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Computational and Complex Network Modeling for Analysis of Sprinter Athletes' Performance in Track Field Tests
Vanessa H Pereira1, Claudio A Gobatto1, Theodore G Lewis2
1Laboratory of Applied Sport Physiology, School of Applied Sciences, University of Campinas, Limeira, Brazil.
Frontiers in Physiology
|July 24, 2018
Summary
This study introduces complex network models to analyze sprinter performance, integrating physiological and biomechanical data. This approach identifies key variables for improving athletic achievement in running and other sports.
Area of Science:
- Sports Science
- Physiology
- Biomathematics
Background:
- Athletic performance analysis often relies on isolated statistics, failing to capture the human body's complexity.
- Network physiology highlights the interconnectedness of bodily systems during activity.
- Limited research exists on applying complex network modeling to sprinter performance.
Purpose of the Study:
- To develop and propose complex network models for analyzing track sprinter athletes.
- To jointly analyze diverse variables including anthropometric, biomechanical, and physiological data.
- To identify critical physiological and biomechanical factors influencing running performance.
Main Methods:
- Utilized complex network modeling and mathematical/computational approaches.
- Integrated untargeted analysis of distinct tests and variables from sprinter athletes.
- Applied models to running exercise conditions, examining interactions between variables.
Main Results:
- Demonstrated the utility of complex network outputs in identifying critical running responses.
- Highlighted the combined importance of physiological basis, aerobic capacity, and biomechanics in performance.
- Provided a framework for coaches and trainers to target specific performance-enhancing outputs.
Conclusions:
- Complex network modeling offers a powerful tool for analyzing athletic achievement in sprinters.
- This methodology can guide targeted training interventions by pinpointing key performance drivers.
- The approach has potential applications for studying other complex sports scenarios.