0001 Associations Between Circadian Factors and Travel Distance with Performance: A retrospective analysis of 2014-2018 National Basketball Association Data
Notice bibliographique
Résumé
Abstract Introduction Frequent travel across time zones and travelling long distances interferes with healthy sleep and disrupts the circadian system, often degrading athletic performance. National Basketball Association (NBA) players face a demanding travel schedule often requiring multiple games per week, with games spanning the continental United States. This investigation aimed to clarify the influence of circadian factors and travel distance on NBA performance using a dataset from the 2014-2018 seasons. Methods NBA (2014-2018) game data were acquired from an open-access source: (https://www.kaggle.com/ionaskel/nba-games-stats-from-2014-to-2018). Circadian variables of time zone change (TZ∆) and adjusted jet lag (AJL) were formulated, with quadratic versions utilized across analyses. TZ∆ captured circadian delay/advance based on travel for a game, with each TZ going eastward and westward reflected by -1 and +1, respectively. AJL advances TZ∆ by allowing acclimation to a novel TZ, with each day resulting in a 1-unit change towards circadian neutral. AJL is a season-long rolling summation, which was computed using two different travel approaches: Approach1 (AJL1) assumes travel the day before each game, whereas Approach2 (AJL2) was designed to prioritize being home. A standardized flight tracker determined travel distance for each game (GameDistance). Team ability differences, characterized as difference in season win percentages (SeasonWinPerDiff), served as an analytic covariate. Game point differential (PointDiff), defined as a team’s score minus their opponent’s score, and a team’s free throw percentage (FreeThrowPer) served as outcome variables. Linear mixed-effects modeling assessed univariate and multivariate associations, with games nested within both team and year. Results AJL2 (β = -0.63; p = .01) and GameDistance (β = -0.73; p<0.0001) significantly associated with PointDiff. TZ∆ (β = -0.002; p = .03), AJL1 (β = -0.002; p =.04) and GameDistance (β = -0.003; p = 0.007) significantly associated with FreeThrowPer. AJL2 and GameDistance maintained significant relationship with PointDiff in fully adjusted model that included AJL2, GameDistance, and SeasonWinPerDiff. Conclusion Results suggest that both circadian delay/advance and greater distance traveled for games negatively influence NBA performance, even when controlling for differences in team ability. Season travel and flight plans could be constructed to reduce the effects of circadian misalignment and travel distance. Support (If Any) None
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».