Optimising Game Tactics for Football

TL;DR

This paper introduces a novel Bayesian and stochastic game-based framework to optimize football tactics and strategies, significantly improving team winning probabilities based on real match data.

Contribution

It presents a new multi-stage game model combining Bayesian and stochastic elements for tactical decision-making in football.

Findings

Optimized tactics increased winning chances by up to 16.1%.

The approach effectively predicts game outcomes and team payoffs.

Empirical results demonstrate the model's practical benefits on real-world data.

Abstract

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.