Related papers: Optimising Game Tactics for Football

Optimising Game Tactics for Football

URL: http://arxiv.org/abs/2003.10294v1
Date: Mon, 23 Mar 2020 14:24:45 GMT
Title: Optimising Game Tactics for Football
Authors: Ryan Beal, Georgios Chalkiadakis, Timothy J. Norman and Sarvapali D. Ramchurn
Abstract summary: 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 the game to model the in-match state transitions and decisions. Building upon this, we develop algorithms to optimise team formation and in-game tactics with different objectives.
Score: 18.135001427294032
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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