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Published VersionCC BY-NC-ND V4.0, Open Access
Journal article
Artificial intelligence applications in the football codes: A systematic review
Journal of Sports Sciences, Vol.42(13), pp.1184-1199
2024
Appears in UniSC Supported Open Access Outputs
Abstract
Artificial Intelligence (AI) is increasingly being adopted across many domains such as transport, healthcare, defence and sport, with football codes no exception. Though there is a range of potential benefits of AI, concern has also been expressed regarding potential risks. An important first step in ensuring that AI applications in football are usable, beneficial, safe and ethical is to understand the current range of applications, the AI models adopted and their proposed functions. This systematic review aimed to identify different applications of AI across football codes to synthesise current knowledge and determine whether potential risks are being considered. The systematic review included 190 peer-reviewed articles. Nine areas of application were found ranging from athlete evaluation and event detection to match outcome prediction and injury detection and prediction. In total, 27 different AI models were identified, with artificial neural networks the most frequently applied. Five AI assessment metrics were identified including specificity, recall, precision, accuracy and F1-score. Four potential risks were identified, concerning data security, usability, data biases and inappropriate athlete load management. It is concluded that, though a wide range of AI applications currently exist, further work is required to develop AI for football and identify and manage potential risks.Artificial Intelligence (AI) is increasingly being adopted across many domains such as transport, healthcare, defence and sport, with football codes no exception. Though there is a range of potential benefits of AI, concern has also been expressed regarding potential risks. An important first step in ensuring that AI applications in football are usable, beneficial, safe and ethical is to understand the current range of applications, the AI models adopted and their proposed functions. This systematic review aimed to identify different applications of AI across football codes to synthesise current knowledge and determine whether potential risks are being considered. The systematic review included 190 peer-reviewed articles. Nine areas of application were found ranging from athlete evaluation and event detection to match outcome prediction and injury detection and prediction. In total, 27 different AI models were identified, with artificial neural networks the most frequently applied. Five AI assessment metrics were identified including specificity, recall, precision, accuracy and F1-score. Four potential risks were identified, concerning data security, usability, data biases and inappropriate athlete load management. It is concluded that, though a wide range of AI applications currently exist, further work is required to develop AI for football and identify and manage potential risks.
Details
- Title
- Artificial intelligence applications in the football codes: A systematic review
- Authors
- Isaiah Elstak (Corresponding Author) - University of the Sunshine Coast, Queensland, Centre for Human Factors and Systems SciencePaul Salmon - University of the Sunshine Coast, Queensland, Centre for Human Factors and Systems ScienceScott McLean - University of the Sunshine Coast, Queensland, Centre for Human Factors and Systems Science
- Publication details
- Journal of Sports Sciences, Vol.42(13), pp.1184-1199
- Publisher
- Routledge
- Date published
- 2024
- DOI
- 10.1080/02640414.2024.2383065
- ISSN
- 1466-447X; 0264-0414
- Copyright note
- © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
- Grant note
- The paper was funded through ISaiah Elstak’s PhD scholarship.
- Organisation Unit
- Centre for Human Factors and Systems Science
- Language
- English
- Record Identifier
- 991054259102621
- Output Type
- Journal article
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- Web Of Science research areas
- Sport Sciences