Event Surprise Factor
In prediction markets, prices represent collective expectations before an event occurs. The event surprise factor describes how far the actual outcome deviates from those expectations.
A high surprise factor occurs when an outcome had a low implied probability but still happened. A low surprise factor means the outcome closely matched what the market already expected. Surprise is not only about the final result. Interim events, announcements, or partial outcomes can also generate surprise if they differ from what probabilities implied at the time.
Event surprise factor is closely tied to belief updates and volatility. Large surprises often trigger sharp posterior belief shifts and temporary instability in prediction markets data.
For analysts, surprise factor helps explain why some events cause major market disruption while others barely move prices. It provides a structured way to quantify how informative an event actually was to the market. Over time, tracking surprise factors across events helps evaluate forecasting performance. Markets that consistently show low surprise may be well-calibrated, while frequent high-surprise outcomes suggest systematic blind spots.
Why It Matters
Event surprise factor shows how much new information an event delivered. It helps users understand whether prediction markets were truly prepared for an outcome or caught off guard.
What is the event surprise factor in prediction markets?
In prediction markets, the event surprise factor measures how unexpected an outcome was based on prior probabilities. It compares what the market implied before the event with what actually happened. Larger gaps indicate greater surprise. This helps assess informational impact.
How does the event surprise factor affect prediction markets data?
High surprise factors often lead to abrupt probability shifts and volatility in prediction markets data. Low surprise events usually result in minimal adjustment. Analysts use surprise levels to explain post-event behavior and belief updates. It adds context to price reactions.
Why is the event surprise factor important when using prediction markets APIs?
Prediction markets APIs provide historical probability snapshots needed to calculate surprise factors. Analysts can compare pre-event probabilities with final outcomes programmatically. This supports accuracy evaluation, calibration analysis, and model testing. APIs make surprise measurement consistent across markets.
Real-World Example
On Kalshi, an economic indicator printing far outside expectations would have a high event surprise factor. A result close to consensus would have a low surprise factor, even if the number itself is large.
Event Surprise Factor and FinFeedAPI
FinFeedAPI’s Prediction Markets API provides time-stamped prediction markets data required to measure event surprise factors. Analysts can capture probabilities immediately before events and compare them to resolved outcomes. This supports calibration analysis, forecasting evaluation, and information impact studies. The API enables systematic tracking of surprise across prediction markets.