In forecasting, the prior probability represents the baseline belief about an event’s likelihood. This estimate is made before traders react to new signals or before a prediction market gathers meaningful activity. Priors may be based on historical data, expert opinions, early reports, or simple assumptions when information is limited.
Once the market opens, traders begin updating this prior probability as new evidence emerges. The prior gradually transforms into a more refined forecast as trades reflect changing sentiment. This process—moving from prior to updated probability—is central to how prediction markets generate meaningful prediction markets data. It helps reveal how early expectations compare to later consensus.
Priors are especially important in markets with long horizons or limited early information. A well-chosen prior helps stabilize early forecasts, while a weak or biased prior can distort the initial probability path until stronger signals appear. Analysts use prior probabilities to understand where the market began and how far it has evolved.
Prior probabilities provide essential context for interpreting early forecasts. They help analysts understand how prediction markets data shifts from initial assumptions to evidence-based expectations.
Prior probabilities matter because every forecast starts somewhere. Without a baseline belief, early market probabilities would swing randomly or depend heavily on the first few trades. Priors add structure and context, allowing analysts to evaluate whether later forecasts represent strong changes or natural refinements. They also help ensure prediction markets data begins from a reasonable starting point.
Prior probabilities influence early trading behavior by shaping expectations before information begins to flow. If the prior is high, traders must see strong contrary evidence to push the probability down. If the prior is low, early optimism can shift the price upward quickly. These early dynamics shape the initial segment of prediction markets data, affecting how the probability curve evolves over time.
Analysts can measure how dramatically traders revised their beliefs, identify early mispricing, and detect overconfidence or underconfidence in initial assumptions. Large gaps between prior and posterior values often signal new information or shifting sentiment. Studying these changes helps analysts evaluate market responsiveness and understand how prediction markets data transforms from an initial guess into a refined forecast.
A prediction market forecasts whether a new policy will pass in the upcoming legislative session. The prior probability starts low because similar policies have historically struggled. As lawmakers release statements and committees show support, traders update their beliefs, pushing the probability upward—revealing how the market moved from baseline assumptions to evidence-driven expectations.
Comparing priors to later forecasts requires clean historical probability paths. FinFeed's Prediction Markets API provides structured prediction markets data—including early market values, time-stamped updates, and final outcomes—allowing developers to analyze how priors evolve into posterior probabilities and build tools that visualize belief progression.
