0001 Associations Between Circadian Factors and Travel Distance with Performance: A retrospective analysis of 2014-2018 National Basketball Association Data

Abstract

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