Predicting eggbeater kick performances from hip joint function testing in artistic swimming
Bibliographic record
Abstract
The eggbeater kick is an important skill in artistic swimming necessary to lift the body above water level. Previous attempts to model its performance included complex biomechanical parameters that cannot be easily used to guide strength and conditioning training. The objective of this study was to model the relationship between hip strength and eggbeater performance through a machine learning algorithm. We assessed hip function of 92 elite artistic swimmers with six easily performed isometric tests. These data were fed to a gradient boosting model to predict three technical variables: body boost height [BB-H], eggbeater height [EB-H] and eggbeater force [EB-F]. Group mean differences () between predicted and measured variables were reported. Then, the model was used to propose training tips for two hypothetical case studies. Our model predicted performances with errors within the resolution of the scale used during competitions: absolute error of and in EB-H and BB-H, respectively. The predicted performance was similar to the measured one for all technical tests (EB-F: ; EB-H: ; BB-H: ). We illustrated some of the important predictors (hip internal rotation, abduction, and left-right imbalances) of the eggbeater kick performance and highlighted personalized strategies to improve performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".