Cycling performance in competitive Dutch youth cyclists: More than just power?

The determinants of youth cycling performance are still understudied. This study aims to relate multidimensional performance characteristics of road cyclists (U17-category) to a ‘Youth Seasonal Cycling Performance Score’ (YSCPS). This score represents the weighted average of a cyclist's race results over a season. In total 264 cyclists (209 male, age 15.6 ± 0.5, cycling experience 5.3 ± 2.5 years) were assessed on anthropometrical, physiological, technical and psychological tests in a Dutch talent development programme. The relation between test results and YSCPS was investigated using (1) correlational analysis; and (2) machine learning (XGBoost). In males, significant correlations with YSCPS (|ρ| = 0.23–0.63) were found for (in decreasing importance): physiological variables (5 min average power output, sprinting and climbing performance), training history, age, technical skills, and anthropometric variables (maturity status, height, body mass, BMI), but not for psychological variables. In females, YSCPS significantly correlated with 5 min. average power output only (ρ = 0.63). The accuracies (R 2 ) of the machine learning models for cycling performance were 0.44 (males) and 0.30 (females), with physiological variables and training history being the most important predictors of YSCPS for both genders. It can be concluded that physiological variables mainly relate to cycling performance for Dutch U17 cyclists. Acknowledging the relation between task requirements and cyclists’ performance characteristics as well as the distinction between current performance and the development thereof, the results cannot be generalized to cyclists in other countries or older populations without consideration. Still, practitioners can use these results to identify and develop youth cycling talent in The Netherlands.