DFS 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.

DFS Desk — Model Anchor Calibration

We are building model anchors for DFS (Daily Fantasy Sports) projection accuracy on Kalshi contest markets.

Questions:

1. How should DFS projections differ from prop model anchors? (Ownership matters, correlation matters)
2. How should slate-specific projections work? (Main slate vs single-game showdown)
3. How should ownership projection interact with value detection? (Low-owned + high projection = leverage)
4. How should game environment (Vegas total, spread) drive DFS projections?
5. How should lineup correlation (stacking) be modeled? Bring-back correlation?
6. How should salary efficiency (points/dollar) be optimized across positions?
7. What fantasy point scoring system adjustments per platform (DraftKings vs FanDuel)?
8. How should ceiling vs floor projections differ for GPP vs cash games?
9. How should confirmed lineups/scratches cascade through DFS projections?
10. Give specific numbers, formulas, and implementation-ready recommendations.