About the Predictive Value of a 3D Multiple Object Tracking Device for Talent Identification in Elite Ice Hockey Players
Bibliographic record
Abstract
Purpose: The aim of this study is to verify if a single session on the NeuroTracker has predictive value in talent identification in ice hockey. Methods: Thirty-five male ice hockey players (aged 16-20) from the highest Canadian competition level for that age group participated in the study. A battery of tests (attention, working memory, time reproduction, pattern recognition, temporal equivalence, technical ability, and decision-making) was administered to verify the relation between various cognitive abilities, on-ice performance, and the baseline score on the NeuroTracker, which is claimed to solicit multiple cognitive functions. On-ice performance indicators were game-related statistics: games played, points (mean per game), on-ice goals differential, and draft rank. Results: Results show that the baseline score on the NeuroTracker is not associated with draft ranking, nor is it able to predict which players will perform best based on game-related statistics. However, the NeuroTracker baseline score does correlate with various tests involving working memory and attention. Conclusion: Currently, NeuroTracker is not specific enough to allow talent identification among same-level elite athletes in ice hockey.
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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 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".