Analyzes 600 million Polymarket orderbook datapoints, finding ~70% of one-cent price moves do not continue in the same direction. Coins 'semantic tick size' to describe how a prediction market's minimum price increment doubles as a narrative unit—each penny reads as a one-percentage-point probability change, creating overreactions that a contrarian fade strategy can profitably harvest. Frames this against Tetlock's TradeSports microstructure research, where passive limit order walls slow price discovery while impatient market orders amplify short-term noise.

Data-driven analysis of Kalshi's business model using all 203 million trades across $41.7B in volume. Reveals that Kalshi functions more like a poker rake than a sportsbook, charging fees via the formula fee = 0.07 × C × P × (1-P), which incentivizes trading near 50% probability. Key finding: sports comprise 82% of total volume, making Kalshi functionally a sports betting platform despite its CFTC-regulated derivatives positioning. Includes clear explanations of order book mechanics, binary contract pricing, and the regulatory framework (clearinghouse structure, no-action letters) alongside original data visualizations of volume distribution and resolution patterns.

Quantifies when prediction markets become structurally cheaper than derivatives for pricing binary institutional risk. Analyzes 87 contracts across 11 categories and finds that high-VRP categories like Bitcoin (4.83%) and elections already cross the displacement threshold, while FOMC markets compressed from a 12 percentage point cost gap to under 2 points between 2024 and 2026. Frames the cost differential as "apparatus rent" paid for constructed dealer infrastructure that event contracts can bypass.

Decomposes 222 million Polymarket trades into directional and execution components and finds that forecasting accuracy does not predict profitability. Traders who pick the right side still lose money because they arrive late and pay unfavorable prices, while automated traders with near-random directional skill profit by paying 2.52 cents less per contract.

Argues that First-Come-First-Served order matching in prediction markets creates perverse incentives: latency wars between traders and defensive spread widening by market makers. Proposes priority batch auctions that process cancellations before maker orders before taker orders, allowing market makers to quote tighter spreads and improving price quality for all participants.

Compares the traditional sportsbook house model with prediction market exchanges to explain why exchanges offer better odds. Uses data showing Betfair's ~3% overround versus bookmakers' ~12% to argue that peer-to-peer exchange models produce fairer pricing and welcome all winners, unlike sportsbooks that limit successful bettors.