Predictor Variables for A 100-km Race Time in Male Ultra-Marathoners

Abstract

In 169 male 100-km ultra-marathoners, the variables of anthropometry, training, and prerace experience, in order to predict race time, were investigated. In the bivariate analysis, age (r = .24), body mass (r = .20), Body Mass Index (r = .29), circumference of upper arm (r = .26), percent body fat (r = .45), mean weekly running hours (r = –.21), mean weekly running kilometers (r = –.43), mean speed in training (r = –.56), personal best time in a marathon (r = .65), the number of finished 100-km ultra-runs (r = .24), and the personal best time in a 100-km ultra-run (r = .72) were associated with race time. Stepwise multiple regression showed that training speed (p < .0001), mean weekly running kilometers (p < .0001), and age (p < .0001) were the best correlations for a 100-km race time. Performance may be predicted (n = 169, r² = .43) by the following equation: 100-km race time (min) = 1, 085.60 – 36.26 × (training speed, km/hr.) −1.43 × (training volume, km/wk.) + 2.50 × (age, yr.). Overall, intensity of training might be more important for a successful outcome in a 100-km race than anthropometric attributes. Motivation to train intensely for such an ultra-endurance run should be explored as this might be the key for a successful finish.