Analyzes Polymarket's short-duration crypto up/down markets, which now account for 16% of monthly volume but generate ~40% of platform fees. Finds professional algo traders (19 addresses) extract consistent profits through paired trading and maker execution while 69% of retail traders lose money. Argues prediction markets are becoming a game of volatility rather than forecasting.

Isaac Rose-Berman dissects Kalshi's claim that as an exchange rather than a bookmaker, it has no stake in whether users lose. Through a detailed walkthrough of maker-taker fees, adverse selection and the zero-sum structure of prediction markets, he argues the business depends on ordinary users systematically losing to sophisticated market makers just as surely as any sportsbook. A coin-flip bet that should be fair becomes a 3.4% expected loss after fees.

A trader who ran ~10,000 automated trades through Polymarket dissects why most prediction market terminals fail: execution mirages, non-synthesizing research layers, and strategy tabs that are deck screenshots disguised as features. Lays out the baseline infrastructure real traders need and argues only two terminal types survive the next 24 months: institutional API rails and a trader-native terminal built by someone who has actually lived the workflow.

Part 1 of a Ranger Global research series on onchain prediction market microstructure. Walks through why CLOBs beat constant-product AMMs for binary events, the YES/NO minting and merging invariant that lets depth expand whenever matched counterparties exist, and probability-scaled dynamic fees that shrink near 0 and 1. Closes with a regression of prediction market midpoints against BTC spot, finding PM traders systematically underreact to spot moves by 10-20% and that latency under 100ms now captures 73% of arbitrage profits.

Short research-agenda framing from Daedalus Research announcing a paper on institutional liquidity in prediction markets. Argues that as institutional market makers shape these venues, the academic conversation must move beyond forecast accuracy to execution quality, market structure, and trader welfare. Useful as a pointer to shifts in how prediction market research is being scoped.

Data-driven analysis benchmarking Kalshi's sports markets against traditional sportsbooks. Kalshi's monthly volume grew 80x to $14.4B in March 2026, with NCAA March Madness generating $3.3B in notional volume comparable to total US wagered on the tournament. In-game prices correlate at 0.99+ with FanDuel, but Kalshi's taker fees (up to 3.5% at midpoint) and thinner in-game liquidity (76% depth decline vs pre-game) currently limit institutional execution. Includes a valuation comparison showing Kalshi priced as an exchange ($20B) vs sportsbooks trading at 2-4x revenue.

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.