An Educational Review on Machine Learning: A SWOT Analysis for Implementing Machine Learning Techniques in Football

Aug 4, 2026

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Football’s data abundance creates both opportunities and decision-making challenges. This review delivers two goals: a concise practitioner overview of machine learning analysis and a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis for implementing machine learning techniques in professional clubs. It clarifies differences between artificial intelligence and machine learning, and between machine learning and statistical analysis, then summarizes supervised, unsupervised, and reinforcement learning approaches. The SWOT example highlights actions for medical and sport science staff across four dimensions. Machine learning supports injury risk assessment, physiological monitoring, physical fitness evaluation, training optimization, opponent-informed strategies, and talent identification.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages23

Reading Time58 min

AccessFree to download

Abstract

Purpose and objectives

Methods and concepts (AI vs ML, ML vs statistics)

Machine learning learning approaches

SWOT application and four dimensions

Conclusion and practical value

Introduction

Decision-making and data-driven challenges

Role of AI and ML in football

Data mining and ML for practitioner decisions

Accepted author manuscript version reprinted, by permission, from International Journal of Sports Physiology and Performance (IJSPP), 2024, [https://doi.org/10.1123/ijspp.2024-0247](https://doi.org/10.1123/ijspp.2024-0247). © Human Kinetics, Inc. 1 An educational review on machine learning: a SWOT analysis for implementing 2 machine learning techniques in football. 3 4 Marco Beato 1*, Mohamed Hisham Jaward2, George P. Nassis3,4, Pedro Figueiredo3,5, Filipe 5 Manuel Clemente6,7,8, Peter Krustrup4,9 6 7 8 Affiliations 9 1. School of Allied Health Sciences, University of Suffolk, Ipswich, United Kingdom. 10 2. School of School of Technology, Business and Arts, University of Suffolk, Ipswich, 11 United Kingdom. 12 3. Physical Education Department, United Arab Emirates University, Al Ain, United 13 Arab Emirates. 14 4. Department of Sports Science and Clinical Biomechanics, Sport and Health 15 Sciences Cluster (SHSC), University of Southern Denmark, Denmark 16 5. Research Center in Sports Sciences, Health, Sciences and Human Development, 17 CIDESD, Vila Real, Portugal. 18 6. Escola Superior Desporto e Lazer, Instituto Politécnico de Viana do Castelo, Rua 19 Escola Industrial e Comercial de Nun’Álvares, 4900-347 Viana do Castelo, 20 Portugal. 21 7. Gdansk University of Physical Education and Sport, 80-336 Gdańsk, Poland. 22 8. Sport physical activity and health research innovation and technology center 23 (SPRINT), 4900-347 Viana do Castelo, Portugal. 24 9. Danish Institute for Advanced Study (DIAS), University of Southern Denmark, 25 Odense, Denmark. 26 27 28 *Corresponding author 29 Marco Beato, School of Allied Health Sciences, University of Suffolk, Ipswich, United 30 Kingdom, [email: ](email: m.beato@uos.ac.uk)[m.beato@uos.ac.uk](email: m.beato@uos.ac.uk) 31 32 Short title/running head: A SWOT analysis on machine learning in football. 33 34 36 Abstract 37 Purpose: The abundance of data in football presents both opportunities and challenges for 38 decision-making. Consequently, this review has two primary objectives: first, to provide 39 practitioners with a concise overview of the characteristics of machine learning (ML) analysis; 40 and second, to conduct a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis 41 regarding the implementation of ML techniques in professional football clubs. This review 42 explains the difference between artificial intelligence and ML, and the difference between ML 43 and statistical analysis. Moreover, we summarize and explain the characteristics of ML 44 learning approaches such as supervised learning, unsupervised learning and reinforcement 45 learning. Finally, we present an example of SWOT analysis, which suggests some actions to 46 be considered in applying ML techniques by the medical and sport science staff working in 47 football. Specifically, four dimensions were presented namely the use of strengths to create 48 opportunities and make the most of them, the use of strengths to avoid threats, work on 49 weaknesses to take advantage of opportunities, and upgrade weaknesses to avoid threats. 50 Conclusion: ML analysis can be an invaluable ally for football clubs, sport science and medical 51 departments due to its ability to analyze vast amounts of data and extract meaningful insights. 52 Moreover, ML can enhance performance by assessing the risk of injury occurrence, 53 physiological parameters, physical fitness, and optimizing training, recommending strategies 54 based on opponent analysis, and identifying talent and assessing player suitability. 55 56 Key Points: Strengths, Weaknesses, Opportunities, Threats, decision-making, performance 57 prediction, injury risk assessment, Soccer 58 59 60 61 62 63 64 65 66 67 INTRODUCTION 68 The decision-making process plays a critical role for practitioners working in football. 69 Practitioners aim to optimize the tr

FAQs

What are the two main objectives of this educational review?

To provide practitioners with a concise overview of machine learning analysis and to conduct a SWOT analysis on implementing machine learning techniques in professional football clubs.

How does the review distinguish artificial intelligence from machine learning?

AI is defined as developing computer systems that perform tasks normally requiring human intelligence, while ML refers to technologies and algorithms that identify patterns, make decisions, and improve through experience as a subset of AI.

What benefits does machine learning analysis offer to football clubs?

It can analyze large datasets to extract meaningful insights, enhance performance, and support tasks such as injury risk assessment, monitoring physiological parameters, optimizing training, opponent-based strategy recommendation, and talent identification.

Aug 4, 2026

Upload by Oliver