Optimising Long-Term Outcomes using Real-World Fluent Objectives: An Application to Football

TL;DR

This paper introduces a novel method for improving long-term football team performance by using fluent objectives modeled through Markov chain Monte Carlo and deep learning, based on real match data.

Contribution

It presents a new approach to optimize football tactics over a season by modeling evolving objectives and applying advanced algorithms to enhance long-term outcomes.

Findings

Teams' expected league position increased by up to 35.6%.

The approach effectively learns from historical match data.

Simulations demonstrate improved strategic decision-making.

Abstract

In this paper, we present a novel approach for optimising long-term tactical and strategic decision-making in football (soccer) by encapsulating events in a league environment across a given time frame. We model the teams' objectives for a season and track how these evolve as games unfold to give a fluent objective that can aid in decision-making games. We develop Markov chain Monte Carlo and deep learning-based algorithms that make use of the fluent objectives in order to learn from prior games and other games in the environment and increase the teams' long-term performance. Simulations of our approach using real-world datasets from 760 matches shows that by using optimised tactics with our fluent objective and prior games, we can on average increase teams mean expected finishing distribution in the league by up to 35.6%.