'Generational differences in top ranked golfers' developmental trajectories'
Bibliographic record
Abstract
The Official World Golf Rankings (OWGR) measure the relative success of professional male golfers' competing around the globe. National Sporting Organizations (NSOs) often view the attainment of a top 100 OWGR as a significant career milestone. While NSOs provide considerable resources to athletes to help achieve this milestone, little objective data exist to support this process. Understanding the ranking trajectories of top 100 ranked athletes is a first step to informing athlete identification and development (AID) programs in men's professional golf. However, ranking data are retrospective in nature, and since sporting systems change over time past data may not be relevant to current and future athletes. In this study, we appraised OWGR data in order to explore developmental trajectories among four age-groups of athletes who reached a top 100 ranking between 1990-2018. Key career ranking milestones (e.g. first turned professional, first top 1000 ranking, etc.), the time taken to transition between milestones, and the overall time to turn professional and obtain a top 100 OWGR were examined across the age groups using multiple One-Way ANOVAs. Results revealed athletes from younger generational cohorts reached key career milestones at earlier ages; moreover, in some cases they spent less time transitioning between milestones and took significantly less time to transition from first turning professional to obtaining their first top 100 ranking. Results suggest NSOs should continue to update and monitor their data in order to be aware of changes in generational trends so policies can be updated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".