The relative age effect among female basketball players in the Israeli Premier League
Bibliographic record
Abstract
In sports, the term relative age effect refers to the asymmetrical distribution of athletes based on their birth dates relative to an arbitrary cut-off date. Some studies indicate that athletes who were born shortly after this cut-off tend to have higher representation in elite sports leagues compared to those who were born later in the year. Yet the literature presents inconsistencies in empirical support for this effect. The aim of this study, therefore, was to examine the relative age effect in female basketball players from the Israeli Premier League, while distinguishing between domestic and foreign players (n = 215, Mage = 24.08 years, SD = 5.17; and n = 120, Mage = 30.33 years, SD = 3.68, respectively), and examining two alternative cut-off dates (January 1 and September 1). Data were collected over six seasons, 2018–2024. Chi-square values and odds ratios were calculated to examine the distribution of birth quarters compared to uniform distribution in general, and to Israeli and U.S. live birth data. The findings reveal that the relative age effect was insignificant among the players, regardless of their nationality. While a higher number of players were found to have been born in the second quarter of the year, this difference was statistically insignificant, regardless of whether a uniform distribution of births or normative population values were applied. As such, the findings of the current study do not support the existence of selection bias among coaches based on the birth dates of female professional basketball players in Israel.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".