On the predictive validity of the National Football League combine: does it forecast future success?
Bibliographic record
Abstract
The National Football League (NFL) Combine provides NFL teams the ability to assess prospective athletes’ medical histories and physical and psychological abilities. Using this information, NFL personnel must then decide whether an athlete is a good fit to their team. Given the combine’s 40-year history and the availability of peer reviewed articles on the function and efficacy of the combine, the purpose of this systematic review was to synthesize the literature evaluating the predictive validity of the combine according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Full-text, peer-reviewed articles containing information relevant to the NFL combine and at least one measure of future success were retained. The search yielded 1954 articles and after screening, 68 articles remained. These remaining articles focused on measures of success pertaining to (a) medical testing and future performance (n = 25), (b) combine tests (n = 12), (c) draft position (n = 10), (d) draft and future performance (n = 8), (e) medical testing (n = 7), (f) career longevity (n = 3), (g) draft and salary (n = 2), and (h) playing performance (n = 1). Due to the mixed results of combine measures on future success, this review highlights the need for more research to investigate the combine’s influence on long-term performance and success.
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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 | 0.028 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".