Complex Networks Models and Spectral Decomposition in the Analysis of Swimming Athletes’ Performance at Olympic Games

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.

Network Physiology of Exercise: Vision and Perspectives

“…These results indicate that strengthening connectivity among physiological systems provides optimal responses to hypoxic exposure and reflects the adaptive adjustment of the cardiorespiratory system in trained individuals. Furthermore, it has been recently demonstrated that the Network Physiology approach applied to exercise exhibits high sensitivity to quantify the performance of elite athletes participating at Olympic Games and to differentiate between fitness levels of those who win medals and those who do not (Pereira-Ferrero et al, 2019).…”

The New Field of Network Physiology: Building the Human Physiolome

“…In fact, in recent years, we have already witnessed the broad impact of introducing novel concepts and methods derived from modern statistical physics and network theory to biology and medicine, shifting the paradigm from reductionism to a new integrative framework essential to address fundamentally new problems in systems biology (Yao et al, 2019;Prats-Puig et al, 2020;Corkey and Deeney, 2020;Rizi et al, 2021;Barajas-Martínez et al, 2020), neuroscience (Castelluzzo et al, 2020;Pa¨eske et al, 2020;Fesce, 2020;Stramaglia et al, 2021), physiology (Podobnik et al, 2020;Zmazek et al, 2021), clinical medicine (Loscalzo and Barabasi, 2011;Delussi et al, 2020;Li et al, 2020;Liu et al, 2020;McNorgan et al, 2020;Tan et al, 2020;Haug et al, 2021;Liu et al, 2021) and even drug discovery (Hopkins, 2008). A central focus of research within this integrative framework is the interplay between structural connectivity and functional dependency, a key problem in neuroscience, brain research (Bullmore and Sporns, 2009;Gallos et al, 2012;Rothkegel and Lehnertz, 2014;Liu et al, 2015a;Bolton et al, 2020;Wang and Liu, 2020) and human physiology (Pereira-Ferrero et al, 2019;Lavanga et al, 2020;Barajas-Martínez et al, 2021;Gao et al, 2018;Balagué et al, 2020;Porta et al, 2017;Lioi et al, 2017;Jiang et al, 2021). As a result, new physical models have been motivated and proposed to investigate the dynamical consequences of adaptive networks…”

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

“…The observed embryonic abnormalities include pericardial edema, yolk sac edema, craniofacial malformations, spinal deformities, delayed swim bladder inflation, and mortality. The network connections (links) between nodes are assigned weights of association indicating the strength of the co-occurrence [ 22 , 23 , 24 ]. Weights are computed utilizing the significance of association from Fisher’s exact test as reported in Figure 2 .…”