Coaches perceptions of talent: A case study of high-school ice hockey players
Bibliographic record
Abstract
In selecting athletes to teams, coaches should consider many aspects of assessment including physical, psychological and sociological (Reilly et al., 2000). However, the majority of coaches focus on physical assessment with the goal of predicting the athlete's future success (Lidor et al., 2005). The purpose of this study was to assess coaches' perception of talent using a series of hockey drills identified by coaches as reflecting necessary skills for hockey success in a sample of 14-18 year old (n=13) high school hockey players. Step 1 involved surveying coaches as to the most important traits and skills used in identifying talent in teenage players. In Step 2, data from the surveys were used to create a list of hockey drills that could be used to identify talent in youth hockey players. In Step 3, 13 players were videotaped executing these drills, and in Step 4, seven high school coaches and two expert coaches ranked the player videos into the top 5 and bottom 5 players. Overall, two players were consistently identified by all coaches as being in the top 5, while two players were consistently identified as being in the bottom 5. The lack of agreement on the majority of players highlights the difficulty in identifying "talent" in this population. Potentially most interesting was the extent to which the coaches disagreed on players. There were 7 players who had rankings in both the top and bottom 5 by both high school and expert coaches. Results will be discussed with respect to the suitability of using physical drills for team selection and will provide suggestions for future research.Acknowledgments: SSHRC
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".