Genetic associations with personality and mental toughness profiles of English academy football players: An exploratory study
Bibliographic record
Abstract
Psychological characteristics influence the performance of youth football players and are significant predictors of development and success at adulthood. Although genetic factors may explain a considerable portion of inter-individual differences in psychological traits, psychogenetic research in football is scarce. As such, the purpose of this study was to examine the association of ten single nucleotide polymorphisms (SNPs) with personality and mental toughness profiles of academy football players. Seventy-three male under-12 to under-18 football players from a Category 3 English academy were genotyped for ten SNPs. Personality and mental toughness were assessed using a 50-item IPIP Big Five personality traits questionnaire and the Mental Toughness Index, respectively. Simple linear regression was used to analyse individual SNP associations with personality dimensions and mental toughness, whereas both unweighted and weighted total genotype scores (TGSs; TWGSs) were computed to measure the combined influence of all SNPs. There was a significant association between DRD3 (rs167771) and agreeableness (p = .043), where A/A homozygotes scored higher than G allele carriers. TGSs and/or TWGSs were significantly correlated with mental toughness and each personality dimension except openness, explaining between 3 and 17% of the variance. The results of this study suggest psychological characteristics of youth football players are partly determined by genetic factors.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".