Chang, Victor, Sajeev, Sreeram, Xu, Qianwen Ariel, Tan, Mengmeng and Wang, Hai (2024). Football Analytics: Assessing the Correlation between Workload, Injury and Performance of Football Players in the English Premier League. Applied Sciences, 14 (16),
Abstract
The aim of this research is to shed light on the complex interactions between player workload, traits, match-related factors, football performance, and injuries in the English Premier League. Using a range of statistical and machine learning techniques, this study analyzed a comprehensive dataset that included variables such as player workload, personal traits, and match statistics. The dataset comprises information on 532 players across 20 football clubs for the 2020–2021 English Premier League season. Key findings suggest that data, age, average minutes played per game, and club affiliations are significant indicators of both performance and injury incidence. The most effective model for predicting performance was Ridge Regression, whereas Extreme Gradient Boosting (XGBoost) was superior for predicting injuries. These insights are invaluable for data-driven decision-making in sports science and football teams, aiding in injury prevention and performance enhancement. The study’s methodology and results have broad applications, extending beyond football to impact other areas of sports analytics and contributing to a flexible framework designed to enhance individual performance and fitness.
Publication DOI: | https://doi.org/10.3390/app14167217 |
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Divisions: | College of Business and Social Sciences > Aston Business School > Operations & Information Management College of Business and Social Sciences College of Business and Social Sciences > Aston Business School College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies > Software Engineering & Cybersecurity |
Additional Information: | Copyright © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/) |
Uncontrolled Keywords: | football analytics,injury occurrence analysis,machine learning in sports,predictive modelling,sports data analysis,General Engineering,Instrumentation,Fluid Flow and Transfer Processes,Process Chemistry and Technology,General Materials Science,Computer Science Applications |
Publication ISSN: | 2076-3417 |
Last Modified: | 11 Oct 2024 07:36 |
Date Deposited: | 02 Sep 2024 17:24 |
Full Text Link: | |
Related URLs: |
https://www.mdp ... 3417/14/16/7217
(Publisher URL) http://www.scop ... tnerID=8YFLogxK (Scopus URL) |
PURE Output Type: | Article |
Published Date: | 2024-08 |
Published Online Date: | 2024-08-16 |
Accepted Date: | 2024-08-09 |
Authors: |
Chang, Victor
(
0000-0002-8012-5852)
Sajeev, Sreeram Xu, Qianwen Ariel ( 0000-0003-0360-7193) Tan, Mengmeng Wang, Hai ( 0000-0002-4192-5363) |