The effects of body size and training environment on the physical performance of adolescent basketball players: the INEX study
Bibliographic record
Abstract
BACKGROUND: Although adolescent basketballers differ in body size, shape, and composition, less is known about how these factors interact during physical development. AIM: We used ontogenetic allometry to identify the optimal body size and shape characteristics associated with physical performance in adolescent basketball players, and investigated the effects of training experience, training volume, maturity status, and club characteristics on physical performance development. SUBJECTS AND METHODS: Two hundred and sixty-four male basketballers, from five age-cohorts (11-15 years of age), were followed consecutively over three years. Three physical performance components, anthropometrics, training information, and biological maturation were assessed bi-annually. Longitudinal multiplicative allometric models were developed. RESULTS: Players with a physique that had a dominant ectomorphic component performed better in all physical performance components. When adjusting for confounders other than size, the development of running speed was independent of body size. Players advanced in maturation were physically fitter. Training data had no significant effect on developmental trajectories of running speed or lower body explosive strength. Club characteristics had no significant association with any physical performance trajectories. CONCLUSION: Leaner players have advantages in physical performance and individual characteristics play an important role, over and beyond club structure, in developing physical performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".