Previous experience, aerobic capacity and body composition are the best predictors for Olympic distance triathlon performance

Abstract

Objective: Present study examines predictors of the overall race time and disciplines in the Olympic distance triathlon.
Methods: Thirty-nine male and six female triathletes were evaluated for anthropometric, physiological, genetic, training, clinical and circadian characteristics. Body composition, maximum capacity for oxygen uptake (V˙O2max), maximum aerobic velocity (MAV), anaerobic threshold (AT), triathlon experience (TE) and XX genotype for α-actinin 3 affected total race time (p<0.05).
Results: Total race time can be predicted by MAV (ß = -0.430, t = -3.225, p = 0.003), TE (ß = -0.378, t = -3.605, p = 0.001), and percentage of lean mass (%LM) (ß = -0.332, t = -2.503, p = 0.017). Swimming can be predicted by MAV (ß = -0.403, t = -3.239, p = 0.002), TE (ß = -0.339, t = -2.876, p = 0.007), and AT%V˙O2max (ß = 0.281, t = 2.278, p = 0.028). Cycling can be predicted by MAV (ß = -0.341, t = -2.333, p = 0.025), TE (ß = -0.363, t = -3.172, p = 0.003), and %LM (ß = -0.326, t = -2.265, p = 0.029). In running split, MAV (ß = -0.768, t = -6.222, p < 0.001) was the only parameter present in the best multiple linear regression model.
Conclusion: The most important variables in multiple regression models for estimating performance were MAV, TE, AT and %LM.