Sport analytics promises to use Big Data and sophisticated statistical methods to identify effective strategies in sports—“the Moneyball moment.” However, much like alchemy, sport analytics is characterized by opacity and secrecy, and outside of baseball, evidence of success that would meet the usual scientific criteria is limited. An example is used to demonstrate that quite simple models can match more complex ones in terms of prediction. Like alchemy, sport analytics can deliver important advances in our understanding, but some problems need to be addressed. These include the need to incorporate theory, reconciling the pursuit of profit with scientific principles, and focusing on prediction as a measure of progress.
Nicholas M. Watanabe, Stephen Shapiro, and Joris Drayer
-driven decision making within sport organizations ( Szymanski, 2020 ). The academic literature in this area has focused primarily on big data and on-field performance ( Goes et al., 2020 ; Morgulev, Azar, & Lidor, 2018 ; Van den Berg, Coetzee, & Mearns, 2020 ) with less attention dedicated to big data and sport