# Optimising Game Tactics for Football

arXiv: https://arxiv.org/abs/2003.10294 · HTML: https://mlsanity.com/paper/2003.10294/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2020.

## Built on by (most cited first, 9 of 9)

- [SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap](https://mlsanity.com/paper/2404.11335/) (2024)
- [ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton](https://mlsanity.com/paper/2112.01044/) (2021)
- [What Happened Next? Using Deep Learning to Value Defensive Actions in Football Event-Data](https://mlsanity.com/paper/2106.01786/) (2021)
- [Inferring Player Location in Sports Matches: Multi-Agent Spatial Imputation from Limited Observations](https://mlsanity.com/paper/2302.06569/) (2023)
- [Data-driven Analysis for Understanding Team Sports Behaviors](https://mlsanity.com/paper/2102.07545/) (2021)
- [Optimising Long-Term Outcomes using Real-World Fluent Objectives: An Application to Football](https://mlsanity.com/paper/2102.09469/) (2021)
- [Learning from the Pros: Extracting Professional Goalkeeper Technique from Broadcast Footage](https://mlsanity.com/paper/2202.12259/) (2022)
- [Computing an Optimal Pitching Strategy in a Baseball At-Bat](https://mlsanity.com/paper/2110.04321/) (2021)
- [Strategic Multi-Agent Learning for Interpretable Action Valuation of All Players in Football](https://mlsanity.com/paper/2610.05961/) (2026)

## Abstract (arXiv)

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.