295 Impacts of travel distance and travel direction on back-to-back games in the National Basketball Association (NBA)
Bibliographic record
Abstract
Abstract Introduction Travel fatigue and circadian disruptions are known factors that can hinder performance in professional athletes. The present exploratory study focused on investigating the impact of travel distance and direction on back-to-back games over the 2013–2020 seasons in the National Basketball Association (NBA). Methods Data from away and home games of back-to-back sequences, in two different cities, from the 2013 to 2020 seasons in the National Basketball Association were included in this study. Information from every selected game was retrieved from the official website of the NBA (www.nba.com). The outcomes were based on winning percentage with additional covariates including the direction of travel (eastward or westward) and the distance travelled (0-500km – 501-1000km – 1001-1500km – 1501km and more). If a team played both games of a back-to-back sequence on the road, they were considered Away-Away; if a team played the first game of a back-to-back sequence at home they were considered Home-Away; if a team played the first game of a back-to-back sequence on the road they were considered Away-Home. Results The sequence Away-Home significantly increases the likelihood of winning compared to the Away-Away and Home-Away sequences 54.4% (95%CI: 54.4,54.5); 39.2% (95%CI: 37.2,41.2), and 36.8%, (95%CI: 36.7,36.8), respectively. Following a road game, when teams travel back home, every additional 500km reduces the likelihood of winning by approximately 4% (p = 0.038). Finally, after withdrawing the Away-Home sequence, travelling eastward significantly increases the chance of winning (p = 0.024) compared to westward travel but has no significant impact on the probability of winning compared to neutral time zone travel (p = 0.091). Conclusion The accumulation of travel fatigue and the chronic circadian desynchronization that occurs over the NBA season can acutely disturb sleep and recovery. It appears that tailored sleep and recovery strategies need to be dynamically developed throughout the season to overcome the different challenges of the NBA schedule. 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".