Sport-Specific Agility and Change of Direction in Water Polo: The Reliability and Validity of Two Newly Developed Tests
Bibliographic record
Abstract
Abstract Dong, L, Paradelo, D, Delorme, A, Oliveira, J, Parillo, B, Croteau, F, Romeas, T, Dubé, E, Bieuzen, F, Billaut, F, and Berryman, N. Sport-specific agility and change of direction in water polo: The reliability and validity of two newly developed tests. J Strength Cond Res 35(12S): S111–S118, 2021—There is a gap in water-based agility testing that considers both the change-of-direction (COD) and perceptive-reactive components of agility. This study sought to develop easily implementable, sport-specific in-water agility tests for water polo and to verify the reliability and validity of these new tests: the in-water Stop and Go (SG) and Jump and Go (JG). Female water polo athletes at the Senior ( n = 12, age = 22.1 ± 2.1 years), Junior ( n = 19, age = 18.5 ± 1.0 years), and Youth ( n = 11, age = 16.5 ± 0.8 years) national levels performed 3 trials of each of the SG, JG, and the existing Functional Test for Agility Performance (FTAP). Senior athletes performed an additional experimental session to assess reliability parameters. Relative reliability for agility and COD versions of the SG and JG was high or very high (intraclass correlation coefficient [ICC] = 0.76–0.95). For construct validity analyses, significant between-group differences for each of the new tests ( p < 0.05) were found. In contrast, the FTAP was moderately reliable (ICC = 0.57) and was unsuccessful in discriminating between playing levels. Considering the favorable metrological properties of the SG and JG, their fidelity to in-game demands, and their accessible setups, these new tests represent viable options to implement at grassroots and elite levels for the assessment and training of water polo–specific agility.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".