The Roles of Growth, Maturation, Physical Fitness, and Technical Skills on Selection for a Portuguese Under-14 Years Basketball Team
Bibliographic record
Abstract
This study investigated the roles of growth, maturation, physical fitness, and technical skills on selection onto an under-14 years basketball team. The sample consisted of 150 male players, aged 13.3 ± 0.7 years, divided into selected (SE—top players chosen by coaching staff to form an elite regional team) and non-selected (NSE—remaining players) groups. Anthropometry, body composition, biological maturation, and training experience data were collected using standard procedures. Physical fitness was assessed using the Yo-Yo IE2, sit-ups, handgrip, squat jump, countermovement jump, 3 kg medicine ball throw, 20 m sprint, and T-Test. Technical skills were acquired using the American Alliance for Health, Physical Education, Recreation, and Dance (AAHPERD)’s basketball-specific test battery. Groups were compared using a Student’s t test and multivariate analysis of covariance (MANCOVA), with training experience and biological maturation as covariates. A forward stepwise discriminant function analysis was employed to identify variables that maximized the separation between groups. The results showed that SE players were taller, had greater fat-free mass, greater strength, power, and agility, and were technically more skillful compared with NSE players (p < 0.05) when controlling for training experience and maturation. It was also found that players were best discriminated by the 3 kg medicine ball throw and control dribble, revealing the importance of qualified training to achieve excellence in youth basketball. 92.7% of the basketballers were correctly classified into their original groups. It is therefore confirmed that the additional effects of training experience and biological maturation positively influenced the performance of young basketball players. We recommend that coaches focus not only on players’ body sizes, but also on their skill level, especially during adolescence, when selecting teams in order to promote sustainable long-term development.
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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 | 0.001 | 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".