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Optimising Game Tactics for Football
arXiv - CS - Computer Science and Game Theory Pub Date : 2020-03-23 , DOI: arxiv-2003.10294
Ryan Beal; Georgios Chalkiadakis; Timothy J. Norman; Sarvapali D. Ramchurn

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.
更新日期:2020-03-24

 

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