Esports 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.

Esports Desk — Model Anchor Calibration

We are building model anchors for esports betting markets on Kalshi: match winner, map spreads, total maps.

Questions:

1. What team/player metrics best predict match outcomes per game? (Elo by game title, win rate, map pool depth?)
2. How should map-specific performance factor in? Teams have different map pools.
3. How many matches until team Elo stabilizes per game? k values? (Esports rosters change frequently)
4. How should roster changes (player swaps) affect team rating? Reset or gradual adjustment?
5. How to model map count totals? What distribution for best-of-3 vs best-of-5?
6. How should LAN vs online performance differ?
7. How should patch/meta changes affect projections? (Game updates can shift team strength)
8. How should regional strength differences factor in? (Korean teams in LoL, EU in CS2)
9. What EWMA alpha for team win rates?
10. How should recent form weight vs historical rating for teams with frequent roster changes?
11. Give specific numbers, formulas, and implementation-ready recommendations.