Where Bolt and Bekele meet: the analytical basis of running performance estimates

This paper proposes a self-contained analytical model for the prediction of individual running performances. The model uses two personal bests for calibration, and then allows the prediction of the athlete’s personal bests for any other distance. It is based on a simple, first order estimate of the way lap time increments with total distance. Also, the model accounts for delays that occur during start-up. It therefore covers a wide range of events including endurance and sprinting distances. The model is validated with empirical data of a variety of world class and sub top athletes. Outcomes display valid and reliable predictions with inaccuracies typically around 1%. It greatly outperforms existing models (typically 3% or higher). Importantly, the model is transparent, since it is based on theoretical principles rather than arbitrariness and negotiation. It is self-contained, easy to use and affordable, because it does not require any physiological or biomechanical tests to be carried out first. Also, the model displays a universal validity, as the results suggest its applicability for any speed and distance related sports event, including running, speed skating and swimming