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

arXiv: https://arxiv.org/abs/2402.04898 · HTML: https://mlsanity.com/paper/2402.04898/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 7 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Inferring Player Location in Sports Matches: Multi-Agent Spatial Imputation from Limited Observations](https://mlsanity.com/paper/2302.06569/) (2023)
- [Optimising Long-Term Outcomes using Real-World Fluent Objectives: An Application to Football](https://mlsanity.com/paper/2102.09469/) (2021)

## Abstract (arXiv)

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.