The research team, led by Ahlan B. Lima and Humberto M. Carvalho of the Federal University of Santa Catarina and Lund University, systematically searched MEDLINE, Web of Science, and Scopus from database inception to October 2025. Following PRISMA guidelines and a protocol pre-registered on PROSPERO, the team screened thousands of records and ultimately included 28 longitudinal studies containing 41 independent samples spanning nine sports, from football and rugby to tennis, volleyball, and freestyle wrestling. Study durations ranged from 30 weeks to 11 years, with sample sizes between 10 and 849 participants and between three and twelve repeated measurement occasions per athlete. Only male athletes were analyzed, because the number of eligible studies including female athletes was insufficient to support separate modeling.

What sets this synthesis apart is its statistical machinery. Rather than averaging single snapshots of performance, the researchers applied Bayesian multilevel growth curve modeling to the repeated-measures data, fitting polynomial functions up to the cubic term to capture the nonlinear character of adolescent development. The multilevel structure acknowledged that repeated measurements are nested within individuals, individuals within studies, and studies within sports, while weakly regularizing priors kept estimates within biologically plausible ranges. From the fitted curves, the team derived the age at peak velocity—the moment when each fitness component improves fastest—using the first and second derivatives of the growth functions. Sensitivity analyses across alternative model specifications confirmed that the headline results were robust.

The central discovery is a striking split in developmental patterns. Vertical jump performance followed a pronounced curvilinear trajectory, rising from roughly 27 centimeters at age 10 to about 41 centimeters at age 17, with peak velocity occurring at 13.2 years. Endurance performance traced a similarly curved path, with peak velocity at 13.8 years. Both ages overlap the typical window of peak height velocity in adolescent boys, the period of most rapid growth in stature. Sprint performance, by contrast, improved in an almost perfectly linear fashion across ages 12 to 17, with 10-meter sprint times falling steadily from about 1.98 seconds to 1.83 seconds, without any distinct acceleration period corresponding to the pubertal growth spurt.

The physiological logic behind this divergence is compelling. Explosive power and endurance are tightly coupled to the biological upheavals of mid-puberty: surging muscle mass, increasing stature, enhanced cardiac stroke volume, greater capillary density, and improved oxidative enzyme capacity all peak in their developmental rate around the same years. Short-distance acceleration, however, appears to depend more on gradual neuromuscular adaptation and the slow accumulation of technical refinement in running mechanics—qualities that build consistently with training exposure rather than surging with a maturational trigger. The authors are careful to note that the temporal coincidence between fitness peaks and peak height velocity is a descriptive observation, not proof of causation, since biological maturity was not assessed in most included studies and training exposure could not be quantified.

Sport-specific curves added another layer of nuance. Rugby players posted the highest absolute vertical jump values, climbing from roughly 35 centimeters at age 10 to nearly 49 centimeters at 17, a profile consistent with the collision demands and resisted accelerations of their sport. Football and handball occupied intermediate positions, while basketball and volleyball—despite being jump-heavy sports—showed lower absolute values, likely reflecting the technical specificity of sport-specific jumping mechanics rather than inferior power development. Freestyle wrestling began with the lowest values but showed steep progression. Crucially, however, the shape of the developmental curves was broadly similar across sports within each fitness component, and sprint development showed minimal sport differences at all, suggesting that short-acceleration capacity is governed by general neuromuscular maturation shared across disciplines.

The practical payoff comes in the form of reference values that coaches can apply immediately. A countermovement jump without arm swing is expected to increase from approximately 25.5 centimeters at age 10 to 41.6 centimeters at 17; Yo-Yo IR1 distance rises from about 893 meters at age 12 to 1,610 meters at 17. The reported 68 percent credible intervals capture the range within which roughly two-thirds of athletes of the same age and sport are expected to fall, mirroring the convention used in standard pediatric growth charts. Performance persistently below the lower bound may warrant closer monitoring, while values above the upper bound may simply signal early maturation or advanced training exposure rather than exceptional talent.

The study is candid about its limitations. Aligning data by chronological age rather than biological maturity means individual variation in the timing of the growth spurt—roughly plus or minus one year around the population mean—is only partially captured. Publication bias assessments revealed asymmetry for sprint and endurance outcomes, so those pooled trajectories deserve somewhat more caution, whereas the vertical jump estimates showed no such asymmetry. Attrition is another concern: coach-driven deselection of weaker athletes could progressively inflate performance estimates at older ages. And because the analysis is restricted to males, the authors stress that female athletes, who follow distinct maturational timelines, require dedicated longitudinal evidence that does not yet exist in sufficient quantity.

Even with those caveats, the implications for youth sport are hard to overstate. The findings argue against early selection based on current performance levels, since a 12-year-old’s ranking may reflect nothing more than where he sits on his own maturational clock. Instead, they support long-term development models that prioritize progressive, individualized adaptation: expecting the fastest jump and endurance gains between ages 12 and 15, valuing steady year-on-year sprint improvement without demanding pubertal leaps, and interpreting every test result against age-referenced norms rather than against the teammate standing nearby. For a field long dominated by intuition and cross-sectional snapshots, this synthesis of nearly three decades of longitudinal data offers something rare—a rigorous, probabilistic map of what normal athletic development actually looks like.

Subject of Research: Longitudinal development of vertical jump, sprint, and endurance performance in male youth athletes

Article Title: Development of Physical Fitness in Young Athletes: A Longitudinal Meta-analysis

Article References: Lima, A. B., Quinaud, R. T., Reis, G. F., Karasiak, F. C., Ageberg, E., Clemente, F. M., Gonçalves, C. E., & Carvalho, H. M. (2026). Development of Physical Fitness in Young Athletes: A Longitudinal Meta-analysis. Sports Medicine – Open, 12(1), Article 144. https://doi.org/10.1186/s40798-026-01109-7

Image Credits: AI Generated

DOI: 10.1186/s40798-026-01109-7

Keywords: youth athletes, physical fitness, longitudinal meta-analysis, Bayesian modeling, vertical jump, sprint performance, endurance, peak height velocity, adolescent development, growth curves, sports medicine, athlete monitoring