THE RELATIVE AGE EFFECT IN WOMEN’S ICE HOCKEY: INTERNATIONAL AND POSITIONAL COMPARISONS
Bibliographic record
Abstract
A relative age effect (RAE), or chronological age differences among individuals within the same age group, has been observed in 14 sports (Cobley et al., 2009), including men’s ice hockey (Barnsley et al., 1985). In contrast, research on RAE in women’s ice hockey is limited (Wattie et al., 2007; Weir et al., 2010) and findings are equivocal. PURPOSE: To determine whether there is a RAE in women’s ice hockey, and if it varies by country or player position. METHODS: Participants were 117 female ice hockey players (age=19.9±2.3 yr) on 2 Swedish elite-level club teams (n=54) and 4 Canadian university teams (n=63). Players reported birthdate and position (Forward=F, Defenseman=D, Goalie=G). Birthdates were coded by quartiles (Q1: Jan.-March, Q2: April-June, Q3: July-Sept., Q4: Oct.-Dec.) and by half-year, and were submitted to chi-square (x2) analyses for the sample, by country, and by position. SPSS 17.0 for Windows was used for all analyses (significance level: p<0.05). RESULTS: A significant RAE was observed for the entire sample by quartile (Q1: 28.2%, Q2: 34.2%, Q3: 25.6%, Q4: 12.0%; x2=12.402, p=0.006). More players were born in the first half of the year than the second (62.4% vs. 37.6%, respectively; x2=7.188, p=0.007). In contrast, Q4 was underrepresented for the sample and by country (Canada: 12.7%, Sweden: 11.1%). A RAE was present for the Canadian players by quartile (x2: 13.381, p=0.004) and half-year (x2: 9.921, p=0.002); however, no RAE was observed for Swedish players. In addition, a significant RAE was observed for the entire sample by position for G and D by quartiles (G - x2: 10.077, p=0.018; D - x2: 8.444, p=0.038) and half year (G - x2: 6.231, p=0.013; D - x2: 4.000, p=0.046), but not for F. CONCLUSIONS: The significant RAE in this sample is consistent with that in men’s ice hockey. RAE absence in Swedish players may reflect lower participant number, competitive level, and sociocultural support, as well as greater variation in skill level. The significant RAE observed in Canadian players and by position supports the findings of Weir et al. (2010), but the positional differences found were inconsistent, perhaps due to differences in sample size and competitive level between studies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".