Birth Advantages in Male Italian Soccer: How They Influence Players Youth Career and Their Future Career Status
Bibliographic record
Abstract
Soccer organizations generally adopt deterministic models within their talent pathways. In this framework, early ability and results are emphasized, leading to selection biases, such as birth advantages (i.e., relative age effects and birthplace effects), which research has shown affect both early developmental experiences and continued sporting involvement. Accordingly, this study aimed to (a) provide further test of birth advantages in Italian youth soccer by exploring the birth quarter (BQ) and birthplace (BP) distribution of 1050 male Italian players born between 1999 and 2001 who competed in the national U17 championship throughout the 2015–16 season and (b) investigate how birth advantages influenced selected players’ future career status. Chi-square goodness-of-fit tests revealed early born players, and players born in North Italy were overrepresented at the youth level (p-values < 0.0001). Successive prospective analysis revealed only 18% of players developed into professional-level soccer players. Chi-square tests of independence indicated that players’ BP was associated with their future career status (p < 0.0001), whereas their BQ was not (p = 0.459). Odds ratios showed players born in North Italy were five times more likely to complete the youth-to-senior transition than those born in South Italy. These findings highlighted environmental factors influence Italian players’ early developmental experiences and their future career status.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".