Visor use among National Hockey League players and its relationship to on-ice performance
Bibliographic record
Abstract
OBJECTIVE: To assess the use of half and three-quarter visors among National Hockey League (NHL) players and investigate the relationship between skill level and on-ice statistics such as points, penalty minutes and ice time and visor use. DESIGN: All players who played at least one game during the 2014-2015 NHL season were included in the study. Visor usage including three-quarter visor use was determined using official in-game images and video. Player information and statistics were obtained from a statistical hockey database. A multiple logistic regression model was used to study how the different player statistics influenced the probability of a player wearing a visor. RESULTS: Visor use was 87.1% among all NHL players (N=881) and 81.7% among all non-rookie players (N=612). Players who wore a visor were on average younger, played more games during the season, had more points, goals, assists and received more playing time. Players who did not wear a visor had 3 times more penalty minutes for every 100 min played. Only 11 (1.2%) players wore a three-quarter visor and these players were much older and contributed more to their team's offence when compared with the players who wore a one-half visor. CONCLUSIONS: Visor usage in the NHL continues to increase independent of new legislation making it mandatory for rookie players to wear a visor. Based on the results and the logistic regression model built in the study, those players who have the highest risk for not wearing a visor can be identified to help establish targeted interventions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".