Compares spreads across DraftKings, FanDuel, Polymarket, and Kalshi on identical sports markets to isolate how counterparty identification versus maker competition affects pricing. Finds PM prices are 100-300 bps better on liquid markets but long-tail markets on prediction markets have 10-50% spreads. Argues sportsbooks tighten spreads through counterparty-aware pricing while PMs compensate through maker competition and order book transparency.

Skeptical counter to the democratizing-finance framing around prediction markets. Cites Polymarket data showing 70% of 1.7 million addresses lost money and that the top 0.04% captured over 70% of the $3.7B in realized profits, and argues the structure predictably funnels retail into informed counterparties (including platform-operated market-making desks at Kalshi and Crypto.com). Concedes prediction markets are genuinely useful as an information layer while arguing they are a poor retail trading product.

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.

On-chain analysis of Polymarket's fast crypto markets from September 2025 through March 2026. Five-minute contracts overtook 15-minute in weeks ($2.3B vs $795M in notional volume), bots control 55-62% of volume across fast markets, and Bitcoin drives 77% of turnover. The piece argues that with $23.7M in taker fees collected in 83 days through a maker-rebate fee model, Polymarket has structurally converged with a derivatives exchange.

Uses the case of a trader sniping Polymarket's geopolitical strike markets at 10 cents to argue that continuous order books are structurally broken for binary assets. In traditional markets, sniping costs basis points; in prediction markets, it costs 80 cents on the dollar because prices jump from 0.10 to 0.99 on a single tweet. Proposes frequent batch auctions (citing Budish, Cramton, and Shim) to shift competition from speed to price accuracy, and introduces the concept of a 'liquidity mirage' where the highest social-value markets are precisely those where passive liquidity is unsustainable.

Applies adverse selection concepts specifically to prediction markets. Compares market making difficulty across Indian options (easy), crypto (medium), and prediction markets (legendary). Argues gap risk is effectively worse than any other asset class because informed counterparties can have near-perfect information and take out entire order books.

Educational deep-dive on adverse selection and market making fundamentals. Uses the classic Jane Street coffee interview question to illustrate why conditional on someone trading with you, you should be less confident your trade was good. Explains how market makers profit from retail flow while avoiding toxic informed counterparties.