Analysis of Pushing Forces During the Water Polo Eggbeater: Reliability and Validity of a Novel Approach
Bibliographic record
Abstract
Water polo players benefit from greater odds of success when maintaining their tactical position against their opponents. This study evaluated the reliability and validity of a water-based resistance test to replicate this skill.Thirty-three water polo players participated in this study (19 males and 14 females, 14 from senior and 19 from junior national teams). Data were collected during two regular training sessions, separated by one week, using a load cell to instrument a weight stack resistance setup on the pool deck. Performance parameters such as mean force, maximum force, mean peak force and total impulse were defined with custom Python scripts. Test-retest reliability was assessed using intra-class correlations (ICC3,1). Group comparisons were explored between male and female players. Level of significance was set at p < 0.05. The reliability findings were high to very high for the mean force, maximum force, mean peak force, inter-stroke range, and total impulse (ICC 0.85-0.93, p < 0.01). Group comparisons showed significantly greater values in male players for these variables (p < 0.01, ES = 1.05-9.36) with large to very large effect sizes. However, there was no significant difference in endurance measured between sexes (p = 0.88, ES = 0.04). This study presents a methodology with satisfactory metrological qualities for field applications using simple and affordable equipment. The testing apparatus presented in this study can readily be replicated in a variety of training environments by practitioners working with water polo teams. Coaches can use this approach to evaluate individual player progress or to compare performance across a group of water polo players.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".