  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.

Artificial Intelligence;Computer Science and Game Theory;Multiagent Systems