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Complex Networks Models and Spectral Decomposition in the Analysis of Swimming Athletes’ Performance at Olympic Games
This study aims to present complex network models which analyze professional swimmers of 50-m freestyle Olympic competitions, comparing characteristics and variables that are considered performance determinants. This comparative research includes Olympic medalists’ versus non-medalists’ behavior. Us...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6733958/ https://www.ncbi.nlm.nih.gov/pubmed/31551810 http://dx.doi.org/10.3389/fphys.2019.01134 |
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author | Pereira-Ferrero, Vanessa Helena Lewis, Theodore Gyle Ferrero, Luciane Graziele Pereira Duarte, Leonardo Tomazeli |
author_facet | Pereira-Ferrero, Vanessa Helena Lewis, Theodore Gyle Ferrero, Luciane Graziele Pereira Duarte, Leonardo Tomazeli |
author_sort | Pereira-Ferrero, Vanessa Helena |
collection | PubMed |
description | This study aims to present complex network models which analyze professional swimmers of 50-m freestyle Olympic competitions, comparing characteristics and variables that are considered performance determinants. This comparative research includes Olympic medalists’ versus non-medalists’ behavior. Using data from 40 athletes with a mean age, weight and height of 26 ± 2.9 years, 87 ± 5.59 kg, 193 ± 3.85 cm, respectively, at the Olympics of 2000, 2004, 2008, 2012, and 2016 (16-year interval), we built two types of complex networks (graphs) for each edition, using mathematical correlations, metrics and the spectral decomposition analysis. It is possible to show that complex metrics behave differently between medalists and non-medalists. The spectral radius (SR) proved to be an important form of evaluation since in all 5 editions it was higher among medalists (SR results: 3.75, 3.5, 3.39, 2.91, and 3.66) compared to non-medalists (2.18, 2.51, 2.23, 2.07, and 2.04), with significantly differences between. This study introduces a remarkable tool in the evaluation of the performance of groups of swimming athletes by complex networks, and is relevant to athletes, coaches, and even amateurs, regarding how individual variables relate to competition results and are reflected in the SR for the best performance. In addition, this is a general method and may, in the future, be developed in the analysis of other competitive sports. |
format | Online Article Text |
id | pubmed-6733958 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-67339582019-09-24 Complex Networks Models and Spectral Decomposition in the Analysis of Swimming Athletes’ Performance at Olympic Games Pereira-Ferrero, Vanessa Helena Lewis, Theodore Gyle Ferrero, Luciane Graziele Pereira Duarte, Leonardo Tomazeli Front Physiol Physiology This study aims to present complex network models which analyze professional swimmers of 50-m freestyle Olympic competitions, comparing characteristics and variables that are considered performance determinants. This comparative research includes Olympic medalists’ versus non-medalists’ behavior. Using data from 40 athletes with a mean age, weight and height of 26 ± 2.9 years, 87 ± 5.59 kg, 193 ± 3.85 cm, respectively, at the Olympics of 2000, 2004, 2008, 2012, and 2016 (16-year interval), we built two types of complex networks (graphs) for each edition, using mathematical correlations, metrics and the spectral decomposition analysis. It is possible to show that complex metrics behave differently between medalists and non-medalists. The spectral radius (SR) proved to be an important form of evaluation since in all 5 editions it was higher among medalists (SR results: 3.75, 3.5, 3.39, 2.91, and 3.66) compared to non-medalists (2.18, 2.51, 2.23, 2.07, and 2.04), with significantly differences between. This study introduces a remarkable tool in the evaluation of the performance of groups of swimming athletes by complex networks, and is relevant to athletes, coaches, and even amateurs, regarding how individual variables relate to competition results and are reflected in the SR for the best performance. In addition, this is a general method and may, in the future, be developed in the analysis of other competitive sports. Frontiers Media S.A. 2019-09-03 /pmc/articles/PMC6733958/ /pubmed/31551810 http://dx.doi.org/10.3389/fphys.2019.01134 Text en Copyright © 2019 Pereira-Ferrero, Lewis, Ferrero and Duarte. http://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 Pereira-Ferrero, Vanessa Helena Lewis, Theodore Gyle Ferrero, Luciane Graziele Pereira Duarte, Leonardo Tomazeli Complex Networks Models and Spectral Decomposition in the Analysis of Swimming Athletes’ Performance at Olympic Games |
title | Complex Networks Models and Spectral Decomposition in the Analysis of Swimming Athletes’ Performance at Olympic Games |
title_full | Complex Networks Models and Spectral Decomposition in the Analysis of Swimming Athletes’ Performance at Olympic Games |
title_fullStr | Complex Networks Models and Spectral Decomposition in the Analysis of Swimming Athletes’ Performance at Olympic Games |
title_full_unstemmed | Complex Networks Models and Spectral Decomposition in the Analysis of Swimming Athletes’ Performance at Olympic Games |
title_short | Complex Networks Models and Spectral Decomposition in the Analysis of Swimming Athletes’ Performance at Olympic Games |
title_sort | complex networks models and spectral decomposition in the analysis of swimming athletes’ performance at olympic games |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6733958/ https://www.ncbi.nlm.nih.gov/pubmed/31551810 http://dx.doi.org/10.3389/fphys.2019.01134 |
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