Retrospective analysis of accumulated structured practice: A Bayesian multilevel analysis of elite Brazilian volleyball players
Bibliographic record
Abstract
The patterns of cumulated structured volleyball practice and other structured sports activities of elite adult Brazilian players, considering age of specialization in volleyball and achievement of international competition representing the national team, were examined using Bayesian multilevel models. Elite volleyball players (n = 78) with an average age of 19.2 (SD = 0.9) years were considered. We used retrospective quantitative questionnaire to track individual training history. The mean age of specialization in volleyball was 10.7 (95% CI 10.3 to 11.0) for players that specialized early (before age 12), 14.1 (95% CI 13.9 to 14.3) for players that specialized intermediate (between ages 13 and 15), and 16.2 (95% CI 15.7 to 16.7) for players that specialized late (after age 16). Consequently, the earlier the specialization age in volleyball, the more years of training experience were accumulated. International and national level players were similar in both specialization age and pattern of engagement in other structured sport activities. Conditional on the data and models, attainment of expertise in volleyball may be favored by the accumulation of nonspecific sport experiences at early ages, and specialization may occur at a rather late age during adolescence.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".