Performance differences between winning and losing basketball teams during close, balanced and unbalanced quarters
Bibliographic record
Abstract
Previous studies in basketball performance have tended to assess differences between winners and losers of games.This methodology does not consider the fluctuating nature of scoring within games.Consequently winning and losing performance for each quarter of 26 games of the Hungarian basketball league in 2007/08 were compared with the difference in points scored used as an independent variable with three levels (identified through cluster analysis as close (1 to 5 points), balanced (6 to 11 points) and unbalanced periods (12 to 22 points)).Wilcoxon signed ranks tests identified significant differences between winning and losing quarter performance for 20 performance indicators when all quarters were analysed (n = 100) in comparison to just 5 for close quarters only (n = 42).The five performance indicators (number of successful free throws, number of defensive rebounds, total amount of rebounds and rebounding percentage in offence and defence) suggest that mainly the success in rebounding might be the critical factor that determines winning and losing in these close situations.Kruskal Wallis H tests and Mann Whitney U post-hoc tests revealed differences between winning performances from close, balanced and unbalanced quarters for the 3 point performance (number of successful 3 point shots, number of 3 point attempts and 3 point shooting performance), the number of assist passes and turnovers; these findings could be explained by the significantly different features of defensive resistance during different types of periods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".