Modelling the dynamics of change in the technical skills of young basketball players: The INEX study
Bibliographic record
Abstract
Although technical skills are a prerequisite for success in basketball, little is known about how they develop over time. In this study, we model the trajectories of technical skill development in young basketball players and investigate the effects of training experience, training volume, body composition, maturity status, physical performance, and club characteristics on skill development. A total of 264 male basketballers from five age-cohorts (11 to 15 years of age) were followed consecutively over three years using a mixed-longitudinal design. Technical skills, training experience and volume, basic anthropometrics, body composition, biological maturation and physical performance were assessed bi-annually. A multilevel hierarchical linear model was used for trajectory analysis. Non-linear trends (p < 0.01) were observed in speed shot shooting, control dribble, defensive movement, slalom sprint, and slalom dribble. Being more experienced and physically fitter had a significant (p < 0.05) positive effect on technical skill development; greater fat-free mass negatively affected skills demanding quick running and rapid changes of direction with or without the ball (p < 0.05). Training volume and biological age did not explain differences in technical skill development (p > 0.05). Moreover, belonging to different clubs had no significant influence on the technical skills trajectories of players. Our findings highlight the important role that individual differences play, over and beyond club structure, in developing skills. Findings improve our understanding on how technical skills develop during adolescence through training, growth, and biological maturation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".