Expected value markets focus on predicting a numerical result rather than a binary outcome. Instead of asking whether something will happen, they estimate how much, how high, or what number an event will produce. Traders buy and sell positions that correspond to different values, and the market aggregates these trades into an implied expected outcome.
Platforms like Polymarket, Kalshi, Myriad, and Manifold often run expected value–style markets for economic indicators, performance metrics, or numeric predictions. These markets produce detailed probability distributions rather than a single yes/no probability. The resulting prediction markets data helps analysts understand not only the most likely outcome, but also the market’s estimate of its magnitude or level.
Expected value markets are especially useful when the result isn’t about a binary event but about forecasting a precise real-world measurement.
Expected value markets provide richer forecasting signals by predicting actual numeric results. They generate prediction markets data that helps analysts model the size, intensity, or level of an outcome—not just whether it will occur.
They are helpful because many important events—such as economic releases, performance stats, or price levels—require numeric forecasts. Expected value markets let traders encode their beliefs about these values directly into prices. This produces more detailed prediction markets data, capturing both central expectations and how markets interpret uncertainty around the number.
Binary markets answer yes/no questions, while expected value markets predict a numerical outcome. Instead of a single probability, the market outputs an implied expected value or distribution. This structure helps analysts see where traders think the number will land and how strongly they hold those beliefs. The prediction markets data becomes deeper and more expressive compared to simple binary outcomes.
Analysts can extract implied means, variances, and full probability distributions from market activity. They can study how expectations shift before major releases, detect uncertainty levels, and evaluate whether traders anticipate over- or undershoots. Expected value markets provide more granular prediction markets data, improving forecasting models and decision-making.
On Kalshi, expected value–style markets tied to monthly inflation or employment numbers allow traders to estimate the actual figure. As analysts release new projections or government officials provide hints, the market adjusts, reflecting the crowd’s evolving expectation of the final number.
Expected value markets rely on continuous, high-resolution forecast data. FinFeed's Prediction Markets API provides structured prediction markets data—including probability paths and outcomes—that developers can use to model expected values, build visualization tools, and study how numeric forecasts evolve over time.
