How good are professional sports drafts at predicting career performance
Bibliographic record
Abstract
The forecasting of talented players is a crucial aspect of building a successful sports franchise. Professional sports invest significant resources in making player choices in sport 'drafts'. The current study examined career performance against draft round for the NFL, NHL, NBA and MLB for players drafted from 1980-1989 (N=10,800) against the assumption of a linear relationship between draft round and performance (i.e., that players with the most potential will be selected before players of lower potential). Multiple linear regression analyses calculated the relationship between career performance variables and draft round. Within the NHL and NBA there was no relationship between career performance and draft round beyond the first round of the draft. In the NFL weak relationships were found for both offensive and defensive positions between draft round and career performance. MLB pitchers showed a medium relationship between draft round and statistical performance and a low correlation for career longevity, where batters showed a small relationship for both statistics and longevity. Results highlight the challenges of accurately evaluating amateur talent. Findings will be discussed within the context of previous literature on the accuracy of professional drafts.
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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 | 0.007 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".