The Strain of Success: A Predictive Model for Injury Risk Mitigation and
  Team Success in Soccer
Gregory Everett Ryan Beal Tim Matthews Timothy J. Norman Sarvapali D. Ramchurn
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.