The Normalization of Explosive Functional Movements in a Diverse Population of Elite American Football Players
Bibliographic record
Abstract
The objective of this study was to investigate the need to normalize, for body mass, explosive functional tasks in a population exhibiting diverse body masses. Measures investigated in elite college American football players attending the National Football League's annual combine (n = 1,136) were the 9.1-, 18.3-, and 36.6-m sprints, vertical and horizontal jumps, 18.3-m shuttle, and 3-cone drill. To determine the relationship between body mass and performance outcomes, Pearson's correlation coefficients (r) were generated using log-transformed data. Task-specific allometric exponents, accounting for body mass, were also determined. The strength of the correlations suggests that sprint and jump abilities are associated with body mass, whereas change-of-direction ability is not. The determined allometric exponents range between 0.296 and -0.463 for the sprint and jump tasks and are -0.022 and -0.006 for the 18.3-m shuttle and the 3-cone drill, respectively. In populations exhibiting relatively large variations in body mass, normalization of sprint and jump abilities is recommended, whereas normalization of change-of-direction ability is unwarranted. Novel suggestions derived from the present research are that sprint and jump abilities in diverse populations warrant normalization and that physical attributes associated with explosive functional movements deserve attribute-specific consideration when contemplating normalization.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".