The Strain of Success: A Predictive Model for Injury Risk Mitigation and Team Success in Soccer
CoRR(2024)
摘要
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
demonstrating the potential to reduce costs and improve player welfare in
real-world soccer teams.
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