294 Impacts of travel and time zone differences in the National Hockey League (NHL)
Bibliographic record
Abstract
Abstract Introduction Elite athletes are at risk of poor sleep which can be exacerbated by frequent travel. The present exploratory study investigated the impact of travel on the winning percentage, number of goals scored in the 3rd period and the number of penalties in the 3rd period over the 2013–2020 seasons in the National Hockey League (NHL). Methods Data from away and home games from the 2013–2020 seasons in the NHL were included in this study. The outcomes were based on winning percentage with additional covariates including home and away games; timing of the game (afternoon/17:30 or earlier; evening/18:00 or later; number of time zones travelled (one, two or three); direction of the travel (eastward or westward); length of the game (regular, overtime or shootout). Additionally, data exclusively from the 3rd period were assessed for the number of penalties received and the number of goals scored for and against. Data were analyzed with logistic regressions to evaluate the effects of the aforementioned variables on winning percentage for both eastern and western conference teams. Results Regardless of the length of the game, results indicated no difference between eastern and western teams on winning percentage. However, there was a significant impact of home-ice on winning percentage for both conferences (p<0.001). In addition, there was no difference on the winning percentage based on the travel direction and the number of time zones crossed (p = 0.747) or the time of the day (p=0.991). Moreover, visiting teams received significantly more 3rd period penalties than home teams (p<0.001), regardless of travel and while travelling within the same time zone compared to eastward travel (p<0.001) but not westward travel (p=0.078). Finally, there was an increased risk of being scored against when team travelled three time zones (p=0.03), regardless of the direction. Conclusion This 7-year investigation of data from the NHL demonstrates an unexplored aspect of the impact that travel and circadian factors may have on emotion regulation and performance. Translational application of this knowledge to enhance general public health and performance would be warranted. Support (if any):
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".