Evaluation of rule changes affecting head contact in North American professional ice hockey using video review of head impact frequency and magnitude between 2003–04 and 2016–17 seasons
Bibliographic record
Abstract
Ice hockey is a sport with a high incidence of concussion, but growing attention has been placed on the broader exposure to head impacts, regardless of clinical symptoms, due to their potential cumulative effects. While player education, protective equipment, and rule modifications have been introduced to reduce head impact risk, the effect of rule changes on head impact exposure in adult professional ice hockey has not been well quantified. This study compared the frequency and magnitude of head impacts between two NHL seasons of 2003-04 and 2016-17, that bracketed a period of rule changes targeting contact to the head. Twenty regular-season games from each season were analyzed using a combination of video analysis, physical reconstructions, and finite element modeling to estimate brain tissue strain for each head impact. Total head impact frequency per game did not differ significantly between seasons. However, a significant decrease in glove-to-head contacts and fight-related impacts was observed in 2016-17, reflecting the enforcement of stricter penalties for these actions. Additionally, players in the 2016-17 season experienced a significantly higher frequency of impacts classified within the Low brain strain category. These results suggest that while targeted rules may reduce specific types of dangerous contact, they may also shift the nature of impacts without reducing overall exposure. Understanding how such shifts influence cumulative biomechanical load is essential for guiding future rule evaluations and injury prevention strategies in professional ice hockey.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".