To draft or not to draft? A systematic review of North American sports’ entry draft
Bibliographic record
Abstract
In theory, professional sport "entry drafts" are designed to promote parity by granting poorly performing teams with early selections and winning teams with later selections. While this process has intentions to "level the playing field", mixed findings exist in the literature. The aim of this review is to identify and synthesize the literature examining the efficacy of the draft for professional, North American sport leagues. A systematic review of four databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. Full-text articles containing relevant data on the draft system for the four major professional North American sports were identified. Further restrictions were made to include articles focusing on a specific outcome regarding future success (i.e., whether the draft related to a measure of future performance). The search returned 10 962 records and after screening, 18 articles were synthesized. Of the articles examined, the measures of future success with relation to draft order were (a) career length and/or number of games played at the majors (n = 8), (b) future performance statistics at the professional level (n = 5), (c) change in winning percentage and/or number of wins produced (n = 3), (d) financial compensation (n = 1), and (e) a combination of measures (a) to (d), (n = 1). Most commonly, the first/early rounds most accurately predicted future measures of success (ie, number of games played, signing bonuses, and playing statistics) across sports. The middle and late rounds were less accurate, with the degree of accuracy increasing slightly in the last rounds. This review highlights several opportunities to better understand the draft process (e.g., potential improvements in middle round picks) and emphasizes the need for more research on analyzing and scrutinizing the draft.
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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 | 0.064 | 0.233 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".