![]() ![]() ![]() Race time predictors are widely available on the Web, and typically predict time of a future race on the basis of previous race of a different distance. A second important question for long-distance runners concerns race time prediction, critical for pacing during the early stages of a race. ![]() Modifiable factors, such as training, may suggest changes that a runner might make to improve race times factors that cannot be modified, such as age or sex, can be used to make fair comparisons between different runners. One key question for such runners concerns factors associated with performance. Many millions of recreational runners compete in long-distance races each year. Our findings can be used to inform race training and to provide more accurate race time predictions for better pacing. ![]() 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). We built two models to predict marathon time. 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. 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 difference in velocity between males and females decreased with increasing distance. Sex, age, BMI and race training were associated with mean race velocity for all race distances. The cohort was split 2:1 into a training set and validation set to create models to predict race time. An Internet survey was used to collect data from recreational endurance runners ( Nā=ā2303). We examined factors associated with race performance and explored methods for race time prediction using information routinely available to a recreational runner. Studies of endurance running have typically involved elite athletes, small sample sizes and measures that require special expertise or equipment. ![]()
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