A NEW NOMOGRAM FOR ESTIMATING PEAK LEG POWER OUTPUT IN THE VERTICAL JUMP
Bibliographic record
Abstract
Leg power is an important component in assessing both performance-related and health-related fitness. Historically, leg power has been estimated from vertical jump height using the Lewis equation and nomogram. Recent research has illustrated convincingly that the Lewis equation estimates average leg power (Sayers et al., Med. Sci. Sports Exerc. 31:572–577, 1999). However, peak leg power is more applicable to the functional requirements of health-related fitness and it is a better predictor of jump height. Therefore, the Sayers equation was developed to predict peak leg power output: Peak Power (W) = 60.7 × (vertical jump height [cm]) + 45.3 × (body mass [kg]) - 2055. PURPOSE To develop a nomogram based on the Sayers equation for simplified use in fitness assessments. METHODS The Sayers equation for peak leg power output in the vertical jump was transformed to an alignment nomogram and evaluated for ease of use and accuracy. The nomogram is simple to reproduce in any standard spreadsheet program by inputting power in 100 W increments, body mass in 5 kg increments, and then solving the Sayers equation for vertical jump height (in cm). Our evaluation of the nomogram consisted of comparing the peak leg power determined by two unaccustomed technicians, when inputting body mass and jump height pairs distributed throughout the range of values observed in the general population. RESULTS The vertical jump alignment nomogram is easy to use and generates values for peak leg power that are well within the precision of the regression equation (r >0.99, CV <0.2%). The error introduced by the new nomogram is ±12 W, in comparison to the standard error estimate of 355 W for the Sayers equation itself. Between observer error was less than 0.3% with a correlation greater than 0.99. CONCLUSION The Keir nomogram provides an easy to use and accurate representation of the Sayers equation.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".