Predictive validity of pattern recall skills for talent selection in handball
Bibliographic record
Abstract
Perceptual-cognitive skills play an important role in complex game sports. They are often equated with tactical skills in performance analyses and talent selection decisions. However, few studies have investigated short-term and long-term predictions of performance using perceptual-cognitive skills. Accordingly, the aim of the present study was to objectively assess perceptual-cognitive skills of young handball players and to analyze the usefulness of these data for talent selection decisions. A total of 447 adolescent handball players participated in the talent selection of the German Handball Federation. A pattern recall test with animated handball game situations was conducted. At the end of the talent selection, players were selected for the national youth team (short-term). In addition, the highest league of all players was determined ten years later (long-term). A comparison of short-term effects on selected and non-selected players showed no statistically significant differences. Longitudinal comparisons of players (ANOVAs) from 1st and 2nd leagues, 3rd league, and 4th and lower leagues showed a statistically significant effect for female players, F(2,136) = 3.79, p = .02, f = 0.24, CI 90%(0.06; 0.36), and a non-significant effect for male players, F(2,159) = 1.66, p = .19, f = 0.14. Overall, pattern recall skill was not related to short-term in either group, but was a long-term predictor of success in female, but not male players. This difference may reflect variability in systemic support and opportunities in the female versus male pathways.
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.
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 | 0.002 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".