LAUSR

This study aimed to identify predictive variables of performance for a 100-km race (Perf100-km) and develop an equation for predicting this performance using individual data, recent marathon performance (Perfmarathon), and environmental conditions at the start of the 100-km race. All runners who had performed official Perfmarathon and Perf100-km in France, both in 2019, were recruited. For each runner, gender, weight, height, body mass index (BMI), age, the personal marathon record (PRmarathon), date of the Perfmarathon and Perf100-km, and environmental conditions during the 100-km race (i.e., minimal and maximal air temperatures, wind speed, total amount of precipitation, relative humidity and barometric pressure) were collected. Correlations between the data were examined, and prediction equations were then developed using stepwise multiple linear regression analyses. Significant bivariate correlations were found between Perfmarathon (p<0.001, r = 0.838), wind speed (p<0.001, r = -0.545), barometric pressure (p<0.001, r = 0.535), age (p = 0.034, r = 0.246), BMI (p = 0.034, r = 0.245), PRmarathon (p = 0.065, r = 0.204) and Perf100-km in 56 athletes The, 2 prediction equations with larger sample (n = 591) were developed to predict Perf100-km, one including Perfmarathon, wind speed and PRmarathon (model 1, r² = 0.549; standard errors of the estimate, SEE = 13.2%), and the other including only Perfmarathon and PRmarathon (model 2, r² = 0.494; SEE = 14.0%). Perf100-km can be predicted with an acceptable level of accuracy from only recent Perfmarathon and PRmarathon, in amateur athletes who want to perform a 100 km for the first time.