Knowing what to do and doing it: Differences in self-assessed tactical skills of regional, sub-elite, and elite youth field hockey players
Bibliographic record
Abstract
To determine whether youth athletes with an "average" (regional), "high" (sub-elite), and "very high" (elite) level of performance differ with respect to their self-assessed tactical skills, 191 youth field hockey players (mean age 15.5 years, s = 1.6) completed the Tactical Skills Inventory for Sports (TACSIS) with scales for declarative ("knowing what to do") and procedural ("doing it") knowledge. Multivariate analyses of covariance with age as covariate showed that elite and sub-elite players outscored regional players on all tactical skills (P < 0.05), whereas elite players had better scores than sub-elite players on "positioning and deciding" (P < 0.05) only. The sex of the athletes had no influence on the scores (P > 0.05). With increasing level of performance, scores on declarative and procedural knowledge were higher. Close to expert performance, declarative knowledge no longer differentiated between elite and sub-elite players (P > 0.05), in contrast to an aspect of procedural knowledge (i.e. positioning and deciding), where elite players outscored sub-elite players (P < 0.05). These results may have implications for the development of talented athletes.
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".