Relative age affects among hockey draftees: An analysis of the Ontario Hockey League Priority Selection and Under-18 drafts.
Bibliographic record
Abstract
Athletes born in the months immediately after a cut-off date often benefit from selection advantages known as relative age effects (RAEs; Barnsley et al., 1985). The purpose of this study was to compare birth distributions of athletes selected in the Ontario Hockey League (OHL) Priority Selection and U18 drafts against the larger populations from which they were derived. We sought to determine if the addition of the U18 draft in 2017, which gives players an extra year to develop, would reduce RAEs. Birthdate information for athletes drafted from 2017 through 2020 were retrieved from ontariohockeyleague.com. Chi-square goodness of fit tests were used to compare the birth distributions of athletes from both drafts to what would be expected based upon Canadian population birth rates and Ontario Hockey Federation (OHF) birth rates for "Midget" players. A supplementary analysis was performed to identify differences between the Priority Selection and U18 drafts. Our results showed a significant overrepresentation in the number of players born in the first quartile of the year in both the Priority Selection and U18 drafts compared to Canadian birth rates, providing evidence of RAEs. Similarly, RAEs were found among players in the Priority Selection draft when compared against OHF birthrates, but not for players in the U18 draft. Finally, the birth distribution of players in the Priority Selection were significantly different from those in the U18 draft, with the RAE trend being less pronounced among U18 players. Our findings suggest RAEs remain a prominent issue in the OHL.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".