Key determinants of time to 5 m in different ventral swimming start techniques
Bibliographic record
Abstract
The aim of this study was to determine the biomechanical parameters that explain ventral start performance in swimming. For this purpose, 13 elite swimmers performed different variants of the ventral start technique. Two-dimensional video analyses of the aerial and underwater phases were used to assess 16 kinematic parameters from the starting signal to 5 m, and an instrumented starting block was used to assess kinetic data. A Lasso regression was used to reduce the number of parameters, providing the main determinants to starting performance, revealing different combinations of key determinants, depending on the variant (r² ≥ 0.90), with flight distance being the most relevant to all variants (r ≤ -0.80; p < .001). Also, special attention should be given to the total horizontal impulse in the grab start (r = -0.79; p < .001) and to the back foot action in the track and kick starts (r ≤ 0.61; p < .001). In addition, we provide two equations that could be easily used to predict starting performance by assessing block time and flight time (r² = 0.66) or block time and flight distance (r² = 0.83). These data provide relevant contributions to the further understanding of the biomechanics of swimming starts as well as insights for performance analysis and targeted interventions to improve athlete 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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".