Should all athletes use the same twisting strategy? The role of anthropometry in the personalisation of optimal acrobatic techniques
Bibliographic record
Abstract
Choosing the best acrobatic technique for each athlete remains a challenge for coaches. Predictive simulations may support coaches, but only a few athlete morphologies have been simulated yet. It is assumed that the optimal acrobatic techniques are somehow generalisable across athletes. However, anthropometry characteristics can influence the twist rotation outcome of an acrobatic technique. Our objective was to assess the differences in optimal techniques caused by the anthropometric differences between athletes. Anthropometry-specific techniques of double pike forward somersaults ending with 112 or 212 twists were generated using predictive simulations and the measurements of 18 acrobatic athletes presenting a wide range of anthropometry. We found that anthropometry had an impact on the optimal acrobatic techniques by modifying the amplitude of the strategies used or, more drastically, by modifying the strategies used. Some athletes had a morphological advantage for twist creation, which was measured using the combined twist potential, a metric introduced in the current study. This metric was very strongly correlated with the complexity of the techniques; models with an advantage for twist creation needed fewer/shorter limb movements to generate twists. This research shows that coaches should consider their athletes’ anthropometry to offer them better guidance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".