Analyzes three structural reasons prediction market prices diverge from true probabilities even with rational participants: favorite-longshot bias from Kelly betting, risk-premium distortion from market correlation, and risk-neutral forward pricing in long-dated contracts. Argues markets still outperform individuals because they weight capital-backed beliefs rather than equal-weighted opinions.

Survey of recent research on why conventional market making fails in prediction markets. Covers cross-venue fragmentation (the same contract at 58-67 cents on different platforms), the January 2026 Polymarket XRP exploit that paid $231K on thin weekend liquidity, Kalshi's structural longshot bias, and evidence from 150M Polymarket trades that the top 5% skilled traders earned $228M while spread capture barely moves P&L. Concludes passive LPs on these venues behave more like underwriters of terminal risk than classical market makers.

Analyzes 7,292 resolved Polymarket markets and 28,793 on-chain trades to test whether yes bias exists in trade data. Finds that traders buy whichever token is cheaper, not whichever is labeled YES, and that the apparent bias is a compound effect of longshot preference channeled through Polymarket's "Will X happen?" question framing, which systematically assigns the longshot to the YES token.

Analyzes tick-level order flow across Polymarket and Kalshi to decompose market bias by trader type. Finds that the classic favorite-longshot bias may be a statistical artifact masking a pervasive "yes bias" driven by temporal volatility and incomplete controls for contract lifecycle. Also shows that whales are not the sharpest participants: heavily capitalized traders systematically bleed expected value to small-order traders, likely driven by ideological conviction rather than informational edge.

Fits a Bayesian hierarchical model to 292 million trades across 327,000 contracts on Kalshi and Polymarket to decompose calibration errors into structured components: universal horizon effects, domain-specific biases, and trade-size scale effects, which together explain 87.3% of variance on Kalshi. Finds persistent underconfidence in political markets where prices compress toward 50%, and shows that large trades amplify this pattern on Kalshi but not on Polymarket, pointing to platform-specific microstructure differences.

Summarizes research analyzing 72 million Kalshi trades. Identifies three persistent biases: longshot bias (5c contracts win only 4.18% of the time), maker-taker asymmetry (makers outperform at 80 of 99 price levels), and YES/NO asymmetry (YES buyers average -1.02% returns vs +0.83% for NO buyers). Finance markets are most efficient (0.17% spread) while crypto is least (2.69%).

Analyzes 72.1 million trades ($18.26B volume) on Kalshi and documents a systematic wealth transfer from takers to makers averaging 1.12% excess returns on each side. Takers disproportionately buy YES longshots, accepting returns 64 percentage points lower than equivalent NO positions. Shows this transfer only emerged after Kalshi's October 2024 legal victory attracted professional algorithmic market makers, and that market efficiency varies sharply by category: finance markets are near-efficient while entertainment and media markets show gaps exceeding 7 percentage points.

Proposes a DeFi primitive for borrowing against prediction market positions, arguing that collateralization solves the capital lock-up problem in long-dated markets. Walks through how position lending could improve liquidity, correct persistent mispricings like longshot mispricing, and open composability with the broader financial ecosystem, while flagging the liquidation risks unique to binary outcomes.