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

arXiv: https://arxiv.org/abs/2102.09469 · HTML: https://mlsanity.com/paper/2102.09469/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 26 references that are in the map.
- First posted on arXiv 18 Feb 2021.

## Builds on (most cited first, 2 of 2)

- [Observational Learning by Reinforcement Learning](https://mlsanity.com/paper/1706.06617/) (2017)
- [Optimising Game Tactics for Football](https://mlsanity.com/paper/2003.10294/) (2020)

## Built on by (most cited first, 3 of 3)

- [Inferring Player Location in Sports Matches: Multi-Agent Spatial Imputation from Limited Observations](https://mlsanity.com/paper/2302.06569/) (2023)
- [Computing an Optimal Pitching Strategy in a Baseball At-Bat](https://mlsanity.com/paper/2110.04321/) (2021)
- [The Strain of Success: A Predictive Model for Injury Risk Mitigation and Team Success in Soccer](https://mlsanity.com/paper/2402.04898/) (2024)

## Abstract (arXiv)

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%.