The Impact of the Change of Water Polo Rules on the Game Dynamics
Bibliographic record
Abstract
This research aimed to identify a change in the dynamics of the water polo game related to identifying differences in the number of goals scored before and after the introduction of the current 2019 rules. The sample analyzed 96 matches from the 2018 and 2020 European Water Polo Championships. The sample of variables included five variables for both subsamples, which referred to the total number of goals scored in the match, as well as the total number of goals per quarter. By applying the T-test for small independent samples, it was determined that at a statistically significant level, a higher number of goals was achieved in the third and fourth quarters, as well as the total number of goals in the 2020 European Championship in Hungary, compared to the 2018 European Championship in Spain. No statistically significant differences were found in the variables related to the total number of goals in the first and second quarters of the water polo match. The rule change, which came into force in 2019, was driven by changes in the game that directly reflected in a more dynamic game that resulted in more goals per game as well as in the final quarter of the game. These changes were undoubtedly preceded by changes in the total number of attacks, faster swimming, more frequent shots, which should definitely be determined by additional research.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".