Questioning the validity and reliability of using a video-based test to assess decision making among female and male water polo players
Bibliographic record
Abstract
This study aimed to evaluate the validity and reliability of a water polo video-based test to assess decision making. Ninety-five female and male elite/tier 4 (T4) or highly trained/tier 3 (T3) athletes participated using their smartphones. Males repeated the test one week later for reliability analyses. Coaches assessed males’ in-water decision making and females were noted as selected or nonselected for the national team. Although response accuracy was significantly different between T3 and T4 athletes ( p < .001) and correlated with age (r s (88) = 0.43), sex-specific analyses identified that the only significant differences in accuracy were between T3 females and the other three groups (T4 females, T3 males, and T4 males). There was no correlation between males’ accuracy and coach-rated decision making skill, and no difference in accuracy between selected and nonselected females. Reliability analyses comparing performance between weeks revealed an ICC of 0.75, a standard error of measurement of 3.41%, and a significant improvement from week 1 to week 2 among T4 males ( p = .018). Despite associations between accuracy and age, the test was not able to distinguish between more similar groups of athletes. Considering the nonrepresentative design of the test, the construct assessed was declarative game knowledge rather than decision making skill, with the results suggesting that the former is not critical for evaluating elite players. The performance improvement between weeks among T4 males reinforces that video-based designs should be used cautiously in high-performance sport. However, there may still be practical applications for video-based designs, such as in video review sessions or as a pedagogical tool.
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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 | 0.007 | 0.022 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".