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

TL;DR

This paper introduces a predictive model for soccer team selection that accounts for player injuries, optimizing team performance and reducing injury-related costs over a season.

Contribution

The paper presents a novel sequential team selection model using Monte-Carlo Tree Search that incorporates injury risk to improve team management in soccer.

Findings

Reduces first-team injuries by approximately 13%.

Decreases costs associated with injured players by about 11%.

Maintains comparable season points to benchmark methods.

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.