Predictions of the Distance Running Performances of Female Runners Using Different Tools

Abstract

This study examined the validity and compared the precision and accuracy of a distance-time linear model (DTLM), a power law and a nomogram to predict the distance running performances of female runners. Official rankings of French women for the 3000-m, 5000-m, and 10,000-m track-running events from 2005 to 2019 were examined. Each performance was predicted from two other performances. Between the actual and predicted performances, only DTLM showed a difference (p < 0.05). The magnitude of the differences in these predicted performances was small if not trivial. All predicted performances were significantly correlated with the actual ones, with a very high correlation coefficient (p < 0.001; r > 0.90), except for DTLM in the 3000-m, which showed a high correlation coefficient (p < 0.001; r > 0.895). Bias and 95% limits of agreement were acceptable because, whatever the method, they were ≤ -3.7 ± 10.8% on the 3000-m, 1.4 ± 4.3% on the 5000-m, and -2.5 ± 7.4% on the 10,000-m. The study confirms the validity of the three methods to predict track-running performance and suggests that the most accurate and precise model was the nomogram followed by the power law, with the DTLM being the least accurate.