Optimising game tactics for football
Optimising game tactics for football
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.
Association for Computing Machinery
Beal, Ryan
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Norman, Timothy
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Chalkiadakis, Georgios
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Ramchurn, Sarvapali
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Beal, Ryan
d9874cb0-bd92-4a16-8576-78d769b41ff7
Norman, Timothy
663e522f-807c-4569-9201-dc141c8eb50d
Chalkiadakis, Georgios
06a66d27-705a-46a2-beeb-b3b7d443ffdb
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Beal, Ryan, Norman, Timothy, Chalkiadakis, Georgios and Ramchurn, Sarvapali
(2020)
Optimising game tactics for football.
In Nineteenth International Conference on Autonomous Agents and Multi-Agent Systems.
Association for Computing Machinery.
9 pp
.
(In Press)
Record type:
Conference or Workshop Item
(Paper)
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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Accepted/In Press date: 15 January 2020
Venue - Dates:
Nineteenth International Conference on Autonomous Agents and Multi-Agent Systems, Auckland, New Zealand, Auckland, New Zealand, 2020-05-09 - 2020-05-13
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Local EPrints ID: 438461
URI: http://eprints.soton.ac.uk/id/eprint/438461
PURE UUID: a7e85eab-8027-40d3-998a-489fb94f20ef
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Date deposited: 10 Mar 2020 17:33
Last modified: 17 Mar 2024 03:41
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Contributors
Author:
Ryan Beal
Author:
Georgios Chalkiadakis
Author:
Sarvapali Ramchurn
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