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Optimising game tactics for football

Beal Ryan, Chalkiadakis Georgios, Norman Timothy J., Ramchurn Sarvapali D.

Πλήρης Εγγραφή


URI: http://purl.tuc.gr/dl/dias/833F3533-470E-451D-B850-1C380A396F74
Έτος 2020
Τύπος Πλήρης Δημοσίευση σε Συνέδριο
Άδεια Χρήσης
Λεπτομέρειες
Βιβλιογραφική Αναφορά R. Beal, G. Chalkiadakis, T. J. Norman, and S. D. Ramchurn, “Optimising game tactics for football,” In Proc. of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020), vol 2020, B. An, N. Yorke-Smith, A. El Fallah Seghrouchni, G. Sukthankar, Eds., USA: IFAAMAS, 2020, pp. 141-149.
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Περίληψη

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. Usingthis 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 byusing optimised tactics from our Bayesian and stochastic games, we increase a team chances of winning by 16.1% and 3.4% respectively.

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