Optimising game tactics for footballOptimising game tactics for football Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2022-07-262020enThis research is supported by the AXA Research Fund and the EPSRC NPIF doctoral training grant number EP/S515590/1.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 ingame 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 increase a team chances of winning by 16.1% and 3.4% respectively.http://creativecommons.org/licenses/by/4.0/141-14919th International Conference on Autonomous Agents and Multiagent SystemsProceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems Beal Ryan Chalkiadakis Georgios Χαλκιαδακης Γεωργιος Norman Timothy J. Ramchurn Sarvapali D. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) Autonomous agents Football Game theory Multi agent systems Bayesian game Stochastic game