Associations of circadian change, travel distance, and their interaction with basketball performance: a retrospective analysis of 2014–2018 National Basketball Association data

Abstract

ABSTRACT This investigation aimed to clarify the influence of circadian change and travel distance on National Basketball Association (NBA) team performance using a dataset from the 2014–2018 seasons. Data from 9,840 games were acquired from an open-access source. Game point differential and team free-throw percentage served as outcome variables. Time zone change (TZΔ) captured raw circadian delay/advance based on travel for a game and adjusted TZΔ (AdjTZΔ) evolved TZΔ by allowing acclimation to a novel TZ. We also further categorized AdjTZΔ into AdjTZΔ_A, which assumed travel the day before each game and AdjTZΔ_B, which assumed teams spent as many days in their home city as possible. Travel distance for each game was calculated. Linear mixed-effects modeling estimated associations, with games nested within team and year. Adjusted associations accounted for differences in team ability, whether the game was home or away, and whether the game occurred on the second half of a back-to-back game sequence. Greater circadian misalignment, regardless of delay or advance, and increasing travel distance negatively influenced NBA game performance. Yet, results suggest that performance outcomes may be more influenced by travel distance than circadian misalignment. Moreover, circadian misalignment and travel distance interacted to significantly influence game point differential. Furthermore, differences in results across analyses were observed between AdjTZΔ_A and AdjTZΔ_B, which suggests that subtle differences in constructed travel schedules can have notable impact on NBA performance outcomes. Lastly, playing on the second half of a back-to-back sequence emerged as a robust predictor of performance disadvantage, which corroborates the existing literature and provides further support for NBA schedule changes purposed to enhance competitive equity by reducing the number of back-to-back games across a season. These findings can help guide NBA teams on key strategies for reducing travel-related disadvantages and inform schedule makers on critical factors to prioritize across future schedules to attenuate competitive inequity from travel. Furthermore, they can help direct teams towards scenarios that are best to target for load management purposes due to the cumulative disadvantage arising from travel-related factors, opponent quality, game location, and game sequence.