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SoccerNet: A Gated Recurrent Unit-based model to predict soccer match winners

journal contribution
submitted on 2024-06-24, 13:37 and posted on 2024-06-24, 13:38 authored by Jassim AlMulla, Mohammad Tariqul Islam, Hamada R. H. Al-Absi, Tanvir Alam

Winning football matches is the major goal of all football clubs in the world. Football being the most popular game in the world, many studies have been conducted to analyze and predict match winners based on players’ physical and technical performance. In this study, we analyzed the matches from the professional football league of Qatar Stars League (QSL) covering the matches held in the last ten seasons. We incorporated the highest number of professional matches from the last ten seasons covering from 2011 up to 2022 and proposed SoccerNet, a Gated Recurrent Unit (GRU)-based deep learning-based model to predict match winners with over 80% accuracy. We considered match- and player-related information captured by STATS platform in a time slot of 15 minutes. Then we analyzed players’ performance at different positions on the field at different stages of the match. Our results indicated that in QSL, the defenders’ role in matches is more dominant than midfielders and forwarders. Moreover, our analysis suggests that the last 15–30 minutes of match segments of the matches from QSL have a more significant impact on the match result than other match segments. To the best of our knowledge, the proposed model is the first DL-based model in predicting match winners from any professional football leagues in the Middle East and North Africa (MENA) region. We believe the results will support the coaching staff and team management for QSL in designing game strategies and improve the overall quality of performance of the players.

Other Information

Published in: PLOS ONE
License: http://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1371/journal.pone.0288933

Funding

Qatar National Research Fund (GSRA7-1-0412-20017), PhD-Machine Learning Models for the performance and injury analysis for the football players from QSL.

History

Language

  • English

Publisher

Public Library of Science (PLoS)

Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • College of Science and Engineering - HBKU
  • Qatar Football Association

Geographic coverage

Qatar