Using Golf Analytics to Determine the Optimal Age Range for Golfers on the PGA Tour
Bibliographic record
Abstract
The study sought to address the belief that professional male golfers suffer a decline in performance as they age. Thus, the purpose of this research was to understand if age affects male golfers’ performance on their entire performance for the PGA Tour for five seasons starting with 2013 and ending with 2017. The researcher sampled the top one hundred professional male golfers from the stated five seasons. The continuous variable was the average score of the golfers, while the categorical variable was the age range (20-29 years old, 30-39 years old, 40+ years old). Based on the amount of variables and the question to answer, the researcher decided to run an ANOVA test. After running this analytical test, the author concluded that there is no impact on the male golfers’ performance based on their age range (20-29 years old, 30-39 years old, 40+ years old). These results are not on par with what was suggested from past findings.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".