294 Impacts of travel and time zone differences in the National Hockey League (NHL)
Notice bibliographique
Résumé
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):
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 |
| 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 ».