Prediction Market Data for Institutions

The real opportunity is selling prediction market data as market infrastructure, not just serving retail traders. The traders moving most of the dollars are already concentrated among whales, small hedge funds, and tiny algorithmic shops, so Dome can grow by packaging cleaner cross venue data, faster feeds, and execution tools in the formats professional desks already use. That turns scattered event prices into something closer to a tradable data product.

• Prediction markets are already useful to professional users because one event often trades on multiple venues. Dome matches equivalent markets across platforms and routes orders to the best venue, which lets a desk compare prices, backtest signals, and trade against a deeper combined order book instead of one thin market.
  1 sacra
• The buyer profile is already more institutional than the consumer apps suggest. Around 25% of Dome's users are using data for backtesting, analytics, and high frequency trading, and the active firms entering today are mostly 1 to 10 person trading shops and small hedge funds rather than giant firms like Citadel or Jane Street.
  1 sacra
• This follows the broader shape of the market. Kalshi and Polymarket are becoming infrastructure and liquidity layers that sportsbooks, brokerages, and crypto platforms can plug into, while sports has become the highest volume category because contracts resolve fast and capital can be reused quickly. That makes data feeds more valuable to both trading desks and odds setters.
  2 sacra 3 sacra

The next phase is the buildout of institutional plumbing. As volumes deepen and more venues stay open to external integrations, the winners will look less like consumer apps and more like Bloomberg for event contracts, with normalized data, routing, compliance outputs, and embedded access for funds, brokers, and sportsbook operators.