ArticleFrontiers in physiology2019
Complex Networks Models and Spectral Decomposition in the Analysis of Swimming Athletes' Performance at Olympic Games.
Article in Frontiers in physiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Multivariate dynamic association analysis between sprint interval training and adolescent freestyle swimming performance.BMC sports science, medicine & rehabilitation · 2026Article
- Development of a Dynamic Network Model to Identify Temporal Patterns of Structural Malformations in Zebrafish Embryos Exposed to a Model Toxicant, Tris(4-chlorophenyl)methanol.Journal of xenobiotics · 2023Article
- Associations among sleep, hematologic profile, and aerobic and anerobic capacity of young swimmers: A complex network approach.Frontiers in physiology · 2022Article
- The New Field of Network Physiology: Building the Human Physiolome.Frontiers in network physiology · 2021Article
- Network Physiology of Exercise: Vision and Perspectives.Frontiers in physiology · 2020Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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