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.

本文提出了一种新颖的足球连续球队选择模型，通过对现实世界足球数据中学习到的球员特定信息建模，以模拟球员受伤和不可用性的随机过程。通过对球员受伤概率进行推理，利用蒙特卡洛树搜索 (Monte-Carlo Tree Search) 来选择在整个足球赛季中优化长期团队表现的比赛队伍。我们将我们的方法与 2018/19 英超联赛季的基准解决方案进行验证。我们的模型在减少一线队伤病方面达到了类似的赛季预期积分，并将在受伤球员上浪费的金钱减少了约 11%，从而证明了在现实世界的足球队中降低成本和改善球员福利的潜力。