The Home Advantage in the National Basketball Association Conference Finals and Finals Series From 1979 to 2019: A Mediation Analysis of Offensive and Defensive Skills

Home is where the hustle is: the influence of crowds on effort and home advantage in the National Basketball Association

Studies have consistently shown crowds contribute to home advantage in the National Basketball Association (NBA) by inspiring home team effort, distracting opponents, and influencing referees. Quantifying the effect of crowds is challenging, however, due to potential co-occurring drivers of home advantage (e.g., travel, location familiarity). Our aim was to isolate the crowd effect using a "natural experiment" created by the Coronavirus disease 2019 (COVID-19) pandemic, which eliminated crowds in 53.4% of 2020/2021 NBA regular season games (N = 1080). Using mixed linear models, we show, in games with crowds, home teams won 58.65% of games and, on average, outrebounded and outscored their opponents. This was a significant improvement compared to games without crowds, of which home teams won 50.60% of games and, on average, failed to outrebound or outscore their opponents. Further, the crowd-related increase in rebound differential mediated the relationship between crowds and points differential. Taken together, these results suggest home advantage in the 2020/2021 NBA season was predominately driven by the presence of home crowds and their influence on the effort exerted to rebound the basketball. These findings are of considerable significance to a league where marginal gains can have immense competitive, financial, and historic consequences.

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

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.