10 km running performance predicted by a multiple linear regression model with allometrically adjusted variables

The aim of this study was to verify the power of VO(2max), peak treadmill running velocity (PTV), and running economy (RE), unadjusted or allometrically adjusted, in predicting 10 km running performance. Eighteen male endurance runners performed: 1) an incremental test to exhaustion to determine VO(2max) and PTV; 2) a constant submaximal run at 12 km·h(−1) on an outdoor track for RE determination; and 3) a 10 km running race. Unadjusted (VO(2max), PTV and RE) and adjusted variables (VO(2max)(0.72), PTV(0.72) and RE(0.60)) were investigated through independent multiple regression models to predict 10 km running race time. There were no significant correlations between 10 km running time and either the adjusted or unadjusted VO(2max). Significant correlations (p < 0.01) were found between 10 km running time and adjusted and unadjusted RE and PTV, providing models with effect size > 0.84 and power > 0.88. The allometrically adjusted predictive model was composed of PTV(0.72) and RE(0.60) and explained 83% of the variance in 10 km running time with a standard error of the estimate (SEE) of 1.5 min. The unadjusted model composed of a single PVT accounted for 72% of the variance in 10 km running time (SEE of 1.9 min). Both regression models provided powerful estimates of 10 km running time; however, the unadjusted PTV may provide an uncomplicated estimation.