A Note on Team-Specific Home Advantage in the NBA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recently it was reported that in the NBA as a whole, two thirds of the home advantage which teams enjoy when playing at home is accumulated in the first quarter. Home advantage can also be determined for individual teams, and there is good reason for doing so. For example, the relation of home advantage to team statistics such as assists, rebounds, and turnovers can be studied team-specifically but not in the league as a whole. Before any such project is undertaken, however, a major technical problem must be addressed. Formally, team-specific home advantage is a difference score between positively correlated variables (games won at home minus games won away), and difference scores are notoriously unreliable. This unreliability, moreover, is not just an empirical generalization. There is a formal basis for it in the theory of mental tests. This study reports that over a four-year period in the NBA the estimated reliability of team-specific home advantage was 0.284, even though the estimated reliabilities of games won at home and games won away were 0.772 and 0.833 respectively. The implications for research on home advantage are discussed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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 it