Starting and Specialisation Ages of Elite Athletes across Olympic Sports: An International Cross-sectional Study
Bibliographic record
Abstract
Talent development models are the frameworks that guide sports stakeholders in developing potential athletes, within which early specialisation and diversification remain contradictory strategies. This paper presents new insights into the starting and specialisation ages of world-class athletes in various Olympic sports. A total of 2,838 athletes from 13 nations and 44 Olympic sports were included in this study. The results show that world-class athletes started with their current sport at the age of 10.6 (±5.3) and decided to focus on this sport at the age of 15.6 (±5.0), with obvious variations in these ages across different sports. The study showed a moderate relationship between athletes’ starting and specialisation ages (r = 0.639), which demonstrates the variable duration of the multiple sport sampling period. This period, during which athletes pursue a variety of sports, lasts 4.9 years on average. There is a high degree of variation among different athletes in starting and specialisation ages, even within the same sport. All sports in the study can be classified into five categories based on a combination of their starting ages (early/intermediate/late) and specialisation ages (early/intermediate/late). The Developmental Model of Sports Participation provides the age guidelines for the categories. The five categories contain (I) early specialisation sports, (II) intermediate starting and specialisation sports, (III) late specialisation sports, (IV) late starting sports, and (V) late starting and late specialisation sports. The study concludes by proposing that there is a need for sport-specific talent development models with increased attention to each sport’s starting age, sampling period, and specialisation age.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".