Effect of Rule Changes on Performance Parameters for Women's Water Polo Over Three Seasons
Bibliographic record
Abstract
Water polo’s rule changes are proposed to improve performance throw a faster game with more goals, less wrestling and more technical skills. The rule changes may modify the players' perception and game actions. However, there are some critical differences between male and female water polo teams. To date, we have not found any study about female water polo rule changes effects over the seasons. This study aimed to verify the effects of water polo rule changes on the performance of female elite-level teams over the three seasons. The data were collected through official game reports from the European water polo league for female tournaments, totaling 63 matches. Goals, goals per quarter, exclusion fouls, and penalty fouls were registered and analyzed. Mean, standard deviation, and 95% confidence intervals were calculated for all variables. Generalized estimating equations were applied to compare the variables in the three moments. Effect sizes (Cohen's d) were calculated. SPSS 20.0 was used in all analyses. The alpha significance level was established at 0.05. No statistical differences were found over seasons' post-water polo rule changes for goals, goals per quarter, exclusion fouls, and penalty fouls variables, and the effect sizes were just from trivial to small. The 2019 and 2021 water polo rule changes do not provoke statistical effects in female water polo teams over the 2019/2020, 2021/2022 and 2022/2023 seasons.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".