2017/05/09 by Iztok Fister, Fister, Iztok, Dušan Fister +7
Computer Science · Economics, Econometrics and Finance · Psychology · #Artificial Intelligence in Games #Educational Games and Gamification #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE) #Sports Analytics and Performance
paper · pdf · doi:10.48550/arxiv.1705.03302
openalex publication_date 2017/05/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.