成功的代价：足球伤病风险缓解与团队成功的预测模型
The Strain of Success: A Predictive Model for Injury Risk Mitigation and Team Success in Soccer
AI总结：
提出基于蒙特卡洛树搜索的足球阵容选择模型，通过建模球员伤病概率优化赛季长期表现，在英超数据上减少一线队伤病约13%并降低无效花费约11%。
摘要：
本文提出了一种新颖的足球顺序团队选择模型。具体而言，我们利用从真实足球数据中学到的球员特定信息，对球员伤病和不可用性的随机过程进行建模。我们使用蒙特卡洛树搜索来选择比赛阵容，通过推理球员伤病概率来优化整个足球赛季的长期团队表现。我们针对2018/19赛季英格兰足球超级联赛的基准解决方案验证了我们的方法。我们的模型在赛季预期积分上与基准相当，同时将一线队伤病减少了约13%，并将因伤病球员而低效花费的资金减少了约11%——展示了在真实足球队中降低成本和提高球员福利的潜力。
英文摘要：
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% 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
