Throughout human history, measurement has been a common means of building knowledge and allowing for the evaluation of the physical world (Kuhn 1961; Mari 2003), with the advantage of generating forms of information that facilitate shared interpretation (Rossi 2007). Although systems of measurement often appear natural and incontestable, render- ing a complex phenomenon into numbers involves an authoritative dec- laration of what is to be valued and what the essence of an activity is. The translation of qualities into quantities requires abstracting from context and assigning a numerical value that can be communicated to distant places and people (Porter 1994; Power 2004). These characteristics of measurement have given the numbers it generates an important role in sports. In what follows, we outline how previous research has focused on the way metrics have perpetuated or widened already existing power rela- tionships: first, in the context of management and governance; second, as the quantification of movement; third, in the coaching environment; and finally, through the sport-media nexus.
As a range of authors have observed, the use of scientific data or met- rics as a management mechanism has become commonplace in sport and viewed as a form of managerialism with national or international govern- ing bodies demanding high levels of accountability from sports organisa- tions who report to them (see Baerg 2017; Hutchins 2016; Macris and Sam 2014; Williams and Manley 2016). In this context, the quantitative component allows for the definitive measuring of success. For example, in many countries, the number of Olympic medals won, or the numeric placing of an athlete, determines the amount of government funding that the sport or athlete will receive (Grix and Carmichael 2012). As Sam and Macris (2014) outline, these tactics can result in problematic behaviours from sports organisations as they adopt practices purely to acquire money rather than promoting sport.
Indeed, a potential threat of the value of sport being overshadowed by the emphasis on numbers was identified by Colás (2017), who traces the history of quantification in the sport of basketball and questions the dominance of numbers both as an analytic tool and for keeping score. He
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notes that in the past, it had been argued that basketball may be improved without the score, since the sport is essentially about physical movement prowess. He suggests that by focusing on the score so strongly, the more important dimensions of the game, such as the quality of movement per- formed, are de-emphasised. Konoval (2018) makes the same observation in the context of endurance running training, where numbers in the form of athletes’ times become the focus of attention to a far greater extent than the physical act of running. A similar argument has been applied in the talent identification literature by social scientists who have examined the appropriateness of such a strong quantitative emphasis. In this con- text, physical ability testing has traditionally been used as a method to quantifiably measure an athlete’s talent (Lidor et al. 2009). Quantitative psychology or psychometric tests have also been used (Collins and Cruickshank 2017). However, recently, a range of authors have ques- tioned the dominance of quantified tests in talent identification and development, arguing that these have poor predictive ability in compari- son to the subjective views of experienced coaches who are able to account for a far wider number of variables than those that can be quantitatively measured (Christensen 2009; Lidor et al. 2009; Miller et al. 2015).
At the same time, studies have found that coaches too are increas- ingly relying on quantitative data for their decision-making processes.
Both Williams and Manley (2016) and Denison and Mills (2014) argue that quantitative data can work as a control mechanism within the coaching context. It works in a similar way to the previously discussed managerial mechanism; it occurs at the individual level and has been found to produce some problematic outcomes for athlete welfare. While Denison and Mills (2014) utilise Foucault, Williams and Manley (2016) draw on the work of Gilles Deleuze, who in Postscript on the Societies of Control (1992) focused on the role of numeric data in the twenty-first century as contributing to a new form of society of control. In their study of the use of quantitative data in a rugby club, Williams and Manley (2016) argue that the data was used by coaches to survey and discipline the athletes, and that as a result of the reliance on the data, each athlete was reduced to nothing more than a data set. In their cri- tique of Williams and Manley’s (2016)1 work, Collins et al. (2015)
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claim that while they disagree with Williams and Manley’s (2016) over- all interpretation, they agree that quantification can be detrimental when it is solely relied upon. Specifically, they point out the risk when coaches focus only on variables that can be quantified, even though sporting success relies on many facets that cannot be quantified. For example, they note that the sport of rugby union involves mastering a range of complexities, including quick decision- making, which is not easily quantifiable. Baerg (2017) agrees that the monitoring of athletes through data, by coaches and others is a troubling development as it creates a digital divide where the athletes are unempowered due to their lack of access to digital data. He suggests that athletes could potentially overcome this scenario if they were to collect their own health data and use it for their own ends, or if they were to resist the collection of data for reasons of privacy.
Continuing the theme of access and inequalities, in the sport media context, Hutchins (2016) notes how the prizing of big data in sport has opened up considerable new commercial opportunities. He identifies how the increasing use of data has exacerbated already wide inequalities between lucrative team sports that dominate the media and other, less visible, men’s teams or women’s sports (Hutchins 2016, 495). By con- trast, Baerg (2013) notes how analytics can reveal unexpected results that provide opportunities for less-celebrated athletes. Using the work of Latour, Baerg (2013) focuses on the agentic role of numbers in being able to reveal what is not apparent to the human eye. For example, he argues that while individuals directly observe what is happening at the game, surprises can be revealed through numeric analysis. For example, Baerg refers to a case where a player who has not performed in a spec- tacular way and therefore may have gone unnoticed by fans, may in fact end up with the best ratings of the season due to consistently good per- formances. Baerg (2013) also notes the way that analytics can allow the detection of flaws in a team’s performance that otherwise would not be seen. Clashes between quantitative-focused analysts and those who observe with more traditional qualitative eyes have also been found to arise (Baerg 2013).
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