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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".