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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/833F3533-470E-451D-B850-1C380A396F74 | - |
| Identifier | www.ifaamas.org/proceedings.html | - |
| Identifier | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096654096&partnerID=40&md5=ce843458300f4b248f93e6827a5ad262 | - |
| Identifier | 978-145037518-4 | - |
| Language | en | - |
| Extent | 9 pages | en |
| Title | Optimising game tactics for football | en |
| Creator | Beal Ryan | en |
| Creator | Chalkiadakis Georgios | en |
| Creator | Χαλκιαδακης Γεωργιος | el |
| Creator | Norman Timothy J. | en |
| Creator | Ramchurn Sarvapali D. | en |
| Publisher | International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) | en |
| Content Summary | 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. | en |
| Type of Item | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Type of Item | Conference Full Paper | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2022-07-26 | - |
| Date of Publication | 2020 | - |
| Subject | Autonomous agents | en |
| Subject | Football | en |
| Subject | Game theory | en |
| Subject | Multi agent systems | en |
| Subject | Bayesian game | en |
| Subject | Stochastic game | en |
| Bibliographic Citation | 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. | en |
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