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Record W3206291193 · doi:10.1111/sms.14076

To draft or not to draft? A systematic review of North American sports’ entry draft

2021· review· en· W3206291193 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScandinavian Journal of Medicine and Science in Sports · 2021
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsYork University
Fundersnot available
KeywordsHullAeronauticsPolitical scienceEngineeringOperations researchMarine engineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.196
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.000
Bibliometrics0.0020.005
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.052
GPT teacher head0.330
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it