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Longitudinal performance development in youth track and field: A population-level study
1School of Economics and Business, Norwegian University of Life Sciences (NMBU), Norway.
Journal of Science and Medicine in Sport
|July 25, 2026
Summary
Longitudinal analysis reveals that competitive youth athletes develop beyond cross-sectional benchmarks. These individual performance trajectories offer a better guide for coaching and talent identification than static performance curves.
Area of Science:
- Sports Science
- Human Performance
- Developmental Trajectories
Background:
- Cross-sectional performance benchmarks are commonly used to assess youth athletic development.
- These benchmarks may not accurately reflect individual athlete progression over time.
- Understanding sex-specific developmental patterns is crucial for targeted training.
Purpose of the Study:
- To construct longitudinal developmental trajectories for competitive youth athletes.
- To document sex-specific athletic development patterns.
- To evaluate the accuracy of cross-sectional performance benchmarks against individual development.
Main Methods:
- Retrospective longitudinal cohort study analyzing season-best performances from a large national athletics database (31,028 athletes, ages 10-25).
- Utilized within-athlete mixed-effects models and descriptive panel trajectories for 60m, 800m, high jump, and long jump.
- Compared longitudinal data with cross-sectional top-100 benchmarks and analyzed relative age effects.
Main Results:
- Individual performance improved through the late teens and early twenties, surpassing cross-sectional benchmarks which plateaued earlier.
- Significant sex-specific divergence in performance emerged around puberty (ages 13-14), stabilizing by age 18.
- Cross-sectional benchmarks substantially underestimated actual development, with gaps up to 48 percentage points in high jump.
Conclusions:
- Cross-sectional top-N curves inaccurately represent individual athletic development.
- Longitudinal trajectories provide a more realistic depiction of athlete progression.
- Longitudinal data are superior for coaching insights and talent identification in youth sports.
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