Outcome Distribution
An outcome distribution looks at the full set of possible results and how likely each one is. Instead of focusing on a single probability, it shows the entire picture of what the market believes could happen. This is especially important when outcomes are more than just yes or no.
In prediction markets, outcome distributions are formed through trading across all outcomes. As participants buy and sell different positions, probability mass shifts between options. On platforms like Polymarket, Kalshi, Myriad, and Manifold, this appears in prediction markets data as probabilities that rise for some outcomes while falling for others, often in response to the same piece of information.
Outcome distributions make uncertainty visible. A tight distribution signals consensus, while a wide or flat distribution signals disagreement or unresolved information.
Why It Matters
Outcome distributions show how confident or uncertain a forecast really is. They add depth to prediction markets data by revealing where belief is concentrated and where doubt remains.
How is an outcome distribution different from a single probability?
A single probability highlights the most likely outcome. An outcome distribution shows all plausible outcomes and their relative likelihoods. This helps analysts understand trade-offs and uncertainty that are hidden in headline numbers within prediction markets data.
What can outcome distributions reveal about market confidence?
When most probability is concentrated in one outcome, confidence is high. When probability is spread evenly, the market is unsure. Tracking how distributions tighten or widen over time helps interpret shifts in confidence using prediction markets data.
How are outcome distributions used in analysis?
They are used to study risk, scenario ranges, and forecast quality. Analysts compare distributions across similar events or over time to see how beliefs evolve. This makes outcome distributions a core tool for working with prediction markets data beyond simple odds.
Real-World Example
A Kalshi market offers several outcomes tied to an economic indicator range. Early on, probabilities are spread across many ranges. As official data approaches, probability concentrates into fewer outcomes, showing how the outcome distribution narrows with clarity.
Outcome Distribution and FinFeedAPI
Working with outcome distributions requires access to all outcome-level probabilities. FinFeed's Prediction Markets API provides structured prediction markets data—including orderbooks, OHLCV, trades, and resolutions—so developers and analysts can analyze distributions, track how belief shifts, and build advanced forecasting tools.