EFFECTS OF STARTING QUARTER SCORE, GAME LOCATION, AND QUALITY OF OPPOSITION IN QUARTER SCORE IN ELITE WOMEN'S BASKETBALL
Bibliographic record
Abstract
The aim of the present study was to identify the effects of a starting score-line on the game quarter final score (final quarter outcome except for the first period) when considering the quality of the opposition and game location.The sample comprised 1,456 game quarters from the Spanish women's professional league (seasons 2009/2010 and 2010/2011).A k-means cluster analysis classified the game quarters as balanced (difference in score equal or below by six points, n=1,000) and unbalanced game quarters (difference in score above six points, n=456).The effects of the situational variables in the game quarter outcome (difference between points scored and points conceded) in an entire game and in the second, third, and fourth game quarters were analyzed using linear regression analysis.The results showed the importance of a starting quarter score only during the second game quarters when one analyzed the entire game and unbalanced quarters.Also, the results showed that the situational variables of game location and quality of the opposition affected during the entire game quarters and unbalanced game quarters.These results established that the game dynamics in women's basketball are strongly influenced by situational variables.The results of the present study help the coaches to develop appropriate game strategies considering the situational variables and the game dynamics.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".