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

Journal: International journal of sports physiology and performance
PMID:

Abstract

PURPOSE: The abundance of data in football presents both opportunities and challenges for decision making. Consequently, this review has 2 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 a SWOT analysis that suggests some actions to be considered in applying ML techniques by medical and sport science staff working in football. Specifically, 4 dimensions are presented: the use of strengths to create opportunities and make the most of them, the use of strengths to avoid threats, working on weaknesses to take advantage of opportunities, and upgrading weaknesses to avoid threats.

Authors

  • Marco Beato
    School of Health and Sports Science, University of Suffolk, United Kingdom.
  • Mohamed Hisham Jaward
    School of Engineering,Electrical and Computer Systems Engineering, Monash University Malaysia, 47500 Selangor, Malaysia.
  • George P Nassis
    Physical Education Department, College of Education, United Arab Emirates University, Al Ain, United Arab Emirates.
  • Pedro Figueiredo
    Physical Education Department, College of Education, United Arab Emirates University, Al Ain, United Arab Emirates.
  • Filipe Manuel Clemente
    Escola Superior Desporto e Lazer, Instituto Politécnico de Viana do Castelo, Rua Escola Industrial e Comercial de Nun'Álvares, Viana do Castelo, Portugal.
  • Peter Krustrup
    Department of Sports Science and Clinical Biomechanics, SDU Sport and Health Sciences Cluster (SHSC), University of Southern Denmark, Odense 3450, Denmark.