Muscular Strength Spurts in Adolescent Male Basketball Players: The INEX Study
Bibliographic record
Abstract
Although successful performance in basketball requires high levels of muscular strength during adolescence, its development is confounded by the effects of normal growth. We examine the timing, intensity and sequence of muscular strength according to biological age (years from peak height velocity (PHV)) and hypothesize that young basketball players attain their peak muscular strength spurts around PHV. A total of 160 adolescent male basketballers, aged 11–15 years, were followed bi-annually over 3 consecutive years. The years from attainment of PHV and peak weight velocity (PWV) were estimated and five muscular strength measures (sit-ups, handgrip, seated medicine ball throw, squat jump and countermovement jump) were aligned to years from PHV in 3-month intervals. Strength velocities were estimated using a non-smooth mathematical model. The mean ages at-PHV and at-PWV were 13.90 ± 1.40 years and 13.90 ± 1.79 years, respectively. Maximal velocity in sit-ups was attained 6 months prior to attainment of PHV (intensity = 10.69 repetitions·year−1), whereas maximal velocity in squat jump occurred 6 months after-PHV (intensity = 3.93 cm·year−1). Handgrip strength, seated medicine ball throw and countermovement jump maximal velocity peaked at-PHV (intensity = 8.47 kgf·year−1, intensity = 0.75 m·year−1, intensity = 5.59 cm·year−1, respectively). In general, maximal velocity spurts did not differ in their timing, with the velocities reaching a peak concurrent with PHV and PWV or within 6 months of its attainment. Basketball coaches, as well as strength and conditioning trainers, should consider individual differences in strength development and be aware of rapid periods of growth in stature when planning and designing muscular strength training regimes.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".