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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".