The Strain of Success: A Predictive Model for Injury Risk Mitigation and Team Success in Soccer

Abstract

In this paper, we present a novel sequential team selection model in soccer. Specifically, we model the stochastic process of player injury and unavailability using player-specific information learned from real-world soccer data. Monte-Carlo Tree Search is used to select teams for games that optimise long-term team performance across a soccer season by reasoning over player injury probability. We validate our approach compared to benchmark solutions for the 2018/19 English Premier League season. Our model achieves similar season expected points to the benchmark whilst reducing first-team injuries by ~13% and the money inefficiently spent on injured players by ~11% - demonstrating the potential to reduce costs and improve player welfare in real-world soccer teams.

19 pages (16 main, 2 references, 1 appendix), 10 figures (9 main, 1 appendix). Accepted at the MIT Sloan Sports Analytics Conference 2024 Research Paper Competition