Successful talent development in popular game sports in Switzerland: The case of ice hockey
Bibliographic record
Abstract
There is continuing discussion in talent research on the best approach to developing sporting expertise through learning activities during early sport participation. Among other concepts, the specialized sampling model describes a pathway between early specialization and early sampling and yields promising results in Swiss football. As successful constellations of early sport participation might be affected by sport-specific constraints (e.g., age of peak performance, selection pressure, and physiological/psychological requirements), other popular game sports may show similar promising pathways. This study investigates whether ice hockey, another popular game sport in Switzerland, shows similar successful constellations of early sport participation. A sample of 98 former Swiss junior national team players born between 1984 and 1994 reported on early sport participation through a retrospective questionnaire. Using the person-oriented Linking of Clusters after removal of a Residue (LICUR) method, volumes of in-club practice, free play, and activities besides ice hockey until 12 years of age were analyzed, along with player’s age at initial club participation. The results indicate that ice hockey enthusiasts with the most free play and above-average in-club practice had a greater chance of reaching professional level compared to other groups. This implies that high domain specificity with varied sampling experiences is the most promising approach to developing sporting expertise in ice hockey. As similar results were previously found in Swiss football, comparable sport-specific constraints might indeed require similar constellations of learning activities during early sport participation. Therefore, in popular game sports in Switzerland, the specialized sampling model seems to be most promising.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".