Are There Sensitive Periods for Skill Development in Male Adolescent Basketball Players?
Bibliographic record
Abstract
PURPOSE: Although spurts in physical capacities during adolescence are well known, little is known about the existence of such spurts in sport-specific skill development, especially during the period of rapid growth in stature. Our aims were to examine the timing, intensity, and sequence of basketball-specific skill spurts aligned with biological (years from peak height velocity (PHV)) rather than chronological age. We then defined putative sensitive periods (windows of optimal development) for each skill aligned to the adolescent growth spurt. METHODS: Altogether, 160 adolescent male basketballers aged 11-15 yr were tested biannually over 3 consecutive years. The years from attainment of PHV was estimated, and six skill tests were aligned to each year from PHV in 3-month intervals. Skill velocities were estimated using a nonsmooth polynomial model. RESULTS: Maximal gains in slalom dribble occurred 12 months before PHV attainment (intensity, 0.18 m·s -1 ·yr -1 ), whereas in speed shot shooting (intensity, 9.91 pts·yr -1 ), passing (intensity, 19.13 pts·yr -1 ), and slalom sprint (intensity, 0.19 m·s -1 ·yr -1 ), these skill spurts were attained 6 months before PHV attainment. The mean gains in control dribble (intensity, 0.10 m·s -1 ·yr -1 ) and defensive movement (intensity, 0.12 m·s -1 ·yr -1 ) peaks coincided with attainment of PHV. We identified different sized windows for optimal development for each skill. CONCLUSIONS: Peak spurts in skill development, for most basketball skills, were attained at the same time as PHV. The multiple peaks observed within the defined windows of optimal development suggest that there is room for skill improvement even if gains might be greater earlier rather than later in practice. Our findings highlight the need to make coaches aware of where their players are relative to the attainment of PHV because different skills appear to develop differently relative to PHV. Such knowledge may help in designing more relevant training regimes that incorporate the athlete's current growth status so that skill development can be maximized.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".