Analysing short-track speed skating performance factors
Bibliographic record
Abstract
Short-track speed skating is an incredibly precise sport, where even the smallest technical or physiological adjustment can profoundly impact performance, underscoring the importance of its study for achieving success. This study aims to identify short-track speed skating performance factors and quantify their impacts on athletes' performances. Twenty-nine short-track speed skaters (16 males and 13 females), with two different skill levels (National Elite and Junior Elite athletes), participated in this study. Movella IMU Link suits and Python scripts were employed to record and analyse one on-ice high-speed trial per athlete, focusing on lower body kinematics. From an initial pool of 535 tested factors, Pearson's product moment correlations and stepwise multiple linear regression identified seven significantly associated with lap time, with notable differences between sex and skill levels. Results revealed that inter-foot spacing and pelvic height, which are actionable by athletes, could each contribute to a gain of up to 0.51 seconds per lap if improved by 10 cm. For an average lap on a 500 m race, this means a 5.2%-time improvement. This research enhances the understanding of short-track speed skating performances by identifying factors that can improve lap times, offering practical implications for coaching strategies and athlete training programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".