Comparing the home advantage in regulation and overtime in the National Basketball Association
Bibliographic record
Abstract
The home advantage reflects the finding that the home team wins over 50% of the games played under a balanced home and away schedule (Courneya & Carron, 1992). Although teams competing in their home venue are more likely to win, Jones (2007) found the advantage diminishes throughout the game and into overtime in the National Basketball Association (NBA). Jones’ examination, however, was restricted to two seasons encompassing only 157 overtime games whereas typical home advantage studies examine several seasons’ worth of results to determine trends. Thus, the purpose of this project was to examine potential differences in the home advantage for games completed in regulation time versus those decided by overtime using a larger sample consisting of 12 NBA seasons. Home winning percentages for both regulation time and overtime games were obtained for 32 teams. A paired sample t-test was executed to compare winning percentages of regulation time (14,429) and overtime (906) games for each team in the NBA over 12 seasons. The results demonstrated that there was a significant decrease in winning percentage for the home team from regulation time (M = 60%, SD = .08) to overtime (M = 56%, SD = .12); t(31) = 2.20, p = .03, r = .238. As explained by Carron, Loughead, and Bray (2005), the home advantage is dependent on a variety of factors including the crowd, learning, travel, and rules, which then affect psychological and physiological states, critical behavioural states, and performance outcomes. Discussion is centered around salient explanations for the observed decrease in winning percentage in overtime.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".