“Let's Pick Him!”: Ratings of Skill Level on the Basis of in-Game Playing Behaviour in Bantam League Junior ICE Hockey
Bibliographic record
Abstract
Rating a player's skill level is an essential task for coaches to select the players with greatest potential to reach the top and to further be able to adjust the training program to the skill level of the player in order to most optimally facilitate the player's learning and performance. However, limited research exists on how they do this. This study examined the association between in-game playing behaviours and observers' ratings of skill level. Three observers rated 71 junior ice hockey players (House League Level, Bantam Division in Canada). Results revealed that players were more often rated high skilled when they executed a greater numbers of actions, of which relatively more shots and carries compared to passes, and when they were involved in the play more often (p < .05). Conversely, players were rated more often low skilled when they executed fewer actions, of which relatively more passes, and when they were involved in the play less often (p < .05). The in-game playing behaviours appeared to be used as information sources for player ratings. The results presented highlight the need for further research on the sources of information used by coaches as it will increase awareness about their coaching's process with regard to player selection and player development.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".