Catching Rising Stars: Preempting the Athlete Endorsement Market
Bibliographic record
Abstract
We examine whether athletes’ performance on low-level circuits can predict success on a top-level circuit to assist endorsement preemption decisions. As athletes compete during their inaugural season on the second-tier tour, their tournament performances provide cumulative information that allow us to make weekly predictions about the probability of players’ future success on the first-tier tour. Combining several high-caliber data sets, including information for every player from every PGA TOUR tournament during the 2002–2019 period, we employ 106 success predictors using random forests, a machine learning algorithm. Our results allow us to identify the endorsers whose future value will be highest on the first-tier tour of the PGA TOUR many months, or in some case years, before athletes achieve it. We argue that predicting athlete’s probability of success can enhance the value of athlete endorsements because it helps identify scarce talent before its market price skyrockets.
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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 | 0.002 | 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 | 0.000 |
| 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".