Contribution of psychological characteristics to talent identification in ice-hockey
Bibliographic record
Abstract
Talent identification and selection are crucial for the success of elite sport organizations. Scouts and managers generally select the most promising young athletes based on their current performances, physiological characteristics, and gut feelings. However, psychological characteristics (including perceptual-cognitive and self-regulation abilities) might still be overlooked by selectors. This study aimed at verifying the relationship between psychological characteristics and performance in elite ice-hockey. Eighty-eight youth elite ice-hockey players (forwards and defensemen) eligible for a Major Junior selection draft participated in the study. They were measured at 15 years old on perceptual-cognitive skills (decision-making and anticipation with eye-tracking at a temporal occlusion task) and self-regulated learning abilities (self-reported questionnaire). In addition, their current (draft rank and scouts' subjective appreciation) and future (points, games played, differential for the following four years) performances were recorded. Multiple linear regression models showed that the scouts' subjective appreciation was the best predictor of current and future performance. However, when scouts' appreciation is removed from the models or when positions are analyzed separately, self-regulated learning abilities (effort, planning and reflection subscales) and decision-making could add to the prediction. Overall, this study shows that psychological characteristics could help scouts in the talent identification and selection process, but measuring these characteristics cannot replace their judgment.
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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 | 0.000 | 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) | 0.002 | 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".