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Record W3106604258 · doi:10.3389/fspor.2020.591072

Origins of Relative Age Effects in Youth Football—A Nationwide Analysis

2020· article· en· W3106604258 on OpenAlex
Michael Romann, Eva Rüeger, Mirjam Hintermann, Raphael Kern, Oliver Faude

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFrontiers in Sports and Active Living · 2020
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsDemographyFootballOddsAthletesQuarter (Canadian coin)Selection (genetic algorithm)Odds ratioConfidence intervalMedicineAge groupsPsychologyGeographyPhysical therapyInternal medicineSociologyComputer science

Abstract

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Introduction: Relative age effects (RAEs) refer to the overrepresentation of players born earlier in the selection year compared to late-born players within the same age category. To date, the origins and mechanisms of RAEs are still unclear. To evaluate the development of RAEs in terms of age group and selection level, we analyzed data of all registered child and adolescent football players in Switzerland. Methods: Age category, selection level, and birthdate from all licensed 101,991 Swiss child and youth football players assigned to a specific team [9,149 girls (9.0%) and 92,842 boys (91.0%); age range: 4.6–19.6 years] were analyzed. Additionally, out of 1,128 clubs, 54 clubs provided their documented waiting lists (1,224 players). Birthdate distributions were split by age category, sex, and birth quarter (Q1 = January to March, Q4 = October to December). RAEs were calculated using odds ratios (Q1 vs. Q4) with 95% confidence intervals (95% CI). Results: We found small RAEs among U8 players (OR 1.44 [95% CI 1.31, 1.59]) and U10 (OR 1.24 [95% CI 1.16, 1.32]). The RAE was negligible in all other age categories, independent of gender. In children's football, 5,584 (71.3%) teams performed selections. In teams without selection, there were no obvious RAEs. However, teams with selections for the same age category showed small RAEs with an overrepresentation of Q1 athletes in the first team (OR = 1.29 [95% CI 1.24, 1.35]) and inverse RAEs with an underrepresentation of Q1 athletes in the last team (OR = 0.85 [95% CI 0.82, 0.89]). Only small RAEs were observed on the waiting lists for the U8 (OR = 1.48 [1.13, 1.95]). Discussion and Conclusion: RAEs have a small, but consistent effect on participation in Swiss children's football at the grassroots level. Contrary to expectations, no inverse RAEs were found on the waiting lists. Nonetheless, first time coach selections seem to be the origin of RAEs. To protect young athletes from discrimination, RAE biases should be analyzed and eliminated at all stages of sport participation, selection, and dropout situations. Modifications to the organizational structure of sport and athlete development systems are recommended to prevent RAE-related discrimination in youth sports.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.272
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it