Born at the Wrong Time: Selection Bias in the NHL Draft
Bibliographic record
Abstract
Relative age effects (RAEs) occur when those who are relatively older for their age group are more likely to succeed. RAEs occur reliably in some educational and athletic contexts, yet the causal mechanisms remain unclear. Here we provide the first direct test of one mechanism, selection bias, which can be defined as evaluators granting fewer opportunities to relatively younger individuals than is warranted by their latent ability. Because RAEs are well-established in hockey, we analyzed National Hockey League (NHL) drafts from 1980 to 2006. Compared to those born in the first quarter (i.e., January-March), those born in the third and fourth quarters were drafted more than 40 slots later than their productivity warranted, and they were roughly twice as likely to reach career benchmarks, such as 400 games played or 200 points scored. This selection bias in drafting did not decrease over time, apparently continues to occur, and reduces the playing opportunities of relatively younger players. This bias is remarkable because it is exhibited by professional decision makers evaluating adults in a context where RAEs have been widely publicized. Thus, selection bias based on relative age may be pervasive.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".