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A machine learning framework for quantifying in-game space-control efficiency in football

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journal contribution
posted on 2023-12-07, 14:08 authored by Aaron GuAaron Gu, Varuna De-SilvaVaruna De-Silva, Mike Caine

Analysis of player tracking and event data in football matches is used by the coaching staff to evaluate team performance and to inform tactical decision-making, whereas using Machine Learning methods to gain useful insights from the data is still an open research question. The objective of our research is to discover the football team's space-control efficiency using a novel Machine Learning approach and evaluate the team performance based on its space-control efficiency. We develop a novel Possession Evaluation Model through deep generative machine learning to predict the football team's space-control capability utilising tracking and event data. The developed model is used to quantify the efficiency of attacking and defending for a given sequence of play. Performance analysis results demonstrate that this novel method of space-control efficiency quantification is objective and precise. The superior performance of the model is attributed to the utilization of deep generative modelling on image datasets and conditioning in the prediction with contextual factors. This study presents a novel approach to football analysis in evaluating team performance and providing tactical insights for the coach to make data-informed adjustments.

Funding

MIMIc: Multimodal Imitation Learning in MultI-Agent Environments

Engineering and Physical Sciences Research Council

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History

School

  • Loughborough University London

Published in

Knowledge-Based Systems

Volume

283

Publisher

Elsevier

Version

  • VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

2023-10-28

Publication date

2023-11-13

Copyright date

2023

ISSN

0950-7051

eISSN

1872-7409

Language

  • en

Depositor

Dr Varuna De Silva. Deposit date: 22 November 2023

Article number

111123