Should coaches use personality assessments in the talent identification process? A 15 year predictive study on professional hockey players
Bibliographic record
Abstract
Making an accurate and valid prediction about an athlete’s long term success in professional sport is likely a difficult aspect of a professional coach’s role. Therefore, to aid them in this evaluative process coaches routinely employ a battery of tests, all of which are intended to inform their eventual selection decision. To date however, personality inventories have yet to become common place within this evaluative process; and thus, their predictive utility within the talent identification process has not yet been adequately tested (Aidman, 2007). Those research efforts that have been concerned with personality’s role in predicting athletic success have been overwhelmingly cross-sectional and descriptive in nature, and therefore do not mirror the applied use (e.g., longitudinal prediction) of these instruments by coaches. Consequently, the purpose of the current investigation was to address these previous limitations by employing a normative measure of personality (SportsPro ; Marshall, 1979) and assessing its relationship ™ to athletic performance over a 15 year time period. Potential draft choices of a Canadian National Hockey League team (N=124) were profiled prior to the 1991-92 entry draft and were followed until the end of the 2005-06 NHL season. The proposed selection model was found to be a significant predictor of a player’s total NHL goals, NHL assists, and their overall NHL points. Overall, when performance is assessed longitudinally within a relatively homogenous sample of athletes, personality measures appear to add to a coach’s ability to predict an athlete’s longitudinal athletic attainment.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".