Relative Age Effect in Russian Elite Hockey
Bibliographic record
Abstract
Bezuglov, E, Shvets, E, Lyubushkina, A, Lazarev, A, Valova, Y, Zholinsky, A, and Waśkiewicz, Z. Relative age effect in Russian elite hockey. J Strength Cond Res 34(9): 2522-2527, 2020-A considerable amount of literature has been published on relative age effect (RAE) in many sports; however, only a few studies have investigated this phenomenon in European elite ice hockey. The objective of this research was to study RAE prevalence in Russian elite ice hockey, which for years has been holding leading positions in global ice hockey. To estimate RAE prevalence, birthdates of recruits of leading ice hockey academies, players of the leading junior and adult teams, and most successful Russian-born National Hockey League (worlds' strongest ice hockey league) players were identified (n = 2,285). A high prevalence of RAE was identified. The number of players born in the first half of the year was higher than those born later in the year-65.5 and 34.5%, respectively. The RAE prevalence was high among all age groups of recruits of the leading Russian hockey academies and junior teams. In contrast to junior hockey, more players born in the fourth quarter of the year were identified in elite adult teams. The high prevalence of RAE in Russian hockey might be explained by the fact of a high level of competition among young players during recruitment to hockey academies. Moreover, the coaches aim to achieve immediate progress, thus selecting more mature players who are better physically developed. However, "later-born" are widely present in elite adult ice hockey leagues.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".