Bibliographic record
Abstract
Many researchers have examined the relative age effect (RAE) among hockey players from the grassroots through to the professional level. In nearly every case, a significant RAE has been found. To the best of our knowledge, nobody has studied whether this effect lingers beyond one’s hockey playing career into the coaching realm. Cobley et al. (2008) had shown a RAE when examining German soccer coaches of the Bundesliga that were brought up as players in the German youth system. On the contrary, Schorer et al. (2011) found no RAE among coaches of the First German basketball league. The goal of the present study is to explore whether a RAE exists among National Hockey League (NHL) head coaches and the extent to which this effect may have carried over from their earlier playing careers. Chi-square analyses were used to compare the birth distributions of NHL coaches (n = 351) against those of NHL players born before and since 1951, as per the findings of Addona and Yates (2010). We also compared the birth distribution of NHL coaches who played in the Canadian Hockey League (CHL) against the distribution of players from data collected by Barnsley et al. (1985). No significant differences were found between the overall birth distributions of the NHL coaches and the playing populations from which they were derived. However, our analysis of NHL coaches who had competed in the CHL revealed a significant reverse RAE (n = 89, X2 = 9.95, df= 3, p = .019, φ = .33), such that there was an over-representation of coaches born in the latter months, and an under-representation of coaches born in the earlier months of the selection year. While having an early birthday is advantageous at a highly competitive level, this effect diminishes as players progress into the coaching ranks.
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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 | 0.004 | 0.018 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".