Academic timing and the relative age effect in NCAA Basketball
Bibliographic record
Abstract
The aim of the current investigation was to examine the interrelationship between academic timing and the relative age effect (RAE) in men and women’s intercollegiate basketball. Academic timing is particularly important within the context of intercollegiate sport as it occurs within an academic setting, and has received virtually no attention since Glamser and Marciani (1992) explored the topic more than two decades ago. Although previous studies have examined the RAE within the National Basketball Association and the youth/junior representative level, none have explored its presence in an intercollegiate setting. The birthdates of female (n=475) and male (n=429) NCAA Division I basketball players were collected from the top 30 ranked team rosters and identified as either academically on-time or delayed based on their birth date and academic eligibility. From this sample, 265 female and 136 male student-athletes were found to be on-time, while 210 female and 293 male student-athletes were considered academically delayed. Our examination revealed that on-time student-athletes were more commonly born in the early months of the year (i.e., displaying a traditional RAE), whereas academically delayed student-athletes were more likely to be born in the latter months of the year (i.e., displaying a reversal of the traditional RAE). Both patterns are significantly different (p < .001) from what would be expected within the general U.S. population. Our results reveal that on-time athletes born late in the year are disadvantaged and that delaying entrance to university may be an option for equalizing the playing field and/or gaining an advantage.
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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 | 0.002 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".