\n \n

\n \n \n \n

\n $\"Russia$ \n

\n \n

\n Experiments in Prediction Probabilities as a Consumer Product\n

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The Two Audiences Problem

\n \n

\n Prediction markets are on the rise, but there are actually two distinct demographics that can make use of them: those who bet on them, and the much larger percentage of people who might consume them for information. Think of it like the distinction between journalists and news consumers – a small circle of creators serving a much larger circle of consumers.\n

\n\n

\n Currently, prediction market platforms serve bettors, which makes sense and is needed for the markets to be useful at all. But these platforms are dizzying to the average person and look nothing like how they normally consume news. We've made progress on the first challenge – attracting traders and liquidity – but what doesn't appear to have been established is how to translate that generated data into something consumable by the average informed citizen.\n

\n\n \n

\n $\"Platform$ \n $\"Platform$ \n $\"Platform$ \n

Bettor friendly maybe. Normie friendly? Not so much.

\n\n

\n Prediction markets won't become mainstream until they're packaged in a way that appeals to folks who are unlikely to consider betting on them.\n

\n\n

\n My instinct: instead of trying to aggregate many predictions about many events, get one really good page going for one single topic, then expand from there.\n

\n\n

The Deep Dive Approach

\n\n

\n Picture this: the best page on the internet for understanding \"When will the war in Ukraine end?\" Every time the market moves, we research why it moved, building a digestible narrative from that movement.\n

\n\n

\n When odds shift from 15% to 23% for \"war ends by December 2024,\" readers don't just see numbers – they get context. What news broke? Which analysts are talking? What historical parallels apply?\n

\n\n

Russia Timeline Prototype:

\n $\"Russia$ \n

\n\n

\n (This would probably work brilliantly as a newsletter format too – imagine getting a weekly digest that not only tells you how the market moved, but why it moved and what that means.)\n

\n\n

From Prototype to Proof of Concept

\n\n

\n I built a single-page treatment for my prediction market about the USA World Cup roster. Instead of covering every sport, I went deep on one question that soccer fans genuinely care about.\n

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\n USA World Cup Roster Prediction Market\n \n

\n \n

\n\n

Why This Works

\n\n

\n The traditional aggregator approach: you need lots of markets to be useful, but you need users to make markets liquid. By starting with one exceptional page, you can perfect the format, build expertise, and create something people actually bookmark.\n

\n\n

Tools for Building Bespoke Market Pages

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\n I built a site where you can add URLs from Manifold or Polymarket markets, and it creates a dashboard that shows them all nicely formatted.\n

\n\n

\n The Rundown - Market Dashboard Maker\n \n

\n \n

\n\n

\n I also built a bot (@manifoldmaven) that tweets whenever a Manifold market moves by more than 35%. Manifold Maven itself is just another aggregator of prediction markets, of course, but it does make an attempt to show the \"story\" of the day by only tweeting markets that have moved significantly. It also exists in the context of Twitter, meaning that as you're seeing lots of editorialization (maybe too much), you're also getting pieces of more solid probabilities. Prediction market platforms have the opposite problem in that sense.\n

\n\n \n

Market: Will Musk still be on good terms with Trump at the end of 2025? (URL: https://t.co/UNd3aifXFl)
Change: 42.8% down
From: 48.2% to 5.4%
Duration: 3 hours
— manamaven (@manifoldmaven) June 5, 2025

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The Bigger Picture

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\n We don't want to be overwhelmed with data, we want to be informed by intelligence. The difference between showing someone a market price and helping them understand what that price means is the difference between a tool and a service.\n

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\n Sometimes the best way to aggregate everything is to start by perfecting something.\n

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\n What do you think? Are there specific markets or topics where this deep-dive approach could work particularly well? I'd love to hear from other folks pursuing interesting ideas in this space.\n

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