Beato, Marco, Jaward, Hisham, Nassis, George, Figueiredo, Pedro, Clemente, Filipe and Krustrup, Peter (2024) An educational review on machine learning: a SWOT analysis for implementing machine learning techniques in football. International Journal of Sports Physiology and Performance. ISSN 1555-0265 (In Press)

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Abstract

The abundance of data in football presents both opportunities and challenges for decision-making. Consequently, this review has two primary objectives: first, to provide practitioners with a concise overview of the characteristics of machine learning (ML) analysis; and second, to conduct a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis regarding the implementation of ML techniques in professional football clubs. This review explains the difference between artificial intelligence and ML, and the difference between ML and statistical analysis. Moreover, we summarize and explain the characteristics of ML learning approaches such as supervised learning, unsupervised learning and reinforcement learning. Finally, we present an example of SWOT analysis, which suggests some actions to be considered in applying ML techniques by the medical and sport science staff working in football. Specifically, four dimensions were presented namely the use of strengths to create opportunities and make the most of them, the use of strengths to avoid threats, work on weaknesses to take advantage of opportunities, and upgrade weaknesses to avoid threats.
Conclusion: ML analysis can be an invaluable ally for football clubs, sport science and medical departments due to its ability to analyze vast amounts of data and extract meaningful insights. Moreover, ML can enhance performance by assessing the risk of injury occurrence, physiological parameters, physical fitness, and optimizing training, recommending strategies based on opponent analysis, and identifying talent and assessing player suitability.

Item Type:	Article
Uncontrolled Keywords:	Machine learning, ML, strengths weaknesses opportunities and threats, SWOT
Subjects:	Q Science > QP Physiology
Divisions:	Faculty of Health & Science > School of Allied Health Sciences
Depositing User:	Marco Beato
Date Deposited:	08 Oct 2024 08:14
Last Modified:	08 Oct 2024 08:14
URI:	https://oars.uos.ac.uk/id/eprint/4315

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