The Strain of Success: A Predictive Model for Injury Risk Mitigation and Team Success in Soccer
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929236504215552 |
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| author | Everett, Gregory Beal, Ryan Matthews, Tim Norman, Timothy J. Ramchurn, Sarvapali D. |
| author_facet | Everett, Gregory Beal, Ryan Matthews, Tim Norman, Timothy J. Ramchurn, Sarvapali D. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_04898 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Strain of Success: A Predictive Model for Injury Risk Mitigation and Team Success in Soccer Everett, Gregory Beal, Ryan Matthews, Tim Norman, Timothy J. Ramchurn, Sarvapali D. Artificial Intelligence Machine Learning 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. |
| title | The Strain of Success: A Predictive Model for Injury Risk Mitigation and Team Success in Soccer |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2402.04898 |