Calculation of Critical Speed from Raw Training Data in Recreational Marathon Runners

ntroduction. Critical speed (CS) represents the highest intensity at which a physiological steady state may be reached. The aim of this study was to evaluate whether estimations of CS obtained from raw training data can predict performance and pacing in marathons. Methods. We investigated running activities logged into an online fitness platform by >25,000 runners prior to big-city marathons. Each activity contained time, distance, and elevation every 100 m. We computed grade-adjusted pacing and the fastest pace recorded for a set of target distances (400, 800, 1000, 1500, 3000, 5000 m). CS was determined as the slope of the distance-time relationship using all combinations of, at least, three target distances. Results. The relationship between distance and time was linear, irrespective of the target distances used (pooled mean ± standard deviation: R2 = 0.9999±0.0001). T he estimated values of CS from all models were not different (3.74±0.08 m∙s-1), and all models correlated with marathon performance (R2 = 0.672±0.036, error = 8.01±0.51%). CS from the model including 400, 800 and 5000 m best predicted performance (R2 = 0.695, error = 7.67%), and was used in further analysis. Runners completed the marathon at 84.8±13.6% CS, with faster runners competing at speeds closer to CS (93.0 % CS for 150 min marathon times vs. 78.9% CS for 360 min marathon times). Runners who completed the first half of the marathon at >94% of their CS, and particularly faster than CS, were more likely slowdown by more than 25% in the second half of race. Conclusion. This study suggests that estimations of CS from raw training data can successfully predict marathon performance and provide useful pacing information.