Greater circadian disadvantage during evening games for the National Basketball Association (<scp>NBA</scp>), National Hockey League (<scp>NHL</scp>) and National Football League (<scp>NFL</scp>) teams travelling westward
Bibliographic record
Abstract
Summary We investigated the effects of a circadian disadvantage (i.e. playing in a different time zone) on the winning percentages in three major sport leagues in North America: the National Basketball Association, the National Hockey League and the National Football League. We reviewed 5 years of regular season games in the National Basketball Association, National Hockey League and National Football League, and noted the winning percentage of the visiting team depending on the direction of travel (west, east, and same time zone) and game time (day and evening games). T‐tests and analysis of variance were performed to evaluate the effects of the circadian disadvantage, its direction, the number of time zones travelled, and the game time on winning percentages in each major league. The results showed an association between the winning percentages and the number of time zones traveled for the away evening games, with a clear disadvantage for the teams travelling westward. There was a significant difference in the teams' winning percentages depending on the travelling direction in the National Basketball Association (F2,5908 = 16.12, P < 0.0001) and the National Hockey League (F2,5639 = 4.48, P = 0.011), and a trend was found in the National Football League (F2,1279 = 2.86, P = 0.058). The effect of the circadian disadvantage transcends the type of sport and needs to be addressed for greater equity among the western and eastern teams in professional sports. These results also highlight the importance of circadian rhythms in sport performance and athletic competitions.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".