Computational and complex network modeling for analysis of sprinter athletes' performance in track field tests

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.