The relative age effect within the Little League World Series
Bibliographic record
Abstract
While the relative age effect (RAE) has been well documented and exists in a range of sports across competitive levels, there has been comparatively less focus on young athletes, particularly within the sport of baseball. Thompson, Barnsley and Stebelsky (1992) authored the early literature examining the effects of the RAE within youth baseball, and this area remains virtually unexamined since their initial work. The purpose of the current study was to investigate the RAE in the Little League World Series (LLWS). Birthdates from the 2011 and 2012 LLWS were collected from team rosters (n=399). These athletes were then identified as being born in one of the quarters of the year, based on the cut-off date of May 1st for the LLWS. For example, quarter one represents athletes born within May-July 1999, while quarter four represents athletes born from February-April 2000. Furthermore, quarters five and eight represent athletes born in May-July 2000 and February-April 2001, respectively. For the combined 2011 and 2012 seasons, 123 athletes were born in quarter one, while only nine were born in quarter eight, which is significantly different from the expected distribution (p < 0.001). This suggests that it is advantageous to be born in the months immediately after the prescribed cut-off date in order to play for teams that reach the highest level of competition for this age group and raises concerns about inequity for those born in the latter months of the selection period.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.006 | 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".