Analyzes 36,777 Polymarket events to understand what happens when continuous questions are split into dozens of independent binary contracts. Volume follows an extreme Pareto distribution: the top 3 markets capture over 75% of trading activity regardless of event size, leaving a large fraction as untradeable ghost markets. The $0.01 tick size compounds the problem, creating a rounding tax that makes low-probability contracts structurally imprecise.

Uses the March 2026 Strait of Hormuz crisis to argue that binary order-book prediction markets hit an architectural ceiling when pricing granular, multi-outcome risk. Compares how traditional options solve this for tradable assets, then explains how automated market scoring rules (LMSR/CLMSR) offer protocol-native liquidity, coherent pricing, and capital efficiency for events without underlying assets. Walks through a concrete WTI crude oil scenario showing how scoring-rule markets reward precise thesis expression over simple directional bets.

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.

Traces the quantitative finance toolkit from backtesting (Deflated Sharpe Ratio, combinatorial purged cross-validation) through factor models, Black-Litterman portfolio optimization, Bayesian regime detection, and machine learning, then argues each technique transfers directly to prediction markets. The core claim is that prediction markets are the purest testing environment for investment theory because binary resolution eliminates the unobservable noise that obscures strategy quality in traditional finance. Uses LMSR's mathematical identity with the softmax function to bridge quant finance and prediction market pricing.

Surveys the landscape of teams trying to add leverage to prediction markets and explains why most are converging on 1x to 1.5x rather than the 10x or 20x they advertise. The core problem is gap risk: binary outcomes resolve instantly, skipping the intermediate prices that liquidation engines need to function. Uses dYdX's TRUMPWIN perp on election night 2024 as a case study where sophisticated safeguards still broke under real conditions, then categorizes current approaches into three camps: constrain leverage, engineer around it with dynamic fees and circuit breakers, or ship and iterate.