prediction-market-strategylisted

# Prediction Market Strategy Binary prediction markets price contracts as probabilities. This skill covers the *strategy, sizing, and validation* layer that applies across all venues and market types. API mechanics live in `kalshi-api` / `polymarket-api`; contract semantics and settlement live in `kalshi-weather-markets` / `kalshi-crypto-index-markets`. This is the strategy/sizing/validation layer that applies across all of them. ## Core Thesis **Price = implied probability.** A contract priced at $0.18 claims an 18% chance of resolving YES. Brackets in a series sum to just above $1.00 — the overround is the house margin (roughly 5–8% for weather markets on Kalshi). **Takers systematically lose; makers systematically win.** Across 300k+ Kalshi contracts, the average pre-fee return is ≈ −20%, concentrated in takers (market-order users) and in longshot buyers. Makers (resting limit orders) earn positive returns. On Polymarket (588M+ trades), the top ~1% of accounts capture ~76.5% of profit, predominantly by resting limit orders. This is the foundational result. **Favorite–longshot bias is the durable mechanism.** Cheap longshots are systematically overpriced: a $0.05 contract historically wins ~2%; sub-$0.10 contracts lose ~60% of stake to buyers. Favorites are fairly- to slightly-underpriced. The repeatable expression is **selling the overpriced longshot tail, maker-side** — resting NO bids on brackets priced ~$0.05–$0.20, diversified across many events to survive the rar