What Does It Take to Become a Professional Cyclist? A Laboratory-Based Longitudinal Analysis in Competitive Young Riders

Int J Sports Physiol Perform. 2023 Aug 23;18(11):1275-1282. doi: 10.1123/ijspp.2023-0083. Print 2023 Nov 1.

Abstract

Purpose: Laboratory-based indicators are commonly used for performance assessment in young cyclists. However, evidence supporting the use of these indicators mostly comes from cross-sectional research, and their validity as predictors of potential future performance remains unclear. We aimed to assess the role of laboratory variables for predicting transition from U23 (under 23 y) to professional category in young cyclists.

Methods: Sixty-five U23 male road cyclists (19.6 [1.5] y) were studied. Endurance (maximal graded test and simulated 8-min time trial [TT]), muscle strength/power (squat, lunge, and hip thrust), and body composition (assessed with dual-energy X-ray absorptiometry) indicators were determined. Participants were subsequently followed and categorized attending to whether they had transitioned ("Pro") or not ("Non-Pro") to the professional category during the study period.

Results: The median follow-up period was 3 years. Pro cyclists (n = 16) showed significantly higher values than Non-Pro riders (n = 49) for ventilatory thresholds, peak power output, peak oxygen uptake, and TT performance (all P < .05, effect size > 0.69) and lower levels of fat mass and bone mineral content/density (P < .05, effect size > 0.63). However, no significant differences were found for muscle strength/power indicators (P > .05, effect size < 49). The most accurate individual predictor was TT performance (overall predictive value = 76% for a cutoff value of 5.6 W·kg-1). However, some variables that did not reach statistical significance in univariate analyses contributed significantly to a multivariate model (R2 = .79, overall predictive value = 94%).

Conclusions: Although different "classic" laboratory-based endurance indicators can predict the potential of reaching the professional category in U23 cyclists, a practical indicator such as 8-minute TT performance showed the highest prediction accuracy.

Keywords: endurance; prediction; talent identification.

MeSH terms

  • Absorptiometry, Photon
  • Bicycling / physiology
  • Body Composition*
  • Bone Density
  • Cross-Sectional Studies
  • Humans
  • Male
  • Muscle Strength*
  • Oxygen Consumption