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
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