comes complete with advanced metrics or
that football game now features player tracking data? The next chapter of the dissertation will
take this question as its focus. As will be explained, datavisuality finally brings media face-to-
face with the critiques gradually emerging out of big data scholarship.

1
John Thornton Caldwell, Televisuality: Style, Crisis, and Authority in American Television (Rutgers University
Press, 1995), 4.
2
Ibid., 5.
3
Ibid., 353.
4
As is frequently the case with television scholarship, sports are little mentioned within Caldwell’s seminal
work. Nonetheless, sports television was very much enveloped in the broader changes Caldwell tracks in
Televisuality. In “Everything New Is Old Again: Sport Television, Innovation, and Tradition for a Multi-
Platform Era,” in Beyond Prime Time: Television Programming in the Post-Network Era, ed. Amanda D Lotz
(New York: Routledge, 2009), 124, Victoria E. Johnson explains, for instance, how sports television has long
conspicuously maximized television’s formal capabilities by way of features like hovering Skycams and
extreme slow-motion camerawork. Some of the links between sports television and Caldwell’s work are
particularly explicit. For example, the emergence of digital graphics is a significant part of Caldwell’s
theorizations, representing how “stylishness” can be easily reproduced at the touch of a button. This
emergence was rapid. As Caldwell writes, graphics – relatively scarce not long before he authored the work –
quickly came to “so dominate mass-market television that they have become an obligatory – even if
157

unremarkable – part of the cutting-edge package” (134). Sports television was not left out of this rapid shift.
Through the 1980s, graphics were a rare part of sports broadcasts. Snippets of text might flash briefly on
screen, displaying an announcer’s name or the score of the game, but that was about as graphically complex
as games would get. That was soon to change, though. Score boxes became a regular part of most sports
broadcasts starting in the mid-1990s, while the now-ubiquitous scrolling score tickers became round-the-
clock features soon after. And, fitting Caldwell’s argument, these graphics were to become easily available to
all types of sports broadcasters – ranging from national networks to regional sports networks. Thanks to the
brisk development of standardized sports graphics systems, any producer – whether operating out of Los
Angeles or Milwaukee – suddenly had the ability to “parade and perform visual style before the viewers”
(158).
5
Caldwell, Televisuality, 4.
6
Ibid., 5.
7
Ibid., 5.
8
Ibid., 6.
9
Ibid., 7.
10
Ibid., 7.
11
Ibid., 9.
12
Ibid., 9.
13
Ibid., 5.
14
Ibid., 10.
15
Joe Flint and Shalini Ramachandran, “ESPN Tightens Its Belt as Pressure on It Mounts,” Wall Street Journal,
July 10, 2015, sec. Business.
16
“Giants-Nationals, PGA Greenbrier, SportsCenter, World Cup Tonight Lead Cable Sports for Sunday, July 5,
2015,” Sports TV Ratings, accessed October 12, 2015, http://sportstvratings.com/giants-nationals-pga-
greenbrier-sportscenter-world-cup-tonight-lead-cable-sports-for-sunday-july-5-2015/3056/.
17
Alan Schwarz, The Numbers Game: Baseball’s Lifelong Fascination with Statistics (St. Martin’s Griffin, 2005),
164.
18
Jason Dachman, “ESPN Commits to K-Zone Live on Every Pitch for MLB Coverage,” Sportvision, April 3,
2015, https://www.sportvision.com/news/espn-commits-k-zone-live-every-pitch-mlb-coverage-0.
19
See Johnson, “Everything New Is Old Again: Sport Television, Innovation, and Tradition for a Multi-Platform
Era.”
20
Rob Neyer, “The Power of JABO,” FOX Sports, accessed October 12, 2015,
http://www.foxsports.com/mlb/just-a-bit-outside/baseball-joe/blog/the-power-of-jabo-100114.
21
Neil Weinberg, “Can Sabermetrics Thrive on Television Broadcasts?” Beyond the Box Score, accessed
October 12, 2015, http://www.beyondtheboxscore.com/2014/10/13/6967385/sabermetrics-television-
broadcasts-stats-jabo.
158

22
Caldwell, Televisuality, 88.
23
Ibid., 88.
24
Hutchins, “Tales of the Digital Sublime,” 7.
25
Kirk Goldsberry, “Analytics Illustrated” (MIT Sloan Sports Analytics Conference, Boston Convention and
Exhibition Center, Boston, MA, February 28, 2015).
26
Cornelius Puschmann and Jean Burgess. “Metaphors of Big Data.” International Journal of Communication 8
(2014), 1693.
27
Mark Andrejevic. “Big Data, Big Questions: The Big Data Divide.” International Journal of Communication 8
(2014), 1675.
28
Schwarz, The Numbers Game, xiv.
29
Ibid., 164.
30
Michael Lewis, Moneyball: The Art of Winning an Unfair Game (W. W. Norton & Company, 2003), 7.
31
Cindy Boren. “Charles Barkley Really, REALLY Hates NBA Analytics.” The Washington Post, February 11,
2015. https://www.washingtonpost.com/news/early-lead/wp/2015/02/11/charles-barkley-really-really-
hates-nba-analytics/.
32
Matthew Futterman. “Baseball After Moneyball.” Wall Street Journal, September 30, 2011, sec. Sports.
http://www.wsj.com/articles/SB10001424053111903791504576584691683234216.
33
Jay London. “Data-Driven: MIT Infuses the Rise of Sports Analytics.” MIT News, February 26, 2014.
http://news.mit.edu/2014/data-driven-mit-infuses-the-rise-of-sports-analytics.
34
Lindsey Willhite. “Baseball Prospectus More than a Diamond in the Rough.” Daily Herald, June 16, 2009.
35
London, “Data-Driven.”
36
Futterman, “Baseball After Moneyball.”
37
Michael Lewis. “The No-Stats All-Star.” The New York Times, February 13, 2009.
http://www.nytimes.com/2009/02/15/magazine/15Battier-t.html.
38
Ibid.
39
Ibid.
40
Ibid.
41
Ibid.
42
Ibid.
43
Pablo Torre. “The 76ers’ Plan to Win (Yes, Really).” ESPN The Magazine, March 2, 2015.
http://espn.go.com/nba/story/_/id/12318808.
44
Ibid.
45
“Danny Chau, “Trusting the Process: How the Rest of the League Out-Hinkie’d the Sixers at the NBA Draft,”
Grantland, June 26, 2015, http://grantland.com/the-triangle/trusting-the-process-how-the-rest-of-the-
league-out-hinkied-the-sixers-at-the-nba-draft/.
46
Torre, “The 76ers’ Plan to Win.”
159

47
Ibid.
48
Ibid.
49
Ibid.
50
Tom Sunnergren. “Philadelphia’s Victory Lab.” ESPN.com, December 3, 2013.
http://espn.go.com/blog/truehoop/post/_/id/64381.
51
Maurice Peebles. “The Man Behind the NBA’s Revolutionary Player Tracking Technology Reveals What
Really Matters In The NBA Finals.” Complex, June 5, 2014. http://www.complex.com/pop-
culture/2014/06/sportvu.
52
Rich Hofmann. “Blackjack and Battier: Liberty Ballers Talks with Sam Hinkie.” Liberty Ballers. Accessed
November 5, 2015. http://www.libertyballers.com/2013/10/31/5046996/76ers-sixers-gm-sam-hinkie-
interview-nba-draft.
53
Zach Harper. “Eight Teams Will Use a Movement Tracking Device in 2013-14 Season.” CBSSports.com,
August 19, 2013. http://www.cbssports.com/nba/eye-on-basketball/23223295/eight-teams-will-use-a-
movement-tracking-device-in-201314-season.
54
Sunnergren, “Philadelphia’s Victory Lab.”
55
Tom Haberstroh and Pablo Torre, “New NBA Biometric Testing Is Less Michael Lewis, More George Orwell,”
ESPN The Magazine, October 27, 2014, http://espn.go.com/nba/story/_/id/11629773.
56
Ibid.
57
Ibid.
58
Ibid.
59
Alex Konrad, “The Australian Tech That’s Improving The World’s Best Athletes,” Forbes, May 27, 2013; and
Tim McManus, “Inside Voices: Deeper Into the Sports Science,” Philadelphia Magazine, December 5, 2013,
http://www.phillymag.com/birds247/2013/12/05/inside-voices-2/.
60
Caldwell, Televisuality, 88.
61
Ibid., 7.
62
“Troika | Time Warner Cable SportsNet/Deportes Launch,” accessed November 5, 2015,
http://www.troika.tv/time-warner-cable-sportsnet-deportes-brand-identity/.
63
Jordan Shorthouse, “Approaches in Graphics and Show-Open Design” (SVG Sports Graphics Forum, HBO
Theater, New York, February 25, 2015).
64
John Ellis, “Interstitials: How the ‘Bits in Between’ Define the Programmes,” in Ephemeral Media: Transitory
Screen Culture from Television to YouTube, ed. Paul Grainge (Palgrave Macmillan, 2011), 90.
65
Ibid., 103.
66
Richard Sandomir, “The Innovation That Grew and Grew,” The New York Times, June 12, 2014,
http://www.nytimes.com/2014/06/13/sports/the-tv-score-box-that-grew-and-grew.html.
67
Michael Hiestand, “Dedicated Staff Keeps Close Watch on ESPN’s Bottom Line,” USA Today, March 6, 2008.
160

68
Timothy Clark, “Which Sports Network Wastes The Most Space With Its Scores Ticker?” Deadspin, May 29,
2014, http://regressing.deadspin.com/which-sports-network-wastes-the-most-space-with-its-sco-
1582821805.
69
drive, “Fox Sports Insert & Scoring System,” |drive|, accessed November 5, 2015,
http://drivestudio.com/projects/fox-sports-2014-insert-system1/.
70
“NBC Sports Brand Update,” Troika, accessed November 5, 2015, http://www.troika.tv/nbc-sports-
identity-case-study/.
71
Bill Squadron, “The Bonus: The Story behind Football’s Innovative Yellow First down Line,” SI.com, July 18,
2013, http://www.si.com/nfl/2013/07/18/nfl-birth-yellow-line.
72
Tomas Robertsson, “Virtual Graphics and Analysis” (SVG Sports Graphics Forum, HBO Theater, New York,
February 25, 2015).
73
“FOX Sports Loads Golf Bag With High-Tech Arsenal For Network’s First U.S. Open,” Press Release, FOX
Sports Press Pass, (June 10, 2015), http://www.foxsports.com/presspass/latestnews/2015/06/10/fox-
sports-loads-golf-bag-with-high-tech-arsenal-for-network-s-f.
74
Mac Nwulu, “For MLS Cup, ESPN Will Deploy an Optical Player Tracking System,” ESPN Front Row, accessed
November 5, 2015, http://www.espnfrontrow.com/2014/12/for-mls-cup-espn-will-deploy-an-optical-
player-tracking-system/; and Brandon Costa, “MLS Cup 2014: ESPN To Roll Out Live Player Tracking
Graphics,” Sports Video Group, December 5, 2014, http://sportsvideo.org/main/blog/2014/12/05/mls-cup-
2014-espn-to-roll-out-live-player-tracking-graphics/.
75
“Player Tracking: TRACAB Player Tracking System,” ChyronHego, n.d., http://chyronhego.com/sports-
data/player-tracking.
76
“Data Feeds Overview,” Opta, n.d., http://www.optasports.com/services/media/data-feeds/opta-data-
feeds-overview.aspx.
77
Steve Hart, “Broadcast Graphics: Technology Update” (SVG Sports Graphics Forum, HBO Theater, New York,
February 25, 2015).
78
David Melfi, “Broadcast Graphics: Technology Update” (SVG Sports Graphics Forum, HBO Theater, New
York, February 25, 2015).
79
David Jorba, “Broadcast Graphics: Technology Update” (SVG Sports Graphics Forum, HBO Theater, New
York, February 25, 2015).
80
Soren Kjellin, “Broadcast Graphics: Technology Update” (SVG Sports Graphics Forum, HBO Theater, New
York, February 25, 2015).
81
Ibid.
82
Mario Sousa, “Broadcast Graphics: Technology Update” (SVG Sports Graphics Forum, HBO Theater, New
York, February 25, 2015).
83
“NFL Combine Analytics / NFL Media,” Reality Check Systems, accessed November 5, 2015,
http://www.realitychecksystems.com/portfolio/6024/.
161

84
Kjellin, “Broadcast Graphics.”
85
Sousa, “Broadcast Graphics.”
86
Tomas Robertsson, “Sports Graphics - What’s to Come,” Deltatre, February 27, 2015,
http://www.deltatre.com/2015/02/sports-graphics-whats-to-come/.
87
Jorba, “Broadcast Graphics.”
88
Robertsson, “Sports Graphics.”
89
Jason Cohen, “Virtual Graphics and Analysis” (SVG Sports Graphics Forum, HBO Theater, New York,
February 25, 2015).
90
Gerard Hall, “Virtual Graphics and Analysis” (SVG Sports Graphics Forum, HBO Theater, New York,
February 25, 2015).
91
Robertsson, “Sports Graphics.”
92
Melfi, “Broadcast Graphics.”
93
Ibid.
94
Robertsson, “Sports Graphics.”
95
Joe Inzerillo and Dirk Van Dall, “Case Study: MLB Advanced Media’s Player Tracking & Visualization
System” (SVG Sports Graphics Forum, HBO Theater, New York, February 25, 2015).
96
Ibid.
97
Cohen, “Virtual Graphics and Analysis.”
98
Hall, “Virtual Graphics and Analysis.”
99
Mike Jakob, “Virtual Graphics and Analysis” (SVG Sports Graphics Forum, HBO Theater, New York, February
25, 2015).
100
Aaron Gordon, “Lame Puck,” Slate, January 28, 2014,
http://www.slate.com/articles/sports/sports_nut/2014/01/foxtrax_glowing_puck_was_it_the_worst_blunde
r_in_tv_sports_history_or_was.html.
101
Jakob, “Virtual Graphics and Analysis.”
102
Bill Squadron, “Virtual Graphics and Analysis” (SVG Sports Graphics Forum, HBO Theater, New York,
February 25, 2015).
103
Robertsson, “Virtual Graphics and Analysis.”
104
Hall, “Virtual Graphics and Analysis.”
105
Robertsson, “Virtual Graphics and Analysis.”
106
Jakob, “Virtual Graphics and Analysis.”
107
Robertsson, “Virtual Graphics and Analysis.”
108
Squadron, “Virtual Graphics and Analysis.”
109
Hall, “Virtual Graphics and Analysis.”
110
Richard Sandomir, “ESPN Plans To Purchase Sports Wire,” The New York Times, November 8, 1994, sec.
Business, http://www.nytimes.com/1994/11/08/business/the-media-business-espn-plans-to-purchase-
162

sports-wire.html; In 2000, ESPN would purchase the remaining 20% of the company, as explained in “Dow
Jones to Sell Remaining Stake In SportsTicker Enterprises to ESPN,” Wall Street Journal, June 16, 2000, sec.
Marketplace, http://www.wsj.com/articles/SB96117234941783381.
111
Sandomir, “ESPN Plans to Purchase Sports Wire.”
112
“PA Sport Acquires SportsTicker,” PR Newswire, accessed November 5, 2015,
http://www.prnewswire.com/news-releases/pa-sport-acquires-sportsticker-69975047.html.
113
Ibid.
114
ESPN employee “Clark,” interview by Branden Buehler, Phone, August 21, 2015.
115
ESPN employee “Roger,” interview by Branden Buehler, Phone, September 4, 2015.
116
As James Andrew Miller, the television reporter who has long chronicled the ups and downs of ESPN,
noted upon Walsh’s retirement in 2015, Walsh left ESPN “easily ranking as one of the most influential
executives in the company’s history and as a transformative figure in the larger world of sports journalism.”
See James Andrew Miller, “John Walsh: His Own Unique Self,” SportsBusiness Journal, May 11, 2015.
117
ESPN employee “Alan,” interview by Branden Buehler, Phone, September 3, 2015.
118
Roger, interview.
119
Alan, interview.
120
Roger, interview.
121
Ibid.
122
Alan, interview.
123
Roger, interview.
124
Bill Simmons, “Don’t Deny NBA Stat Geeks the Truth,” ESPN The Magazine, March 24, 2009,
http://sports.espn.go.com/espnmag/story?id=4011524.
125
London, “Data-Driven”; and “2015 | MIT Sloan Sports Analytics Conference,” accessed November 5, 2015,
http://www.sloansportsconference.com/?page_id=16783.
126
“2015 MIT Sloan Sports Analytics Conference: List of Attendees,” n.d.
127
Zach Slaton, “Why The Sloan Conference Is The Super Bowl Of Sports Analytics,” Forbes, February 20,
2013, http://www.forbes.com/sites/zachslaton/2013/02/20/why-the-sloan-conference-is-the-super-bowl-
of-sports-analytics/; and Paul Flannery, “Help Needed, Stat! Is the Sloan Sports Analytics Conference Too
Big?” Boston Magazine, March 2014, http://www.bostonmagazine.com/news/article/2014/02/25/mit-
sloan-sports-analytics-conference/.
128
Roger, interview.
129
“2009 | MIT Sloan Sports Analytics Conference,” accessed November 5, 2015,
http://www.sloansportsconference.com/?page_id=2661.
130
“2015 MIT Sloan Sports Analytics Conference: List of Attendees.”
131
Alan, interview; Roger, interview.
163

132
It is perhaps in delineating this mission that it is easiest to draw a distinction between the Analytics Team
and FiveThirtyEight, the ESPN-owned website launched by Nate Silver in March 2014. While both groups
feature sports analytics experts, the purposes of the two groups vary greatly. As Greg explains in our phone
call, the goal of the Analytics Team is to support all of ESPN’s many properties, whereas FiveThirtyEight’s
staffers are primarily concerned with creating content for the FiveThirtyEight website. Indeed,
FiveThirtyEight’s operations are largely separated from the rest of ESPN, with the group hewing to its own
structure and operating out of New York City instead of ESPN’s headquarters in Bristol, Connecticut. Of
course, too, FiveThirtyEight is not solely focused on sports. Rather, it also produces content related to politics
and entertainment. For the purposes of this project, origins are also significant. While the creation and
growth of the Analytics Team reflects the place of analytics at ESPN, the creation of FiveThirtyEight would
seem to mainly reflect ESPN’s courtship of Nate Silver, who was wooed away from The New York Times
following the 2012 election cycle. That said, it might also be mentioned that FiveThirtyEight and the Analytics
Team are increasingly working together. After all, FiveThirtyEight represents another ESPN platform that the
Analytics Team might service – a slight difference being that FiveThirtyEight offers its own analytics
knowledge in return. Greg explains, “We definitely see them as partners and resources, and I think they see
us the same.”
133
ESPN employee “Greg,” interview by Branden Buehler, Phone, September 8, 2015.
134
Benjamin Hochman, “Former Denver Nuggets Stat Man Dean Oliver Is Headed to ESPN to Help with NBA
and Other Sports Stats,” The Denver Post: Nuggets Ink, March 3, 2011,
http://blogs.denverpost.com/nuggets/2011/03/03/espn-just-got-a-whole-lot-smarter-with-ex-
nugget/3256/.
135
Greg, interview.
136
Alan, interview.
137
Ibid.
138
Ibid.
139
ESPN employee “Nick,” interview by Branden Buehler, Phone, August 25, 2015.
140
Greg, interview.
141
Nick, interview.
142
Clark, interview.
143
Nick, interview.
144
Alan, interview.
145
Matt Yoder, “Exploding Rights Fees Putting the Squeeze on Sports Networks,” Awful Announcing, July 6,
2015, http://awfulannouncing.com/2015/exploding-rights-fees-putting-the-squeeze-on-sports-
networks.html.
146
Joe Flint and Shalini Ramachandran, “ESPN Tightens Its Belt as Pressure on It Mounts,” Wall Street Journal,
July 10, 2015, sec. Business, http://www.wsj.com/articles/espn-tightens-its-belt-as-pressure-on-it-mounts-
164

1436485852; and Frank Pallotta, “Disney’s Bob Iger: ESPN Could Be Sold Directly to Consumers,” CNNMoney,
July 27, 2015, http://money.cnn.com/2015/07/27/media/disney-bob-iger-espn-direct-to-
consumer/index.html.
147
Yoder, “Exploding Rights Fees.”
148
Jason McIntyre, “Layoffs Are Coming to ESPN,” The Big Lead, September 21, 2015,
http://thebiglead.com/2015/09/21/layoffs-are-coming-to-espn/.
149
Matt Bonesteel, “ESPN Layoffs Will Gut the Network’s Production Staff,” The Washington Post, October 22,
2015, https://www.washingtonpost.com/news/early-lead/wp/2015/10/22/espn-layoffs-will-gut-the-
networks-production-staff/.
150
Alan, interview.
151
Roger, interview.
152
Nick, interview.
153
Clark, interview.
154
Roger, interview.
155
Ibid.
156
Greg, interview.
157
Alan, interview.
158
Clark, interview.
159
Nick, interview.
160
Roger, interview.
161
Nick, interview.
162
Alan, interview.
163
Ibid.
164
Clark, interview; and Roger, interview.
165
Nick, interview.
166
Alan, interview.
167
Nick, interview.
168
Roger, interview.
169
Ibid.
170
Alan, interview.
165

Chapter Three: Critiquing Datavisuality

The previous chapter established that sports television is increasingly showing the effects
of a new mode of broadcasting I have termed “datavisuality.” Moreover, the previous chapter
established how datavisuality arose from a confluence of developments in both the sports
industry and the sports television industry. What is left, then, is to grapple with the
consequences and larger meanings of datavisuality’s ascendance. In order to approach this task,
this chapter will engage the big data scholarship referenced earlier. However, matching big data
scholarship with the changes in sports television is not simply a matter of copying and pasting
previous critiques onto a new industry.
As has been mentioned above, sports television – by way of datavisuality – is very much
unique in its tendency to bring big data to the surface. Again, media studies scholars have
previously approached big data by analyzing its behind-the-scenes presence, as in Nick Couldry
and Joseph Turow’s critique of personalized advertising or Jeremy Wade Morris’s analysis of
“infomediaries” – approaches that reflects big data’s tendency to remain out of sight. Predictive
analytics can, for example, determine which shows are recommended by a streaming service or,
in the case of Couldry and Turow’s case study, which ads are presented to the viewer, but these
algorithms remain hidden – an opaqueness that is part of big data’s potential perniciousness.
With the rise of datavisuality, though, viewers are coming face-to-face with big data, seeing
glimmers of how large data sets are collected and measured. This visibility, then, calls for new
angles into previous critiques. To that end, this chapter will examine how datavisuality
alternatively reflects and hides the divides in access, skill and ways of knowing that have come
to characterize big data – first by detailing the theoretical explorations of these divides, then by
166

showing the relevance of these divides to the sports world, and finally by illustrating how
datavisuality reifies these divides while also creating new ones.

Big Data Divides
As Jonah Bossewitch and Aram Sinnreich explain, “informatic volume” continues to
increase in contemporary information societies, with individuals, communities and institutions
storing and exchanging ever more information. However, as they detail, the net flow of
information between these entities is usually uneven – a fact that has great meaning for the
“emerging knowledge/power dynamics” that mark this new era of information quantity.
1
In
response, then, they develop a model of “information flux” in order to discuss information
exchange. Generally, they break down the model like so:
Simply put, regardless of the quantity or nature of the information being captured, information
flows can be divided into three broad geometrical outcomes: (a) positive flux − you are leaking
information, and others have access to more than you do, (b) negative flux − you gather and retain
more information than you emit, (c) neutral flux − everyone has equal access to everyone else’s
information, a situation one could describe as a form of perfect transparency.
2

According to Bossewitch and Sinnreich, the multiplicity of potential information flows
has meant that there are a variety of ways to model information exchange. For example, they
offer the case of “disinformation campaigns,” in which individuals might choose to “propagate
disinformation, thereby reducing the flow of accurate information outward and producing a more
negative flux overall” – a strategy, they note, that has been employed by any number of wary
social network users.
3
Despite the many new models that the scholars offer, though, it would
nonetheless seem that the dominant model for the average individual remains that of the
“traditional panopticon,” which can be “construed as a positive flux of information emanating
167

from the individual outward to the institutions of power.”
4
Indeed, the most consistent critique
of big data offered by academics has involved such a drastically uneven exchange. More
specifically, scholars have pointed out the tendency of big data methodology to set up stark
divides between the database “haves” and the “have-nots” – i.e. between those able to make use
of big data tools and those unable do so (or unaware of such flows in the first place).
The “big data divide” unfurls on multiple levels. On a basic level, it is a question of
access and skill. Lev Manovich, for example, explains, “The explosion of data and the
emergence of computational data analysis as the key scientific and economic approach in
contemporary societies create new kinds of divisions.” He continues by specifying, “People and
organizations are divided into three categories: those who create data (both consciously and by
leaving digital footprints), those who have the means to collect it, and those who have expertise
to analyze it.”
5
Danah boyd and Kate Crawford make a similar argument and cite Manovich in
their call for big data critiques. They write, “The current ecosystem around Big Data creates a
new kind of digital divide: the Big Data rich and the Big Data poor.”
6
The rich, they explain,
have the ability to collect and access large pools of data, while the poor do not. Writing with big
data research in mind, they express particular frustration with the way that corporations have
restricted the use of their data by academics, much less by regular users. They lament, “Large
data companies have no responsibility to make their data available, and they have total control
over who gets to see them.”
7
Moreover, there is not just an access divide, but also a skill divide,
for it is still – as Manovich also notes – a small group with the computational abilities to
“wrangle” big data. A skill gap, they note, that “sets up new hierarchies around ‘who can read
the numbers’” and that also speaks to other divisions.
8
Most researchers with big data skills, for
instance, are male and economically privileged.
168

The scholar who has perhaps most thoroughly explored the “big data divide” is Mark
Andrejevic. Andrejevic notes the gaps in access and skill, but may be even more interested in
the way that asymmetric access portends different ways of understanding the world. He
explains:
"For those with access, the way in which data is understood and used will be fundamentally
transformed. There will be no attempt to read and comprehend all of the available data - the task
would be all but impossible. Correlations can be unearthed and acted upon, but only by those with
access to the database and the processing power. Two different information cultures will come to
exist side by side: on the one hand, the familiar, ‘old-fashioned’ one in which people attempt to
make sense of the world based on the information they can access: news reports, blog posts, the
words of others and the evidence of their own experience. On the other hand, computers equipped
with algorithms that can ‘teach’ themselves will advance the instrumental pragmatics of the
database: the ability to use tremendous amounts of data without understanding it."
9

As this quote makes clear, what is at stake with the rise of big data is the ability to use large data
sets to find patterns, which can then be used to make predictions – thereby generating actions
based on correlation, but not necessarily comprehension. “What is ‘known,’” Andrejevic
explains, “is not an underlying cause or explanation but rather a set of probabilistic
predictions.”
10
Such a shift is potentially troubling, he details, not just because machine
capabilities come to outpace human comprehension, but also because the constant expectation
that machines are unearthing unforeseeable patterns normalizes ongoing surveillance. As
Andrejevic argues, users have come to expect companies like Google to mine their data,
continually searching for new ways to target their advertising. “This level of normalcy,” he
suggests, “helps pave the way for forms of population-level police surveillance that might
previously have seemed intrusive or otherwise inappropriate.” He continues, “It is hard to avoid
the notion that we are living in an era of rampant surveillance creep.”
11
Moreover, Andrejevic
169

writes that this surveillance creep is particularly troubling because users cannot anticipate what
patterns will be uncovered when they turn over their data to corporations and governments. He
explains, “Individuals users have no way to anticipate fully how information about them might
provide for particular forms of decision making,” whether that might mean determining their
credit worthiness or whether or not they are a security risk.
12

The new ways of knowing provided to the “database rich” speak back to original
questions of access and skill because they show that providing access to individual users is by no
means a solution of the “big data divide.” That is to say, users have long demanded to know
exactly what sorts of data are being collected about them. However, even if corporations or
governments were to provide individuals with their data, it would be relatively meaningless
without proper context. As Andrejevic explains, the “database rich” are only able to take
advantage of an individual’s data because they are able to make comparisons across vast pools of
information. By aggregating and sorting, in other words, they can begin to identify patterns and
make predictions – thus bringing a user’s data into proper relief. Individual users, though, would
lack all of this other data. They would, to put it rather simply, have no ability to contextualize
themselves. Andrejevic summarizes, “Allowing users access to their own data does not fully
address the discrepancies associated with the data divide: that is, differential capacities for
putting data to use.”
13
The big data divide, then, is a particularly pernicious problem.

Sporting Divisions
The scholarship around the big data divide has been worth illustrating at length because
almost every single point noted above has begun rearing its head within the sports world. As
mentioned in the introduction, Brett Hutchins has identified a big data divide between the major
170

professional leagues and smaller organizations. Perhaps more distressing, though, is the
growing divide within these major professional leagues between teams and athletes. To begin,
there are major gaps in access and skill. As mentioned above, teams have been compiling more
and more information about players – a trend that has only accelerated as leagues have
introduced player tracking tools that provide real-time positioning data. Unsurprisingly, this has
been largely a one-way street or, to use Bossewitch and Sinnreich’s language, very much a
“positive flux” for the players. That is to say, they are the source for all of the data, but have
little access to it. Of course, too, there is a skills gap, with players generally lacking the technical
skills that are now often a necessity for employment in a front office. This is a particularly
jarring gap because front office jobs have long been a common post-playing career option for
athletes. Indeed, ESPN executive Nick tells me that players have recently asked him how they
might better understand analytics so that these front office options remain open. He says,
“Players that I talk to in the NBA, they want to get educated on analytics – not just because they
think it will help them now, but more so they know that once their playing career is done, if they
don't understand that, they're not going to get front office jobs.”
14

Unsurprisingly, too, these gaps in access and skill have led to front offices and players
operating with different forms of knowledge. In other words, teams are making data-driven
decisions that players have little insight into. An example from Moneyball foreshadows this
development. In the book, Lewis recounts the story of how Oakland acquired Scott Hatteberg, a
first baseman who would later be portrayed by Chris Pratt in the film version of the story. As
Lewis details, Hatteberg had been a reliable catcher for the Red Sox, but after developing elbow
problems, he suddenly became unwanted across the league. The Athletics, though, were highly
interested in Hatteberg’s impressive OBP (on base percentage) – a rather peculiar interest at the
171

time. While the rest of the league largely discounted the stat, the Athletics had drawn on years of
data and their own formulations to develop a philosophy that centered on this metric. Thus, as
soon as free agency began, the team was on the phone with the Hatteberg. Lewis writes:
“This was truly odd. Hatteberg hadn’t the slightest idea why the Oakland A’s were so interested
in him. All he saw was that one major league baseball team treated him like a used carpet in a
Moroccan garage sale, twenty-eight other teams had no interest in him whatsoever, and one team
was so wildly enthusiastic about him they couldn’t wait till the morning to make him an offer.”
15

As this quote reveals, Hatteberg was mystified by Oakland’s interest in him because they were
working with data that the rest of the league was largely ignoring – including the players
themselves. Oakland had found a correlation and acted on it, while the player remained unaware
the correlation existed. While leagues – again, including the players – are now more aware of
the importance of certain statistics, like OBP, the difference in knowledge between teams and
players has only amplified as teams rely on increasingly complex systems able to generate
correlations even less straightforward than the one between OBP and wins.
Going forward, the biggest divide between players and front offices may involve
biometric data. As mentioned in the previous chapter, teams are collecting more and more of
this type of data – even when players head home. Teams, seemingly aware of the optics of this
growing knowledge divide, have emphasized that biometric data collection uniquely benefits the
players. At the 2015 MIT Sloan Sports Analytics Conference, executive after executive – both
from teams and vendors – mentioned that new devices, like GPS tracking units, would improve
player health and, accordingly, elongate careers. In this way, then, the discourse around
biometric data in sports very much recalls the larger discussion around wearable devices. As
Kate Crawford, Jessa Lingel and Tero Karppi have detailed, “A fundamental claim of wearable
devices is that data will bestow self-knowledge that will create a fitter, happier, more productive
172

person” – a claim that not only mirrors promises in the sports world, but also mirrors promises
made around the weight scales many decades prior.
16
As they then detail, though, wearable
devices are different from previous devices in that there is no longer a “private data relation”
between the device and the person. Instead, data slips out of control – gathered by the wearable
manufacturer and then exchanged with an unknown number of third parties. “From this
perspective,” the scholars write, “when people start using these devices they enter into a relation
that is an inherently uneven exchange – they are providing more data than they receive, and have
little input as to the life of that data – where it is stored, whether it can be deleted and with whom
it is shared.”
17
It’s a situation that parallels the sports world and, increasingly, athletes are
becoming aware. Shane Battier, the former NBA player, tells Tom Haberstroh and Pablo Torre
ahead of his retirement, “I’m glad I’m done … I think all fluids will be extracted in five years.”
He continues, “Big data is scary because you don't know where it's going and who's seen it. I'm
not saying that they'd sell research to anyone, but I don't trust where my blood sample will end
up and what eyes will look at it and what people outside the NBA will know about it."
18

There are, of course, very real consequences to the big data divide between players and
front offices – ones that stem from the privacy concerns expressed by Battier. As Crawford et al.
explain, one of the most pressing anxieties related to wearables is that data will not just slip out
of an individual’s control, but that it will also be used in “direct contravention” to their interests
– perhaps, most disturbingly, by their employers.
19
In the sports world, that potentially has very
much arrived. Vendors and team executives may preach the benefits of player health, but there
is no doubt that biometric data will also be used to gauge player performance. Indeed, brochures
that Catapult handed out at the Sloan conference boast, “With athletes using Catapult, you’ll
never need to guess when athlete performance is deteriorating.” Such a purpose begins to
173

diverge from the player health rationale. As argued by Alan C. Milstein, a lawyer quoted by
Torre and Haberstroh, “If the purpose is to predict performance, that's not a health care purpose.
That's an economic purpose.”
20

What’s behind that economic purpose? To begin, there certainly is an economic rationale
to teams maximizing player health; teams have a strong incentive to keep their best players on
the field. But economic motivations stretch beyond that. As athletes have begun to argue, teams
will likely be looking to bring performance data – whether derived from optical tracking systems
or wearable devices – into salary negotiations and roster decisions. This is a prime concern for
Cleveland Browns wide receiver Andrew Hawkins, one of the few athletes to be featured on a
Sloan panel. Teams, Hawkins began, have started accumulating significant data on every player.
Thus, when their performance begins to decline, teams will immediately notice – much as
Catapult would have it. Negotiations, then, will be heavily stacked against players, with teams
able to present irrefutable evidence that players are declining. “That sucks,” Hawkins
concludes.
21
Hawkins is not alone in this grim prognosis. In Torre and Haberstroh’s piece, they
talk to Dallas Mavericks center Brandan Wright and discuss the prospect of contract negotiations
based on biometric data. Sounding much like Hawkins, Wright says, “Honestly, I think it'll hurt
guys.”
22

For Hawkins, one of the most frustrating parts of the brave new world of contract
negotiations, in which teams may come armed with reams of performance data, is that tracking
systems cannot track the intangible attributes that he finds particularly important. “You can’t
measure heart and desire,” he tells the crowd.
23
Again, this echoes the scholarship on wearables.
“By wearing a self-tracker, people are … creating a split narrative in the way their lives are
measured,” Crawford writes. “There is their experience, and there is the data about them: these
174

may converge or diverge.”
24
“But in this divided account,” Crawford et al. later elaborate, “the
data are perceived as more objective and reliable than a subjective human account.”
25
Of course,
this is troubling not just because it discounts the human account – which can include, in the case
of athletes, felt attributes like “heart” – but also because it is assumed the tracking data is correct.
However, as Crawford et al. point out, data tracking systems are very much fallible. Discussing
the use of wearables in legal cases, they write, “By giving these systems the power to represent
‘truth’ in a court case, we are accepting the irregularities of their hardware and software, while
also establishing a set of unaccountable algorithmic intermediaries.” They continue, “The
wearable, and the systems that subtend it, become unreliable witnesses masquerading as fact.”
26

As Hawkins fears, this may come to pass in the sports world, too, with performance data
becoming the “fact” that undergirds everything from contract negotiations to player acquisitions.
As Torre and Haberstroh ask in their article, “How long will it be until biometric details impact
contract negotiations? How long until graphs of off-court behavior are leaked to other teams or
the press? How long until employment hinges on embracing technology that some find
invasive?”
27

Players unions are certainly aware of these questions and considering the implications for
future collective bargaining agreements. “I think everyone is paying close attention to this,” says
Tara Greco, an NBA Players Association spokeswoman quoted by Rick Maese in The
Washington Post.
28
As in the larger conversation around big data, sharing access to data has
been put forward as a possible solution. Hawkins, for example, expresses a desire to have all the
information that the front office has once negotiations roll around. A few team executives say
this is a possibility. On the same Sloan panel as Hawkins, Kirk Lacob, the director of basketball
operations for the Golden State Warriors (and the son of the team’s owner), says that
175

“transparency is huge” and expresses a willingness to share data with athletes. “If a guy wants to
know [something], we’ll usually tell them,” he says.
29
Similarly, in Torre and Haberstroh’s
piece, Kings then-general manager Pete D'Alessandro says, “It doesn't have to be us vs. you. It
can be a partnership.”
30
Given the aforementioned scholarship on big data, though, it is perhaps
not surprising teams would be willing to share access to individual data. Again, individual data
only makes sense when aggregated and sorted. Left unsaid is that players – even if granted
access to their own data – will not have access to the wider pool. The big data divide would
remain.

Datavisuality Divides
A discussion of big data divides in the sports world brings us, finally, to datavisuality.
To begin, it might be suggested that datavisuality superficially reifies the big data divide that
now marks the sports world. That is to say, datavisuality – on the surface – mirrors the giant
gaps in access, skill, and ways of knowing that have emerged between players and front offices –
apparently placing viewers in the privileged position of the “big data rich.” To explain, it might
be helpful to pull from an example. Again, then, here are two screenshot pulled from baseball
broadcasts, in this case from a studio show, MLB Network’s Baseball Tonight:
176

3.1 Use of tracking technology during MLB Network’s Baseball Tonight.

3.2 Use of tracking technology during MLB Network’s Baseball Tonight.
In the full clip that accompanies the second screenshot, the show’s three commentators discuss
the imposing spin rate of Boston Red Sox pitcher Koji Uehara, explaining that spin rate can
serve as a helpful metric in complementing the traditional emphasis on pitch velocity. Impressed
by the optical and radar tracking systems that have enabled this measurement – and this graphic
– one of the commentators, longtime Colorado Rockies GM Dan O’Dowd, enthusiastically
declares, “It allows you to look inside a player’s ability so much deeper … it should allow you to
make so much better evaluations of your players.” He continues, “You can’t measure that with
the naked eye.” The “you” in question here is ambiguous. While it nominally refers to
177

professional scouts, it also implicates the viewer. The viewer, it is inferred, now has the ability
to use advanced statistical analysis, including big data tools, to evaluate players – just like the
newest generation of front office executives.
In positioning the viewer as a data-driven executive, whether or not it is done as
explicitly as in this clip, datavisuality begins to place the viewer on the advantaged side of the
big data divide. It begins with access. As seen in the screenshots, datavisuality grants viewers a
glimpse of the same tracking systems and advanced statistics that teams are now using. During
the broadcast, then, viewers are granted knowledge that players do not have. As O’Dowd
declares, this is information that escapes “the naked eye” – thus, only those sitting at home have
the ability to examine it. After access, datavisuality offers skill. Of course, the average viewer
does not have the ability to make sense of big data systems or many of the advanced metrics that
teams employ. However, datavisuality ostensibly teaches viewers how to use these new
evaluation tools. As mentioned in the previous chapter, industry executives are focused on
making advanced statistical analysis digestible for casual viewers – turning complicated models
into features that can be easily interpreted. The screenshots show this process at work. The first,
for example, displays a statistic called “route efficiency” – a number that, according to language
buried on the MLB website, is a “measure of the shortest point-to-point distance between where
the player starts and where he ends, relative to how far he actually traveled.” The league is thus
processing a great deal of player tracking information and turning it into an easy-to-read
percentage number that can be easily compared player to player. In the clip that accompanies the
second screenshot, meanwhile, former GM O’Dowd even gushes about the power of the new
tools. The viewer, then, is allowed to become something of an expert, or at least a more
“informed” viewer, even without a full grasp on the system’s operation. Finally, datavisuality
178

offers viewers unique ways of knowing – granting them, to use terms from Andrejevic’s Infoglut
– entrance into a distinct “information culture” where unforeseen correlations outpace human
understanding. Unlike the athletes playing in the game, limited to understanding games based on
their own subjective experiences, viewers are able to mine new correlations that promise more
efficient ways of parsing games. Route efficiency, for example, offers a primitive form of
algorithmic player evaluation that seems to discard the “gut” prejudices of Moneyball’s biased
scouts in favor of something, to use O’Dowd’s phrasing, “better.” However, it is a form of
knowledge only available to those with televisions.
It would appear, then, that datavisuality offers the same uneven flow of information that
has marked the introduction of big data into the sports industry – the only element that has
changed is that viewers are placed into the role of the “big data rich” instead of front office
executives. Athletes, meanwhile, remain on the other side of the dividing line. It is a troubling
gap. As Andrejevic writes, “The concentration of control over data is a form of power and
control, one that differentiates the haves from the have-nots and thereby undergirds new forms of
hierarchy and inequality.”
31
With datavisuality ascendant, viewers are not just transformed into
more “informed” and “efficient” viewers, but they are also placed in an apparent position of
power and control over the athletes on the field. Also worth exploring, though, is how this
uneven flow of information is represented. After all, part of what makes datavisuality unique is
that it brings big data on to television screens for the first time.
In order to discuss how exactly visualization reinforces the data divide, it is useful to turn
to scholarship from the digital humanities, where debates over the use of visualization have been
fierce. Johanna Drucker, for example, has expressed reservations about using data visualization,
arguing that visualizations are always loaded with unspoken premises. In her words, “Graphical
179

tools are a kind of intellectual Trojan horse, a vehicle through which assumptions about what
constitutes information swarm with potent force.”
32
What are these assumptions? First, there is
the problem of the “data” used to construct visualizations. Drucker argues that designers tend to
proceed forward with the idea that they are working with pre-existing “data” offering a
transparent window into world, assuming that the statistical information they are depicting
unproblematically represents “a priori conditions” – a contention that echoes critics of big data
who see big data methodology frustratingly equated with “truth.” Drucker’s second, related
argument is that visualizations tend not just to be based on this flawed conception of “data,” but
also that they conceal such faulty assumptions. She explains, “The rendering of statistical
information into graphical form gives it a simplicity and legibility that hides every aspect of the
original interpretative framework on which the statistical data were constructed.” Elaborating,
she adds that graphics “do not present themselves as categories of interpretation, riven with
ambiguity and uncertainty.”
33
The route efficiency graphic, for instance, does not just take for
granted that the measurement is accurate, but also that the measurement matters. That is to say,
does route efficiency indicate anything other than an outfielder’s ability to run in a straight line?
Does it really do anything to tell us whether an outfielder is good at his job? The answers are
unclear, but the graphic, as Drucker argues, aspires to remove uncertainty. A simple fact is
presented on screen encased in smooth gradients, so the assumption is that it is relevant, accurate
and even true.
In a slightly different vein, Drucker asserts that the most commonly used forms of
visualization, like the bar graph, have very specific historical roots that are too frequently
forgotten.
34
Such forms, she points out, have links to centuries-old notions of bureaucracy,
management and objectification that structure their presentation. The flow chart, for example,
180

arises out of bureaucratic structures and, accordingly, “The directional force of power relations
and movement of goods through a production system often have a conspicuous absence of
human agents, as if processes were an inevitable and natural fact.” Examining these contexts,
Drucker concludes, “The structured relations among information elements is as much an
expression of a way of thinking as any other intellectual form.” In other words, “graphical
structures are rhetorical arguments.”
35
Again, though, such assumptions are obscured. A similar
critique comes from Lauren Klein’s “The Image of Absence.” In the article, Klein cites
Drucker’s “Trojan horse” metaphor as she examines Thomas Jefferson’s papers and the graphs
within. Klein notices how Jefferson’s visualizations went hand-in-hand with “subjugation and
control— that is, the reduction of persons to objects, and stories to names.”
36
This observation,
she argues, “serves as a reminder of the violence that can be enacted through visual display,” in
turn reminding digital humanists “to examine the underlying assumptions and biases embedded
in the research methods, database structures, and modes of display” that they use in their work.
37

It is useful to apply Drucker and Klein’s arguments to datavisuality. On the simplest
level, it is not unheard of for datavisuality to draw on the same forms of visualization that they
examine in their scholarship, like the line graph:

3.3 Use of line graph during NFL telecast.
181

Of course, though, the argument stretches beyond the continued use of bar graphs and line
graphs. The greater significance is that the visualizations that are part of datavisuality – whether
they are simple bar graphs or 3D marvels that represent the latest in virtual graphics technology –
continue the project of configuring power relations in uneven ways and, in the process, removing
human agency. The graphic in the second Statcast screenshot above, for example, equates Koji
Uehara with his spin rate; he is what he pitches. The viewer does not need to know his
background, or even how he got to this pitch within the narrower context of this game – all that
matters in this graphic is the spin. The act of visualization, then, reinforces the uneven flow of
information already characteristic of datavisuality. Not only are viewers apparently positioned
on the side of the database “haves,” having been given access to big data tools and subsequent
entrance into a new “information culture,” but this chasm is also accompanied by the power of
graphics to obscure potential flaws in big data methodology and, moreover, to further objectify
players.

“Trust the process”
This chapter thus far has built to a tidy conclusion: that datavisuality offers a mirror of
the big data divide that characterizes the sports industry as a whole, with management on one
side and athletes on the other. However, it is also worth considering whether datavisuality may,
on the one hand, be implicating viewers in this neoliberal process of control, and, on the other
hand, reinforcing another big data divide: that between the industry and fans.
As has been mentioned above, a massive data divide has opened between players and
management. While teams continue to collect more and more information about players, players
have seen very little access to that data. Unmentioned thus far, though, is that the divide between
182

the industry and fans is also growing. This divide operates on two levels, the first involving
information about athletes and the second involving information about the fans themselves.
First, it can be briefly stated that teams are generally very guarded about their big data
methodology, essentially treating their analytics strategies like trade secrets. Indeed, a popular
critique of the Sloan conference has been that its panels have become increasingly vague as
teams have become warier about leaking information. In 2009, for instance, ESPN commentator
Bill Simmons visited the conference and left annoyed that NBA teams keep almost all of their
analytical advances internal and, thus, hidden from curious fans. “NBA teams need to stop
acting like they're protecting nuclear info during the Cold War,” he lamented before concluding
with his plans to get Daryl Morey “drunk on tequila – or laser-printer fumes” so that he would
spill his secrets.
38
Several years later, while summarizing a 2014 visit to the conference,
Deadspin‘s Kyle Wagner similarly observed, “The conference has degenerated into a sort of
TED Talks in cleats” with the main panels having turned to “mush.”
39
Because teams have
become so secretive, both at Sloan and in their day-to-day interactions with the press, fans only
get the haziest sense of how teams are collecting data and how they are using it. Emblematic of
this state of affairs is the Philadelphia 76ers aforementioned plea to “trust the process.” Implicit
in this statement is that fans will not know how the “process” actually works – the team is
suggesting that their decision making is too complex for fans to understand it fully. Fans must
“trust,” then, in the decision makers and their big data methodology, whether that means
incorporating data from SportVU, biometric sensors, or elsewhere.
This gap between teams and fans is significant here because it is reified by datavisuality.
That is to say, even though datavisuality implicates viewers in the GM role, using big data
methodology to evaluate players, it offers only an incomplete perspective on the GM role. As
183

ESPN’s employees suggested, networks are operating at a simpler level than most teams,
assuming that additional complexity would drive away viewers. For instance, while tracking
data is becoming an increasing part of broadcasts, fans are still just getting small glimpses at how
that technology can be used. Fans, then, may develop a familiarity with big data methodology,
but they do not get a chance to develop any sort of actual expertise. This outcome is particularly
disappointing because – in an ideal scenario – datavisuality might have offered fans a thorough
breakdown of big data methodology, including both its advantages and its limitations. This, in
turn, could be a valuable set of knowledge to possess in a world that is increasingly moving
toward data-driven decision-making and in which more and more laborers may find their
employment status subject to big data analytics. However, datavisuality offers only a basic
introduction to big data tools. This ensures an ongoing big data divide both in terms of access
and skill.
Such a big data divide is not just consequential because it represents a missed opportunity
to enrich the discourse around big data, but also because it could actively harm this discourse.
As boyd and Crawford write, big data is a phenomenon that does not just entail new technology
and new means of analysis, but also a distinct mythology. That is to say, big data methodology
is often accompanied by “the widespread belief that large data sets offer a higher form of
intelligence and knowledge that can generate insights that were previously impossible, with the
aura of truth, objectivity, and accuracy.”
40
Of course, such a belief is problematic. As they
argue, numbers do not speak for themselves – even if some big data proponents may suggest that
they do. As might be expected given the strength of the big data mythology, this simple fact –
that numbers do not speak for themselves – can get lost within datavisuality. Take, for instance,
the previously mentioned quote from former Rockies GM Dan O’Dowd, who reacts to new big
184

data tools by saying, “It allows you to look inside a player’s ability so much deeper … it should
allow you to make so much better evaluations of your players.” The keywords here – “deeper,”
“better” – are very much in line with the mythology that boyd and Crawford describe. What is
needed as a counter, then, is additional complexity – complexity that speaks to big data’s
constructedness and its inability to deliver an objective “truth.” Such complexity, though, will
not come as long networks deliberately offer viewers a surface-level perspective on analytics.
The big data mythology, then, may continue to go unchallenged.
Turning now to the second layer of the divide between teams and fans, teams are
collecting more and more information about fans. As an example, we can return again to the
Sloan conference. Undoubtedly, the emphasis at the Sloan conference is on athlete data.
However, amidst the many panels and vendors emphasizing player performance are attendees
whose primary concern is “fan analytics” – that is to say, the systemized collection and
application of data about fans. Consulting firm Booz Allen Hamilton, for instance, hands out
brochures that tout its ability to use analytics to “enhance business and off-the-field planning.”
Included in its offerings are promises to identify and engage “power users” as well to examine
social media use. On one of the main stages, meanwhile, German software company SAP puts
on a sponsored panel entitled “Technology amplifies success: How analytics is changing the
game,” featuring an SAP executive as well as several of the company’s clients and partners.
During the panel, the speakers emphasize the company’s success in compiling “fan data.” At
one point, the company highlights its partnership with Phizzle, a company that has used SAP
technology to develop a “fan tracker” that provides a “real-time view of fan behaviors,” which in
turn allows for “personalized marketing.”
41
As an example of potential “fan tracker” use, a
Phizzle executive proposes a hypothetical situation in which a fan’s daughter has broken her leg
185

in a soccer game. By monitoring the fan’s social media activity and purchasing activity, the
executive suggests that the “fan tracker” would be able to determine that the fan is now “sad” as
a result of his daughter’s injury. When delivering an email newsletter, then, a team could
include a “get better” message.
Of course, many teams are already fully aware of “fan analytics” and do not need to be
sold by besuited vendors. On one of the first Sloan panels, entitled “If you build it,” team
representatives, architects, and app designers gather for a discussion of the in-stadium fan
experience. Before long, discussion turns toward data collection. Priya Narasimhan, the CEO
and founder of a company that develops custom venue apps, uses this turn as an opportunity to
discuss how teams are data-mining fans to deliver “personalized experiences.”
42
She mentions,
for instance, “beacon analytics” that can use the company’s apps to track where users spend their
time in stadiums and which entrances they use, amongst other location information. Once
combined with other data, like information about concession and merchandise purchases, she
points to the possibility of using this location data to create astoundingly comprehensive fan
profiles. As Chris Granger, president of the Sacramento Kings, summarizes, teams are all
looking for a “360 degree view of the fan.”
43

What all of these examples point to is a gaping divide between the industry and fans, with
teams and other entities possessing more and more information about their audience. Again,
datavisuality reflects this gap. Or, to be more precise, it amplifies this gap by facilitating further
data collection and analysis. For example, it was mentioned above that ESPN partially values
analytics because new metrics have the potential to attract more users to second screens and,
importantly, keep them there. As ESPN employee Clark comments, the company is hoping that
the addition of win probability data to ESPN’s NFL game trackers will make them “stickier” –
186

i.e. more likely to keep viewers on the page. Undoubtedly, the primary reason the company
wants this stickiness is to generate ad revenue, as these pages are typically sponsored. Another
reason, though, is to gather additional information about audiences, as ESPN’s pages are loaded
with web trackers that collect data about visitors – data that can be either used by ESPN or sold
to third parties. ESPN’s tennis scoreboard page, for example, comes with nine different trackers.
One from web analytics firm Gravity tracks data like the user’s browser and hardware
configurations, IP address and search history, while another one from Nielsen also pulls the
user’s demographic information. Similarly, WatchESPN, the web app that streams ESPN’s
programming – including alternative broadcasts, like the many college football “Megacast”
channels – comes with several trackers. The second screen experience, then, is one rich with
tracking.
Datavisuality, then, does not just position viewers as the “big data rich,” but it
simultaneously – and rather surreptitiously – facilitates a divide that puts fans back on the side of
the “big data poor.” In other words, while viewers take in television that aligns them with data-
driven decision-makers, evaluating players based on advanced metrics and new tracking data,
they are all the while being tracked themselves. In this way, datavisuality recalls Andrejevic’s
earlier article, “Watching Television Without Pity,” which documents the growing need for
television viewers to “become not only more efficient but also more informed and even more
critical.”
44
In the essay, Andrejevic specifically examines the “productivity” of television fans
who visit and post on the recap website Television Without Pity. As Andrejevic details, the site
becomes an “incitation to participate in the work of being watched.”
45
That is to say, in
engaging with the site, users become an “instant focus group” that can be monitored by the
industry.
46
According to Andrejevic, this type of relationship not an isolated phenomenon
187

unique to message boards, but rather a developing characteristic associated with interactive
media. “The work” that viewers do, Andrejevic writes, “the work of making their preferences
transparent, of allowing themselves to be watched as they do their watching – is an increasingly
important component of the emerging interactive economy.”
47
The proliferation of web tracking
technology and its use in second screen applications would seem to align with this idea. Second
screen experiences, in other words, promise greater interactivity, but that interactivity enables
viewers to be tracked as never before.

Predictive Analysis
In attempting to explain contemporary sports media, this dissertation project is involved
in the complicated task of historicizing the present. As such, there is some speculation involved.
The underlying conceit of this chapter, for example, is that datavisuality will continue to play a
large role in sports broadcasting as networks and leagues collect and analyze more and more
data. In closing this chapter, though, it might be worthwhile to more fully engage in speculation
– projecting, as do the data scientists that work for sports teams, what the future may hold. For
instance, we might wonder what datavisuality would look like if networks were to begin
incorporating biometric data. As Roger, the ESPN executive, indicates, the network has
definitely considered the televisual possibilities that might be allowed by such information. He
muses, “What a cool story that would be if you could say, 'Look at this guy's heart rate – it goes
up on average this much every time he's in a clutch free throw shooting situation,' or, 'Look at
this person's heart rate – it never changes in clutch free throw situations.'” He adds, “There's a
lot of cool stories you could tell with biometric data.”
48
However, this possibility is unlikely to
come to fruition and, thus, there are limited gains in pondering what the intersection of sports
188

television and biometric data might look like. As Roger and figures in the graphics industry
explain, there are a number of obstacles in the path toward biometric broadcasting. Roger,
though excited about the possibilities of biometric data, admits, “We're not going to ever
probably going to get biometric data because of HIPAA and a million other reasons why … the
unions would never want that, either.” He summarizes, “It's on our radar, but we know that's not
where we should be putting our priorities or our efforts right now.”
49

According to the industry figures quoted in the previous chapter, two phenomena that are
more likely to have increasingly wide effects in the coming years are second screen broadcasts
and “microcasts.” As mentioned, the popularization of these alternative broadcast experiences is
very much tied to the rise of datavisuality, for many of these experiences have attempted to
complement traditional broadcasts with data-driven additions. Although these alternative
experiences are still far from routine – particularly in the case of microcasts – there is every
reason to think they will grow more common – not just because of television’s continued spread
across platforms, but also because of how they are seen to naturally accommodate data and data
visualization. As mentioned in the previous chapter, ESPN executive Alan explains that it may
not be long until data-driven second screen experiences are totally automated, thus making them
particularly appealing for networks looking to roll out extra features without incurring additional
labor costs.
50
Roger, meanwhile, mentions that ESPN has discussed rolling out more streaming
broadcasts that resemble the “data center” deployed during the college football championship.
He declares, “We love that stuff.”
51
ESPN is not alone in this “love,” either. Alan, for instance,
explains that the leagues are also increasingly looking towards data-driven broadcast alternatives,
citing the NFL’s recent deal with Microsoft that will allow Xbox users to access player tracking
data.
52

189

Because second screen and microcast experiences appear to be on track for an
increasingly large role in sports broadcasting over the next several years, it is worth exploring the
broader ramifications of this growth. More specifically, what are the ramifications for television
audiences? As mentioned in the previous chapter, Victoria E. Johnson has documented how
sports television has recently evolved into a multi-platform affair – an account that largely
focuses on experiences that would fall under the category of second screen broadcasting. In
detailing this evolution, she notes that sports television has become a “unique hybrid … between
network television’s traditional role as the site of ‘mass’ audience … and the post-network era’s
characteristic proliferation of content and its co-branded migration between media forms.”
53

That is to say, sports television is at once able to maintain large audiences – and thus provide the
all-important “water cooler talk” – and also adapt to the post-network terrain where television
extends across platforms. To a large extent, this description covers the alternative broadcasts
spurred on by datavisuality. ESPN’s current work on adding win probability to its website, for
example, is partially meant to provide second screen content for fans watching Monday Night
Football on their primary screens. Monday Night Football remains the main fodder for attention
and will continue to draw massive ratings, while the option to look at win probability provides a
way into using other platforms, whether they be phones or other sorts of devices, to personalize
the experience. Just as Johnson suggests, such an experience appears to give sports television a
“unique” status straddling old media and new media.
However, what Johnson’s account does not quite cover is the emergence of the microcast,
which essentially turns niche content – of the sort often associated with second screens – into a
primary screen experience. This is a significant development because with the rise of these
alternative primary screen broadcasts, both the sports television text and the sports television
190

audience have split in unprecedented ways. Whereas sport’s initial forays across platforms
preserved television’s “role as the site of ‘mass’ audience,” now audiences are being offered
multiple versions of the same event – fracturing the “communal” engagement that has long
defined sports television. There are several possible consequences of this split that are worth
considering. For one, there are the potential ramifications for democracy – a point basically
outlined by Couldry and Turow in their article on big data and advertising. As the scholars
describe, advertisers are shying away from traditional outlets, like print newspapers, meant to
reach large audiences all at once. Instead, they write, advertisers are moving toward more
“personalized” approaches that rely on data mining to reach targeted audiences at carefully
selected times and locations – a feat enabled by new technology. A video streaming service like
Hulu, for example, allows advertisers to use demographic and tracking data to methodically aim
their ads at specific audiences, such that one viewer watching Empire may receive very different
ads than another viewer who has also loaded up the same show. While such careful targeting
may benefit marketers, the scholars fret that it may also have troubling consequences for society
at-large. More specifically, they draw on political theorists to argue that democracy requires its
citizens to have shared reference points that allow them to recognize a “common social and
political space.”
54
However, the personalization of ads – and, going forward, the personalization
of other pieces of media – helps “erode” that shared “system of reference.” They explain,
“Media’s capacity to circulate material that builds connections between otherwise diverse groups
is not helped, but rather undermined, by the pressures toward personalization.” They conclude
by asking, “What landscape can we expect to find if we continue much farther down the path
charted in this article?” They answer, “A landscape … that has been cleared of one basic
191

ingredient of democratic life: the reliable and regular exchange of common ideas, facts, and
reference points about matters of common concern.”
55

Given Couldry and Turow’s emphasis on shared “ways of living,” it is very possible that
they might find the rise of the microcast to be even more troubling than the rise of personalized
ads. After all, sports television has arguably offered the most consistently shared “system of
reference” within the media landscape, with major events like the Super Bowl and the college
football championship continuing to dominate year-end ratings and to provide ample fodder for
social media exchanges. The fracturing of these events into multiple audiences each watching
their own tailored versions of the event, then, would appear to have more severe consequences
for “democratic life” than the personalization of ads. With one viewer watching a baseball
playoff game by way of a traditional game broadcast and others watching by way of an analytics-
focused broadcast, there are fewer people partaking in the same “collective experience.” As
Couldry and Turow argue, the ramifications of this split could be severe, for the fragmentation of
major events would be another key cog in the erosion of the shared social space that undergirds
our democratic system. However, the effects might not be so linear. ESPN executive Roger, for
instance, argues that the microcast might just enhance discourse because the broadcasts are not
completely split. Rather, they still revolve around the same event and just offer different
perspectives – a dynamic that reflects traditional ways of watching and discussing major events.
He argues, “Even when we're all watching the same thing, we still experience it a little bit
differently.” He continues, “That's part of the fun of the water cooler talk is that we're all
standing around saying, 'I can't believe this happened' and then somebody else might say, 'What
do you mean, that was great!'”
56
In this view, then, personalized TV is not a fast track to
anarchy, but rather an exciting way to foster discussion – thus mirroring democracy in its finest
192

state. Ultimately, though, the truth may lie somewhere in the middle. As Roger argues,
microcasts do offer a shared core experience; whether viewers are watching the traditional
broadcast or an alternative data-driven broadcast, they will still see the same basic sporting
event. That said, audiences are nonetheless losing some of the shared experience. Linking back
to Couldry and Turow’s article, part of the appeal of microcasts is the ability to more precisely
target viewers with ads. The analytics broadcast, for example, is intended to attract a certain
niche of fans and, thus, may feature a different set of ads than the traditional broadcast.
Reference points, then, are necessarily lost. The divide facilitated by microcasts, then, may not
be complete, but it is still a divide.
On top of these concerns about what microcasts might mean for the health of the public
sphere, the rise of microcasts also intersects with the politics of gender, race, class, and age. To
begin, it can be asked who is being targeted by these microcasts. In an earlier analysis of ESPN
original programming, Johnson argues that a network’s “‘convergent’ media strategies” come
loaded with “presumptions about who ‘counts’ and what public(s) should be served.”
57
That is
to say, some demographic groups are valued more than others and it is these valued groups that
end up on the receiving end of the network’s attention and financial investments. In the case of
data-driven microcasts, industry figures are hesitant to admit that they are targeting any
particular niche with their analytics products, but they do admit that analytics has been most
enthusiastically embraced by highly-educated, young, male fans – generally, a widely coveted
demographic by advertisers.
58
Thus, it would not be a major leap to assume this is the
demographic being targeted by microcast experiments like FOX’s JABO broadcast and that this
is the group being granted extra programming. Of course, too, there is a related question of
access, as microcasts tend to be positioned on outlets with smaller reach. In the case of the
193

JABO broadcast, for example, the main broadcast aired on FOX, which is available to all
viewers, while the microcast aired on FS1, which necessitates a cable package and only reaches
72% of households with televisions.
59
Thus, data-driven microcasts may not only be specifically
tailored to relatively affluent audiences, but they may also be unavailable to other viewers. As
Johnson suggests, such issues of access and targeting deserve particular attention in the case of
sports television, for it has traditionally been easily available to all audiences. The rise of
microcasts, of course, does not necessarily mean the end of the mass appeal of sports – rather
they represent an attempt to simultaneously draw both mass audiences and niche audiences – but
they do deserve scrutiny for their implicit valuing of one niche over the many others that
compose the wider broadcast audience.
In considering the ideology of the data-driven microcast, the content also merits further
analysis. To that end, it is useful to refer to Lisa Parks’s article, “Flexible Microcasting” – an
essay that pre-dates the type of “microcasting” referred to in this chapter by a decade, yet shares
an interest in the industry’s continuing quest towards “personalized TV” and the incorporation of
new technology into the broadcasting landscape. As Parks details, the incorporation of
technology into network television came loaded with “hegemonic assumptions.”
60
As a key
example, she points to the rise of network quiz shows in the late 1990s and early 2000s, with
Who Wants to Be a Millionaire serving as the most prominent example. Scrutinizing
Millionaire, Parks observes that the show – which prominently features a quasi-computer
interface – overwhelmingly features white males, thus positioning them as the primary users of
the show’s technology. In the process, Parks argues, the show “tends to reinforce white, middle-
class masculine control over new media.”
61
Proceeding forward, she points to other broadcasts
beyond network TV that “can be seen as posing a challenge to this logic,” such as Oxygen
194

Media, which encourages “viewers to reimagine the television screen as a democratic Internet
portal that gives everyone equal access to knowledge about computer technologies and
cyberculture.”
62

Turning to sports television and microcasts, this relationship is reversed. Backing up, it
can first be observed that sports television is also a site of information and technology.
Announcers, for example, are positioned as founts of knowledge about a given sport and use
various technologies to impart that knowledge to viewers. A typical football game, for example,
will see a color commentator offer detailed analysis and then use a telestrator to illustrate that
insight. The announcing booth remains very much a male-dominated space, but has become
somewhat diverse in terms of race and age. Several of FOX and CBS’s NFL commentators, for
example, are people of color, including Charles Davis, Ronde Barber and Solomon Wilcots.
While the sample size remains limited, it seems that data-driven microcasts might potentially
work against whatever diversity the networks have achieved in their announcing crews. FOX’s
JABO broadcast is here the main example, for it featured a desk of several white men at the
control of the new technology and metrics that are a part of advanced analytics. Rather than
offering a more “democratic” space, then, the microcast echoes 1990s-era network television in
reinforcing “white, middle-class masculine control over new media.”
Again, the sample size for data-driven microcasts is small, but there is perhaps extra
reason to be skeptical given larger problems within the sports industry. For most of the
industry’s history, it has been rife with racial bias. Professional leagues like the NFL and MLB,
of course, were long segregated, and managerial positions – both in terms of coaches and front
office executives – have continued to be dominated by whites. The managerial discrepancy is
particularly noticeable given the current demographics of the leagues. 69% of the NFL’s
195

players, for example, are black, but in 2001, all but two of the league’s coaches were white.
63
In
response to this vast gap – as well as outside pressure – the league eventually adopted a new
policy in 2003, termed the “Rooney Rule,” that required teams to interview at least one minority
candidate when hiring new head coaches.
64
In 2009, the policy was extended to cover front
office positions, as well.
65

The NFL is not the only league to feature such wide discrepancies, either. Players of
color make up nearly 42% of MLB rosters, yet only four people of color serve as GMs.
66
In light
of such disparities, it is difficult not to conceive of analytics as having a racial component. In
short, a playing career has long been a gateway towards managerial positions. While racial
discrimination has long prevented candidates of color from receiving fair treatment, policies like
the Rooney Rule have helped eliminate some of the barriers. The NFL, for instance, now has
seven GMs of color, four of whom were once players in the league. However, the analytics
discourse, as was mentioned above, devalues playing experience. While playing experience is
not a hindrance to a job and playing experience does not preclude a grasp of analytics, a playing
career is not necessarily going to carry the same cachet it once did. This, then, necessarily sets
up another burden for players of color attempting to transition to managerial roles. The “faces of
analytics,” meanwhile, continue to be white men like Billy Beane, Paul DePodesta, Daryl Morey
and Sam Hinkie. Indeed, in the NBA – where the number of black coaches has declined by 50
percent over the last three seasons – there is the sense that this homogeneity is no coincidence.
Investigating the disappearance of black coaches, Howard Beck writes, “Some coaches blame
teams' embrace of analytics—a theory that suggests black coaches are perceived as either less
intelligent or less willing to adapt.” Beck quotes USC professor Todd Boyd, who says, “I think
there's this perception, perhaps unconscious and perhaps unspoken, that a lot of black guys just
196

aren't smart enough to do the job. And when you throw something like analytics in the mix, it
adds to that even more.”
67
For microcasts, then, to solely position white men as analytics experts
– in total control of new technology and new metrics – is not just troubling in itself, but also has
the disconcerting effect of reinforcing the racial divides that continue to wrack the industry.
In multiple ways, then, datavisuality is a mode of broadcasting characterized by divide.
Not only does it reify the big data divides that have lurked around big data methodology, but it
also hints at a coming division of the sports television audience. However, as this conclusion has
concerned itself with speculation, it might be worth momentarily re-envisioning the future of
datavisuality in an attempt to hypothetically close these divides. As was briefly mentioned in the
previous section, big data remains a largely amorphous phenomenon. Although it is a term that
has “spread like kudzu,” it is also one that has been hard to concretize – a phenomenon that again
speaks to big data divides. As Andrejevic writes, “The workings of the algorithm are opaque.”
68

Algorithmic decision-making, he continues, is “largely invisible, inscrutable, and perhaps even
incomprehensible.”
69
And, as he argues, this is very much by design, for governments and
corporations have no interest in sharing their data or how they use that data. Instead, then, big
data is often referred to by its effects, including within media studies, where one might talk about
the ramifications of Hulu’s recommendation algorithms or Netflix’s use of viewer data to make
programming decisions. It remains unclear, though, how exactly these algorithms work and
what data sets exist behind the services’ slick user interfaces.
The opacity of big data, of course, has consequences. As Andrejevic notes in another
piece in which he turns to survey data to study big data divides, people are very much aware that
data about them is continually being collected and, moreover, are very much aware that this data
collection is being driven by commercial interests. What Andrejevic seizes on, though, is how
197

“powerless” people feel in the face of this data collection – a feeling linked to the invisibility and
inscrutability of data mining.
70
Here Andrejevic quotes one respondent who says, “We really
don’t know where things collected about us go—we don’t understand how they interact in such a
complex environment.”
71
Andrejevic rightly points out that such powerlessness is not the result
of consumers’ failures to educate themselves – they cannot know, after all, what exactly
corporations and governments will do with their data. However, the question lingers whether
education could still play a part in addressing how helpless people feel in the face of big data –
particularly if they were given a better sense of what “big data” means and how it operates. In
other words, might people feel less powerless if they had a better understanding of the general
outlines of data collection and data-driven decision-making? Or, to use the quote from
Andrejevic’s respondent, if they were aware of what the “complex environment” entailed?
This, then, brings us back to datavisuality. As has been mentioned above, sports
television represents the rare location within the media landscape where big data is discussed and
visualized. Sport television, then, offers a major opportunity – an opportunity, that is, to better
educate viewers about what a massive dataset looks like and how it is used. As Crawford et al.
argue, “Rather than invest in big data as an all-knowing prognosticator or a shortcut to ground
truth, we need to recognize and make plain its complexities and dimensionality as an emerging
theory of knowledge.”
72
Undoubtedly, sports television has the ability to do this – to outline the
complexities and complications of big data methodology. This, then, could offer viewers an
improved understanding of how big data might be used in the wider world beyond sports. But,
as was argued above, sports television has not seized this opportunity. Rather, it has reified big
data divides by offering a superficial version of big data. Viewers, for example, may be offered
information about a baseball player’s speed to a ball – thus imparting some knowledge about
198

tracking technology and how that technology can be used to evaluate players – but they are
offered relatively little insight into how this type of data is gathered and how it might be used by
teams in their decision-making processes. The question, then, is how to offer an experience that
goes beyond the superficial. The answer, to return to the previous chapter, might be found in
“context.” As ESPN executive Roger argues, context is vital to creating a fulfilling analytics
experience. He says:
We don't want to put graphics up on television tomorrow that just have a bunch squiggly lines and
dots and that kind of stuff, or shows that someone runs from home to first base at 19 miles per
hour. That tells us nothing. You need context around it. We want to tell you that if someone runs
to first base 19 miles an hour, that's the fastest or the slowest. We want some context around all of
those things.
73

Roger is right to argue for context, but we can also ask what context matters and how to add
context that offers viewers the same complexity that the industry so values. To not, then, just ask
if some player was the “fastest,” but to instead ask where that number came from, why teams
might collect it and what they could potentially do with it. Of course, such information is not
going to instantly close the big data divides articulated by Andrejevic and others, but it would
allow a peek – a rare moment of transparency – into a major industry’s use of big data
methodology. As big data continues to play an increasingly large role in everyday life, such
clarity – even if limited – could have immense value.

1
Jonah Bossewitch and Aram Sinnreich, “The End of Forgetting: Strategic Agency beyond the Panopticon,”
New Media & Society, July 23, 2012, 227.
2
Ibid., 228.
3
Ibid., 235.
4
Ibid., 230.
5
Lev Manovich, “Trending: The Promises and the Challenges of Big Social Data,” in Debates in the Digital
Humanities, ed. Matthew K. Gold (U of Minnesota Press, 2012), 470.
199

6
danah boyd and Kate Crawford, “Critical Questions for Big Data,” Information, Communication & Society 15,
no. 5 (June 1, 2012), 674.
7
Ibid., 674.
8
Ibid., 674.
9
Mark Andrejevic, Infoglut: How Too Much Information Is Changing the Way We Think and Know (Routledge,
2013), 34.
10
Mark Andrejevic, “Big Data, Big Questions: The Big Data Divide,” International Journal of Communication 8
(June 16, 2014), 1679.
11
Andrejevic, Infoglut, 30.
12
Andrejevic, “Big Data, Big Questions,” 1681.
13
Ibid., 1674.
14
ESPN employee “Nick,” interview by Branden Buehler, Phone, August 25, 2015.
15
Michael Lewis, Moneyball: The Art of Winning an Unfair Game (W. W. Norton & Company, 2003), 163.
16
Kate Crawford, Jessa Lingel, and Tero Karppi, “Our Metrics, Ourselves: A Hundred Years of Self-Tracking
from the Weight Scale to the Wrist Wearable Device,” European Journal of Cultural Studies 18, no. 4–5 (August
2015), 480.
17
Ibid., 493.
18
Tom Haberstroh and Pablo Torre, “New NBA Biometric Testing Is Less Michael Lewis, More George Orwell,”
ESPN The Magazine, October 27, 2014, http://espn.go.com/nba/story/_/id/11629773.
19
Crawford et al., “Our Metrics, Ourselves,” 490.
20
Haberstroh and Torre, “New NBA Biometric Testing.”
21
Andrew Hawkins, “Wearable Technology: Athlete Analytics (Presented by Zebra)” (MIT Sloan Sports
Analytics Conference, Boston Convention and Exhibition Center, Boston, MA, February 28, 2015).
22
Haberstroh and Torre, “New NBA Biometric Testing.”
23
Hawkins, “Wearable Technology.”
24
Kate Crawford, “When Fitbit Is the Expert Witness,” The Atlantic, November 19, 2014.
25
Crawford et al., “Our Metrics, Ourselves,” 492.
26
Ibid., 493.
27
Haberstroh and Torre, “New NBA Biometric Testing.”
28
Rick Maese, “Moneyball 2.0: Keeping Players Healthy,” The Washington Post, August 24, 2015.
29
Kirk Lacob, “Wearable Technology: Athlete Analytics (Presented by Zebra)” (MIT Sloan Sports Analytics
Conference, Boston Convention and Exhibition Center, Boston, MA, February 28, 2015).
30
Haberstroh and Torre, “New NBA Biometric Testing.”
31
Andrejevic, Infoglut, 149.
32
Johanna Drucker, “Humanities Approaches to Graphical Display,” Digital Humanities Quarterly 5, no. 1
(2011).
200

33
Ibid.
34
See also Lev Manovich, “What Is Visualization?” Visual Studies 26, no. 1 (2011): 36–49.
35
Drucker, “Humanities Approaches to Graphical Display.”
36
Lauren F. Klein, “The Image of Absence: Archival Silence, Data Visualization, and James Hemings,” American
Literature 85, no. 4 (December 1, 2013), 678.
37
Ibid., 678.
38
Bill Simmons, “Don’t Deny NBA Stat Geeks the Truth,” ESPN The Magazine, March 24, 2009,
http://sports.espn.go.com/espnmag/story?id=4011524.
39
Kyle Wagner, “Two Days At Sloan: How Sports Analytics Got Lost In The Fog,” Deadspin, March 7, 2014,
http://regressing.deadspin.com/two-days-at-sloan-how-sports-analytics-got-lost-in-the-1535046292.
40
boyd and Crawford, “Critical Questions for Big Data,” 663.
41
Ben Davis, “Technology Amplifies Success: How Analytics Is Changing the Game” (MIT Sloan Sports
Analytics Conference, Boston Convention and Exhibition Center, Boston, MA, February 27, 2015).
42
Priya Narasimhan, “If You Build It” (MIT Sloan Sports Analytics Conference, Boston Convention and
Exhibition Center, Boston, MA, February 27, 2015).
43
Chris Granger, “If You Build It” (MIT Sloan Sports Analytics Conference, Boston Convention and Exhibition
Center, Boston, MA, February 27, 2015).
44
Mark Andrejevic, “Watching Television Without Pity: The Productivity of Online Fans,” Television and New
Media 9, no. 1 (2008), 34.
45
Ibid., 28.
46
Ibid., 44.
47
Ibid., 33.
48
ESPN employee “Roger,” interview by Branden Buehler, Phone, September 4, 2015.
49
Ibid.
50
ESPN employee “Alan,” interview by Branden Buehler, Phone, September 3, 2015.
51
Roger, interview.
52
Alan, interview.
53
Victoria E. Johnson, “Everything New Is Old Again: Sport Television, Innovation, and Tradition for a Multi-
Platform Era,” in Beyond Prime Time: Television Programming in the Post-Network Era, ed. Amanda D Lotz
(New York: Routledge, 2009), 116.
54
Nick Couldry and Joseph Turow, “Advertising, Big Data, and the Clearance of the Public Realm: Marketers’
New Approaches to the Content Subsidy,” International Journal of Communication 8 (2014), 1711.
55
Ibid., 1722.
56
Roger, interview.
201

57
Victoria E. Johnson, “Historicizing TV Networking: Broadcasting, Cable, and the Case of ESPN,” in Media
Industries: History, Theory, and Method, ed. Jennifer Holt and Alisa Perren (Chichester, West Sussex; Malden,
MA: Wiley-Blackwell, 2009), 65.
58
ESPN employee “Clark,” interview by Branden Buehler, Phone, August 21, 2015.
59
“How Many More Homes Is ESPN in than Fox Sports 1 and NBC Sports Network?,” Sports TV Ratings,
accessed November 5, 2015, http://sportstvratings.com/how-many-more-homes-is-espn-in-than-fox-sports-
1-and-nbc-sports-network/1515/.
60
Lisa Parks, “Flexible Microcasting: Gender, Genderation, and Television-Internet Convergence,” in
Television after TV: Essays on a Medium in Transition, ed. Lynn Spigel and Jan Olsson (Duke University Press,
2004), 144.
61
Ibid., 142.
62
Ibid., 143.
63
Richard Lapchick and Leroy Robinson, “The 2015 Racial and Gender Report Card: National Football
League” (The Institute for Diversity and Ethics in Sport, September 10, 2015).
64
David Waldstein, “Success and Shortfalls in Effort to Diversify N.F.L. Coaching,” The New York Times,
January 20, 2015, http://www.nytimes.com/2015/01/21/sports/football/jets-hiring-of-todd-bowles-leaves-
nfl-far-short-of-goal-on-diversity.html.
65
Mark Maske, “NFL Extends Rooney Rule To Encourage Hiring of Minorities in Front Offices,” The
Washington Post, June 16, 2009, sec. Sports, http://www.washingtonpost.com/wp-
dyn/content/article/2009/06/15/AR2009061502806.html.
66
Richard Lapchick and Diego Salas, “The 2015 Racial and Gender Report Card: Major League Baseball” (The
Institute for Diversity and Ethics in Sport, April 15, 2015).
67
Howard Beck, “Where Are All the Black NBA Coaches?” Bleacher Report, accessed November 6, 2015,
http://bleacherreport.com/articles/2584463-where-are-all-the-black-nba-coaches-examining-a-sudden-
silent-disappearance.
68
Andrejevic, Infoglut, 283.
69
Ibid., 284.
70
Andrejevic, “Big Data, Big Questions,” 1684.
71
Ibid., 1685.
72
Kate Crawford, Kate Miltner, and Mary L. Gray, “Critiquing Big Data: Politics, Ethics, Epistemology,”
International Journal of Communication 8 (2014): 1670.
73
Roger, interview.

202

Conclusion

The two parts of this dissertation have each examined GM TV by highlighting particular
aspects of this phenomenon – a granular approach that has allowed this project to offer grounded
and detailed insights into sports TV’s recent, sweeping interest in management. The first
chapter, for example, solely focused on specific managerial lessons offered by GM TV,
particularly in relation to similar lessons provided by reality TV. The second and third chapters,
meanwhile, exclusively explored the increasingly large role of data within sports TV. What is
left to do in this conclusion, then, is to step back and attempt the messier task of grappling with
the GM TV phenomenon as a whole – asking what it means, in a broader sense, for sports TV to
have moved toward a managerial mindset.
As a way into this dauntingly expansive question, we might turn not toward television
scholarship, as the rest of this dissertation has largely done, but rather toward new media
scholarship. As was briefly mentioned in the introduction, the rising interest in management
across the sports media landscape has not gone completely unnoticed by critics and scholars –
particularly critics and scholars interested in how new media has reflected certain aspects of this
phenomenon. As will be detailed below, several scholars of sport have posited that managerial
digital sports texts are deeply imbricated in issues of control, as exemplified by quantitative
player rating systems that position athletes as arrays of numbers. This, the scholars suggest, is a
rather worrisome development. However, as this conclusion will argue, feminist videogame
scholars offer insights that allow for a potentially more optimistic reading of digital sports texts –
a reading that might also be applied to GM TV.

203

Digital Sports Texts
As stated in the introduction, there already exists a small collection of scholarship that
has taken note – whether directly or, more commonly, indirectly – of the recent managerial slant
of sports media. In seeking to make sense of GM TV as a whole, then, it is perhaps reasonable
to begin with this work and, in the process, provide a more detailed examination than what was
offered in the introduction. As previously mentioned, though, this scholarship has largely
excluded mentions of television. Rather, this scholarship has primarily focused on digital sports
texts – namely, sports videogames and fantasy sports sites. We might start, then, with a brief
introduction to the study of these digital texts.
A variety of videogame scholars have noted that sports videogames have thus far
received relatively little academic attention – a lack, they emphasize, that betrays both the
genre’s significant place in gaming history (e.g. Pong) and its continuing popularity with masses
of gamers across the globe.
1
Indeed, the most recent versions of the dominant franchises FIFA,
Madden, and NBA2K all made their way into the 2015 list of the ten best-selling games in the
US.
2
In the UK, meanwhile, Football Manager 2016 made for the 2
nd
bestselling PC game of
the year, while Football Manager 2015 came in right behind at number three.
3
Fantasy sports,
too, have received relatively scant scholarly attention, particularly of the critical variety. As
Andrew Baerg notes, “the minimal scholarly attention paid to fantasy” has generally concerned
itself with issues like the effects of fantasy sports on fans’ media usage and fans’ relations to
“real sport.”
4
Again, this lack of critical attention betrays the massive popularity of the subject at
hand. To repeat from the introduction, the Fantasy Sports Trade Association claims 56.8 million
Americans and Canadians partook in fantasy sports in 2015.
5

204

Although sports videogames and fantasy sports may not have received scholarly attention
in line with their combined cultural ubiquity, it would also be unfair to say that they have been
completely ignored by academics – particularly in recent years. While in 2006 David Leonard
was able to confidently proclaim that the study of sports videogames represented “a barren
wasteland of knowledge,” in 2014, Baerg re-visited that claim and suggested that although the
study of sports videogames continued to lag in comparison to other genres, it was also true that
“scholars have slowly begun to cultivate this wasteland.”
6
Similarly, two years later Baerg could
point to several scholars who have begun to pay critical heed to fantasy sports.
Of particular interest for this project, of course, is the sparse amount of scholarship that
has narrowed in on the managerial aspects of sports videogames and fantasy sports. Despite the
small size of this collection, it is nonetheless disparate, drawing on several different
methodologies and focused on a range of subjects. Significantly, though, this scholarship is
united by a shared interest in the issue of “control” – a shared interest that provides an entryway
into this work. For Baerg, who has examined both sports videogames and fantasy sports in a
number of pieces, issues of control largely spring out of his recurrent interest in the classification
of virtual bodies, particularly by way of quantitative classification systems. As Baerg notes, both
sports videogames specifically concerned with management simulation – as in Football Manager
– and those more ostensibly concerned with action simulation – as in FIFA and Madden – rely
heavily on player rating systems. That is to say, all of these games translate athletes’ abilities
into numeric ratings. In FIFA, for example, one player might be rated as 70 out of 100 on their
“dribbling” ability, while another might be rated as 85 out of 100. In Football Manager,
meanwhile, one player might be rated as 10 out of 20 on the “bravery” category, while another
might be rated as 5 out of 20. These ratings, then, become a major part of gameplay. If, to
205

continue the FIFA example, a virtual athlete has a poor “dribbling rating,” they will be more
likely to lose the ball over the course of a game.
As Baerg explains in several pieces, sports videogames’ reliance on ratings systems
comes loaded with ideological implications. Of particular note here is how ratings systems lay
down an inescapable grid of rationality over the messy reality of sporting life. Writing on
Football Manager 2010, for instance, Baerg comments that the game’s “persistent imperative to
feed the user numbers cloaks the idea that some things cannot be understood apart from
quantitative information.”
7
By assigning ratings to intangibles like the aforementioned “bravery”
category, Baerg continues, the game “transforms the subjective into the apparently objective via
enumeration.” The game, then, “becomes an exercise in … believing the notion that the
incalculable can always be rendered calculable.”
8
Similarly, writing on FIFA in another article,
Baerg argues that the text “offers gamers a sanitized, uncluttered version of real-world sport.”
He continues, “The numbers that make up player ratings establish a veneer of clinically pristine
calculation over the often messy, dirt-filled uncertainties of sport.”
9
Again, the argument is that
sports videogames position the incalculable as calculable. The world – and all of its
complexities – is rendered down to tidy ratings categories.
For Baerg, sports videogames’ emphasis on rendering the incalculable calculable is
wrapped up in issues of control. As he explains, the ability to render athletes as calculable is
inevitably a matter of power. On one level, this is power exercised by game developers, as they
are able to control both what constitutes an athlete and an athlete’s ability, as well as how users
might interact with virtual athletes. On another level, though, this is power exercised by users, as
“the quantification of the athletic body through the system positions gamers to exert power
through an instrumental rationality.”
10
In other words, numeric ratings systems transform
206

athletes into “resources to be supervised, assessed, and deployed in the service of the gamers'
interests.”
11
He continues, “As athletes are transformed into resources by the game and as
gamers necessarily treat them as resources through the affordances of the player-rating system,
any sense of athletic subjectivity is erased.”
12
In the case of FIFA, for instance, players “exist in
its rating system as data aggregations to be manipulated rather than as subjects with agency.”
13

While for Baerg, meditations on control eventually result from a broader examination of
classification systems in sports videogames, for Thomas Oates, considerations of control are part
and parcel of his broader concept of “vicarious management.” As Oates argues, digital football
texts, including videogames and fantasy sports websites, are “marked by a single distinctive
feature: the presentation of athletes as commodities to be consumed selectively and self-
consciously by sports fans.”
14
As this statement makes clear, this singular feature – “vicarious
management” – very much implies an exercise of power. Oates makes this even clearer through
his examples. The Madden franchise, for example, “offers up fantasies where the skills of a
tycoon merge with control over elite athletes.”
15
Fantasy football sites, meanwhile, also offer up
the “perspective of imagined control” over elite athletes, explicitly using the “contemporary
marketplace” as the predominant metaphor for that control.
16
While, as Oates admits, sports
coverage has long invited “fans to imagine elite athletes as property,” digital sports texts engage
“this tendency with unprecedented focus, depth, and deliberateness.”
17
Like Baerg, then, Oates
finds digital sports texts positioning athletes as resources for fans to control – a process that
necessarily erases athletes’ agency.
Raising similar points as Baerg and Oates, Garry Crawford also finds digital sports texts
to be imbricated in issues of control. Recalling Baerg, for instance, Crawford notes that games
like Football Manager “turn life into calculations, where choices … are goal oriented and
207

rationalized.”
18
Significantly, though, Crawford turns his attention squarely toward gamers – a
focus that aligns with Crawford’s previous work studying videogame audiences. According to
Crawford, the quantification at the heart of sports videogames such as Football Manager
provides gamers “a sense of control.” Drawing on his prior research, he elaborates that for many
gamers, “(the illusion of) control is a key part of the appeal of sports-themed videogames.”
19
As
he further explains, this sense of control is particularly important in light of the shifting
landscape of the modern sports world. That is to say, as sports leagues increasingly become the
terrain of big business, fans may feel more and more disconnected from the teams “that they
once felt to be theirs.”
20
Videogames, then, offer “the ability to give the gamer a sense of
control, which may be absent elsewhere in their lives.”
21
And, as he elaborates, this search for
control may speak to larger issues pertaining to life within the tumult of capitalism. Drawing on
Zygmunt Bauman, Crawford writes, “In an increasingly uncertain and liquid world, more and
more we seek a sense of control, of certainly of authorship, in a world where we increasingly
have none.”
22

Although each of these scholars approach “control” from slightly different angles, they
are all fairly clear in emphasizing just how problematic the “control” of digital sports texts can
be. Baerg, for instance, argues that sports videogames’ emphasis on quantification naturalizes
“broader neoliberal discourses of scientifically and actuarially-inflected risk management.”
23

Similarly, he posits that the notion of control offered by fantasy sports – one that is “directly
related to managing others” – “may have the effect of moving participants to perceive other
decisions that need to be made through this neoliberal, risk-oriented perspective.”
24
Ominously,
Baerg writes, “Fantasy sports participation may have much more far-reaching social and cultural
effects than we might imagine.”
25
Oates, meanwhile, links the concept of “vicarious
208

management” to what he terms “racial androcentrism” – “a system of authority rooted in White
supremacy and hegemonic masculinity, and which polices sexuality on those terms.”
26

Managerial texts like Madden, Oates argues, “serve to contain Black masculinity.”
27
He
elaborates, “By transforming the bodies of elite athletes into commodities that can be shaped and
molded toward the user’s ends, elite athletes are becoming the target of various corporeal
ceremonies of power, and become docile bodies.”
28
Again, then, the “control” offered by
managerial texts is seen as reinforcing unsettling ideologies.
Baerg and Oates, then, are largely troubled by the effects of digital sports texts.
Crawford, though, in contextualizing the “sense of control” offered by sport videogames within
the larger uncertainty of capitalist society, would appear to hint at the potentially redeeming
qualities of the “control” afforded by digital sports texts. Possibly feeling adrift in a complex
world, gamers have the option of turning to sports videogames (and, it might also be suggested,
fantasy sports) for a bit of direction. And, as Crawford would seem to imply, there could be real
pleasure in that sensation – of being able to reclaim a more intimate connection to sporting life
and, perhaps, society at large. However, Crawford, like Baerg and Oates, is skeptical about the
“control” offered to users by digital sports texts. For Crawford, though, this skepticism is
grounded in the shallowness of the “control.” As implied by the aforementioned quote regarding
“(the illusion of) control,” Crawford sees the “control” provided by digital sports texts to be just
that – an “illusion.” He elaborates, “Following Althusser, it is important to see any sense of
individual freed or control as ideology propagated (and often marketed and sold) by
capitalism.”
29
Continuing, he writes, “This is an elusive, and imagined, sense of control.”
30

Gamers, in other words, are not being offered anything truly meaningful. While a game like
209

Football Manager may allow users to fantasize an escape from the “control and alienation of the
everyday,” actual escape is impossible.
31

We see, then, scholars of sport taking a generally grim view of managerial digital sports
texts. According to the aforementioned scholars, the power relations figured by these texts are
highly problematic. Not only do these games naturalize a troublesome logic in which athletes
are figured as resources to be optimized, but they also serve as shallow fantasies that do little
more for gamers than provide them yet another way to feed into the big business of modern
sport. The question for this project, of course, is whether the same insights apply beyond digital
sports texts. That is to say, whether GM TV suggests similar power relations and, in turn,
whether this leads to similar worries.
Before jumping back to GM TV, though, it is worth lingering on digital sports texts for
slightly longer. Curiously, little of the scholarship surrounding sports videogames and fantasy
sports delves into what it looks like to play these digital sports texts. If it did, it might be hard to
avoid the resemblance between sports videogames, fantasy sports, and much of the work now
required by the “information economy”/”knowledge economy”/”post-industrial economy”/”post-
Fordism.” For example, fantasy sports websites are, in essence, glorified tables that very much
recall spreadsheet software programs like Microsoft Excel. To play, users manipulate table cells
as if they were preparing a budget for an upcoming work project. Football Manager takes this
resemblance even further. Users do not just manage spreadsheets, but also receive messages in a
window meant to recall email clients like Microsoft Outlook. It is not hard to imagine a worker
logging out of their work email, heading home, and then immediately loading up their “play”
email.
210

Suffice it to say, managerial digital sports texts blur the lines between work and play. As
such, digital sports texts speak to previous scholarship that has explored the complex relationship
between videogames and work. Nick Yee, for instance, has documented how the work
performed in videogames, particularly MMORPGs (Massively Multiplayer Online Role-Playing
Games) is “increasingly similar to actual work in business corporations.”
32
Players in World of
Warcraft guilds, for example, can accidentally find themselves in “tedious management roles”
that involve tasks like recruiting and interviewing new members, scheduling meetings, and
mediating disputes.
33
Looking across the gaming landscape, Yee summarizes, “Video games are
blurring the boundaries between work and play very rapidly.”
34
Other scholars have made
similar claims about MMORPGs. Scott Rettberg, for instance, argues that World of Warcraft
asks its players “to climb the corporate ladder, to lead projects, to achieve sales goals, to earn and
save, to work hard for better possessions, to play the markets, to win respect from their peers and
their customers, to direct and encourage and cajole their underlings to outperform.”
35
He, like
Yee, summarizes, “Though playing the game is itself a form of escapism from the demands of
life in the real world, it is somewhat paradoxically a kind of escapism into a second professional
life, a world of work.”
36
Lukacs et al., meanwhile, also turn their attention to World of Warcraft
and find that the “game structure establishes social organizations resembling Taylorist
management and control practices,” and furthermore, suggest that the game requires “emotion
management and emotional labor.”
37
They conclude, “Modern virtual realms are simultaneously
play and work environments: to make the distinction between the two is counterproductive.”
38

Of particular interest to this dissertation project, though, is not this work on MMORPGs,
but rather two pieces of feminist media scholarship that have investigated the work/play dynamic
of videogames by way of the casual game genre, which they note has proven to be especially
211

popular with women. The first of these pieces, by Shira Chess, argues that videogames be
treated as open texts inviting a multitude of player interpretations – an argument she then applies
to the Diner Dash franchise of games, which she suggests “are designed in a way that invites a
variety of textual analysis and broad (often contradictory) interpretations.”
39
This extends to the
issue of work/play, as Chess argues that the games establish a complicated relationship between
the two. One the one hand, for example, the game’s narrative suggests the need to escape from
the drudgery of office work, but on the other hand, the game figures that escape – the diner in
which the player dashes – as a place defined by its repetitive, menial tasks. As Chess writes,
there are risks to the way the game entangles work and play. For players, “playtime could
simply become work time” and in-game stresses might “re-enact stresses” that female players
encounter in their work lives.
40
However, Chess reminds us that interpretations are “based on
the personal experiences, beliefs, and negotiations of players,” and as such, can vary.
41
The
Diner Dash games, for example, call for emotional management, as gamers must work to please
their fictional customers. Chess posits, then, “Just as emotional labor takes a toll on many
women, so might emotional play.”
42
However, Chess adds, “This is not one-sided or fair to
assume that all women players might have a negative experience with this kind of emotional
labor/play.” She continues:
“In reality the Dash games offer players something that real world labor cannot: control. By
giving players the opportunity to win the game, to please customers, and to see rewards for care
giving, it has the potential of being satisfying in a way that might be meaningful to many women.
The individual and personal experiences each player has becomes a nuanced negotiation of their
own system of beliefs and helps to construct a game space that helps to retell their own personal
experiences (or, perhaps, even set them right). Where the Dash games illustrate problems, they
also create (at least temporary in-game) solutions.”
43

212

There are potentially very real benefits, then, to the way the game interlaces work and play. As
Chess writes, this entanglement might be draining for some players, but for others, it may help
them make sense of their lived experiences.
In an article that arrived not long after Chess’s, Audrey Anable also tackles the Diner
Dash franchise and, in the process, explores several of the same issues as Chess, including the
games’ complex relationship between work and play. Driving the piece is Anable’s argument
that casual games represent “affective systems” that “work on us and work us over in terms of
impinging on our feelings, our identities, and our everyday lives” – an argument she eventually
applies to the Diner Dash series.
44
These games, Anable explains, “speak to a longing for a
different, less fraught, relationship to labor.” Drawing on Lauren Berlant’s work on the “female
complaint” genre within mid-twentieth century film and literature, Anable argues that “time
management” games like Diner Dash put forward complaints about “the ways in work culture
and labor conditions in the 21
st
century seem to exacerbate gender inequality while at the same
time universalizing women’s precarious status as workers to massive segments of the population,
regardless of gender.” She continues, adding these games “create affective situations that call
into question the myths and failures of the digital workplace, the constantly increasing bleed of
work into our private lives, and the role of emotional labor.”
45
Thus, while Anable is careful to
point out that casual games are not necessarily “radical or even progressive media forms,” she,
like Chess, sees the potential of casual games to help players work through what it means to be
in the world.
46

While Chess and Anable are both interested in how casual games operate “as mediations
of ‘women’s work,’” it is also productive to apply their work more broadly and to extend their
insights toward digital sports texts.
47
As the aforementioned scholars of sport suggest, and as the
213

MMORPG scholarship would also imply, there is a strong argument to be made for how digital
sports texts train their players to serve as cogs within capitalist hierarchies. However, that
reading is perhaps too conspicuous to be further developed here. The game Football Manager,
after all, has the word “manager” in its title. Quite clearly, the game is going to embrace a
middle manager subject position. What is a bit more interesting, then, is to interrogate how
digital sports texts operate as mediations of “information economy” work.
Managerial digital sports texts, like the casual games that Chess and Anable critique,
figure a complex – and often contradictory – relationship between work and play. These games
are promoted, like most other videogames, as a form of escape. In this case, it is the fantasy of
being involved in the exclusive world of sports, of being able to take charge of one’s favorite
team. Despite this promise, though, the experience of playing the game can be a grind. Anable
asserts that the “rhythm and aesthetic” of time management games like Diner Dash recall Sianne
Ngai’s argument that post-Fordism emphasizes the “aesthetic category of zaniness,” which Ngai
defines as “the experience of an agent confronted by – and endangered by – too many things
coming at her at once.”
48
Managerial digital sports texts would appear to share this link. To play
Football Manager is to volunteer for an information deluge, as players are bombarded by the
aforementioned virtual emails. Meanwhile, virtual athletes grow disgruntled and virtual budgets
become strained. Similar concerns extend to games like FIFA and Madden, as well as fantasy
sports sites. To make a quick link to Yee’s work, serving as a fantasy baseball commissioner is
not too dissimilar from serving as a World of Warcraft guild leader. New members must be
scouted, events must be scheduled, emails must be exchanged, disputes must be settled, etc.
What to make of this zaniness? As Chess notes, it is possible that the stresses of virtual
worlds could serve to exacerbate stresses encountered elsewhere in one’s daily life. It is easy to
214

imagine, for instance, a retail worker spending their day anxiously juggling the personalities of
their co-workers and customers, then coming home to Football Manger and immediately fretting
about how to improve the morale of their grumpy virtual soccer players whilst also being
assailed by various other managerial tasks, such as hiring a new physical trainer or implementing
a new team formation. But this is not the only way one might arrive to managerial digital sports
texts. As mentioned above, Crawford suggests that one of the primary appeals of managerial
games is that they provide their players a sense of control. Crawford primarily treats this sense
of control as something like an ideological sleight of hand, while Baerg and Oates warn against
the wider ramifications of the control of virtual workers, but Chess and Anable remind us that
this sense of control can be incredibly meaningful for players. As Chess explains, control can be
elusive in the course of one’s daily life. In a game world, though, a player might be able to see
clear success. A project at work might deteriorate, but at home, a Madden franchise might
eventually find its way to a championship. Moreover, emotional labor in one’s professional life
(and one’s personal life) can go unacknowledged. In games like Football Manager, this labor is
made visible and, furthermore, players are clearly rewarded for attending to their virtual athletes’
emotional well-being. Much as Chess writes of Diner Dash, these in-game experiences have the
potential of being quite satisfying for players.
Re-visiting Anable’s argument that time management games “create affective situations
that call into question the myths and failures of the digital workplace, the constantly increasing
bleed of work into our private lives, and the role of emotional labor,” we can also suggest that
the zaniness of managerial digital sports texts similarly invites questions about the work of the
knowledge economy.
49
Again, this is not to say that these texts are meant to offer any sort of
radical commentary, but rather that they open up the possibility of criticism. The overwhelm and
215

precariousness engendered by a videogame like NBA 2K could, potentially, lead to questions
about the overwhelm and precariousness that accompanies post-Fordism. The taxing emotional
labor required by these texts could similarly lead to questions about the exhausting emotional
labor required by service economy work.
As Anable suggests, too, these potential lines of questioning would seem to go along with
a longing for something new – for “a different, less fraught, relationship to labor.”
50
On this
note, it would appear deeply significant that players of these digital sports texts – which, as
detailed above, are incredibly popular – so eagerly embrace the very same digital tools that they
may be using in their daily lives. Unlike the casual games that Chess and Anable analyze, which
figure the relationship between work and play by way of exaggerated, pixelated environments
that are “safe, colorful, and full of zany characters,” managerial digital sports texts figure the
relationship between work and play by way of mundane interfaces that, as mentioned, closely
resemble those of the ubiquitous Microsoft Office suite.
51
This resemblance would seem to
reinforce the idea that players may turn to these games not out of longing for a different form of
work, as perhaps one does when they play a game like Euro Truck Simulator (in which players
drive cargo across Europe) or Farm Simulator (in which players manage a farm), but rather a
different connection to their work. Again, the work of managerial sports texts may be fictional
work, but it can allow for sensations that may be fleeting in professional life. Shuffling numbers
in a spreadsheet, for instance, can effect significant change in the virtual world. Similarly,
responding to an email in a virtual world may lead to an immediate and tangible shift in a virtual
workers’ mood.
The work of Baerg, Crawford and Oates must be taken seriously. As they argue,
managerial digital sports texts circulate a troublesome ideology in which athletes are figured as
216

commodities to be quantified and controlled both by game designers and game players.
However, without calling these texts progressive, it is worthwhile to consider how these games
provide pleasure and, moreover, can potentially rouse “longing and complaint” within their
players. These texts, then, while worrisome, also come loaded with the potential for offering
both satisfaction and a gateway toward critique. For the purposes of this dissertation, though, the
larger question is whether this potentiality is limited to digital texts and their unique affordances.
That question in mind, the rest of the conclusion will consider whether this (hopeful) analysis of
managerial digital sports texts might be applied toward GM TV.

GM TV
The two parts of this dissertation – the first addressing GM TV and the second addressing
the related phenomenon of datavisuality – may seem to be a bit disconnected. Whereas the first
part situated sports TV against the backdrop of reality TV and, as such, drew heavily upon reality
TV scholarship, the second part largely grounded itself in both the concept of “televisuality” and
the critical study of big data. Moreover, whereas the first part relied on textual analysis, the
second part chiefly employed a media industries approach reliant upon methods like
ethnographic interviewing. The subjects of analysis differed, too. While the first part of the
project largely focused its analysis on studio programming that explicitly showcases the act of
management, as in Gruden’s QB Camp, the second part of the project mainly analyzed live
telecasts that instead suggest the work of management, as in live broadcasts featuring some of
the same advanced statistics that now drive front offices across the sports world. Despite these
differences, there are significant parallels to be made between the two parts of this project –
parallels that tie back to the new media work reviewed in the previous section.
217

Of course, the primary link between the first and second parts of this project is that they
both reflect the growing prominence of the GM figure within the sports landscape. As detailed
in the introduction, this is a development that has touched everything from films to podcasts to
videogames to, of course, TV. Again, to repeat from the introduction, this has meant an
increasing obsession within the sports TV landscape both with front office executives themselves
– most easily seen in programming that features current and former executives, as in ESPN’s
NFL draft coverage – and the work that they perform, whether that might mean scouting amateur
athletes, negotiating contracts with free agents, running complex statistical analyses, or a host of
other related tasks. Both the first and second parts of this dissertation represent explorations of
this development. Although the methodologies and exact subjects of analysis may have varied,
both parts spoke to a unified interest in sports TV’s move toward management.
Going slightly deeper, though, both parts of this project have been interested in what it
means to manage people and, in turn, what it means to make the work of management the subject
of sports TV. As such, the project has implicitly dealt with many of the issues that have
interested the new media scholars mentioned above. Management, of course, necessarily
involves power. Front office executives are, to put it in basic terms, in charge of athletes – they
supervise them and, ultimately, determine their professional fates. For management to play an
increasingly large role within the sports TV landscape, then, is to necessarily invoke how power
is represented and reified. Thus, both parts have tacitly been about control. In chapter one, that
interest in control manifested itself in discussions of how GM TV celebrates managerial figures
and, even more significantly, how it trains viewers to act like managers – or, in other words, how
to properly exercise control over others. In chapters two and three, meanwhile, that interest in
control largely played out in discussions of datavisuality divides. As detailed, datavisuality
218

grants viewers access to data about players. And, to restate a quote from Mark Andrejevic, “The
concentration of control over data is a form of power and control.”
52

Issues of control, then, have served as an undercurrent throughout this dissertation. We
might naturally wonder, then, how the aforementioned new media scholarship might speak to
this project and its concerns. In several places, there are obvious parallels. For instance, Baerg’s
argument that player ratings turn athletes into numbers and, in the process, strip them of their
agency clearly recalls the suggestion that datavisuality encourages viewers to evaluate players
based on advanced metrics and new tracking data. Meanwhile, Oates’s concept of “vicarious
management” deals, in general terms, with many of the issues at work throughout this project.
More interesting than these basic parallels, though, would be a larger scale comparison. As
detailed, scholars of sport have been largely pessimistic about the ways that digital sports texts
have figured control. Not only was it suggested that the vicarious control of athletes naturalizes
a neoliberal-tinged viewpoint in which other people are seen as mere resources to be optimized,
but it was also argued that that this sense of control was nothing but an illusion. There is much
to agree with here – in fact, similar claims have been made throughout this dissertation. The first
chapter, for instance, argues that GM TV trains viewer to navigate an evolving neoliberalism in
which the ideal of self-management transforms into the ideal of self-and-other-management.
The second and third chapters, meanwhile, warn against the ways in which datavisuality
reinforces big data mythology. Significantly, too, the third chapter suggests that the “control”
figured by datavisuality is, in a sense, an illusion. That is to say, while datavisuality places
viewers in vicarious control of athletes by granting viewers access to player data, this is rather
superficial access because teams and leagues have the ability to work with much greater amounts
of data. Furthermore, viewers also are in the position of having their own data mined as they
219

tune in to sports TV. Viewers, then, might gain a sense of control as a result of datavisuality, but
as Crawford might emphasize, this sense of control is merely “imagined.”
While these agreements are important, and signal a certain amount of shared skepticism
regarding sports media, it is also worth noting this project has consistently tried to steer away
from total pessimism. The first chapter, for example, suggested that there might be real value in
the lessons offered by managerial television – the lessons of GM TV included. The second and
third chapters, meanwhile, argued that datavisuality offers the potential to increase people’s big
data literacy – even if it has not yet taken advantage of that potential. Keeping with this
commitment to silver linings, we might not just look at GM TV in light of the previous work on
managerial digital sports texts, as in the scholarship of Baerg, Crawford and Oates, but also in
light of the work interrogating how videogames blur the lines of work and play, including the
work of Chess and Anable.
That GM TV is so defined by issues of “control” speaks to the fact that GM TV, like
managerial digital sports texts, involves a complex relationship between work and play. As
Bryan Curtis speculated in the introduction, part of the appeal of the GM role for so many sports
fans may lie in the fact that GMs do work that resembles what so many information economy
workers do as part of their daily lives – making phone calls, reviewing paperwork, organizing
information, etc. In highlighting the work of GMs, then, GM TV accordingly spotlights work
characteristic of the information economy. NFL and NBA trade deadline shows, for example,
revolve around the negotiations between various GMs – or, to put it other words, the results of
ongoing email exchanges and teleconferences. NFL and NBA draft coverage, meanwhile,
constantly emphasizes executives’ “draft boards” – that is to say, the tables they use to categorize
their player ratings. To watch GM TV, then, is to re-enter the world of emails, spreadsheets and
220

meetings. As in the case of managerial digital sports texts, GM TV blurs the lines between work
and play.
In assessing the role of “control” within GM TV, then, it is important to consider the
relationship between work and play, as well as what that might mean for viewers. As was
detailed in the previous section, managerial digital sports texts may not be inherently
progressive, but the way they figure the work/play dynamic offers the possibility for pleasure
and critique. TV, of course, is a very different medium. Whereas the mechanics of managerial
digital sports texts dutifully replicate the actions associated with information economy work –
clicking through spreadsheets, wading through emails, etc. – GM TV instead represents
information economy work. Nonetheless, GM TV would also seem to potentially signal
complaint as well as a longing for “a different, less fraught, relationship to labor.”
53

To begin, Chess suggests that time management games offer their players a potentially
affirming – and likely rare – sense of control. Above, it was argued that managerial digital
sports texts might provide something similar. Obviously, GM TV cannot offer anything quite so
direct. In a game, players can see their decisions effect immediate virtual change, whereas GM
TV is not likely to be directly responsive to viewers anytime soon. But GM TV does still offer a
sense of control. As mentioned above, and as detailed throughout this dissertation, GM TV
places viewers in managerial subject positions. In Gruden’s QB Camp, for instance, viewers
scrutinize prospects alongside the energetic Jon Gruden, while in NBA draft coverage, viewers
are effectively asked to consider whom they would draft if they were themselves front office
executives. Datavisuality, meanwhile, offers viewers information about athletes and,
significantly, also offers viewers certainty – that is to say, the confidence that big data is always
better data. Again, it can be suggested that this sense of control, while problematically figuring
221

athletes as quantifiable resources, might also be deeply meaningful to viewers. And again, too,
this might suggest a longing for a different relationship to work.
On top of the way GM TV offers the potential for pleasure is the way it may open up
questions regarding the “myths and failures” of the information economy.
54
To repeat, Anable
argues that the affective situations associated with time management games, namely zaniness,
could invite criticism of the ways in which exhaustion and precariousness have come to define
life in post-Fordism – an argument that the section above extended to managerial digital sports
texts. Once again, it must be pointed out that these digital texts are quite different from GM TV.
GM TV, which does not entail physical action (e.g. clicking, scrolling, sorting), is unlikely to
directly trigger a sensation like exhaustion. Yet GM TV still can invite critical inquiry. Like all
other TV programming, GM TV is open to a multitude of readings. By so transparently treating
athletes as commodities, GM TV might be particularly open to alternate readings – inducing
viewers, for instance, to question the emphasis on quantitative categorization within information
economy work or, as the chapter on datavisuality hoped, question the looming prospect of
biometric measurement in the workplace. Also worth considering, too, is what one might do
with the lessons of GM TV. Take, for instance, datavisuality. As was detailed in the chapters on
datavisuality, the drive towards sports analytics can largely be traced back to fan communities, as
it was fans who first sought to better understand the sport of baseball by experimenting with
statistical techniques – a process that carried with it the implicit understanding that statistical
measures are imperfect and require context. Implied in the chapters on datavisuality is that GM
TV, rather than stressing experimentation, has instead stressed certainty. GM TV, though, may
encourage viewers to dig deeper into the numbers proffered by sports TV and, perhaps, return to
fan communities. Viewers, then, may undertake their own experiments and, in the process,
222

return to the notion that data (even big data) is not objective truth – that all data, in other words,
is constructed. Such an understanding, of course, could be applied beyond sports.
In closing, then, it can be reiterated that the rise of GM TV is something to be wary of.
As other scholars of sport have warned, the “vicarious management” of athletes has, amongst
other things, the disquieting effects of figuring athletes as property to be controlled and the world
as calculable. What this conclusion has sought to do, though, is balance this picture. Managerial
sports texts, whether from the realm of new media or television, point to the complex
relationship between work and play. This means, then, that they offer unique pleasures and,
moreover, unique openings for critique. As sports media increasingly moves toward
management, it will continue to be important to understand all sides of this phenomenon.

1
See, for example, Mia Consalvo, Konstantin Mitgutsch, and Abe Stein, “Introduction: Sports Videogames.
Mapping the Field,” in Sports Videogames, ed. Mia Consalvo, Konstantin Mitgutsch, and Abe Stein (New York:
Routledge, 2013), 1–12; Ian Bogost, “What Are Sports Videogames?,” in Sports Videogames, ed. Mia Consalvo,
Konstantin Mitgutsch, and Abe Stein (New York: Routledge, 2013), 50–66; Andrew Baerg, “Sports,” in The
Routledge Companion to Video Game Studies, ed. Mark J. P. Wolf and Bernard Perron (Routledge, 2014), 267–
74; and Garry Crawford, “Is It in the Game? Reconsidering Play Spaces, Game Definitions, Theming, and Sports
Videogames,” Games and Culture 10, no. 6 (November 1, 2015): 571–92.
2
Jeff Grubb, “2015 NPD: The 10 Best-Selling Games of the Year,” VentureBeat, January 14, 2016,
http://venturebeat.com/2016/01/14/2015-npd-the-10-best-selling-games-of-the-year/.
3
Christopher Dring, “How Did the UK Games Market Perform in 2015?,” MCV UK, January 14, 2016,
http://www.mcvuk.com/news/read/the-year-that-was-2015-in-numbers/0161201.
4
Andrew Baerg, “Draft Day: Risk, Responsibility, and Fantasy Football,” in Fantasy Sports and the Changing
Sports Media Industry: Media, Players, and Society, ed. Nicholas David Bowman, John S. W. Spinda, and Jimmy
Sanderson (Lanham: Lexington Books, 2016), 100.
5
“Industry Demographic Analysis with FSTA” (STATS, n.d.).
6
Andrew Baerg, “Sports,” in The Routledge Companion to Video Game Studies, ed. Mark J. P. Wolf and Bernard
Perron (Routledge, 2014), 268.
7
Andrew Baerg, “Neoliberalism, Risk, and Uncertainty in the Video Game,” in Capital at the Brink: Overcoming
the Destructive Legacies of Neoliberalism, ed. Jeffrey R. Di Leo (Open Humanities Press, 2014),
http://hdl.handle.net/2027/spo.12832551.0001.001.
223

8
Ibid.
9
Andrew Baerg, “Classifying the Digital Athletic Body: Assessing the Implications of the Player-Attribute-
Rating System in Sports Video Games,” International Journal of Sport Communication, no. 4 (2011), 143.
10
Ibid., 144.
11
Ibid.
12
Ibid.
13
Ibid.
14
Thomas Oates, “New Media and the Repackaging of NFL Fandom,” Sociology of Sport Journal 26, no. 1
(2009), 31.
15
Ibid., 40.
16
Ibid.
17
Ibid., 32.
18
Garry Crawford, “Is It in the Game? Reconsidering Play Spaces, Game Definitions, Theming, and Sports
Videogames,” Games and Culture 10, no. 6 (November 1, 2015): 583.
19
Ibid., 584.
20
Ibid.
21
Ibid.
22
Ibid.
23
Baerg, “Neoliberalism, Risk, and Uncertainty in the Video Game.”
24
Baerg, “Draft Day,” 113.
25
Ibid., 114.
26
Oates, “New Media and the Repackaging of NFL Fandom,” 33.
27
Ibid., 44.
28
Ibid., 44.
29
Crawford, “Is It in the Game,” 585.
30
Ibid.
31
Ibid., 577.
32
Nick Yee, “The Labor of Fun: How Video Games Blur the Boundaries of Work and Play,” Games and Culture
1, no. 1 (January 1, 2006), 70.
33
Nick Yee, The Proteus Paradox: How Online Games and Virtual Worlds Change Us—And How They Don’t (New
Haven: Yale University Press, 2014), 74.
34
Yee, “The Labor of Fun,” 70.
35
Scott Rettberg, “Corporate Ideology in World of Warcraft,” in Digital Culture, Play, and Identity: A World of
Warcraft Reader, ed. Hilde Corneliussen and Jill Walker Rettberg (MIT Press, 2008), 20.
36
Ibid., 26.
224

37
Andras Lukacs, David G. Embrick, and Talmadge Wright, “The Managed Hearthstone: Labor and Emotional
Work in the Online Community of World of Warcraft,” in Facets of Virtual Environments: First International
Conference, FaVE 2009, Berlin, Germany, July 27-29, 2009, Revised Selected Papers, ed. Fritz Lehmann-Grube
and Jan Sablatnig (Springer Science & Business Media, 2010), 168.
38
Ibid., 175.
39
Shira Chess, “Going with the Flo,” Feminist Media Studies 12, no. 1 (March 1, 2012), 88.
40
Ibid., 91.
41
Ibid., 88.
42
Ibid., 96.
43
Ibid.
44
Aubrey Anable, “Casual Games, Time Management, and the Work of Affect,” Ada: A Journal of Gender, New
Media, and Technology, no. 2 (June 1, 2013), http://adanewmedia.org/2013/06/issue2-anable/.
45
Ibid.
46
Ibid.
47
Ibid.
48
Sianne Ngai, Our Aesthetic Categories: Zany, Cute, Interesting (Cambridge, Mass: Harvard University Press,
2012), 183, quoted in Anable, “Casual Games, Time Management, and the Work of Affect.”
49
Anable, “Casual Games, Time Management, and the Work of Affect.”
50
Ibid.
51
Ibid.
52
Mark Andrejevic, Infoglut: How Too Much Information Is Changing the Way We Think and Know (Routledge,
2013), 154.
53
Anable, “Casual Games, Time Management, and the Work of Affect.”
54
Ibid.
225

Bibliography

“2009 | MIT Sloan Sports Analytics Conference.” Accessed November 5, 2015.
http://www.sloansportsconference.com/?page_id=2661.
“2015 | MIT Sloan Sports Analytics Conference.” Accessed November 5, 2015.
http://www.sloansportsconference.com/?page_id=16783.
“2015 MIT Sloan Sports Analytics Conference: List of Attendees,” n.d.
“2015 Silicon Valley Index.” Joint Venture Silicon Valley, February 3, 2015.
http://www.jointventure.org/images/stories/pdf/index2015.pdf.
“Airbnb ‘Threatens’ Host over Poor Star Ratings.” GlobalHosting Forum & Blogs. Accessed
March 28, 2016. http://globalhosting.freeforums.net/thread/46/airbnb-threatens-host-over-
ratings.
Alamar, Benjamin, and Vijay Mehrotra. “Beyond ‘Moneyball’:The Rapidly Evolving World of
Sports Analytics.” Analytics, October 2011.
Anable, Aubrey. “Casual Games, Time Management, and the Work of Affect.” Ada: A Journal
of Gender, New Media, and Technology, no. 2 (June 1, 2013).
http://adanewmedia.org/2013/06/issue2-anable/.
Andrejevic, Mark. “Big Data, Big Questions: The Big Data Divide.” International Journal of
Communication 8, no. 0 (June 16, 2014): 17.
———. Infoglut: How Too Much Information Is Changing the Way We Think and Know.
Routledge, 2013.
———. “Watching Television Without Pity: The Productivity of Online Fans.” Television and
New Media 9, no. 1 (2008): 24–46.
Baerg, Andrew. “Classifying the Digital Athletic Body: Assessing the Implications of the Player-
Attribute-Rating System in Sports Video Games.” International Journal of Sport
Communication, no. 4 (2011): 133–47.
———. “Draft Day: Risk, Responsibility, and Fantasy Football.” In Fantasy Sports and the
Changing Sports Media Industry: Media, Players, and Society, edited by Nicholas David
Bowman, John S. W. Spinda, and Jimmy Sanderson, 99–120. Lanham: Lexington Books,
2016.

226

———. “Neoliberalism, Risk, and Uncertainty in the Video Game.” In Capital at the Brink:
Overcoming the Destructive Legacies of Neoliberalism, edited by Jeffrey R. Di Leo. Open
Humanities Press, 2014. http://hdl.handle.net/2027/spo.12832551.0001.001.
———. “Sports.” In The Routledge Companion to Video Game Studies, edited by Mark J. P.
Wolf and Bernard Perron, 267–74. Routledge, 2014.
Barbrook, Richard, and Andy Cameron. “The Californian Ideology.” Science as Culture 6, no. 1
(January 1, 1996): 44–72.
Bellafante, Ginia. “Kitchen Nightmares - TV - Review.” The New York Times, September 19,
2007. http://www.nytimes.com/2007/09/19/arts/television/19kitc.html.
Bennett, Laura. “Beware: Captitalist Sharks in New Reality Show.” The Boston Globe, August 8,
2009.
http://archive.boston.com/ae/tv/articles/2009/08/08/beware_captitalist_sharks_in_new_realit
y_show/.
Bilton, Nick. “Tinder, the Fast-Growing Dating App, Taps an Age-Old Truth.” The New York
Times, October 29, 2014. http://www.nytimes.com/2014/10/30/fashion/tinder-the-fast-
growing-dating-app-taps-an-age-old-truth.html.
Blinder, Alan S. After the Music Stopped: The Financial Crisis, the Response, and the Work
Ahead. Penguin, 2013.
Bogost, Ian. “What Are Sports Videogames?” In Sports Videogames, edited by Mia Consalvo,
Konstantin Mitgutsch, and Abe Stein, 50–66. New York: Routledge, 2013.
Bonesteel, Matt. “ESPN Layoffs Will Gut the Network’s Production Staff.” The Washington
Post, October 22, 2015. https://www.washingtonpost.com/news/early-
lead/wp/2015/10/22/espn-layoffs-will-gut-the-networks-production-staff/.
Boren, Cindy. “Charles Barkley Really, REALLY Hates NBA Analytics.” The Washington Post,
February 11, 2015. https://www.washingtonpost.com/news/early-
lead/wp/2015/02/11/charles-barkley-really-really-hates-nba-analytics/.
Bossewitch, Jonah, and Aram Sinnreich. “The End of Forgetting: Strategic Agency beyond the
Panopticon.” New Media & Society, July 23, 2012, 224–42.
Boulton, Clint, and Michael Hickins. “Billy Beane Expects Big Things from MLB’s Big Data
Play,” March 5, 2014. http://blogs.wsj.com/cio/2014/03/05/billy-beane-expects-big-things-
from-mlbs-big-data-play/.
227

boyd, danah, and Kate Crawford. “Critical Questions for Big Data.” Information,
Communication & Society 15, no. 5 (June 1, 2012): 662–79.
Boyle, Raymond, and Lisa W. Kelly. “The Celebrity Entrepreneur on Television: Profile,
Politics and Power.” Celebrity Studies 1, no. 3 (October 27, 2010): 334–50.
———. The Television Entrepreneurs: Social Change and Public Understanding of Business.
Surrey: Ashgate, 2012.
Boyle, Raymond, and Maggie Magor. “A Nation of Entrepreneurs? Television, Social Change
and the Rise of the Entrepreneur.” International Journal of Media & Cultural Politics 4, no.
2 (June 13, 2008): 125–44.
Brown, Lane. “So How Bad Was Soderbergh’s Revised Moneyball Script?” Vulture, June 23,
2009. http://www.vulture.com/2009/06/so_how_bad_was_soderberghs_rev.html.
Bucholtz, Andrew. “ESPN down 1.5 Million Subscribers as Cable Sports Networks Keep
Shedding Viewers.” Awful Announcing, June 1, 2016.
http://awfulannouncing.com/2016/espn-down-1-5-million-subscribers-as-cable-sports-
networks-keep-shedding-viewers.html.
Buhr, Sarah. “Airbnb Hosted Nearly 17 Million Guests This Summer.” TechCrunch. Accessed
March 28, 2016. http://social.techcrunch.com/2015/09/07/airbnb-hosted-nearly-17-million-
guests-this-summer/.
Burke, Timothy. “Which Sports Network Wastes The Most Space With Its Scores Ticker?”
Deadspin, May 29, 2014. http://regressing.deadspin.com/which-sports-network-wastes-the-
most-space-with-its-sco-1582821805.
Caldwell, John Thornton. Televisuality: Style, Crisis, and Authority in American Television.
Rutgers University Press, 1995.
Chau, Danny. “Trusting the Process: How the Rest of the League Out-Hinkie’d the Sixers at the
NBA Draft.” Grantland, June 26, 2015. http://grantland.com/the-triangle/trusting-the-
process-how-the-rest-of-the-league-out-hinkied-the-sixers-at-the-nba-draft/.
Chess, Shira. “Going with the Flo.” Feminist Media Studies 12, no. 1 (March 1, 2012): 83–99.
doi:10.1080/14680777.2011.558350.
Cohen, Jason. “Virtual Graphics and Analysis.” presented at the SVG Sports Graphics Forum,
HBO Theater, New York, February 25, 2015.
228

Conda, Cesar, and Derek Khanna. “Uber for Welfare.” Politico, January 27, 2016.
http://www.politico.com/agenda/story/2016/1/uber-welfare-sharing-gig-economy-000031.
Consalvo, Mia, Konstantin Mitgutsch, and Abe Stein. “Introduction: Sports Videogames.
Mapping the Field.” In Sports Videogames, edited by Mia Consalvo, Konstantin Mitgutsch,
and Abe Stein, 1–12. New York: Routledge, 2013.
Cook, James. “Uber’s Internal Charts Show How Its Driver-Rating System Actually Works.”
Business Insider, February 11, 2015. http://www.businessinsider.com/leaked-charts-show-
how-ubers-driver-rating-system-works-2015-2.
Costa, Brandon. “MLS Cup 2014: ESPN To Roll Out Live Player Tracking Graphics.” Sports
Video Group, December 5, 2014. http://sportsvideo.org/main/blog/2014/12/05/mls-cup-
2014-espn-to-roll-out-live-player-tracking-graphics/.
Couldry, Nick. “Reality TV, or the Secret Theater of Neoliberalism.” Review of Education,
Pedagogy, and Cultural Studies 30, no. 1 (2008): 3–13.
Couldry, Nick, and Jo Littler. “The Work of Work: Reality TV and the Negotiation of Neoliberal
Labour in The Apprentice.” In Rethinking Documentary: New Perspectives and Practices,
edited by Thomas Austin and Wilma de Jong, 258–67. Maidenhead: Open University Press,
2008.
———. “Work, Power and Performance: Analysing the ‘Reality’ Game of The Apprentice.”
Cultural Sociology 5, no. 2 (July 1, 2011): 263–79.
Couldry, Nick, and Joseph Turow. “Advertising, Big Data, and the Clearance of the Public
Realm: Marketers’ New Approaches to the Content Subsidy.” International Journal of
Communication 8 (2014): 1710–26.
Crawford, Garry. “Is It in the Game? Reconsidering Play Spaces, Game Definitions, Theming,
and Sports Videogames.” Games and Culture 10, no. 6 (November 1, 2015): 571–92.
———. “The Cult of Champ Man: The Culture and Pleasures of Championship
Manager/Football Manager Gamers.” Information, Communication & Society 9, no. 4
(August 1, 2006): 496–514.
Crawford, Kate. “When Fitbit Is the Expert Witness.” The Atlantic, November 19, 2014.
http://www.theatlantic.com/technology/archive/2014/11/when-fitbit-is-the-expert-
witness/382936/.
229

Crawford, Kate, Jessa Lingel, and Tero Karppi. “Our Metrics, Ourselves: A Hundred Years of
Self-Tracking from the Weight Scale to the Wrist Wearable Device.” European Journal of
Cultural Studies 18, no. 4–5 (August 1, 2015): 479–96.
Crawford, Kate, Kate Miltner, and Mary L. Gray. “Critiquing Big Data: Politics, Ethics,
Epistemology.” International Journal of Communication 8 (2014): 1663–72.
Curtis, Bryan. “The Trade Rumor Era.” Grantland, July 7, 2014.
http://grantland.com/features/nba-trade-rumors-espn-yahoo-new-york-post-lebron-james-
jason-kidd-offseason-trade-signing/.
———. Interview by Branden Buehler. Phone, May 9, 2016.
Dachman, Jason. “ESPN Commits to K-Zone Live on Every Pitch for MLB Coverage.”
Sportvision, April 3, 2015. https://www.sportvision.com/news/espn-commits-k-zone-live-
every-pitch-mlb-coverage-0.
“Data Feeds Overview.” Opta, n.d. http://www.optasports.com/services/media/data-feeds/opta-
data-feeds-overview.aspx.
Davis, Ben. “Technology Amplifies Success: How Analytics Is Changing the Game.” presented
at the MIT Sloan Sports Analytics Conference, Boston Convention and Exhibition Center,
Boston, MA, February 27, 2015.
Davis, Noah. “Today’s Soccer Gear: Shorts, Cleats, Shinguards and a GPS Unit.” The New York
Times, July 27, 2015. http://www.nytimes.com/2015/07/28/sports/trying-to-track-every-
move-they-make.html.
Dring, Christopher. “How Did the UK Games Market Perform in 2015?” MCV UK, January 14,
2016. http://www.mcvuk.com/news/read/the-year-that-was-2015-in-numbers/0161201.
drive. “Fox Sports Insert & Scoring System.” |drive|. Accessed November 5, 2015.
http://drivestudio.com/projects/fox-sports-2014-insert-system1/.
Drucker, Johanna. “Humanities Approaches to Graphical Display.” Digital Humanities
Quarterly 5, no. 1 (2011).
Dujarier, Marie-Anne. “The Three Sociological Types of Consumer Work.” Journal of
Consumer Culture, April 8, 2014.
Dzieza, Josh. “The Rating Game: How Uber and Its Peers Turned Us into Horrible Bosses.” The
Verge, October 28, 2015. http://www.theverge.com/2015/10/28/9625968/rating-system-on-
demand-economy-uber-olive-garden.
230

Edelman, Benjamin G., and Michael Luca. “Digital Discrimination: The Case of Airbnb.com.”
Harvard Business School NOM Unit Working Paper 14–054 (January 10, 2014).
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2377353.
Ellis, John. “Interstitials: How the ‘Bits in Between’ Define the Programmes.” In Ephemeral
Media: Transitory Screen Culture from Television to YouTube, edited by Paul Grainge, 89–
106. Palgrave Macmillan, 2011.
Erman, Michael. “Five Years after Lehman, Americans Still Angry at Wall Street: Reuters/Ipsos
Poll.” Reuters, September 15, 2013. http://www.reuters.com/article/us-wallstreet-crisis-
idUSBRE98E06Q20130915.
ESPN employee “Alan.” Interview by Branden Buehler. Phone, September 3, 2015.
ESPN employee “Clark.” Interview by Branden Buehler. Phone, August 21, 2015.
ESPN employee “Greg.” Interview by Branden Buehler. Phone, September 8, 2015.
ESPN employee “Nick.” Interview by Branden Buehler. Phone, August 25, 2015.
ESPN employee “Roger.” Interview by Branden Buehler. Phone, September 4, 2015.
“ESPN Promotional Material,” 1981.
Feintzeig, Rachel. “Tech Workers Get Choosy About Changing Jobs.” Wall Street Journal,
March 17, 2016, sec. Tech. http://www.wsj.com/articles/tech-workers-get-choosy-about-
changing-jobs-1458086282?mod=LS1.
Ferenstein, Greg. “An Attempt To Measure What Silicon Valley Really Thinks About Politics
And The World.” Medium, November 6, 2015. https://medium.com/the-ferenstein-
wire/what-silicon-valley-really-thinks-about-politics-an-attempted-measurement-
d37ed96a9251#.b4atrcuhj.
Flannery, Paul. “Help Needed, Stat! Is the Sloan Sports Analytics Conference Too Big?” Boston
Magazine, March 2014. http://www.bostonmagazine.com/news/article/2014/02/25/mit-sloan-
sports-analytics-conference/.
Flint, Joe, and Shalini Ramachandran. “ESPN Tightens Its Belt as Pressure on It Mounts.” Wall
Street Journal, July 10, 2015, sec. Business. http://www.wsj.com/articles/espn-tightens-its-
belt-as-pressure-on-it-mounts-1436485852.
“FOX Sports Loads Golf Bag With High-Tech Arsenal For Network’s First U.S. Open.” Press
Release. FOX Sports Press Pass, June 10, 2015.
231

http://www.foxsports.com/presspass/latestnews/2015/06/10/fox-sports-loads-golf-bag-with-
high-tech-arsenal-for-network-s-f.
Freeman, Michael. ESPN: The Uncensored History. Rowman & Littlefield, 2001.
Fuller, Linda, and Vicki Smith. “Consumers’ Reports: Management by Customers in a Changing
Economy.” Work, Employment & Society 5, no. 1 (March 1, 1991): 1–16.
Futterman, Matthew. “Baseball After Moneyball.” Wall Street Journal, September 30, 2011, sec.
Sports. http://www.wsj.com/articles/SB10001424053111903791504576584691683234216.
Gabriel, Yiannis, Marek Korczynski, and Kerstin Rieder. “Organizations and Their Consumers:
Bridging Work and Consumption.” Organization 22, no. 5 (September 1, 2015): 629–43.
Gabriel, Yiannis, and Tim Lang. The Unmanageable Consumer. SAGE, 2015.
“Giants-Nationals, PGA Greenbrier, SportsCenter, World Cup Tonight Lead Cable Sports for
Sunday, July 5, 2015.” Sports TV Ratings. Accessed October 12, 2015.
http://sportstvratings.com/giants-nationals-pga-greenbrier-sportscenter-world-cup-tonight-
lead-cable-sports-for-sunday-july-5-2015/3056/.
Goldsberry, Kirk. “Analytics Illustrated.” presented at the MIT Sloan Sports Analytics
Conference, Boston Convention and Exhibition Center, Boston, MA, February 28, 2015.
Gong, Shanna, Jenny Barin, Amanda Nguyen, and Todd Schulte. “Recommendations from the
Tech Community: Entrepreneur Pathways Brief.” FWD.us. Accessed March 28, 2016.
http://www.fwd.us/pathways.
Gordon, Aaron. “Lame Puck.” Slate, January 28, 2014.
http://www.slate.com/articles/sports/sports_nut/2014/01/foxtrax_glowing_puck_was_it_the_
worst_blunder_in_tv_sports_history_or_was.html.
Granger, Chris. “If You Build It.” presented at the MIT Sloan Sports Analytics Conference,
Boston Convention and Exhibition Center, Boston, MA, February 27, 2015.
Grubb, Jeff. “2015 NPD: The 10 Best-Selling Games of the Year.” VentureBeat, January 14,
2016. http://venturebeat.com/2016/01/14/2015-npd-the-10-best-selling-games-of-the-year/.
Haberstroh, Tom, and Pablo Torre. “New NBA Biometric Testing Is Less Michael Lewis, More
George Orwell.” ESPN The Magazine, October 27, 2014.
http://espn.go.com/nba/story/_/id/11629773.
Hall, Gerard. “Virtual Graphics and Analysis.” presented at the SVG Sports Graphics Forum,
HBO Theater, New York, February 25, 2015.
232

Harper, Zach. “Eight Teams Will Use a Movement Tracking Device in 2013-14 Season.”
CBSSports.com, August 19, 2013. http://www.cbssports.com/nba/eye-on-
basketball/23223295/eight-teams-will-use-a-movement-tracking-device-in-201314-season.
Hart, Steve. “Broadcast Graphics: Technology Update.” presented at the SVG Sports Graphics
Forum, HBO Theater, New York, February 25, 2015.
Havrilesky, Heather. “‘Undercover Boss’: Capitalist Fairy Tale.” Salon, February 3, 2010.
http://www.salon.com/2010/02/04/cbs_undercover_boss_fairy_tale/.
Hawkins, Andrew. “Wearable Technology: Athlete Analytics (Presented by Zebra).” presented
at the MIT Sloan Sports Analytics Conference, Boston Convention and Exhibition Center,
Boston, MA, February 28, 2015.
Hay, James. “Unaided Virtues: The (Neo-)Liberalization of the Domestic Sphere.” Television &
New Media 1, no. 1 (February 2000): 53–73.
Hearn, Alison. “Insecure: Narratives and Economies of the Branded Self in Transformation
Television.” Continuum 22, no. 4 (2008): 495–504.
Hiestand, Michael. “Dedicated Staff Keeps Close Watch on ESPN’s Bottom Line.” USA Today,
March 6, 2008.
Hochman, Benjamin. “Former Denver Nuggets Stat Man Dean Oliver Is Headed to ESPN to
Help with NBA and Other Sports Stats.” The Denver Post: Nuggets Ink, March 3, 2011.
http://blogs.denverpost.com/nuggets/2011/03/03/espn-just-got-a-whole-lot-smarter-with-ex-
nugget/3256/.
Hofmann, Rich. “Blackjack and Battier: Liberty Ballers Talks with Sam Hinkie.” Liberty
Ballers. Accessed November 5, 2015.
http://www.libertyballers.com/2013/10/31/5046996/76ers-sixers-gm-sam-hinkie-interview-
nba-draft.
Holmes, Baxter. “New Age of NBA Analytics: Advantage or Overload?” Boston Globe, March
30, 2014.
“How Many More Homes Is ESPN in than Fox Sports 1 and NBC Sports Network?” Sports TV
Ratings. Accessed November 5, 2015. http://sportstvratings.com/how-many-more-homes-is-
espn-in-than-fox-sports-1-and-nbc-sports-network/1515/.
233

Hutchins, Brett. “Tales of the Digital Sublime Tracing the Relationship between Big Data and
Professional Sport.” Convergence: The International Journal of Research into New Media
Technologies, May 21, 2015.
“Industry Demographic Analysis with FSTA.” STATS, n.d.
Inzerillo, Joe, and Dirk Van Dall. “Case Study: MLB Advanced Media’s Player Tracking &
Visualization System.” presented at the SVG Sports Graphics Forum, HBO Theater, New
York, February 25, 2015.
Isaac, Mike, and Leslie Picker. “Uber Valuation Put at $62.5 Billion After a New Investment
Round.” The New York Times, December 3, 2015.
http://www.nytimes.com/2015/12/04/business/dealbook/uber-nears-investment-at-a-62-5-
billion-valuation.html.
Jackson, Cheryl V. “HappyOrNot Lets Customers Give Smiley or Frowny Feedback on the
Spot.” Chicago Tribune, May 26, 2015. http://www.chicagotribune.com/bluesky/originals/ct-
happyornot-bsi-20150526-story.html.
Jakob, Mike. “Virtual Graphics and Analysis.” presented at the SVG Sports Graphics Forum,
HBO Theater, New York, February 25, 2015.
John, Burton St. “The Top Executive on Undercover Boss The Embodied Corporate Persona and
the Valorization of Self-Government.” Journal of Communication Inquiry 39, no. 3 (July 1,
2015): 273–91.
Johnson, Victoria E. “Everything New Is Old Again: Sport Television, Innovation, and Tradition
for a Multi-Platform Era.” In Beyond Prime Time: Television Programming in the Post-
Network Era, edited by Amanda D Lotz, 114–37. New York: Routledge, 2009.
———. “Historicizing TV Networking: Broadcasting, Cable, and the Case of ESPN.” In Media
Industries: History, Theory, and Method, edited by Jennifer Holt and Alisa Perren.
Chichester, West Sussex; Malden, MA: Wiley-Blackwell, 2009.
Jorba, David. “Broadcast Graphics: Technology Update.” presented at the SVG Sports Graphics
Forum, HBO Theater, New York, February 25, 2015.
Katz, Lawrence F. “The Rise of Alternative Work Arrangements & the ‘Gig’ Economy.” March
14, 2016. https://www.scribd.com/doc/306279776/Katz-and-Krueger-Alt-Work-Deck.
234

King, Bill. “Feel the Power — Feel the NFL Draft.” SportsBusiness Journal. Accessed May 31,
2016. http://www.sportsbusinessdaily.com/Journal/Issues/2000/04/20000424/No-Topic-
Name/Feel-The-Power-151-Feel-The-NFL-Draft.aspx.
Kjellin, Soren. “Broadcast Graphics: Technology Update.” presented at the SVG Sports Graphics
Forum, HBO Theater, New York, February 25, 2015.
Klein, Lauren F. “The Image of Absence: Archival Silence, Data Visualization, and James
Hemings.” American Literature 85, no. 4 (December 1, 2013): 661–88.
Konrad, Alex. “The Australian Tech That’s Improving The World’s Best Athletes.” Forbes, May
27, 2013. http://www.forbes.com/sites/alexkonrad/2013/05/08/aussie-tech-catapult-gps/.
Kosoff, Maya. “Silicon Valley Shaken as 19 Start-Ups See Their Valuations Slashed.” Vanity
Fair, February 29, 2016. http://www.vanityfair.com/news/2016/02/silicon-valley-shaken-as-
19-start-ups-see-their-valuations-slashed.
Lacob, Kirk. “Wearable Technology: Athlete Analytics (Presented by Zebra).” presented at the
MIT Sloan Sports Analytics Conference, Boston Convention and Exhibition Center, Boston,
MA, February 28, 2015.
Lapchick, Richard, and Leroy Robinson. “The 2015 Racial and Gender Report Card: National
Football League.” The Institute for Diversity and Ethics in Sport, September 10, 2015.
Lapchick, Richard, and Diego Salas. “The 2015 Racial and Gender Report Card: Major League
Baseball.” The Institute for Diversity and Ethics in Sport, April 15, 2015.
“Leading Global Social Networks 2016 | Statistic.” Statista. Accessed March 28, 2016.
http://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-
users/.
Lemann, Nicholas. “The Network Man.” The New Yorker, October 12, 2015.
http://www.newyorker.com/magazine/2015/10/12/the-network-man.
Lennard, Natasha. “Poll: We Still Hate Wall Street.” Salon. Accessed March 28, 2016.
http://www.salon.com/2013/09/15/poll_we_still_hate_wall_street/.
Levy, Ari. “Alphabet Tops Apple as Most Valuable Firm.” CNBC, February 2, 2016.
http://www.cnbc.com/2016/02/01/google-passes-apple-as-most-valuable-company.html.
Lewis, Michael. Moneyball: The Art of Winning an Unfair Game. W. W. Norton & Company,
2003.
235

———. “The No-Stats All-Star.” The New York Times, February 13, 2009.
http://www.nytimes.com/2009/02/15/magazine/15Battier-t.html.
London, Jay. “Data-Driven: MIT Infuses the Rise of Sports Analytics.” MIT News, February 26,
2014. http://news.mit.edu/2014/data-driven-mit-infuses-the-rise-of-sports-analytics.
Lukacs, Andras, David G. Embrick, and Talmadge Wright. “The Managed Hearthstone: Labor
and Emotional Work in the Online Community of World of Warcraft.” In Facets of Virtual
Environments: First International Conference, FaVE 2009, Berlin, Germany, July 27-29,
2009, Revised Selected Papers, edited by Fritz Lehmann-Grube and Jan Sablatnig, 165–77.
Springer Science & Business Media, 2010.
Maese, Rick. “Moneyball 2.0: Keeping Players Healthy.” The Washington Post, August 24,
2015. https://www.washingtonpost.com/sports/moneyball-20-keeping-players-
healthy/2015/08/24/5011ac54-48e6-11e5-9f53-d1e3ddfd0cda_story.html.
Magder, Ted. “Television 2.0: The Business of American Television in Transition.” In Reality
TV: Remaking Television Culture, edited by Susan Murray and Laurie Ouellette, 141–64.
NYU Press, 2009.
Magness, Judy. “The Profit’s Marcus Lemonis: A Miracle Worker.” The Suit Magazine, July 15,
2014. http://www.thesuitmagazine.com/top-stories/22356-the-profits-marcus-lemonis-a-
miracle-worker.html.
Manovich, Lev. “Trending: The Promises and the Challenges of Big Social Data.” In Debates in
the Digital Humanities, edited by Matthew K. Gold, 460–76. U of Minnesota Press, 2012.
———. “What Is Visualization?” Visual Studies 26, no. 1 (2011): 36–49.
Maske, Mark. “NFL Extends Rooney Rule To Encourage Hiring of Minorities in Front Offices.”
The Washington Post, June 16, 2009, sec. Sports. http://www.washingtonpost.com/wp-
dyn/content/article/2009/06/15/AR2009061502806.html.
Mayer, Jane. “New Koch.” The New Yorker, January 25, 2016.
McClintock, Pamela. “Box Office Report: ‘Moneyball’ Wins Friday, But ‘Lion King,’ ‘Dolphin
Tale’ Still Contenders.” The Hollywood Reporter, September 24, 2011.
http://www.hollywoodreporter.com/news/box-office-moneyball-dolphin-tale-239865.
McIntyre, Jason. “Layoffs Are Coming to ESPN.” The Big Lead, September 21, 2015.
http://thebiglead.com/2015/09/21/layoffs-are-coming-to-espn/.
236

McManus, Tim. “Inside Voices: Deeper Into the Sports Science.” Philadelphia Magazine,
December 5, 2013. http://www.phillymag.com/birds247/2013/12/05/inside-voices-2/.
McMurria, John. “Desperate Citizens and Good Samaritans Neoliberalism and Makeover Reality
TV.” Television & New Media 9, no. 4 (July 1, 2008): 305–32.
doi:10.1177/1527476408315115.
Melfi, David. “Broadcast Graphics: Technology Update.” presented at the SVG Sports Graphics
Forum, HBO Theater, New York, February 25, 2015.
Miller, James Andrew. “John Walsh: His Own Unique Self.” SportsBusiness Journal, May 11,
2015.
Miller, Toby. “Foucault, Marx, Neoliberalism: Unveiling Undercover Boss.” In Foucault Now,
edited by James Faubion, 188–202. John Wiley & Sons, 2014.
“Moneyball (2011) - Box Office.” Box Office Mojo. Accessed May 31, 2016.
http://www.boxofficemojo.com/movies/?id=moneyball.htm.
Morris, Jeremy Wade. “Curation by Code: Infomediaries and the Data Mining of Taste.”
European Journal of Cultural Studies 18, no. 4–5 (August 1, 2015): 446–63.
Murray, Noel. “No Money for Moneyball.” A.V. Club, June 22, 2009.
http://www.avclub.com/article/no-money-for-emmoneyballem-29476.
Narasimhan, Priya. “If You Build It.” presented at the MIT Sloan Sports Analytics Conference,
Boston Convention and Exhibition Center, Boston, MA, February 27, 2015.
“NBC Sports Brand Update.” Troika. Accessed November 5, 2015. http://www.troika.tv/nbc-
sports-identity-case-study/.
Newswires, Dow Jones. “Dow Jones to Sell Remaining Stake In SportsTicker Enterprises to
ESPN.” Wall Street Journal, June 16, 2000, sec. Marketplace.
http://www.wsj.com/articles/SB96117234941783381.
Neyer, Rob. “The Power of JABO.” FOX Sports. Accessed October 12, 2015.
http://www.foxsports.com/mlb/just-a-bit-outside/baseball-joe/blog/the-power-of-jabo-
100114.
“NFL Combine Analytics / NFL Media.” Reality Check Systems. Accessed November 5, 2015.
http://www.realitychecksystems.com/portfolio/6024/.
Ngai, Sianne. Our Aesthetic Categories: Zany, Cute, Interesting. Cambridge, Mass: Harvard
University Press, 2012.
237

Nolan, Hamilton. “Undercover Boss: Now Simply Evil.” Gawker, March 22, 2010.
http://gawker.com/5499057/undercover-boss-now-simply-evil.
Nwulu, Mac. “For MLS Cup, ESPN Will Deploy an Optical Player Tracking System.” ESPN
Front Row. Accessed November 5, 2015. http://www.espnfrontrow.com/2014/12/for-mls-
cup-espn-will-deploy-an-optical-player-tracking-system/.
Oates, Thomas. “New Media and the Repackaging of NFL Fandom.” Sociology of Sport Journal
26, no. 1 (2009): 31–49.
Osterweil, Willie. “The Rise of the Sports Management Movie.” Al Jazeera America, April 27,
2014. http://america.aljazeera.com/opinions/2014/4/sports-
managementmoviemilliondollararmmoneyball.html.
Ouellette, Laurie. “Take Responsibility for Yourself: Judge Judy and the Neoliberal Citizen.” In
Reality TV: Remaking Television Culture, edited by Susan Murray and Laurie Ouellette,
223 ‐242. NYU Press, 2004.
Ouellette, Laurie, and James Hay. Better Living through Reality TV: Television and Post-Welfare
Citizenship. Wiley, 2008.
Ourand, John. “Does Media Rights Bubble Have a Leak?” SportsBusiness Journal, May 2, 2016.
http://www.sportsbusinessdaily.com/Journal/Issues/2016/05/02/In-Depth/Media-rights.aspx.
Packer, George. “Change the World.” The New Yorker, May 27, 2013.
http://www.newyorker.com/magazine/2013/05/27/change-the-world.
Pallotta, Frank. “Disney’s Bob Iger: ESPN Could Be Sold Directly to Consumers.” CNNMoney,
July 27, 2015. http://money.cnn.com/2015/07/27/media/disney-bob-iger-espn-direct-to-
consumer/index.html.
Parks, Lisa. “Flexible Microcasting: Gender, Genderation, and Television-Internet
Convergence.” In Television after TV: Essays on a Medium in Transition, edited by Lynn
Spigel and Jan Olsson, 133–56. Duke University Press, 2004.
“PA Sport Acquires SportsTicker.” PR Newswire. Accessed November 5, 2015.
http://www.prnewswire.com/news-releases/pa-sport-acquires-sportsticker-69975047.html.
Peebles, Maurice. “The Man Behind the NBA’s Revolutionary Player Tracking Technology
Reveals What Really Matters In The NBA Finals.” Complex, June 5, 2014.
http://www.complex.com/pop-culture/2014/06/sportvu.
238

“Player Tracking: TRACAB Player Tracking System.” ChyronHego, n.d.
http://chyronhego.com/sports-data/player-tracking.
Pollack, Neal. “The Cult of the General Manager.” Slate, August 29, 2005.
http://www.slate.com/articles/sports/sports_nut/2005/08/the_cult_of_the_general_manager.ht
ml.
Puschmann, Cornelius, and Jean Burgess. “Metaphors of Big Data.” International Journal of
Communication 8, no. 0 (2014): 19.
Rao, Leena. “PayPal’s Payments App Venmo Has Its Biggest Month Ever - Fortune.” Fortune,
February 16, 2016. http://fortune.com/2016/02/16/venmo-billion/.
Raphael, Chad. “The Political Economic Origins of Reali-TV.” In Reality TV: Remaking
Television Culture, edited by Susan Murray and Laurie Ouellette, 123–40. NYU Press, 2009.
Reischel, Rob. 100 Things Packers Fans Should Know & Do Before They Die. Triumph Books,
2013.
Reisinger, Don. “iPhones in Use in the US Rise to 94M, New Study Suggests.” CNET. Accessed
March 28, 2016. http://www.cnet.com/news/nearly-100m-iphones-in-use-in-the-us-new-
study-shows/.
Rettberg, Scott. “Corporate Ideology in World of Warcraft.” In Digital Culture, Play, and
Identity: A World of Warcraft Reader, edited by Hilde Corneliussen and Jill Walker Rettberg,
19–38. MIT Press, 2008.
Rieder, Kerstin, and G. Gunter Voss. “The Working Customer – an Emerging New Type of
Consumer.” Psychology of Everyday Activity 5, no. 2 (2010): 2–10.
Robertsson, Tomas. “Sports Graphics - What’s to Come.” Deltatre, February 27, 2015.
http://www.deltatre.com/2015/02/sports-graphics-whats-to-come/.
———. “Virtual Graphics and Analysis.” presented at the SVG Sports Graphics Forum, HBO
Theater, New York, February 25, 2015.
Roose, Kevin. Young Money: Inside the Hidden World of Wall Street’s Post-Crash Recruits.
Grand Central Publishing, 2014.
Rosenblat, Alex. “Uber’s Pax: Hidden Bias in Rating Systems.” In Algorithms at Work, 2015.
Rosenblat, Alex, and Luke Stark. “Uber’s Drivers: Information Asymmetries and Control in
Dynamic Work.” SSRN Scholarly Paper. Rochester, NY: Social Science Research Network,
October 15, 2015. http://papers.ssrn.com/abstract=2686227.
239

Rose, Nikolas. “Governing ‘Advanced’ Liberal Democracies.” In Foucault And Political
Reason: Liberalism, Neo-Liberalism And The Rationalities Of Government, edited by
Andrew Barry, Thomas Osborne, and Nikolas Rose, 37–64. Routledge, 1996.
Russakoff, Dale. The Prize: Who’s in Charge of America’s Schools? Houghton Mifflin Harcourt,
2015.
Sandomir, Richard. “ESPN Plans To Purchase Sports Wire.” The New York Times, November 8,
1994, sec. Business. http://www.nytimes.com/1994/11/08/business/the-media-business-espn-
plans-to-purchase-sports-wire.html.
———. “The Innovation That Grew and Grew.” The New York Times, June 12, 2014.
http://www.nytimes.com/2014/06/13/sports/the-tv-score-box-that-grew-and-grew.html.
Scholz, Trebor. “Platform Cooperativism vs. the Sharing Economy.” Medium, December 5,
2014. https://medium.com/@trebors/platform-cooperativism-vs-the-sharing-economy-
2ea737f1b5ad#.qm1xfeho3.
Schwarz, Alan. The Numbers Game: Baseball’s Lifelong Fascination with Statistics. St. Martin’s
Griffin, 2005.
Seifert, Kevin. “GPS Technology Could Be NFL Game-Changer.” ESPN.com NFL Nation,
March 26, 2014. http://espn.go.com/nfl/story/_/page/hotread140326.
Shontell, Alyson. “LEAKED: Internal Uber Deck Reveals Staggering Revenue And Growth
Metrics.” Business Insider. Accessed March 28, 2016. http://www.businessinsider.com/uber-
revenue-rides-drivers-and-fares-2014-11.
Shorthouse, Jordan. “Approaches in Graphics and Show-Open Design.” presented at the SVG
Sports Graphics Forum, HBO Theater, New York, February 25, 2015.
Simmons, Bill. “Don’t Deny NBA Stat Geeks the Truth.” ESPN The Magazine, March 24, 2009.
http://sports.espn.go.com/espnmag/story?id=4011524.
Slaton, Zach. “Why The Sloan Conference Is The Super Bowl Of Sports Analytics.” Forbes,
February 20, 2013. http://www.forbes.com/sites/zachslaton/2013/02/20/why-the-sloan-
conference-is-the-super-bowl-of-sports-analytics/.
Slee, Tom. What’s Yours Is Mine: Against the Sharing Economy. OR Books, 2016.
Sousa, Mario. “Broadcast Graphics: Technology Update.” presented at the SVG Sports Graphics
Forum, HBO Theater, New York, February 25, 2015.
Sports talk show producer “Tyler.” Interview by Branden Buehler. Phone, May 9, 2016.
240

Squadron, Bill. “The Bonus: The Story behind Football’s Innovative Yellow First down Line.”
SI.com, July 18, 2013. http://www.si.com/nfl/2013/07/18/nfl-birth-yellow-line.
———. “Virtual Graphics and Analysis.” presented at the SVG Sports Graphics Forum, HBO
Theater, New York, February 25, 2015.
Standing, Guy. The Precariat: The New Dangerous Class. A&C Black, 2011.
Steinmetz, Katy. “Exclusive: See How Big the Gig Economy Really Is.” Time, January 6, 2016.
http://time.com/4169532/sharing-economy-poll/.
Stoneberg, Allie. “ESPN’s Draft Academy Series Returns April 14.” ESPN MediaZone, March
26, 2015. http://espnmediazone.com/us/press-releases/2015/03/espns-draft-academy-series-
returns-april-14/.
Stuever, Hank. “TV Preview of ‘Undercover Boss’ on CBS.” The Washington Post, February 7,
2010, sec. Arts & Living. http://www.washingtonpost.com/wp-
dyn/content/article/2010/02/04/AR2010020405103.html.
Sunnergren, Tom. “Philadelphia’s Victory Lab.” ESPN.com, December 3, 2013.
http://espn.go.com/blog/truehoop/post/_/id/64381.
“‘The Customer Is Not No. 1’: Marcus Lemonis.” CNBC, June 24, 2015.
http://www.cnbc.com/2015/06/24/the-customer-is-not-no-1-marcus-lemonis.html.
Thomas, Matt. “What I Learned About Donald Trump From Binge-Watching The Apprentice.”
Fusion, January 28, 2016. http://fusion.net/story/261299/donald-trump-2016-the-apprentice/.
Torre, Pablo. “The 76ers’ Plan to Win (yes, Really).” ESPN The Magazine, March 2, 2015.
http://espn.go.com/nba/story/_/id/12318808.
“Troika | Time Warner Cable SportsNet/Deportes Launch.” Accessed November 5, 2015.
http://www.troika.tv/time-warner-cable-sportsnet-deportes-brand-identity/.
“Undercover Boss: Season 1.” Metacritic. Accessed April 15, 2016.
http://www.metacritic.com/tv/undercover-boss/critic-reviews.
Vance, Ashlee. “Elon Musk’s Space Dream Almost Killed Tesla.” Bloomberg Business, May 14,
2015. http://www.bloomberg.com/graphics/2015-elon-musk-spacex/.
VanDerWerff, Todd. “Undercover Boss.” A.V. Club, February 7, 2010.
http://www.avclub.com/tvclub/iundercover-bossi-37961.
241

Wagner, Kyle. “Two Days At Sloan: How Sports Analytics Got Lost In The Fog.” Deadspin,
March 7, 2014. http://regressing.deadspin.com/two-days-at-sloan-how-sports-analytics-got-
lost-in-the-1535046292.
Waldstein, David. “Success and Shortfalls in Effort to Diversify N.F.L. Coaching.” The New
York Times, January 20, 2015. http://www.nytimes.com/2015/01/21/sports/football/jets-
hiring-of-todd-bowles-leaves-nfl-far-short-of-goal-on-diversity.html.
Wang, Selina. “Hedge Funds Pumped Up Silicon Valley. Now They’re Pulling Out.” Bloomberg
Business, March 24, 2016. http://www.bloomberg.com/news/articles/2016-03-24/hedge-
funds-pull-back-in-silicon-valley-as-ipo-market-atrophies.
Weber, Brenda R. Makeover TV: Selfhood, Citizenship, and Celebrity. Duke University Press,
2009.
Weinberg, Neil. “Can Sabermetrics Thrive on Television Broadcasts?” Beyond the Box Score.
Accessed October 12, 2015.
http://www.beyondtheboxscore.com/2014/10/13/6967385/sabermetrics-television-
broadcasts-stats-jabo.
Wells, Georgia. “Stephen Colbert Gets Last Laugh With Tech Execs.” The Wall Street Journal,
September 28, 2015. http://www.bloomberg.com/graphics/2015-elon-musk-spacex/.
Wenner, Lawrence A. “Media, Sports, and Society: The Research Agenda.” In Media, Sports,
and Society, edited by Lawrence A. Wenner, 13–48. Newbury Park, Calif: SAGE
Publications, Inc, 1989.
Willhite, Lindsey. “Baseball Prospectus More than a Diamond in the Rough.” Daily Herald,
June 16, 2009.
Yee, Nick. “The Labor of Fun: How Video Games Blur the Boundaries of Work and Play.”
Games and Culture 1, no. 1 (January 1, 2006): 68–71. doi:10.1177/1555412005281819.
———. The Proteus Paradox: How Online Games and Virtual Worlds Change Us—And How
They Don’t. New Haven: Yale University Press, 2014.
Yoder, Matt. “Exploding Rights Fees Putting the Squeeze on Sports Networks.” Awful
Announcing, July 6, 2015. http://awfulannouncing.com/2015/exploding-rights-fees-putting-
the-squeeze-on-sports-networks.html.
“Ziosk - Industry Leading Tabletop Ordering, Entertainment and Payment Solutions.” Accessed
March 28, 2016. http://www.ziosk.com/.

Abstract (if available)

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

Whose quality is it? Transnational TV flows and power in the global TV market

PDF

#Holocaust: rethinking the relationship between spaces of memory and places of commemoration in the digital age

PDF

The virtual big sister: television and technology in girls' media

PDF

More lives to live?: archiving and repurposing the daytime soap opera

PDF

¡Que naco! Border cinema and Mexican migrant audiences

PDF

Nationalisms in the era of global quality TV: how SVODs main/stream the local

PDF

Revenge of the fanboy: convergence culture and the politics of incorporation

PDF

Producing lesbianism: television, niche marketing, and sexuality in the 21st century

PDF

Color inside the lines: race and representation in the formula dance film

PDF

Virtual documentary: the virtual real and its rhetorical legitimations

PDF

Well-being domesticities: mediating 21st-century femininity through physical, mental, and emotional lifestyles

PDF

The world, abjected: contemporary comedy across media

PDF

Father, son, and the holy dollar: rebuilding the American Dream in post recessionary reality television and mommy blogging

PDF

Video camera technology in the digital age: Industry standards and the culture of videography

PDF

Women showrunners: authorship, identity and representation in US television

PDF

Viral selves: Cellphones, selfies and the self-fashioning subject in contemporary India

PDF

I can't afford the luxury of just being an actress: the politicized work of African American actresses on television

PDF

Unsettled media: documenting refugees and Europe's shifting borders along the Balkan Route

PDF

Hollywood vault: the business of film libraries, 1915-1960

PDF

Channeling Shirley MacLaine: stardom, travel, politics, and beyond

Asset Metadata

Creator Buehler, Branden (author)

Core Title GM TV: sports television and the managerial turn

School School of Cinematic Arts

Degree Doctor of Philosophy

Degree Program Cinematic Arts (Critical Studies)

Publication Date 07/26/2018

Defense Date 06/07/2016

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag datavisuality,GM TV,OAI-PMH Harvest,sports media,sports television,television studies

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor McPherson, Tara (committee chair), Imre, Aniko (committee member), Johnson, Victoria E. (committee member), Kuhn, Virginia (committee member), Seiter, Ellen (committee member)

Creator Email bwbuehle@usc.edu,bwbuehler@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-286077

Unique identifier UC11281499

Identifier etd-BuehlerBra-4672.pdf (filename),usctheses-c40-286077 (legacy record id)

Legacy Identifier etd-BuehlerBra-4672.pdf

Dmrecord 286077

Document Type Dissertation

Format application/pdf (imt)

Rights Buehler, Branden

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA