Predicting Sprint Performance From the Vertical and Horizontal Jumps in National Football League Combine Athletes
Bibliographic record
Abstract
ABSTRACT: Agar-Newman, DJ, MacRae, F, Tsai, M-C, and Klimstra, M. Predicting sprint performance from the vertical and horizontal jumps in National Football League Combine athletes. J Strength Cond Res 38(8): 1433-1439, 2024-Identifying fast athletes is an important part of the National Football League (NFL) Combine. However, not all athletes partake in the 36.58-m sprint, and relying on this single test may miss potentially fast athletes. Therefore, the purpose of this study was to determine whether sprinting times can be predicted using simple anthropometric and jumping measures. Data from the NFL Combine between the years 1999-2020 inclusive were used (n = 4,149). Subjects had a mean (±SD) height = 1.87 ± 0.07 m and body mass = 111.96 ± 20.78 kg. The cross-validation technique was used, partitioning the data into a training set (n = 2,071) to develop regression models to predict time over the 9.14-, 9.14- to 18.29-, 18.29- to 36.58-m, and 36.58-m segments using vertical jump, broad jump, height, and mass as the independent variables. The models were then evaluated against a test set (n = 2,070) for agreement. Statistically significant (p < 0.01) models were determined for 9.14-m time (adjusted R2 = 0.76, SEE = 0.05 seconds), 9.14- to 18.29-m time (adjusted R2 = 0.74, SEE = 0.04 seconds), 18.29- to 36.59-m time (adjusted R2 = 0.79, SEE = 0.07 seconds), and 36.58-m time (adjusted R2 = 0.84, SEE = 0.12 seconds). When evaluated against the test set, the models showed biases of -0.05, -0.04, -0.02, and -0.02 seconds and root-mean-square error of 0.07, 0.05, 0.07, and 0.12 seconds for the 9.14-, 9.14- to 18.29-, 18.29- to 36.58-m, and 36.58-m segments, respectively. However, 5-6% of the predictions lay outside of the limits of agreement. This study provides 4 formulae that can be used to predict sprint performance when the 36.58-m sprint test is not performed, and practitioners can use these equations to determine training areas of opportunity when working with athletes preparing for the NFL Combine.
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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 | 0.002 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".