Relationship Between Weighted Standing Long Jump And 20 Meters Sprint Performance
Bibliographic record
Abstract
The standing long jump (SLJ) is intensively used in fitness preparation as a measure of lower limb power. The SLJ has also been shown to be related to maximal sprint speed. The power deployed during a SLJ can be calculated, but it is unknown what the effect of body weight has on the relationship between sprint speed and power. PURPOSE: Explore the relationship between a 20m sprint and the SLJ under 5 loading conditions (0%, 1%, 3%, 10% and 15% of bodyweight). METHODS: Anthropometric measures (n=13) were taken prior to testing sessions (Age 16.0±0.7 years; Height 1.80±0.10 m; weight, 90.4±20.0 kg). The loads used during different loading conditions were confirmed using a bodyweight scale (Omron, Canada). SLJ distances were measured from toes (starting line) to the closest heel using a jump mat (Javy Sports, Singapore). Peak velocity (PV), peak power (PP) and relative power (RP) to body weight were measured using a linear transducer (TENDO SPORTS MACHINES, London, UK) for each loading condition. The protocol consisted of 2 sprints of 20m with 3 minutes of recovery between sprints. The best of 2 completed attempts per loading condition was retained same for the best sprint time. The time at 10m and 20m were measured with photocell timing gates (Brower Timing System, Utah, USA). Linear regressions and 2-tailed Pearson correlations were calculated (SPSS Ver 26). RESULTS: Multiple significant (p<0,05) correlations were observed (r=0.573 to 0.892). Findings show that PV (r=-0.640, r=-0.619, r=-0.646) with a load of 3, 10 and 15% respectively, RP (r=-0.635) with a load of 3%, and SLJ distance (r=-0.573, r=0.736) with a load of 10 and 15% respectively were significantly correlated with the 10m during sprint time. Also, PV (r=-0.577, r=-0.892) with a load of 1 and 15% respectively, PP (r=-0,656) with a load of 15%, RP (r=-0,859) with a load of 15% were significantly correlated with the 20m sprint time. CONCLUSION: Weighted SLJ using 15% of bodyweight is better correlated to 10m or 20m sprint times than a standard SLJ. We propose different formulas to predict peak velocity, 10m and 20m sprint time all based on SLJ distance. Peak velocity(m/s) = (Distance(m) x 2.50) – 0.88 R2 = 0.601, p ≤ 0.01, SEE=0.38 10m time(s) = 2.98 - (SLJ15% distance(m) x 0.55) R2 = 0.541, p ≤ 0.01, SEE=0.08 20m time(s) = 4.89 – (SLJ15% peak velocity(m/s) x 0,54) R2 = 0.796, p ≤ 0.01, SEE=0.10
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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 | 0.002 |
| 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) | 0.004 | 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".