Effects of starting score‐line, game location, and quality of opposition in basketball quarter score
Bibliographic record
Abstract
Abstract In several team sports, the game starting score‐line may be understood as a measure of performance accomplishment and hence might have an effect on players' subsequent efforts. The aim of this study was to identify the effect of the starting score‐line, game location, and quality of opposition on basketball game quarter final score. The sample comprised 504 game quarters from the Spanish Basketball Professional League and these were classified as balanced (difference in scores equal of 8 points or less, n =194) and unbalanced (difference in scores of more than 8 points, n =310) using k ‐means cluster procedures. The effects of the predictor variables on game quarter outcome (difference between points scored and points received) in the whole game and in the second, third, and fourth game quarters were analysed using linear regression analysis. The starting game quarter score‐line was only significant in unbalanced situations with very similar effects among different game quarters. The greater the difference in accumulated score at the beginning of each quarter, the more points recovered by the teams who were losing. A small effect from the quality of the opponent was found in the second and third quarters, whereas game location only had an effect when analysing the whole game and second quarter using balanced and unbalanced game quarters combined.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".