Leveling the Playing Field: A New Proposed Method to Address Relative Age- and Maturity-Related Bias in UK Male Academy Soccer Players
Bibliographic record
Abstract
Relative age selection bias persists within all major soccer leagues and youth soccer academies across the globe, with the relative age effect (RAE) being typically characterized as the over selection of relatively older players (who have sometimes also been shown to be early maturing). The aim of this study was to examine if a new allocation method (i) eliminates the RAE, and (ii) reduces the presence of any additional maturity-related differences in anthropometric and physical fitness characteristics which may exist between players within the same selection category. In the first phase, 1,003 academy soccer players [under (U) 9-16] from 23 UK professional soccer clubs were sampled and a clear RAE per birth quarter (Q) was observed for the overall sample (Q1 = 45.0% vs. Q4 = 9.8%) as well as for the different age categories. Using the newly suggested reallocation method, youth players were divided by allocation date which was defined as the midway point between the chronological age and the estimated developmental (ED) birthdate. Stature was used as an anthropometric reference point on the P50 of the growth curve to determine the developmental birthdate for this new method. After the reallocation of the players using ED, the distribution of players was more equally spread (Q1 = 25.3%, Q2 = 25.6%, Q3 = 22.4%, Q4 = 26.7%). After reallocation, the mean delta stature was reduced by 16.6 cm (from 40.3 ± 7.08 to 23.7 ± 4.09 cm, d = 2.87). The mean delta body mass difference after allocation was reduced by 6.7kg (from 33.2 ± 6.39 to 26.5 ± 4.85 kg, d = 1.18). The mean age difference increased from 1.8 to 3.9 years. A total of 42.7% of the sample would have been reallocated to a different age group compared to the current one. After reallocation, 45% of the anthropometric and physical fitness comparisons showed reductions in the within-group variation expressed as a percentage of coefficient of variation (CV%). The U10 players demonstrated the largest reduction in CV% (-7.6%) of the anthropometric characteristics. The U10 squad also showed the largest reduction in CV% for various physical fitness characteristics (-7.5%). By both eliminating the RAE and reducing temporary maturity-related anthropometric and physical fitness differences, soccer academies across the world may diversify and increase the size of the talent pool both for clubs and national youth teams. In conclusion, this study provides further evidence that the newly proposed allocation method shows the potential to remove the RAE and to create a more "leveled playing field" by reducing the within-group variation of anthropometric and physical fitness characteristics affording relatively younger, and eventually, late-maturing players the opportunity to develop their talent fairly.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".