The Difficulty of Talent Identification: Inconsistency among Coaches through Skill-Based Assessment of Youth Hockey Players
Bibliographic record
Abstract
Talent identification is the process of recognizing participants with the potential to excel in a particular sport. Coaches and scouts are responsible for making an accurate attempt at identifying talent to select athletes for a team roster. The current study used a multi-step approach to assess coaches' perceptions of talent within a local high school. Part 1 consisted of two steps: a) Eight hockey coaches ranked the skills they felt were important when selecting an athlete for a hockey team, b) video clips showcasing the identified skills found in part a) were developed from 13 high school hockey players. In Part 2 seven coaches and two scouts viewed the video clips and generated a list of the top five and bottom five players. Overall, the most frequently identified skills were skating, speed/agility, puck handling, positional play, and shooting. Ranking the players proved challenging with nine of the 13 players being placed in both the top and bottom groups of five. These findings outline the lack of agreement among the coaches/scouts in selecting players which highlights the difficulty in identifying talent. Despite the heterogeneity of the sample of players, even experienced coaches and scouts disagree about what constitutes a skilled hockey player.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".