Predictive Interval
In prediction markets, prices often imply a probability for a specific outcome. A predictive interval goes a step further by showing a range where the final result is expected to fall, given current information.
This interval is built using historical behavior, market volatility, and probability distributions derived from trading activity. Wider intervals usually mean higher uncertainty, while narrower intervals suggest stronger market confidence.
Predictive intervals are especially useful when outcomes are not binary or when timing and magnitude matter. They help analysts understand not only what the market expects, but how confident it is in that expectation.
Why It Matters
Single probabilities can hide uncertainty. Predictive intervals make prediction markets easier to interpret by showing risk, confidence, and the range of plausible outcomes.
What does a predictive interval represent in prediction markets?
In prediction markets, a predictive interval represents the expected range of outcomes implied by current market activity. It is derived from prices, volume, and historical accuracy of similar markets. Rather than focusing on a single forecast, it highlights uncertainty and dispersion. This helps users assess how confident the market really is.
How are predictive intervals calculated using prediction markets data?
Predictive intervals are calculated by modeling probability distributions from prediction markets data. Analysts may use past market outcomes, volatility patterns, and time-to-resolution factors. These inputs help estimate upper and lower bounds for expected results. The interval adjusts as new data enters the market.
Why are predictive intervals useful in prediction markets APIs?
Prediction markets APIs can expose the raw data needed to construct predictive intervals programmatically. This allows analysts to build models that track uncertainty over time. Predictive intervals are useful for monitoring risk, comparing forecasts, and automating decision systems. They add depth beyond simple probability outputs.
Real-World Example
On Kalshi, a market predicting an economic indicator may imply a central estimate along with uncertainty. A predictive interval can show the range where the final value is most likely to land, based on current trading behavior.
Predictive Interval and FinFeedAPI
FinFeedAPI’s Prediction Markets API provides structured prediction markets data that can be used to build predictive intervals. Analysts can combine probability movements, volatility signals, and historical resolution data. This supports uncertainty modeling, scenario analysis, and confidence tracking. The API enables consistent interval estimation across markets.