Effects of Game Location, Quality of Opposition, and Starting Quarter Score in the Outcome of Elite Water Polo Quarters.
Bibliographic record
Abstract
The notational analysis is used to investigate teams' performance in water polo, especially focused on the determinants of success. Recently, a new topic has emerged "the situational variables," which includes the game conditions that may influence the performance at a behavioral level. The aim of this study was to identify the interactive effects of starting quarter score (SQS) (i.e., score difference at the beginning of each quarter and at the final score) and game location (GL) (i.e., home and away teams) in relation to quality of opposition (i.e., positions of difference between opposing teams at the end-of-season rankings) in elite men's water polo games. Data comprised 528 games (n = 2,112 quarters) from the first Spanish water polo division. A linear regression analysis was applied to show the impact of SQS and GL in relation to quality of opposition (unbalanced and balanced) for quarter (all quarters, and second, third, and fourth quarters). Results showed that SQS has an important effect for all quarters (0.16) and for the second (0.14) and third (0.14) quarters in balanced games (whereas the fourth quarter has an unpredictable outcome), and for each quarter (all quarters: 0.33; second quarter: 0.55; third quarter: 0.44; fourth quarter: 0.26) in unbalanced games. In addition, GL effects emerged for balanced (0.31) and unbalanced (0.45) games for all quarters and specifically for the second quarter of the unbalanced games. Therefore, this study showed that the elite water polo game dynamics, indirectly providing a reference for coaches (i.e., effective tactical approach) and physical trainers (i.e., high performance intensities), plans to improve their players' performance.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".