Computational and Complex Network Modeling for Analysis of Sprinter Athletes' Performance in Track Field Tests.

Vanessa H Pereira,Luiz F P Ribeiro,Theodore G Lewis,Claudio A Gobatto,Fúlvia B Manchado-Gobatto,Filipe A B Sousa,Ivan G M Dos Reis,Wladimir R Beck

doi:10.3389/fphys.2018.00843

Vanessa H Pereira, Luiz F P Ribeiro + Show 6 more

Open Access

https://doi.org/10.3389/fphys.2018.00843

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Abstract

Sports and exercise today are popular for both amateurs and athletes. However, we continue to seek the best ways to analyze best athlete performances and develop specific tools that may help scientists and people in general to analyze athletic achievement. Standard statistics and cause-and-effect research, when applied in isolation, typically do not answer most scientific questions. The human body is a complex holistic system exchanging data during activities, as has been shown in the emerging field of network physiology. However, the literature lacks studies regarding sports performance, running, exercise, and more specifically, sprinter athletes analyzed mathematically through complex network modeling. Here, we propose complex models to jointly analyze distinct tests and variables from track sprinter athletes in an untargeted manner. Through complex propositions, we have incorporated mathematical and computational modeling to analyze anthropometric, biomechanics, and physiological interactions in running exercise conditions. Exercise testing associated with complex network and mathematical outputs make it possible to identify which responses may be critical during running. The physiological basis, aerobic, and biomechanics variables together may play a crucial role in performance. Coaches, trainers, and runners can focus on improving specific outputs that together help toward individuals’ goals. Moreover, our type of analysis can inspire the study and analysis of other complex sport scenarios.

Highlights

Mathematics, exercise, complex networks, and physiology can be integrated to answer questions asked by both the general public and scientific experts (Noakes, 2012)
This complex model is the result of measured and calculated parameters according to the test realized, as mentioned in the Materials and Methods section: (i) Time Limit tests, (ii) maximum accumulated oxygen deficit (MAOD) – Anaerobic Capacity tests, (iii) Incremental tests to determine aerobic power and aerobic capacity parameters and data, and (iv) Anthropometric data
These physiological-related nodes can be viewed as hubs in each structured network of influences, which means that Aerobic Power and Aerobic Capacity individualized evaluations are necessary for specialized athletes to sustain an ideal power intensity output for track competitions under the exercise conditions investigated

Summary

Introduction

Mathematics, exercise, complex networks, and physiology can be integrated to answer questions asked by both the general public and scientific experts (Noakes, 2012). The integration of sports analytics with complex networks is called complex sports analytics This new field helps scientists build predictive models for better decision-making, highlighting the importance of complex analysis that goes beyond standard statistics (Davenport and Harris, 2007). A complex network is a mathematical representation of measurable variables as nodes and its interactions as links (a graph) (Lewis, 2009). Such representation makes it possible to regard complex network structures as a Complex Network on Athletes’ Performance promising tool for predictive models (Bashan et al, 2012). The main novelty here is to determine a distinct manner of analyzing data in an untargeted manner, inspired by others’ work among the first to apply network physiology to analyze physiological signals (Bashan et al, 2012; Bartsch et al, 2015; D’Agostino, 2016) and metabolites in exercise (Lee et al, 2011); we use complex networks, which have proven to be a remarkable tool

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