INVESTIGATION OF THE RELATIONSHIP BETWEEN LEG LENGTHS AND BALANCE AND 30M SPRINT PERFORMANCES OF SHORT DISTANCE
Bibliographic record
Abstract
There are many parameters that affect 30m sprint performance. It is thought that among these parameters, start, step frequency and length, neural factors, muscle structure, anthropometry and physiological parameters come to the fore. The aim of this study is to examine the relationship between leg lengths and balance and 30m sprint performances of short distance runners. A total of 26 sprinters with an average age of 21.27±1.88 years and an average age of 9.27±3.38 years competing in different clubs participated in the study. The leg lengths of the athletes participating in the research were measured with a stadiometer (SECA, Germany), the 30 m sprint test with the Fitlight Trainer (CA/Ontario) and the balance measurements with a flamingo balance device. Pearson Correlation Test and descriptive statistics were applied to the data obtained from the athletes in the SPSS 26.0 package program. According to the findings, it was determined that there is a positive and significant relationship between leg length and 30 m sprint performance (p 0.05). As a result, it was determined that the 30-meter sprint performance of the short-distance runners increased as the length of their legs increased. It is thought that examining the determinants of sprint performance is important for performance. Since the differences in the length of the legs can affect the stride length and frequency, it can be said that the trainers should consider the anthropometric characteristics of sprinters.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".