The Relative Age Effect Reversal among NHL Elite
Bibliographic record
Abstract
December 31 the first quarter of the year, most likely because they are relatively bigger than their younger counterparts born later in the year. As this Relative Age Effect (RAE) has been well-established in junior hockey and across other professional sports, we argue that the long- term impact of this phenomenon is still poorly understood. Using roster data on North American NHL players from 2008 to 2015, we examine the RAE in terms of birth month distribution and the extent that RAE is associated with points (i.e. goals plus assists) and player salaries. We find evidence of an RAE reversal—that players born in the second half of the year (July-December) score more points per season (29-50% more points) and command higher salaries (30%-50% more salary). Among elite players—the highest scoring and highest paid athletes—the scoring gap ranges between 14% and 26% more points for players born in the second half of the year—whereas the salary gap ranges between 18% and 50% greater salary. We argue that results partly support an “underdog” effect in NHL that is greatest among elite players.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".