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  • Title: Predictor variables for a 100-km race time in male ultra-marathoners.
    Author: Knechtle B, Knechtle P, Rosemann T, Lepers R.
    Journal: Percept Mot Skills; 2010 Dec; 111(3):681-93. PubMed ID: 21319608.
    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) = 1085.60 - 36.26 x (training speed, km/hr.) - 1.43 x (training volume, km/wk.) + 2.50 x (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.
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