Development of juvenile sprint performance in boys: analysis of speed phases : a cross-sectional study by age

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Bleeker, Simon ; Siener, Maximilian:
Development of juvenile sprint performance in boys: analysis of speed phases : a cross-sectional study by age.
In: Frontiers in Sports and Active Living. Bd. 7 (2025) . - 1701476.
ISSN 2624-9367
DOI: https://doi.org/10.3389/fspor.2025.1701476

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Abstract

Introduction: Sprinting performance in youth is typically assessed using fixed distances, although sprinting consists of distinct phases that develop differently across age. Little is known about how acceleration, maximum velocity (Vmax), and deceleration phases change during growth. This study aimed to analyze sprint phase distribution in boys aged 12–19 years and to develop practical models for estimating key sprint parameters when advanced measurement systems are unavailable.
Methods: A total of 117 boys performed maximal 100 m sprints, with continuous velocity recorded via a 100 Hz laser distance meter. Polynomial–smoothed velocity curves were used to identify acceleration, Vmax, and deceleration phases. Differences across age groups (U12–U20) were examined using ANOVA and Tukey–HSD. Multiple linear regression assessed the contribution of each phase to 100 m sprint time. Nonlinear regression models estimated Vmax and acceleration distance based on age and 50 m sprint time.
Results: Acceleration distance increased from 18.8 m (U12) to 24.1 m (U20), whereas deceleration distance declined substantially from 52.4 m to 22.3 m. The Vmax phase more than doubled with age (24.7 m to 47.3 m). Acceleration distance was the only significant predictor of 100 m time (p < 0.001). The nonlinear model predicting Vmax demonstrated strong accuracy (R² = 0.784), and the model predicting acceleration distance explained 59.8 of the variance.
Discussion: Sprint phase distribution changes markedly during adolescence, with older boys demonstrating longer acceleration and Vmax phases alongside reduced deceleration. Extended acceleration phases are the strongest determinant of 100 m performance. The presented regression tools offer practical options for estimating Vmax and acceleration characteristics when advanced technology is not available.