Golf Desk — Model Anchor Calibration Prompt

Context: Our MLB Player Props council (2026-04-03) established the universal framework: per-possession/per-PA rates as foundation, Bayesian shrinkage toward career prior, Log-Odds opponent adjustment, Gaussian copula for correlated combo props, dual-anchor system (sportsbook + model) tracked via Brier scores, confidence tiers. This prompt asks for the SPORT-SPECIFIC parameters to plug into that framework.

Golf Desk — Model Anchor Calibration

We are building model anchors for golf betting markets on Kalshi: tournament winner, top 5/10/20, make the cut, H2H matchups, round leaders.

We currently use Strokes Gained data and Monte Carlo simulation for top-N props.

Questions:

1. What SG components matter most per market type? (SG:Total for H2H, SG:Approach for course fit?)
2. How should course-specific history adjust projections? Course DNA classification?
3. How many rounds/tournaments until SG metrics stabilize? k values for EWMA?
4. How should current form (last 4-8 rounds) weight vs season-long SG?
5. Field strength adjustment: how does the field composition affect top-N probabilities?
6. How should weather (wind, temperature) adjust SG projections?
7. How to model make-the-cut probability from SG:Total?
8. How many Monte Carlo simulations needed for stable top-N probabilities?
9. How should grass type (Bermuda/Bentgrass/Poa) adjust putting metrics?
10. Course fit vectors: how to classify courses and match player strengths?
11. How should recent putting form weight differently from ball-striking form?
12. Give specific numbers, formulas, and implementation-ready recommendations.