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Effective injury forecasting in soccer with GPS training data and machine learning
Injuries have a great impact on professional soccer, due to their large influence on team performance and the considerable costs of rehabilitation for players. Existing studies in the literature provide just a preliminary understanding of which factors mostly affect injury risk, while an evaluation...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6059460/ https://www.ncbi.nlm.nih.gov/pubmed/30044858 http://dx.doi.org/10.1371/journal.pone.0201264 |
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author | Rossi, Alessio Pappalardo, Luca Cintia, Paolo Iaia, F. Marcello Fernàndez, Javier Medina, Daniel |
author_facet | Rossi, Alessio Pappalardo, Luca Cintia, Paolo Iaia, F. Marcello Fernàndez, Javier Medina, Daniel |
author_sort | Rossi, Alessio |
collection | PubMed |
description | Injuries have a great impact on professional soccer, due to their large influence on team performance and the considerable costs of rehabilitation for players. Existing studies in the literature provide just a preliminary understanding of which factors mostly affect injury risk, while an evaluation of the potential of statistical models in forecasting injuries is still missing. In this paper, we propose a multi-dimensional approach to injury forecasting in professional soccer that is based on GPS measurements and machine learning. By using GPS tracking technology, we collect data describing the training workload of players in a professional soccer club during a season. We then construct an injury forecaster and show that it is both accurate and interpretable by providing a set of case studies of interest to soccer practitioners. Our approach opens a novel perspective on injury prevention, providing a set of simple and practical rules for evaluating and interpreting the complex relations between injury risk and training performance in professional soccer. |
format | Online Article Text |
id | pubmed-6059460 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-60594602018-08-09 Effective injury forecasting in soccer with GPS training data and machine learning Rossi, Alessio Pappalardo, Luca Cintia, Paolo Iaia, F. Marcello Fernàndez, Javier Medina, Daniel PLoS One Research Article Injuries have a great impact on professional soccer, due to their large influence on team performance and the considerable costs of rehabilitation for players. Existing studies in the literature provide just a preliminary understanding of which factors mostly affect injury risk, while an evaluation of the potential of statistical models in forecasting injuries is still missing. In this paper, we propose a multi-dimensional approach to injury forecasting in professional soccer that is based on GPS measurements and machine learning. By using GPS tracking technology, we collect data describing the training workload of players in a professional soccer club during a season. We then construct an injury forecaster and show that it is both accurate and interpretable by providing a set of case studies of interest to soccer practitioners. Our approach opens a novel perspective on injury prevention, providing a set of simple and practical rules for evaluating and interpreting the complex relations between injury risk and training performance in professional soccer. Public Library of Science 2018-07-25 /pmc/articles/PMC6059460/ /pubmed/30044858 http://dx.doi.org/10.1371/journal.pone.0201264 Text en © 2018 Rossi et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Rossi, Alessio Pappalardo, Luca Cintia, Paolo Iaia, F. Marcello Fernàndez, Javier Medina, Daniel Effective injury forecasting in soccer with GPS training data and machine learning |
title | Effective injury forecasting in soccer with GPS training data and machine learning |
title_full | Effective injury forecasting in soccer with GPS training data and machine learning |
title_fullStr | Effective injury forecasting in soccer with GPS training data and machine learning |
title_full_unstemmed | Effective injury forecasting in soccer with GPS training data and machine learning |
title_short | Effective injury forecasting in soccer with GPS training data and machine learning |
title_sort | effective injury forecasting in soccer with gps training data and machine learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6059460/ https://www.ncbi.nlm.nih.gov/pubmed/30044858 http://dx.doi.org/10.1371/journal.pone.0201264 |
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