Patterns of performance development in elite athletes
Bibliographic record
Abstract
This investigation sought to contrast generalised models of athlete development with the specific pathway trajectories and transitions experienced by 256 elite athletes across 27 different sports. All participants completed the National Athlete Development Survey and within it, the Athlete Development Triangle featuring the differentiation of junior and senior competition experience and progression. Developmental initiation; prevalence, magnitude and direction of pathway trajectory; extent of concurrent junior and senior competitive experience; and variability between sports were examined. Three major trajectories were identified in relation to athlete transition from Nil competition to Elite competition, via junior and senior competition phases. These included Pure ascent (16.4%), Mixed ascent (26.2%) and Mixed descent (57.4%). These were further partitioned into eight sub-trajectories, demonstrating a mix of linear, crossover and concurrent competition profiles. Substantial variability with regard to starting age, pattern of ascent and magnitude of transition was apparent. Non-linear trajectories were experienced by the majority of athletes (83.6%), with pure junior to senior developmental linearity evident in less than 7% of cases. Athletes in cgs sports (those measured in centimetres, grams or seconds) were less likely (43%) to experience a descending trajectory in comparison with non-cgs athletes (70%; p<0.001). The collective findings of this investigation demonstrate that, contrary to the popular pyramidal concept of athlete development, a single linear assault on expertise is rare, and that the common normative junior to senior competition transition is mostly characterised by complex oscillations featuring highly varied transitions. More developmental 'granularity' is needed to advance our understanding of sport expertise.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".