Factors affecting the relative age effect in NHL athletes
Bibliographic record
Abstract
BACKGROUND: The relative age effect (RAE) has been reported for a number of different activities. The RAE is the phenomena whereby players born in the first few months of a competition year are advantaged for selection to elite sports. Much of the literature has identified elite male athletics, such as the National Hockey League (NHL), as having consistently large RAEs. We propose that RAE may be lessened in the NHL since the last examination. METHODS: We examined demographic and selection factors to understand current NHL selection biases. RESULTS: We found that RAE was weak and was only evident when birth dates were broken into year halves. Players born in the first half of the year were relatively advantaged for entry into the NHL. We found that the RAE is smaller than reported in previous studies. Intraplayer comparisons for multiple factors, including place of birth, country of play, type of hockey played, height and weight, revealed no differences. Players who were not drafted (e.g., free agents) or who played university hockey in North America had no apparent RAE. CONCLUSION: We found little evidence of an RAE in the current NHL player rosters. A larger study of all Canadian minor hockey intercity teams could help determine the existence of an RAE.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".