Associations between time zone changes, travel distance and performance: A retrospective analysis of 2013-2020 National Hockey League Data.

Objectives: Many studies have investigated the role that travel plays in athletic performance. However, these studies lacked a holistic representation of travel. For instance, they do not consider travel distance and uniquely focuses on travel direction.
Design: An open source (www.evolving-hockey.com) provided NHL (2013-2020) game data. In total, this resulted in 17,088 regular season games.
Methods: Linear and quadratic versions of time zone change (TZΔ) and adjusted jet lag (AJL) were formulated. TZΔ 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 TZΔ by allowing TZ acclimation, 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: Approach A (AJL_A) assumes travel the day before each game, whereas Approach B (AJL_B) was designed to prioritize being home. A standardized flight tracker determined travel distance for each game. Team ability differences, characterized as difference in total season points, served as an analytic covariate. Outcome variables included goal differential, difference between actual and expected Fenwick save percentage (dFSv%), and goals saved above average (GSAA).
Results: GameDistance (β = -0.14, p = 0.0007), AJL_B ² (β = -0.15, p = 0.0006), and their interaction (p = 0.0004) associated with GoalDifferential. GameDistance (β = -0.18, p = 0.02) and AJL_B ² (β = 0.12, p = 0.03) associated with dFSv%, whereas only AJL_B ² (β = 0.03, p = 0.05) associated with GSAA.
Conclusions: Results suggest that circadian change, in both direction, and greater traveled distance can negatively impact NHL athletes.
Competing Interests: Declaration of Interest Statement The authors do no have any conflict of interest to disclose.
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