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Wellness Forecasting by External and Internal Workloads in Elite Soccer Players: A Machine Learning Approach
Training for success has increasingly become a balance between maintaining high performance standards and avoiding the negative consequences of accumulated fatigue. The aim of this study is to develop a big data analytics framework to predict players’ wellness according to the external and internal...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9240643/ https://www.ncbi.nlm.nih.gov/pubmed/35784892 http://dx.doi.org/10.3389/fphys.2022.896928 |
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author | Rossi, Alessio Perri, Enrico Pappalardo, Luca Cintia, Paolo Alberti, Giampietro Norman, Darcy Iaia, F. Marcello |
author_facet | Rossi, Alessio Perri, Enrico Pappalardo, Luca Cintia, Paolo Alberti, Giampietro Norman, Darcy Iaia, F. Marcello |
author_sort | Rossi, Alessio |
collection | PubMed |
description | Training for success has increasingly become a balance between maintaining high performance standards and avoiding the negative consequences of accumulated fatigue. The aim of this study is to develop a big data analytics framework to predict players’ wellness according to the external and internal workloads performed in previous days. Such a framework is useful for coaches and staff to simulate the players’ response to scheduled training in order to adapt the training stimulus to the players’ fatigue response. 17 players competing in the Italian championship (Serie A) were recruited for this study. Players’ Global Position System (GPS) data was recorded during each training and match. Moreover, every morning each player has filled in a questionnaire about their perceived wellness (WI) that consists of a 7-point Likert scale for 4 items (fatigue, sleep, stress, and muscle soreness). Finally, the rate of perceived exertion (RPE) was used to assess the effort performed by the players after each training or match. The main findings of this study are that it is possible to accurately estimate players’ WI considering their workload history as input. The machine learning framework proposed in this study is useful for sports scientists, athletic trainers, and coaches to maximise the periodization of the training based on the physiological requests of a specific period of the season. |
format | Online Article Text |
id | pubmed-9240643 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92406432022-06-30 Wellness Forecasting by External and Internal Workloads in Elite Soccer Players: A Machine Learning Approach Rossi, Alessio Perri, Enrico Pappalardo, Luca Cintia, Paolo Alberti, Giampietro Norman, Darcy Iaia, F. Marcello Front Physiol Physiology Training for success has increasingly become a balance between maintaining high performance standards and avoiding the negative consequences of accumulated fatigue. The aim of this study is to develop a big data analytics framework to predict players’ wellness according to the external and internal workloads performed in previous days. Such a framework is useful for coaches and staff to simulate the players’ response to scheduled training in order to adapt the training stimulus to the players’ fatigue response. 17 players competing in the Italian championship (Serie A) were recruited for this study. Players’ Global Position System (GPS) data was recorded during each training and match. Moreover, every morning each player has filled in a questionnaire about their perceived wellness (WI) that consists of a 7-point Likert scale for 4 items (fatigue, sleep, stress, and muscle soreness). Finally, the rate of perceived exertion (RPE) was used to assess the effort performed by the players after each training or match. The main findings of this study are that it is possible to accurately estimate players’ WI considering their workload history as input. The machine learning framework proposed in this study is useful for sports scientists, athletic trainers, and coaches to maximise the periodization of the training based on the physiological requests of a specific period of the season. Frontiers Media S.A. 2022-06-15 /pmc/articles/PMC9240643/ /pubmed/35784892 http://dx.doi.org/10.3389/fphys.2022.896928 Text en Copyright © 2022 Rossi, Perri, Pappalardo, Cintia, Alberti, Norman and Iaia. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Rossi, Alessio Perri, Enrico Pappalardo, Luca Cintia, Paolo Alberti, Giampietro Norman, Darcy Iaia, F. Marcello Wellness Forecasting by External and Internal Workloads in Elite Soccer Players: A Machine Learning Approach |
title | Wellness Forecasting by External and Internal Workloads in Elite Soccer Players: A Machine Learning Approach |
title_full | Wellness Forecasting by External and Internal Workloads in Elite Soccer Players: A Machine Learning Approach |
title_fullStr | Wellness Forecasting by External and Internal Workloads in Elite Soccer Players: A Machine Learning Approach |
title_full_unstemmed | Wellness Forecasting by External and Internal Workloads in Elite Soccer Players: A Machine Learning Approach |
title_short | Wellness Forecasting by External and Internal Workloads in Elite Soccer Players: A Machine Learning Approach |
title_sort | wellness forecasting by external and internal workloads in elite soccer players: a machine learning approach |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9240643/ https://www.ncbi.nlm.nih.gov/pubmed/35784892 http://dx.doi.org/10.3389/fphys.2022.896928 |
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