Tracking Technical Skill Development in Young Basketball Players: The INEX Study
Bibliographic record
Abstract
This study investigated developmental stability, or tracking, in the development of technical skills in youth male basketball players and retrospectively profiled stable and unstable tracking patterns over time. A total of 97 basketball players were tracked bi-annually over 3 consecutive years. Players were divided into two age-categories according to their age at baseline: under-12; and under-14. Technical skills were assessed using the American Alliance for Health, Physical Education, Recreation and Dance test battery. Anthropometric, body composition, biological maturation and physical performance data were collected. Cohen's kappa (κ) was used to estimate tracking. With the exception of defensive movement in the under-12 age-category, tracking was low in all skill tests for both under-12 (0.22 ≤ κ ≤ 0.33) and -14 (0.20 ≤ κ ≤ 0.26) groupings. The overall technical skill showed moderate tracking for under-12 players (κ = 0.47) and low tracking for under-14 players (κ = 0.26). At baseline, players who were consistently more skilled or became more skillful (in the under-12 age-category) over time had a better growth-motor performance profile and most of them were selected to be members of regional teams. In conclusion, tracking of individual skill trajectories was low-to-moderate. Moreover, a better growth-motor performance profile seems crucial to maintain high levels of skill performance over time. It is recommended that basketball coaches track the developmental trajectories of their players to better understand the erratic nature of skill development and help design more effective practice 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".