Prediction Market Research
Comprehensive analysis of prediction market microstructure, examining accuracy, efficiency, and trading patterns across 3,587 markets from June 2021 to November 2025.
Market Microstructure Analysis
Data-driven insights from 3,587 markets (June 2021 - November 2025)
How often market probabilities match outcomes
Cost of trading (lower is better)
Sample size from June 2021 - Nov 2025
24-hour trading volume per market
Calibration Curve
How accurately do market probabilities predict outcomes?
Markets are well-calibrated across probability ranges (close to diagonal = accurate)
Volume by Category
Where is the most trading activity?
Politics and Fed Policy drive the highest volumes
Market Efficiency Trend
Bid-ask spreads narrowing as liquidity grows
Tighter spreads indicate improving market efficiency and lower trading costs
Win Rate by Probability Bucket
Actual vs expected outcomes across probability ranges
High probability events (>80%) slightly underperform expectations
Key Insights
Well-calibrated markets: Prediction probabilities closely match actual outcomes across all ranges
Improving efficiency: Bid-ask spreads have compressed 43% as volume increased 166%
Category concentration: Politics and Fed Policy account for 53% of total trading volume
High-probability bias: Events with >80% probability win 84% of the time (6pt underperformance)
Analysis methodology based on "The Microstructure of Wealth Transfer in Prediction Markets" framework
Methodology
This analysis framework examines Kalshi prediction market data to understand wealth transfer mechanisms and market microstructure. The dataset includes comprehensive market and trade records spanning multiple years.
Data Sources
- • Market-level data (pricing, liquidity, lifecycle)
- • Individual trade execution records
- • Bid-ask spreads and order book depth
- • Resolution outcomes (yes/no results)
Key Metrics
- • Calibration curves (predicted vs actual)
- • Bid-ask spread compression over time
- • Volume distribution by category
- • Win rate accuracy by probability bucket
Research Framework by Jon Becker
Analysis methodology based on Jon Becker's open-source prediction market analysis framework for studying market microstructure and efficiency. The framework provides tools for analyzing pricing accuracy, liquidity trends, and wealth transfer mechanisms across prediction market platforms.
View Jon's Framework on GitHubThis research is for informational and educational purposes only. Past performance does not guarantee future results. Trading in prediction markets involves risk.