Predictors of performance in a 4-h mountain-bike race

This study aimed to cross validate previously developed predictive models of mountain biking performance in a new cohort of mountain bikers during a 4-h event (XC₄H). Eight amateur XC₄H cyclists completed a multidimensional assessment battery including a power profile assessment that consisted of maximal efforts between 6 and 600 s, maximal hand grip strength assessments, a video-based decision-making test as well as a XC₄H race. A multiple linear regression model was found to predict XC₄H performance with good accuracy (R² = 0.99; P < 0.01). This model consisted of relative to total cycling mass (body mass including competition clothing and bicycle mass), maximum power output sustained over 60 s relative to total cycling mass, peak left hand grip strength and two-line decision-making score. Previous models for Olympic distance MTB performance demonstrated merit (R² = 0.93; P > 0.05) although subtle changes improved the fit, significance and normal distribution of residuals within the model (R² = 0.99; P < 0.01), highlighting differences between the disciplines. The high level of predictive accuracy of the new XC₄H model further supports the use of a multidimensional approach in predicting MTB performance. The difference between the new, XC₄H and previous Olympic MTB predictive models demonstrates subtle differences in physiological requirements and performance predictors between the two MTB disciplines.