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 aggre- gated 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.

Load more