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.

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.

Frames insider trading as a structural feature of prediction markets, not a bug. Walks through the DOJ case against Master Sergeant Gannon Ken Van Dyke, who made $400k on Polymarket trading the Maduro raid, and the prior Israeli reservist arrests, then quotes Mansour, Coplan, Tenev, and Hanson on how insider flow is what makes prices accurate. Argues platforms face a calibration problem: too permissive and noise traders flee perceiving rigging, too strict and informed flow disappears and prices decay into sentiment. Predicts Polymarket fully drops pseudonymous trading and ramps surveillance over the next year.

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.

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.

Technical research on adapting the Avellaneda-Stoikov market-making framework for prediction markets. Classical models fail because prediction market prices are bounded probabilities (0 to 1) rather than unbounded asset prices, creating non-constant volatility and guaranteed terminal convergence. After a logit-space transformation lost $1,114 in backtest, XO Labs iterated to a probability-space engine with inventory-skewed spreads, volatility regime detection, and multi-outcome coordination that turned profitable at $453. Includes the full mathematical framework and open problems.

Applies the sportsbook industry's square vs. sharp distinction to prediction markets. Argues Kalshi (3.5% take fee) and Polymarket (1.5%+) are building square prediction markets that monetize price-insensitive retail takers, while sharp prediction markets focused on trading efficiency will thrive outside the US. Notes that raising fees from 0.5% to 0.75% on 4casters had no material impact on volume, suggesting sports bettors are less price-sensitive than assumed.

Landscape report comparing four models for adding leverage to prediction markets: lending pools (Gondor/Morpho-style), prime brokers (Ultramarkets), synthetic desks (CFD counterparties), and perpetual futures (dYdX TRUMPWIN). Sizes the fee revenue opportunity at $15M base case to $50.7M bull case, with 87% driven by financing revenue on open interest rather than trading fees. All four models share a structural dependency on CLOB venue architecture that degrades during jump events.

Empirical paper finding that prediction market accuracy is not the wisdom of crowds. Roughly 3% of accounts drive most price discovery: their trades anticipate future prices, respond to news immediately, and improve calibration across a market's lifecycle. The remaining accounts contribute volume and liquidity but minimal information, and their losses fund the informed minority. Reframes the standard story about why prediction markets work and has implications for platform design, surveillance, and how to credibly market accuracy.

Frames prediction market prices as public goods whose benefits are non-excludable but whose liquidity costs fall on a narrow trader base. Proposes cross-subsidization as the growth mechanism: profitable markets fund socially valuable ones that can't sustain themselves, the same way newspaper ads funded investigative journalism. Also argues that accuracy isn't the only axis of value, showing how markets can serve risk transfer (hedging hurricane or policy exposure) and information accountability functions even when prices drift from pure probability.

Screens 93,000 Polymarket markets and flags traders with a 69.9% win rate, more than 60 standard deviations above chance, estimating $143 million in anomalous profits. Documents specific cases from geopolitical events to celebrity announcements where wallets appear to trade on material non-public information. Proposes a regulatory framework combining platform-level registration, contract-level restrictions on high-risk categories, and an extended misappropriation doctrine to close the legal gaps that leave prediction market insider trading largely unpoliced.

Wall Street equity research analysis of anonymized trading data comparing prediction market and sports betting returns. The median prediction market user has an ROI of -8%, worse than sports bettors at -5%, with only traders above $500K in volume achieving positive returns (+2.6%). Finds that prediction markets attract sharper competition than regulated sportsbooks, creating worse outcomes for casual retail participants.

Sets out to defend insider trading in prediction markets but arrives at a more conditional position. Introduces a 'discovery vs betrayal' framework: in distributed-truth markets like elections, informed traders sharpen the signal because no one holds the full answer; in concentrated-truth markets like earnings, insiders monetize sealed results rather than synthesize public fragments. Argues the real question is not whether insiders should be allowed but what kind of informational asymmetry a market can absorb without losing the participation and trust that make the signal useful.

Challenges the smart-versus-dumb money dichotomy in prediction markets by synthesizing research from Snowberg/Wolfers/Zitzewitz, INSEAD's BIN model, and Wharton's cognitive search framework. Argues that noisy traders fund the probability space rather than serve as exit liquidity, and compares how binary CLOBs versus continuous probability markets decompose and harness noise differently.

Data-driven deep dive into Polymarket's order book structure using 600M+ raw datapoints filtered to a 343M research dataset. Categorizes order flow into soft (retail), hard (professional), and AI flow, revealing that Polymarket's liquidity is episodic and attention-driven: the p95 peak hour shows hundreds of millions in open interest while the p50 median is thin. Order book analysis shows surface symmetry at top-of-book but systematic ask-side skew at deeper levels, and market impact data confirms that medium-to-large orders hit liquidity cliffs. Argues the core problem is trapped capital — dollars reserved multiple times against mutually exclusive outcomes — and that better netting and capital efficiency, not more money, is the fix.

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.

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.

A Reforge co-founder's bearish case against prediction markets, structured as 23 distinct failure modes. Covers structural constraints across capital efficiency, liquidity mechanics, adverse selection, oracle governance, and regulatory fragmentation. Argues that prediction markets face fundamental limitations that perpetual futures markets do not, making institutional scaling unlikely under current designs.

Argues that prediction markets face two structural problems preventing them from becoming transformative economic instruments: corporate hedging is impractical due to market fragmentation and basis risk, and insider trading undermines retail participation. Draws parallels to online poker and memecoins to suggest that without structural reforms, prediction markets will remain primarily a sports betting product.

Argues that insider trading in prediction markets is structurally different from traditional securities markets because prediction markets can make almost anything tradable, often in contexts where relevant confidentiality duties are unclear. Proposes solutions across three layers: platform-level detection and position limits scaled to account size, market design mechanisms like dynamic spread widening and market maker insurance pools, and legal frameworks from updated corporate compliance policies to CFTC guidance.

Provides a quantitative framework for distinguishing gambling from systematic trading on prediction markets, including a five-point diagnostic and three trader archetypes classified by profitability. Explains why Polymarket's CLOB creates renewable structural arbitrage by design, and covers Kelly position sizing, adverse selection measurement via fill quality, and probability term structure as tools for building a repeatable edge.

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.

Legal analysis explaining that insider trading in prediction markets is governed by existing fraud law rather than a distinct insider trading statute. The key question is whether a trader has deceptively breached an implied or explicit promise about how confidential information may be used. Argues prediction markets complicate this analysis by expanding tradable events into contexts where no clear company-based duty exists, making insider trading liability increasingly difficult to determine.

Builds a formal framework to decompose why prediction markets have late volume: is it because information arrives late (hazard), or because early entry is punished by adverse selection (toxicity)? Introduces LOX, a metric computed from on-chain trades that measures whether new entrants hesitate more than volume alone would predict. Explains why boxing markets cluster with news markets despite being categorized as sports.

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.

First-person account from a seven-figure prediction market trader. Covers strategy as a 'bond mule' (locking capital for small premiums on near-resolution markets), OSINT information sources (non-English media, Telegram), fractional Kelly sizing, and trading personality-driven markets. Notes edge has compressed as space matured.

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.

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.

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.

Introduction to a series arguing prediction market mass adoption is threatened by structural barriers to market maker participation. Claims binary markets are frequently unhedgeable and suffer from adverse selection, making them a professional market maker's nightmare compared to options or crypto.