The Role of Athletes’ Personality on Talent Identification and Development: A pilot project in university sport
Bibliographic record
Abstract
Talent identification and development (TID) programs are often characterized by high expenses, low success rates, and unclear effects that regrettably result in poor return on investment (Vaeyens et al. 2009). Conscientiousness, self-control, and grit are personality traits conceptually and empirically linked to perseverance and high achievement in several domains including academic, professional, and military environments. In sport, anonymous surveys have shown that these traits predict athletes’ practice quantity/quality, better athletic engagement, and higher skill levels (e.g., Tedesqui & Young, 2018). However, such research had never been carried in a TID setting where athletes are under constant evaluation and, as a result, social desirability bias is likely to occur. Therefore, the purpose of this study was to test whether athletes’ self-reported personality traits predicted (a) talent retention, coaches’ decision to retain athletes for a future season; and (b) talent development, coach-rated athletes’ level of practice engagement over one season. Participants were university athletes (N = 194, 84 female, Mage = 20.67, SD = 1.94) from individual (e.g., golf) and team sports (e.g., rugby), along with their coaches. Hierarchical multiple regressions controlling for age, sex, and social desirability, showed only self-discipline (a facet of self-control) predicted athletes’ likelihood of retention for a future season (B = .27, p < .05). No personality facets associated with indicators of talent development (rs < |.19|, ps > .12). We problematized the scarcity of significant associations between personality facets and talent-related outcomes and considered potential implications for improving the effectiveness of TID programs.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".