Event Probability Surface
An event probability surface expands probability analysis beyond one outcome or one timeline. Instead of tracking a single curve, it maps how probabilities vary across multiple dimensions, such as time, scenarios, thresholds, or conditional states. This creates a richer picture of uncertainty.
In prediction markets, probability surfaces emerge when many related markets exist for the same event. For example, different deadlines, outcome ranges, or conditional paths can all be priced simultaneously. On platforms like Polymarket, Kalshi, Myriad, and Manifold, these relationships appear in prediction markets data as clusters of probabilities that move together or diverge under stress.
The surface helps explain complex belief structures. It shows not just what the market expects, but under which conditions those expectations change.
Why It Matters
Event probability surfaces reveal structure inside uncertainty. They make prediction markets data more useful for analyzing complex, multi-outcome events.
How is an event probability surface different from a probability curve?
A probability curve tracks one outcome over time. A probability surface captures many outcomes or conditions at once. This allows analysts to see dependencies, gradients, and tension points that are invisible in single-line prediction markets data.
What can analysts learn from an event probability surface?
Analysts can identify which scenarios the market finds most plausible, where uncertainty is concentrated, and how belief shifts across conditions. Sharp ridges or gaps in the surface often signal disagreement or fragile assumptions within prediction markets data.
How are probability surfaces used in forecasting?
They are used to model scenarios, stress-test assumptions, and compare conditional outcomes. Forecasting systems can sample across the surface to understand risk ranges rather than relying on one probability. This makes prediction markets data more actionable for planning and decision-making.
Real-World Example
An analyst studies multiple Kalshi markets tied to the same policy decision, each with different deadlines and conditions. Mapping their probabilities together reveals a surface showing high confidence if early steps pass, but sharply lower confidence if delays occur.
Event Probability Surface and FinFeedAPI
Building probability surfaces requires consistent data across related markets. FinFeed's Prediction Markets API provides structured prediction markets data—market metadata, probabilities, resolutions—allowing developers and analysts to construct event probability surfaces and analyze how belief shifts across scenarios.