Beyond the trained eye: An objective method to predict game sense in team sports
Bibliographic record
Abstract
Talent identification in sports requires a prediction of how athletes will perform in the future based on a sample of their behaviors. Perceptual cognitive-skills or 'game sense' in sports jargon is important for performance, yet sport organizations lack objective and validated measures to predict it. This study aimed to establish the degree to which subjective evaluations of athletes' in-match perceptual-cognitive skills could be predicted by their performance on objective perceptual-cognitive tests. The perceptual-cognitive skills of 40 highly-trained ice-hockey players were assessed by their coaches and the results were compared with the athletes' performance on four laboratory perceptual-cognitive tasks (neuropsychological battery, multiple-object tracking, temporal occlusion, virtual reality). Athletes were also assessed by scouts throughout a hockey season and during small-sided games. Scout judgments best predicted coach rankings, with measures from small-sided games, neuropsychological battery, virtual reality and temporal occlusion improving prediction. Results suggest that adding perceptual-cognitive testing could help scouts better measure athletes during talent identification.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.001 | 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".