How Does Biological Maturation and Training Experience Impact the Physical and Technical Performance of 11–14-Year-Old Male Basketball Players?
Why this work is in the frame
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Bibliographic record
Abstract
This study (1) investigated the effects of age, maturity status, anthropometrics, and years of training on 11–14-year-old male basketball players’ physical performance and technical skills development, and (2) estimated the contribution of maturity status and training years on players’ physical and technical performances. The sample consisted of 150 participants, average age 13.3 ± 0.7 years, grouped by early, average, and late maturation. Biological maturation, anthropometry, and training data were collected using standard procedures. Measures of physical performance assessed included: aerobic fitness, abdominal muscular strength and endurance, static strength, lower body explosive power, upper body explosive power, speed, and agility and body control. Basketball-specific technical skills were also recorded. Analysis of variance (ANOVA) and analysis of covariance (ANCOVA) were used to compare group differences. Results indicated that early maturers were taller, heavier, and had greater strength, power, speed, and agility (p < 0.05). When controlling for age, height, and body mass, early maturers remained stronger, quicker, and more agile (p < 0.05). They were also more skillful in the speed shot shooting test (p < 0.05). Apart from tests of aerobic fitness, abdominal muscular strength and endurance, and lower body explosive power, maturity status was the primary contributor to the variance in the physical performance tests. Years of training was the primary contributor to the variance in the technical skills tests. Whilst physical performance was dependent on maturity status, technical skills were influenced by years of training. Since both biological maturation and years of training play an important role in basketball performance, we recommend that coaches consider the effects of these two confounders when recruiting and selecting youth basketballers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it