Optimising Game Tactics for Football
This work addresses tactical optimization for football teams, offering incremental improvements in win probability through a novel modeling approach.
The paper tackles the problem of optimizing tactical and strategic decision-making in football by modeling it as a multi-stage game, combining Bayesian and stochastic games to predict outcomes and payoffs. Empirical results on 760 matches show that using optimized tactics from this model can increase a team's chances of winning by up to 16.1% and 3.4% for different components.
In this paper we present a novel approach to optimise tactical and strategic decision making in football (soccer). We model the game of football as a multi-stage game which is made up from a Bayesian game to model the pre-match decisions and a stochastic game to model the in-match state transitions and decisions. Using this formulation, we propose a method to predict the probability of game outcomes and the payoffs of team actions. Building upon this, we develop algorithms to optimise team formation and in-game tactics with different objectives. Empirical evaluation of our approach on real-world datasets from 760 matches shows that by using optimised tactics from our Bayesian and stochastic games, we can increase a team chances of winning by up to 16.1\% and 3.4\% respectively.