An empirical study of race times in recreational endurance runners. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Vickers, Andrew; Vertosick, Emily
• Is Part Of: BMC sports science, medicine & rehabilitation. Volume 8:Issue 1(2016)
• Page Start: 1
• Page End: 9
• Publication Date: 2016-12
• Subjects: Performance -- Running -- Sports training -- Prediction modeling; Sports medicine -- Periodicals; Sports injuries -- Patients -- Rehabilitation -- Periodicals; Sports Medicine -- Periodicals; Athletic Injuries -- rehabilitation -- Periodicals; 617.102705
• DOI: 10.1186/s13102-016-0052-y
• Languages: English
• ISSNs: 2052-1847

Abstract

Abstract Background Studies of endurance running have typically involved elite athletes, small sample sizes and measures that require special expertise or equipment. Methods We examined factors associated with race performance and explored methods for race time prediction using information routinely available to a recreational runner. An Internet survey was used to collect data from recreational endurance runners (N = 2303). The cohort was split 2:1 into a training set and validation set to create models to predict race time. Results Sex, age, BMI and race training were associated with mean race velocity for all race distances. The difference in velocity between males and females decreased with increasing distance. Tempo runs were more strongly associated with velocity for shorter distances, while typical weekly training mileage and interval training had similar associations with velocity for all race distances. The commonly used Riegel formula for race time prediction was well-calibrated for races up to a half-marathon, but dramatically underestimated marathon time, giving times at least 10 min too fast for half of runners. We built two models to predict marathon time. The mean squared error for Riegel was 381 compared to 228 (model based on one prior race) and 208 (model based on two prior races). Conclusions Our findings can be used to inform race training and to provide more accurate race time predictions for better pacing.