Talent Identification in Youth Basketball: Talent Scouts’ Perceptions of the Key Attributes for Athlete Development
Bibliographic record
Abstract
The objective of this study is to understand the attributes youth coaches and talent scouts perceive as important when identifying skilled youth basketball players. Youth coaches and talent scouts (n = 40) from Australia, Canada, the United Kingdom, and United States with an average of 14.09 (±9.77) years of experience completed an online questionnaire. The questionnaire asked participants to rank and justify attributes for identifying potentially talented youth basketball players according to their perceived importance. In addition, five youth coaches and talent scouts completed a semistructured interview that elaborated on how they identify these attributes in national-level youth players. Results from the questionnaire indicate a hierarchy of attributes coaches/scouts perceive as important for youth basketball performance, including tactical (i.e., decision-making ability), technical (i.e., layup, shooting in the paint, jump shot, rebounding), and psychological attributes (i.e., composure, concentration, adaptability). In addition, the results from the interviews provided more detailed justification for the importance of these attributes within the talent identification process. It is believed talent scouts apply a holistic multidisciplinary approach to talent identification, with the current findings potentially providing evidence to suggest coaches/scouts consider a wide range of tactical, technical, psychological, and physical attributes when identifying youth players.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".