Management mathematics in sport—moneyball and soccer.

This article examines the current state and challenges of analytics in professional soccer, with a focus on England, highlighting that while data-driven methods have grown, soccer lags behind other sports in fully integrating analytics. It emphasizes the importance of causal inference methods to distinguish correlation from causation in player and team evaluation, proposing an approach centered on consistent ranking systems—such as Elo ratings—to assess teams and players across divisions and competitions. The article also discusses barriers to analytics adoption, including communication difficulties within clubs, secrecy in the industry, and the need for leadership support, while noting the potential of artificial intelligence to augment soccer analytics. Finally, it underscores that analytics should complement, not guarantee, success in a complex, low-scoring sport where outcomes are influenced by many factors beyond data.