Predictive value of coaches’ early technical and tactical notational analyses on long-term success of female handball players
Bibliographic record
Abstract
Despite the importance of technique and tactics for athlete performance, there has been surprisingly little research on the value of these skills in talent identification and development. This study investigated the relationship between coaches’ early notational analyses of female youth handball players and the long-term success of these athletes. Participants included sixty-eight female handball players involved in a talent selection camp in Germany when they were between 12 and 14 years of age (mean = 14.42, SD = 0.42). All subsequently ended up as non-, semi- or professional adult players. During the initial selection camp, participants were evaluated on a range of quantitative and qualitative measures of technical and tactical skill. Results indicated significant differences between the groups, but only for the number of actions taken, not for the quality of those actions. While this seems counterintuitive, it may reflect the likelihood that more skilled and/or talented players take more actions. Further work is necessary to explore the validity and implications of these findings.
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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 | 0.001 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".