Is it the number of sports specific practice hours that contributes to attaining expertise or the pace at which these hours are accumulated?
Bibliographic record
Abstract
While hours of practice is an important predictor of expertise, in many sports, other developmental factors (e.g., number of other sports practiced) can affect the relationship between training hours and attainment. Among those factors, the importance of the rate at which practice hours are accumulated over the years currently causes debate, as conflicting results were obtained. Importantly, previous studies examining rates of change were conducted on single sports. This study investigated the degree to which the number of training hours per year was a determinant of attaining elite level across sports. A total of 625 athletes (age 14-49; 55.3% women) from a variety of sports filled the Developmental History of Athletes Questionnaire. Their expected level ranged from local junior to international senior competition. Athletes indicated the number of hours spent in their main sport each year from age 5 to 35 (or current age), across various types of training (e.g., physical vs. mental, group vs. individual, structured vs. unstructured). The total hours accumulated in each category and their slope according to age were calculated for each participant and included in a stepwise linear regression to predict their expected level of competition. The only significant predictor (R² = .13) was the slope of group practice (β = .37). Importantly, most slopes strongly correlated together, suggesting a concern with multicolinearity. These results suggest it may not be the total hours of practice in the main sports that facilitate excellency, but rather the speed at which those hours were obtained.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 teacher head, 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".