Big Data, Sport, and the Digital Divide

JSSXXX10.1177/0193723516673409Journal of Sport and Social IssuesBaerg 673409research-article2016

Article

Journal of Sport and Social Issues 2017, Vol. 41(1) 3–20 Big Data, Sport, and the © The Author(s) 2016 Reprints and permissions: Digital Divide: Theorizing sagepub.com/journalsPermissions.nav DOI: 10.1177/0193723516673409 How Athletes Might journals.sagepub.com/home/jss Respond to Big Data Monitoring

Andrew Baerg1

Abstract This article considers the relationship between Big Data and the athlete. Where Beer and Hutchins have focused on Big Data and sport, this article concentrates on the athlete’s potential response to Big Data monitoring. Drawing on the work of Andrejevic, and Kennedy and Moss, the project speaks to the Big Data–athlete relation through the theoretical framework of the digital divide. It describes Big Data and its relation to the digital divide before tracing out how athletes might respond to Big Data monitoring by presenting concerns about privacy and/or embracing a quantified self. Considering these responses provides a starting point for further work on how athletes should treat Big Data and its implications for sport.

Keywords Big Data, analytics, digital divide, sport, NBA

“I’ve always believed analytics were crap. They’re just some crap that some people who are really smart made up to try to get in the game ‘cause they had no talent” (Charles Barkley, quoted in Curtis, 2015, para.1). Analytics, and its extension into Big Data, have become one of the foremost developments in 21st-century sport. Even as old school athletes like Charles Barkley appear dismissive of their value, many sports organizational decisions increasingly flow from Big Data and the voluminous amounts of quantitative information of which it is comprised. However, in spite of the seeming

1University of Houston-Victoria, TX, USA Corresponding Author: Andrew Baerg, University of Houston-Victoria, 3007 N.Ben Wilson, TX 77901, USA. Email: [email protected] 4 Journal of Sport and Social Issues 41(1) preeminence and increasing predominance of this form of knowledge at the organizational level, the athletes themselves appear to be on the outside looking in. Without the same kinds of tools and access to this information, athletes appear to be subject to a new manifestation of the digital divide. This essay takes up the issue of the digital divide and Big Data with respect to the athlete. The following section establishes the relationship between the digital divide and Big Data by examining both concepts, and then considering the connections between them. The article then moves on to a discussion of how analytics and Big Data bring an accompanying digital divide into sport. The essay then considers two ways in which athletes might respond to this divide by resisting certain forms of data collection on the grounds of privacy and/or by embracing data collection via a quantified self.

The Digital Divide Digital inequalities have long been of interest to media studies and critical cultural studies communication scholars. This notion of digital inequality, what has come to be understood as the digital divide, was initially raised in a report on Internet access dis- parities in the United States two decades ago. Those with the opportunity and capacity to work with and in the digital have been understood to operate with an inherent advantage over those without these digital affordances (National Telecommunications and Information Administration, 1995). This form of inequality is expressed in differ- ences across access, consumption, and production, and with respect to skill and effi- cacy. Digital inequalities also traverse other forms of inequality to further division between the haves and have-nots. The digital divide has been discussed in relation to the conventional critical categories of race (Monroe, 2004), class (Navas-Sabater, 2002), and gender (Cooper, 2003). The digital divide has also been discussed as a global issue separating developed from developing nations (Warschauer, 2003) even as some have argued that the problem will be resolved without formal public policy intervention (Compaine, 2001). No matter the dimension of the divide, digital inequality has the potential to shape the life chances of a variety of people across culture (Robinson et al., 2015). Older forms of the digital divide may still be found around the world; however, some scholars have recently turned their attention to the relationship between Big Data and its relation to a new expression of the digital divide. Before considering the Big Data–digital divide relationship, it is helpful to consider how Big Data might be defined. boyd and Crawford (2012) define Big Data at the intersection of three phenomena: technology, analysis, and mythology. First, in terms of technology, Big Data aggregate, harness, and connect large data sets through the mechanisms of maximal computer power and the algorithms that provide these machines their instructions. Advanced forms of computing and the increased capacity to store information digitally make Big Data possible (see also Andrejevic, 2014; Elmqvist & Irani, 2013; Floridi, 2012). To be sure, the act of collecting data and the processes for data collection are old. However, what is new about contemporary Big Data concerns the digitization of these processes and the amount of data that can be Baerg 5 produced and collected as a consequence (Bollier, 2010; Callebaut, 2012; Mahrt & Scharkow, 2013). Also new is the capacity for real-time monitoring of objects being tracked in ways not imaginable in previous historical contexts. A further extension of this novelty, especially with Big Data, concerns the fact that the data collected through real-time monitoring can also be correlated with data in other databases. Data are not collected and merely placed in individual silos but are linked to other databases in Big Data networks (Ruppert, 2012). Big Data simply cannot exist and function without the technologies that make the collection and processing of these reams of information possible. Second, at the level of analysis, Big Data enable “economic, social, technical, and legal claims” (boyd & Crawford, 2012, p. 663) to be made via the locating of patterns within large sets of data. As data are collected and correlated with previously aggregated data, algorithms parse these pieces for potential connections. Big Data then allow for “deep and dynamic analysis of massive, heterogeneous and multiscale data anytime, anywhere” (Elmqvist & Irani, 2013, p. 86). These analytically based connections then serve as the ground for claims about matters that would otherwise remain imperceptible. Big Data subsequently enable new perspectives on the world that can alter what decisions are made and how they are made. This level also speaks to the issue of techniques used to make these new claims (Wigan & Clarke, 2013). Third, the final cog in boyd and Crawford’s (2012) definition relates to mythology. Big Data have become attached to the notion that sets of large data can produce previously inaccessible knowledge—knowledge that is presented as truthful, objective, and accurate. In being represented this way, this Big Data knowledge also becomes associated with an elevated intelligence. To fail to draw on Big Data and buy into its atten- dant mythology is to be derided as partial and subjective in one’s decision making or to be simply left behind (boyd & Crawford, 2012). Hutchins (2016) theorizes that this commitment to a utopian mythology with respect to Big Data sits within a longer historical trajectory associated with the technological sublime and its contemporary expression in the digital sublime. Many scholars have spoken to Big Data’s expression of a new kind of digital divide (e.g., Andrejevic, 2014; boyd & Crawford, 2012; Kennedy & Moss, 2015; Manovich, 2011). In the arena of Big Data, this divide separates those with access to the means of data collection from those who do not. It also separates those with access to proprietary data from those who do not. The digital divide privileges the insider and/or those with the capital to pay for access. The divide makes disputing claims made on the basis of proprietary data very difficult to challenge, if not impossible to confront altogether. One cannot directly question that which one cannot access or that of which one is ignorant. Andrejevic (2013) speaks to the consequences of this divide for those on the wrong side of the Big Data tracks. Instead of being able to make decisions with access to information that could theoretically benefit them, those without the ability or means to dig into data that have been collected could find themselves left with the epistemological poverty of “gut instinct, affective response, and ‘thin slicing’ (making a snap decision based on a tiny fraction of the evidence)” (p. 17). Those without access to Big 6 Journal of Sport and Social Issues 41(1)

Data could find themselves at an economic and structural disadvantage on one hand and an epistemological disadvantage on the other. At a problematic extreme,

the resulting information landscape is one in which those with access to the database can derive practical, if probabilistic (“post-comprehension”), knowledge about how best to influence populations while members of these populations are left with an outmoded set of critical tools that . . . have little purchase on the forms of knowledge turned back upon them by database-driven apparatuses of influence. (p. 154)

Along similar lines, Kennedy and Moss (2015) orient the Big Data digital divide question around access to methods of data collection. They assert that the tools gathering the public’s data are rarely, if ever, known by said public. The consequence of this meth- odological ignorance yields decreasing levels of privacy, greater levels of surveillance and social sorting (sorting that yields discrimination), and a shift in “how publics come to be represented and so understood” (p. 2). They suggest that publics subject to this data collection should move from being known publics to knowing publics as a way to democratize the asymmetrical nature of data power. Kennedy and Moss call for greater forms of public reflexivity with respect to this data collection, even as this reflexivity and potential contestation of data collection methods may prove incredibly difficult. With this greater reflexivity, the potential for public agency in engaging Big Data increases. This article takes its cue from Andrejevic (2013) and Kennedy and Moss (2015) and their discussion of responding to Big Data. It takes up their work by reflecting on responses to the Big Data–digital divide within the culture of sport. Over the past few years, Big Data have become increasingly attached to sport. At a 2014 panel on Big Data and sports analytics held at the Massachusetts Institute of Technology’s Sloan Sports Conference, panelists from ESPN, analytics technology firm Zebra Technologies, Ticketmaster, and a law firm specializing in intellectual property were still unable to truly declare whether the work they were doing counted as engaging in Big Data. However, at the 2015 Sloan Conference, the question appeared to have been settled. The conference featured a baseball panel titled “Beating the Shift: Baseball Analytics in the Age of Big Data.” This panel simply assumed that Big Data were imbricated in sport, at least in baseball, such that discussing whether its presence could be validated was simply unnecessary. The panelists did not even consider the question. A 2016 Sloan panel also assumed the presence of Big Data in sport given its title, “The Rise of Big Data in Sports.” Clearly, professional sport has embraced Big Data as a viable means of decision-making at all levels of institutions and organizations. Analytics has also slipped easily into Big Data, such that the two have become very much intertwined in contemporary sport. In spite of its growing importance for the culture of sport, scholars have not yet truly begun to explore the implications of the Big Data–sport connection. Although Baerg (2013) and Tulle (2016) have recently discussed the importance of analytics and numbers for contemporary sport, to this point, Beer (2016), Millington and Millington (2015), and Hutchins (2016) appear to be the only scholars who have addressed Big Data’s relation to sport. Baerg 7

Beer (2016) considers Big Data’s relationship to football and the way it renders the sport as a kind of “everyday neoliberalism” (p. 2). He suggests that football exists as a part of the social world and that its transformation through the quantitative processes involved in Big Data speaks to broader social processes attached to metrics. Although metrics have a much longer history in sport, Beer asserts that the scope and breadth of measurement afforded by Big Data intensify “the constitutive and productive power that these systems of measurement might have for the way the social is performed” (p. 4). Football thereby becomes an exemplary site through which one might consider these socially transformative processes. Millington and Millington (2015) chart out some of the implications of the Big Data–sport relation by positioning the quantification of sport, and its growth into Big Data and analytics, in historical context. They look to the broader history of quantification before reflecting on the history of quantification and sport. Millington and Millington assert four postulates in relating this history to the present Big Data–sport relation: the reciprocal relation between Big Data and what they call “sport’s statistical turn” (p. 150); advanced analytics as something represented as a progressive move; Big Data’s increasing influence across sport; and the presence of some unease about Big Data in sport. More germane to this essay, Hutchins (2016) concentrates primarily on how the statistics in data sets have become enmeshed in capital and how the Big Data–sport relation perpetuates ongoing inequalities between those sports with access to capital and, therefore, Big Data, and those without. Hutchins locates the problem of Big Data’s digital divide at the more macro-level of popular, capital-producing professional sport versus less popular, capital-poor professional and amateur sport. Those sport institutions with the greatest financial sway have the capacity to deploy Big Data in ways of which much smaller institutions can only dream. Hutchins (2016) persuasively argues for the existence of a Big Data–sport digital divide at the level of institutions. However, this new expression of the digital divide has begun to express itself between the more the micro-level of sports organizations and athletes and even between the athletes themselves. This essay takes up this issue of the digital divide with respect to the individual athlete. It first argues for the existence of a Big Data–sport digital divide by providing some examples of contemporary athlete surveillance mechanisms. The article then goes on to present two potential responses athletes might have as they engage the digital divide arising out of Big Data surveillance.

Big Data, the Athlete, and the Sporting Digital Divide At the heart of the digital divide in sport with respect to the individual athlete sits the monitoring of athletic performance via intensive forms of quantification. This type of surveillance is certainly not new. These measuring practices sit at the heart of modern, professional sport (Guttmann, 1978; Millington & Millington, 2015) and may go back as far back as the inception of sport itself (Carter & Kruger, 1990). As with other forms of Big Data practice, what is new about Big Data and sport concerns the speed and 8 Journal of Sport and Social Issues 41(1) breadth of data collection. Overwhelmingly, large amounts of data are being collected on athletes on and off the fields and courts. In this historical moment, the two most prominent forms of data collection revolve around sophisticated, stationary camera tracking systems in arenas and stadia and wearable sensor devices embedded in athletes’ equipment on the field or worn as clothing off the field. One of the institutions at the forefront of the Big Data movement has been the NBA. Over the course of the past 4 years, the NBA has partnered with a company called SportVU to collect more data than has ever been possible in the sport of basketball. The SportVU system features six cameras perched high above the court. The SportVU cameras track and aggregate the x- and y-coordinates of each player’s movement 25 times each second (Torre & Haberstoh, 2014). The system subsequently yields 72,000 unique movements each game. Among the movements tracked includes a player’s number of dribbles, the proximity of players on defense and offense, players’ miles run, and passes occurring prior to an assist (Wilson, 2012). Multiply the 72,000 pieces of movement data by the total number of games in an NBA season and the SportVU system yields more than 88 million data artifacts on basketball player movement in a given year. Although the system was only employed by a few teams at its inception, by 2013, SportVU cameras were installed in every NBA arena, such that all teams could have the same access to SportVU data (Lowe, 2015). SportVU data are subsequently fed to a league server where teams can access it and deploy it in their coaching and personnel decisions. More recently, global positioning system (GPS) and biometric tracking devices have been embedded in players’ equipment during practices. The NBA’s San Antonio Spurs and Dallas Mavericks are among those teams using tracking devices to gauge their players’ energy exertion during practices and modifying the physical demands they place on players accordingly (Torre & Haberstoh, 2014). Most recently, the NBA has partnered with the Australian sports data technology company, Catapult, and American sports data firm, STATSports, to have its developmental league (i.e., minor league) players wear GPS and biometric tracking devices during games. These devices sit under a player’s jersey and track a variety of different measurements. The data collected include basic information such as speed and total distance run, but the devices also aggregate more sophisticated numbers linked to acceleration, deceleration, and the force of each jump and landing. These data can reveal whether players might tend to favor particular movements such as a preference for landing more forcefully on a left leg as opposed to a right leg. The result of this type of finding might be that teams could then tailor workouts to prevent an injury that might occur from a perceived flaw in biomechanics. Perhaps more perniciously, the data could uncover which players might be giving maximum effort and which may be taking a night off (Lowe, 2015). Similar types of highly detailed, data-rich monitoring are occurring across a wide range of sports. Major League Baseball employs a camera and a radar system called Statcast that collects information on everything from a baseball’s spin rate after a pitch to a hitter’s launch angle after making contact with the ball (Casella, 2015). Catapult Sports gauges all kinds of biometric and biomechanical data via the company’s OptimEye system across a range of sports from American football to basketball to Baerg 9 kayaking (Catapult Sports, n.d.). Opta and Prozone have become major competitors in the global soccer analytics market as their systems track how players occupy space, and what they do with and without the ball (Bialik, 2014). As these different compa- nies collect and store vast amounts of information via optical tracking cameras and/or wearable devices, they serve a variety of constituencies, including sports leagues, organizations, and sports media. For sport, many statistics are public record, even some statistics produced by Big Data firms such as SportVU and STATSports. The generally more public nature of sports statistics creates expectations when it comes to the proprietary statistics generated by Big Data algorithms. These numbers seem as though they should be more readily available, yet most remain classified to the public and even to the athletes themselves. Perhaps importantly, the algorithms that generate the numbers are not accessible to athletes. Some athletes may indeed have the economic resources to access proprietary algorithms and overcome the digital divide but many do not. Others may be subject to the epistemological poverty of not knowing what to do with the data, even if they were given it. Is it possible for athletes to overcome this expression of the digital divide and possess some control over the conditions of their surveillance? Or is subjecting oneself to corporately controlled surveillance a necessary condition for participation in 21st-century professional sport? What is an athlete to do?

Potential Responses to the Athletic Digital Divide Whether athletes are ready for Big Data, it does appear here to stay. Given the way it functions, a digital divide invariably appears between those athletes with access to the data and knowledge of how to use it and those who either do not have said access or the requisite knowledge of what to do with the data that might be provided to them. The remainder of this essay concentrates on how athletes might respond to this issue of the digital divide and the data generated by their activities. Many responses to ubiquitous Big Data collection are possible; however, this essay focuses on two: emphasiz- ing privacy to a greater degree and moving to becoming a quantified self.

Appeals to Privacy One potential response athletes might have to Big Data monitoring relates to the issue of privacy. Athletes may push for a form of privacy with respect to the information being gathered on them and from them. However, this is where the type of data being gathered may make a difference in terms of a perceived right to privacy. As implied above, historically, athletes have not objected to on-field monitoring of their activity. This type of data would appear akin to statistics in the public domain, even where it sits as algorithmically generated, proprietary information. In being very much like publicly available statistics, new forms of measurement afforded by Big Data appear as mere extensions of existing numbers. With respect to privacy concerns, these statistics would likely not be of note. For example, a basketball player may not object to a statistic that correlates acceleration and jumping height off of a right leg with an ability 10 Journal of Sport and Social Issues 41(1) to block an opponent’s shot. This kind of number appears as merely a more sophisticated version of performance measures that are already occurring. Given the quantification of athletic performance throughout the history of sport, pushing for privacy with respect to these measures may appear petty and would hardly garner any kind of public support. Sports fans generally appreciate statistical information, and sport and media institutions relish opportunities to commodify information that could be packaged to these fans. In addition, it seems a difficult argument for athletes to make on the grounds of privacy if they object to data garnered by mere observation. Fans watching a game might not know how much force a power forward might generate with her jump to block a shot, but they still witness that jump. Suggesting that this type of data, that which is ostensibly seen by the naked eye, be deemed private could make athlete objections to this collection of information appear paranoid. After all, fans already possess considerable statistical data grounded in what they see. Why should athletes be allowed to object to data collection based on activity the public can see? Where Big Data monitoring and privacy concerns could more readily be fore- grounded would be health data, data that derive from off-field monitoring that fans do not see. Certainly, in the past, team management across sport had been interested in what athletes do away from official competition. Their hope has been that players maximize performances by getting enough sleep, eating properly, and avoiding habits that could damage potential productivity. However, in the past, there was no quantitative way to monitor these activities. With biometric tracking devices designed to sur- veil how much athletes sleep, what they eat and drink and where, and when and how much they move, management now has potential access to behaviors that were previously hidden. In the context of biometric surveillance, current Denver Nuggets front office executive, Pete D’Alessandro, has expressed a desire for this kind of information saying, “We need to be able to have impact on these players in their private time” (Torre & Haberstoh, 2014, para. 6). These behaviors and the data they produce could then become part of an athlete’s health record. Perhaps more perniciously, in the same piece, D’Alessandro discussed a hope for even deeper health information about players stating, “The holy grail 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” (Torre & Haberstoh, 2014, para. 41). In the new world of biometric surveillance, the old anecdotes of managers and coaches doing bed checks on their athletes to ensure that they were safely tucked under the covers appear to be a relic of the past. Instead, managerial personnel may be triangulating DNA data with a player’s power output off of one leg with his average number of hours of Rapid Eye Movement (REM) sleep over the past 30 days. As potentially suspect as the push for surveillance into something like the genome might be, more thorough surveillance of athlete performance on the field and athlete behavior off of it may not be the most important privacy issue for athletes to confront. For sports organizations, simple tracking of this activity and possessing these data are insufficient. More significant are the correlations between on-court metrics and off- court behavior. These correlations may become the El Dorado for sports organizations. Baerg 11

As such, it is not the collection of various pieces of data that is vital, but the correlation that can be made between data and the claims that can then be pressed from these correlations. This correlation speaks to a concept associated with Big Data, function creep. Andrejevic (2013) defines function creep as “the repurposing of data collected for one purpose for new unanticipated ones” (p. 24). Andrejevic suggests that function creep is irresistible to those with access to Big Data. To have access to such voluminous amounts of information means that function creep can allow for the identification of data patterns that surprise, patterns that were never intended to be identified upon the initial collection of the data. Function creep allows analytics to establish these patterns at some distant point in the future whether they were initially intended or not. As a result, no piece of data can ever be discarded for no potentially unforeseen correlation can be closed off. Any piece of data may possess some future correlative value, even if that value cannot be discerned at the time of collection. In the context of sport, any aspect of an athlete’s activity and connections that could be drawn from it could com- promise a perceived right to privacy. Function creep could prove threatening for athletes who wish to hold on to some semblance of privacy, given the vagaries of not knowing how their data could one day be deployed. Perhaps the collection of medical data related to an athlete’s off the court activity may move more overtly in the direction of compromising privacy. However, leagues and organizations can still argue that this type of data has been collected for years and is now simply more comprehensive. As a result, they may claim that the athlete’s health is in view to a greater degree than ever before. Privacy arguments opposing Big Data, the correlations it allows, and the unforeseen claims that can be made from these correlations end up being met by a presumed interest in the athlete’s well-being. However, this presumed interest in the athlete’s health leaves some skeptical. Bioethics attorney and NBA player representative Alan C. Milstein sees the situation from a player’s perspective. In the starkest of terms, Milstein asserts,

Employers dictating the health care of their employees is a conflict of interest that cannot be overcome. I just refuse to believe that the purpose of monitoring on any long-term basis is the health of the employee. If the purpose is to predict performance, that’s not a health care purpose. That’s an economic purpose. (Quoted in Torre & Haberstoh, 2014, para. 8)

However, Milstein’s perspective has yet to gain traction with the NBA players’ union. Per Lowe (2015) and contrary to Milstein’s line in the sand, the union has not categori- cally rejected wearable data devices. The union realizes that the data being generated could potentially lead to longer and more lucrative careers. If players can make more money, privacy-based objections to data gathering may not find a hearing among the athletes themselves. Even if the union does support this data gathering, several issues must be sorted out. Lowe (2015) envisions a scenario in which these data reveal some career-threatening and/or debilitating injury risk. Without access to these same data, a player in this 12 Journal of Sport and Social Issues 41(1) situation could be treated very differently in a contract negotiation than if the data did not exist. Michele Roberts, executive director of the NBA players’ union, foresees this contract-data access issue as paramount for future labor agreements saying, “My greatest concern is how some of this information might be leaked or used in contract negotiations” (quoted in Lowe, 2015, para. 11). For sports organizations to be able to generate arguments linking an athlete’s on-field to off-field activity could prove advantageous when it comes to contract negotiations and other assessments of an athlete’s performance. These arguments could be leaked or publicized as a way to garner support for these decisions in ways that work against the athlete’s best interests and wish for privacy. Along a slightly different trajectory, professional athletes on sports technology company, MC10’s Sports Advisory Board have wondered about whether the data collected from the company’s wearable technology could be broadcast without their knowledge or consent (Springer, 2014). Athletes may also ground privacy-based objections to Big Data in a hope for anonymity. Athletes may wish to remain anonymous with respect to potentially sensitive performance and health data, and the correlations made from it. However, objecting to data collection on the grounds of privacy, in its conventional association with anonymity, may not be sufficient to create a wall against the pervasive and potentially intrusive presence of analytics. Again, D’Allesandro claims that something like a DNA database of basketball players would remain private and anonymous in saying, “You wouldn’t have to be identified as a person, you could be identified as a number” (Torre & Haberstoh, 2014, para. 41). Yet, as Andrejevic (2013) implies, the suggestion of anonymity in conjunction with Big Data databases represents a fiction, a part of the mythology of Big Data outlined above. Andrejevic speaks to how “privacy must confront the increasingly fictitious status of anonymity. We will be told that, at least in some instances, the databases do not know our names; but this does not mean that they do not know who we are” (p. 40). Obviously, with sport performance so directly tied to a clearly identifiable identity, privacy concerns would appear nearly irrelevant should an athlete object to data collection on the grounds of privacy violation. To desire to remain anonymous and have that desire respected within the context of sport and Big Data appears nigh impossible. Privacy arguments also run up against the issue of corporate privacy. Athletes may argue for the same access to Big Data as those running the databases. This move would become a push for transparency with respect to one’s theoretically private data and eliminate the digital divide between management and athlete—at least with respect to access. For example, NBA players could be provided the same SportVU data as the organizations who employ them while also being given access to other databases that may be correlated with SportVU data. However, arguing for this access within the context of privacy concerns invariably meets the objection that this information, in being possessed by the athlete’s organization or a third party like SportVU, is itself private. An athlete may desire access to information to protect it or deploy it for her own purposes; however, those who own the data could themselves invoke the privacy argument to keep this information proprietary and away from the athletes who have provided it. As a result, an asymmetrical power relation, based itself on claims to Baerg 13 privacy, reinforces the digital divide between those with these data and the athletes who are restricted from its access. As one response to Big Data, privacy arguments opposing its collection and use may have some purchase. However, the nature of what will count as private and the consequences of a privacy violation will likely require careful negotiation. Without this careful negotiation, sensitive information could be broadly disseminated without the athlete’s consent in what might be a violation of privacy, could be employed in unpredictable, but dubious ways, or could be left entirely inaccessible to the athlete in the name of corporate privacy. No matter, this negotiation would require athletes to assent to Big Data and the broader institutional framework in which it is used. Another possible response to Big Data may allow athletes to step outside this framework without necessarily having to be concerned about privacy issues. The next section of the article speaks to this potential response as one oriented around the embracing of a quantified self.

Embracing a Quantified Self Access to information and how athletes might deploy it relates to another potential response to Big Data, albeit one that shifts the nature of information gathering and interpretation. Athletes might respond to the pervasive, if not increasingly ubiquitous, presence of Big Data via participation in what has been termed “The Quantified Self movement” (Nafus & Sherman, 2014, p. 1785). This Quantified Self (hereafter, QS) movement represents those who reject Big Data’s claims and learn to generate their own forms of analytics. The greatest difference between Big Data and the QS movement concerns sample size. Where Big Data aspire to a potentially limitless sample size, the QS movement emphasizes an n of 1. The QS movement is much more interested in idiosyncrasy than algorithmically generated patterns. The idiosyncratic nature of QS also extends to the types of measuring media used to collect and aggregate data. Authority is not tacitly conferred on technologies that automatically generate alleg- edly objective quantitative data. In being part of the QS movement, one collects data one chooses to collect, in the manner one chooses to collect it, and inputs these data manually as a way to develop a deeper sense of the context in which data are produced (Nafus & Sherman, 2014). As an example, an athlete taking part in the QS movement might develop his or her own determination of health. This athlete might then aggregate personal data to gain a deeper understanding of how and why they are healthy. This assessment might differ from the more conventional, culturally accepted forms of health that are received. For the QS, health will be defined by that individual and not by a technological device or a market definition of health. In this respect, the athlete renders him or herself visible to the self. This visibility opposes that of a sports organization that might employ a proprietary algorithm and its rendering of an athlete’s health in a multiplicity of databases as a way to keep that health information invisible. This shift to a QS subsequently leads to a different type of monitoring. Instead of being tracked surreptitiously, the QS movement grabs hold of some semblance of 14 Journal of Sport and Social Issues 41(1) control of the data being generated. This bottom–up form of analytics is distinct from the top–down expressions of analytics to which individuals are most typically subject. They allow people, at least to a degree, to step outside of a corporate and scientific institutional hegemony when it comes to their data. Nafus and Sherman (2014) argue that becoming a QS shakes up existing externally imposed data collection efforts and butts up against the logics that govern algorithms and their operation. The result of this more individually driven analytics is “a material and social resistance to traditional modes of data aggregation” (p. 1785). A semblance of the QS movement appears to have taken hold with some athletes. In 2013, Sports Illustrated’s Lee Jenkins could claim that “the NBA’s analytical revolution has been confined mainly to front offices. Numbers are dispensed to coaches but rarely do they trickle down to players” (para. 2). However, in recent years, increasing numbers of basketball players have publicized a move toward becoming quantified selves in their own right. Players have started hiring personal data analysts or personal statisticians to help them hone their respective games. One of the pioneers in the field has been Justin Zormelo. Zormelo began his career as a video coordinator for the Chicago Bulls before logging video for analytics service, Synergy Sports Technology (Orton, 2014). In 2009, Oklahoma City Thunder forward and NBA Most Valuable Player, Kevin Durant, hired Zormelo. Zormelo developed an individually tailored training plan designed to help Durant shoot 50% from the field, 40% from the three- point line, and 90% from the free throw line; that is, become part of the 50-40-90 club. After a series of analytics-based training sessions with Zormelo, Durant had optimized the efficiency of his game by locating his strongest areas and internalizing his shooting percentages at each spot on the court (Ballard, 2014; Jenkins, 2013). In 2012-2013, Durant achieved his 50-40-90 goal and became the basketball version of “an optimal human being” (Lupton, 2016, p. 65). In 2011, Zormelo started his own analytics company, Best Ball Analytics, as a way to train athletes in this new way (Orton, 2014). Zormelo has since worked with other NBA players such as Tim Duncan, Rajon Rondo, Dwayne Wade, Derrick Rose, Luol Deng and high school prospects like Thon Maker. Zormelo practices this type of data- driven customization with each one of his clients. After having surveyed countless hours of video, he claims that

By the time I meet a new client on the court, I already know more about their game than they do. I know what happens if they take one dribble to the left versus one dribble to the right and when they have wide open jumpers versus contested jumpers. I know the optimal angles for them on their cuts to the basket and on the release of their jump shots. (Schutz, 2015)

Zormelo is interested in providing his clients with information that team analytics personnel and statisticians do not. He then develops personal plans based on exhaus- tively accumulated data designed to, in Zormelo’s words, “figure out the most effi- cient, easiest way for my players to win. I use statistics to figure the quickest way to reach that number before somebody else reaches that number” (Schutz, 2015). Zormelo Baerg 15 tries to discern different ways to think about efficiency through his own proprietary formulas customized for each client (Cacciola, 2014). Not only has a recognition of a need for personal data analysts become increasingly prevalent for athletes. The sports agent is also beginning to see Big Data and analytics as a space where they might serve their clients. At least one sports agent has begun to recognize that something akin to the QS may be a way to take back a piece of data control from the wrong side of the Big Data digital divide. Baseball agent, Scott Boras, recently spent 7 million dollars for the expertise of several MIT engineers and a complex computer system with its equally complex database. Boras’ staff aggregated data and generated an analytics-based portfolio to serve one of his clients, pitcher Max Sherzer, as a way to maximize Scherzer’s earning power during his free agency in the winter between the 2014 and 2015 seasons. Boras’ team of experts was able to generate analytics-based data Scherzer’s potential new employers did not possess, making the case for signing him more persuasive (Lemire, 2015). As such, agents like Boras bring their own forms of the QS to contract negotiations to maximize their clients’ earning potentials. What prospects do athletes and their agents have for successfully bringing their QS to the negotiating table? The evidence is slim, but as might be expected, early returns leave little room for optimism. This expression of asymmetrical power relations is furthered by executives who may simply ignore any data players and agents bring in support of their position during contract negotiations. Lemire (2015) quotes one baseball executive who dismissively speaks to agent-generated analytics reports as “a total waste. They are a paper weight. You were never going to convince us” (para. 8). This response reflects Andrejevic’s (2014) assertion that individuals have considerably less ability to employ Big Data in their lives than corporations who can collect the same amount of or more information. This is certainly true for the athlete who generally possesses little ability to collect data and has next to no access to the data being aggregated from his or her activity. The data athletes collect themselves and/or by proxy can be eminently dismissed. The “digital footprints” (Thatcher, 2014, p. 1769) athletes leave behind end up being either marginalized or washed away by the tidal waves of power sports organizations possess in keeping this data proprietary. As one of Boras’ fellow agents stated, “[Teams] know more about our player than we do. They’ve got 20 more people working on the stats and sabermetrics” (Lemire, 2015, para. 6). This agent implies that even attempting to compete with the organizations for whom his clients might work is extremely difficult, if not impossible. Although embracing a QS does allow for greater control of one’s data, for athletes to step into and encourage the QS movement could create some significant concerns. Clearly, not every athlete has access to resources the way Scott Boras’ clients have access. Thus, becoming one’s own QS may merely reinforce the fissure between data haves and have-nots, and foster an additional digital divide. Those with more capital could more readily embrace a QS than those with less. As a result, in attempting to close the divide between athlete and management, the QS movement could merely generate another divide—this one between the athletes themselves. As Lupton (2016) argues, the quantified self is indeed overwhelmingly focused on the individual self, 16 Journal of Sport and Social Issues 41(1) improving the self, and developing self-knowledge. As such, those athletes who embrace a quantified self may isolate themselves from their teammates and, more broadly, from a player’s union/association that could more effectively serve a collective interest. The athlete may become the “neoliberal entrepreneurial citizen ideal” (p. 68) but do so at the expense of others who may not be able to enact their own quantified selves. Sports organizations may also actively oppose any shift toward a quantified self. The aforementioned Zormelo makes a concerted effort not to interfere with a client’s coach’s wishes; yet his work still occurs away from the organizational surveillance that would take place at practices, team medical facilities, and in games. Per Rajon Rondo, his employer, the Boston Celtics, was not aware that he was receiving constant feedback from Zormelo after each game during the 2013-2014 season (Cacciola, 2014). Orton (2014) quotes ESPN’s Tom Penn who summarizes the issues that can arise when players work with someone like Zormelo:

You are thrilled that your player wants to work out. You are terrified about the way that he is directed to work. There’s a tremendous risk that the wrong trainer with the wrong ideas can train him in the wrong way and create significant risk of injury. (para. 38)

Zormelo has attempted to remain relatively anonymous but acknowledges the tension that could arise between himself and team officials: “Honestly, I’ve never talked to a coach about what I do. They’re cordial. But I’m sure they don’t like it” (Cacciola, 2014, para. 21). Much like the privacy response cited above, moving toward a quantified self has its own set of challenges. Although developing a quantified self does situate the athlete within the same epistemological frame as sports organizations that employ extensive quantitative data, attempting to develop one’s own sample size outside the parameters of sports organizations and institutions would invariably create friction with the athlete’s employer. Given the capital and access to personal data analysts that is required, an athlete’s embracing of a quantified self potentially furthers a digital divide between athletes as well.

Conclusion So can the athlete truly overcome the digital divide? Most athletes seem to be left adrift in the Big Data sea digitally divided from sports organizations both in terms of access to data and the ability to usefully employ it. This essay has discussed how Big Data tracking is occurring, and provided two potential responses athletes might have to this form of monitoring. Athletes may choose to resist Big Data tracking on the grounds of privacy, or they may opt to embrace a quantified self as a way to generate their own forms of data. Either response poses its own set of challenges, some of which are outlined above. Perhaps both the responses outlined above could be negotiated through collective bargaining. Be it a greater allowance for athletes to opt out of Big Data monitoring and Baerg 17 the database correlations that go with it for privacy reasons or a greater respect for an athlete’s quantified self, advancing these responses through collective bargaining may lend them some additional force. It is this type of agency that scholars such as Kennedy and Moss (2015) see as providing potential for a more effective engagement with Big Data. At this point, none of the four major North American professional sports leagues feature anything in their respective labor agreements that speak to the kinds of data collection being done via the likes of Catapult and SportVU. These agreements certainly speak to medical records, but clarification ought to be made about the ontologi- cal status of information generated by Big Data surveillance devices. At this point, the boundaries on different forms of data and what can be done with them appear hazy. Scholars across a variety of disciplines should continue to ask questions about the Big Data–athlete relationship and move beyond what has been discussed here. What happens to the athlete who refuses to contribute to the growing database of information collected on himself or herself and others? What consequences might follow from the athlete who refuses to wear a Catapult tracking device in practice or submit himself or herself to something like sleep tracking off the field? How should athletes respond to more surrepti- tious forms of data collection like those attached to SportVU—data collection that athletes seem to have little opportunity to resist? What happens to the athlete who chooses to define health in his or her own terms using his or her own data rather than submitting to appointments and reports from team-appointed physicians and therapists? What if an athlete wishes to have part or all of her generated data erased in a wish to be forgotten? At the moment, one suspects that the interests of sports franchises would be to leave data collection provisions as they are to extract increasing amounts of information from athletes without restrictions on what can be done with it. If the digital divide thereby becomes a quantitative chasm, it would certainly seem to serve the interests of those with capital and existing power. Should athletes fail to address the situation, they may find bridging the gap increasingly daunting.

Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article

Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.

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Author Biography Andrew Baerg (PhD, Communication Studies, University of Iowa) is an associate professor of Communication at the University of Houston-Victoria. His research focuses on the social and cultural relationship between sport and media with a specific focus on digital sports games. This work has been published in Sociology of Sport Journal, Communication and Sport, Symploke, and several anthologies. His most recent work has also concentrated on analytics and advanced forms of quantification in sport.