Relative age effects and player pathways in international ice hockey: a longitudinal multi-cohort analysis
Bibliographic record
Abstract
Introduction: Past research shows that relative age effects (RAEs) are highly prevalent in ice hockey. Early-born players benefit from more exposure, especially in the early stages of development, and are frequently considered "more talented." Although RAEs are apparent in these early stages, little is known about how it affects pathways leading to the highest levels of competition. This study aims to look more closely at the associations between RAEs and players' career trajectories in 4 hockey nations: Canada, Finland, Czechia, and Slovakia. Specifically, it aims to: (1) evaluate the prevalence of RAEs in each country, (2) identify players' career pathways and examine the impact of RAEs on the players, and (3) compare these effects for each nation. Methods: Data were drawn from 4,306 players (100% males born between 1992 and 2002), who were invited to national development and selection camps between 2009 and 2019. Trajectory clusters were estimated from the players' participation in 8 career milestones, from U17 to representation of their country at the Olympic Games. Group comparisons were conducted based on birth quartiles and hockey nations. Results: The results confirmed the presence of RAEs in the four hockey nations. Consistent with past research, early-born players are overrepresented in the early career stage, whereas late-born players begin to emerge during transition to junior level (U20). Some nation-specific differences were observed. Discussion: This provides further support for the stakeholders of ice hockey association looking to enhance their national team selection processes and discover structuring pathways that offer development opportunities for all groups of players.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".