MAP Best Performances Prediction for Endurance Runners

The preparation of long-distance runners requires to estimate their potential race performances beforehand. Athlete performances can be modeled based on their past records, but the task is made difficult because of the high variability in runner race performances. This paper presents a maximum a posteriori (MAP) estimation that addresses the issues related to this high variability. The inclusion of athlete priors and a specific residual model are inferred with the help of a large set of race results