Making up for the deficit in a marathon run

TL;DR

This paper introduces an automatic method using Differential Evolution to analyze and compensate for performance deficits in marathon runners, based on real-world data from wearable devices.

Contribution

It proposes a novel application of Differential Evolution for post-race analysis to identify and make up for performance deficits in marathon running.

Findings

Differential Evolution shows promise for analyzing marathon performance deficits.

Real-world data from wearable devices was effectively used in the case study.

Initial experiments indicate potential for future development of this method.

Abstract

To predict the final result of an athlete in a marathon run thoroughly is the eternal desire of each trainer. Usually, the achieved result is weaker than the predicted one due to the objective (e.g., environmental conditions) as well as subjective factors (e.g., athlete's malaise). Therefore, making up for the deficit between predicted and achieved results is the main ingredient of the analysis performed by trainers after the competition. In the analysis, they search for parts of a marathon course where the athlete lost time. This paper proposes an automatic making up for the deficit by using a Differential Evolution algorithm. In this case study, the results that were obtained by a wearable sports-watch by an athlete in a real marathon are analyzed. The first experiments with Differential Evolution show the possibility of using this method in the future.