ISSP position stand: To test or not to test? The use of physical skill tests in talent detection and in early phases of sport development
Bibliographic record
Abstract
Searching for talent and the assessing ability in young prospects from individual and team sports often include measurement, analysis, and evaluation of physical and motor skills. The use of these tests in early stages of talent development has been widely observed in both female and male prospects. The purpose of this paper is to review a series of studies conducted on talented and less‐talented athletes/players that were aimed at distinguishing between the two groups and at predicting the athletes'/players’ future achievements/success. Thirteen studies examining the use of physical and motor skill tests in young prospects are reviewed. Based on this review, four main observations are highlighted and a number of benefits and limitations associated with the use of such tests are discussed. It is recommended that (1) coaches reduce the number of batteries of physical and motor skill tests used in early phases of talent development and (2) coaches and sport scientists specializing in measurement and evaluation cooperate in order to improve the effectiveness of the application and interpretation of physical skill tests given to prospects at early stages of talent development
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".