These Are the Sharps Actually Making Money on Prediction Markets
This OddLots episode features journalist Adam and prediction market traders Brian and Daniel discussing how skilled traders consistently profit from prediction markets through rigorous research, data analysis, and ground-level reporting rather than luck or AI. The conversation explores market efficiency, insider trading concerns, the zero-sum nature of prediction markets, and how professional traders systematically beat retail investors.
Summary
The episode opens with host Joe Weisenthal expressing skepticism about prediction markets and get-rich-quick marketing schemes, noting the zero-sum nature of these markets differs from traditional equity investing. Journalist Adam Gromis discusses his New York Times article identifying 'sharps' who consistently win at prediction markets, explaining he discovered these skilled traders through reporting after losing money himself on Polymarket and Kalshi.
The MAGA Kiwi Club—a Discord group of professional prediction market traders—operates as an 'Avengers model' where members with different specialties share information while maintaining individual portfolios. Members like Brian Golden and Daniel Reitman (Carnitas Taco) originally met through political prediction markets dating back to 2016-2017 on Predict.it. They have expanded expertise across inflation, elections, meteorology, and other markets as capital has grown.
Brian Golden reconstructed the Bureau of Labor Statistics inflation formula in Excel and now predicts inflation better than Bloomberg consensus forecasts and major investment banks—despite having only an undergraduate degree in drama. He attributes institutional forecasters' poor performance to lack of effort rather than superior expertise. Daniel Reitman and his election team conduct original research including commissioned polling and precinct-level historical analysis to model election outcomes superior to polls and broader market expectations.
The discussion explores market efficiency: while traditional markets appear generally efficient, prediction markets show clear mispricings, particularly in election markets driven by siloed media ecosystems and emotional attachments. Recent examples include Spencer Pratt's inflated odds in the LA mayoral race and the 2025 Romanian election where local Romanians outperformed international sharps. The traders emphasize that their edge comes from traditional research methods—calling experts, gathering ground-level data, and rigorous analysis—not from AI tools that merely echo existing forecasts.
On market structure, they discuss holding positions through resolution, avoiding markets prone to insider trading disputes, and leveraging comment sections as contrarian indicators (always bet against the comments). The article and conversation highlight concerns about prediction market regulation and marketing: platforms risk long-term legitimacy by promoting trivial bets while actual sharps focus on consequential economic and political questions. Insider trading remains a serious issue despite platform claims of policing, with specific examples of probable insiders in events like the Critics' Choice Awards and halftime show length.
About this episode
<p>Here's a couple things about prediction markets. A lot of it is pure gambling and speculation, much of it on things with very little economic relevance. Another fact is that in all likelihood, if you yourself started trading right now, you'd probably lose your shirt. But there is money being made by some dedicated traders, really focused on areas like politics and economics. On this episode, we speak with Brian Golden and Daniel Reichman, who are part of a private Discord called Maga Kiwi Club, where serious prediction markets traders swap ideas and make real money. We discuss the remarkable efforts they go to in order to spot opportunities, the systematic biases among traders, how they feel about insider trading, and other major issues that surround the space. Alongside Brian and Daniel, we also speak with NYC-based journalist and producer Adam Iscoe, who <a href="https://www.nytimes.com/2026/05/26/magazine/polymarket-prediction-wall-street.html">recently profiled these traders</a> for <em>The New York Times Magazine</em>.</p> <p>Only Bloomberg.com subscribers can get the Odd Lots newsletter in their inbox, plus unlimited access to the site and app. Sign up at <a href="https://www.bloomberg.com/subscriptions/oddlots?in_source=oddlotspodcast">bloomberg.com/subscriptions/oddlots</a></p><p>See <a href="https://omnystudio.com/listener">omnystudio.com/listener</a> for privacy information.</p>
Key Insights
- Brian Golden's inflation predictions outperform Bloomberg consensus and major investment banks despite having only an undergraduate degree in drama, suggesting institutional forecasting failures reflect insufficient effort rather than capability constraints.
- The MAGA Kiwi Club traders attribute their edge primarily to traditional research methods—calling experts, commissioning original polling, and ground-level analysis—rather than sophisticated modeling or AI tools, which indicates that proprietary data gathering remains scarce in the AI age.
- Prediction markets in elections remain consistently beatable because politics involves emotional and tribal attachments, causing markets to misprice based on siloed media ecosystems, whereas markets like inflation have become progressively harder to beat as more professionals enter.
- Daniel Reitman's election team successfully bet on LA mayoral candidate Nithya Raman to make the top two at 1% market odds by conducting precinct-level historical analysis and monitoring early vote patterns in real-time, while Raman's own campaign was pessimistic about her chances.
- The traders actively avoid betting against insiders by assessing whether counterparties could logically possess superior information, and they cite specific examples like Jacob Elordi's Critics' Choice Award odds jumping from $0.01 to $0.40 overnight as clear indicators of insider betting.
- Comment sections on prediction market platforms serve as reliable contrarian indicators—sharps remain silent while retail traders openly advocate for positions, meaning high comment volume and enthusiasm typically signals the wrong side of a trade.
- When professional investors entered online poker during the early 2000s boom fueled by Chris Moneymaker's World Series victory, the subsequent consolidation of capital among skilled players squeezed out retail players, leaving only platform profits from rake once sharp-versus-sharp games dominated.
- Large language models fail as prediction tools because they tailor responses to confirm whatever position users suggest, maintaining backward-looking training data that is already being arbitraged by existing market participants.
Topics
Transcript
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