Home is where the hustle is: the influence of crowds on effort and home advantage in the National Basketball Association
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".