Identification of “sleeping” talent using psychological characteristics in junior elite ice-hockey players
Bibliographic record
Abstract
Scouts search for “sleepers” who may be initially overlooked but ultimately exceed expectations. The psychological characteristics of those players are often neglected because they are difficult to observe, but hold promise to identify sleepers given for example the self-regulation and perceptual-cognitive skills that those developing players might need to flourish. The aim of this study was to examine whether sleepers could be retrospectively identified using psychological characteristics. Ninety-five junior elite ice-hockey players (aged 15–16) were assessed on self-regulation and perceptual-cognitive skills before the yearly draft. Seventy players were drafted after the second round (37th or later). Three years later, professional scouts identified 15/70 sleepers they would now pick if given the chance. Those identified by the scouts showed higher self-regulation planning, and had distinguishable gaze behaviour (fewer fixations on more AOIs) when performing a video-based decision-making task than other late-drafted players (84.3% correct classification; R2 = .40). In addition, two latent profiles differentiated by self-regulation were found, with the profile with higher scores including 14/15 players selected by the scouts. Psychological characteristics were successful in retrospectively predicting sleepers, and may in future help scouts to make better selections of talent.
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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 | 0.001 |
| 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".