A base rate represents how often an event has occurred in the past or how likely it is in general, independent of current news or sentiment. It gives forecasters an anchor before they adjust their expectations with new signals. In prediction markets, traders often start with a base rate and then update their beliefs as fresh information appears.
On platforms like Polymarket, Kalshi, Myriad, and Manifold, base rates help explain why certain markets open with probabilities near long-term historical averages. For example, recurring political, economic, or regulatory events often begin with predictable baseline probabilities before traders react to current developments. These base rates help structure early prediction markets data and influence how belief updates unfold over time.
Using base rates helps markets avoid overreacting to noise or rare events. They provide a grounding mechanism so forecasting begins with a realistic benchmark.
Base rates help stabilize early forecasts and prevent overconfidence. They improve the foundation of prediction markets data by ensuring probabilities start from realistic, historically informed levels.
They keep forecasts grounded, especially in the early stages of a market when little real-time information is available. Without base rates, markets might overreact to small signals or sensational news. Base rates anchor prediction markets data to long-term patterns before traders adjust probabilities with new information.
They ensure that traders begin from a realistic expectation rather than guesses or emotional reactions. As new data arrives, markets update probabilities from this baseline. This creates cleaner, more calibrated prediction markets data, especially for recurring events where historical patterns matter.
Analysts can see how closely markets follow historical norms, detect when traders deviate too far from established patterns, and understand where sentiment may be overpowering long-term trends. Comparing market probabilities to base rates helps identify mispricing signals and improves interpretation of prediction markets data.
A recurring election market on Polymarket begins around a base rate informed by historical win probabilities for incumbents. As new polls and statements emerge, traders move the probability away from that baseline—revealing how the market updates beliefs relative to long-term patterns.
Using base rates effectively requires clean historical and real-time data. FinFeed's Prediction Markets API provides structured prediction markets data—past outcomes and probability histories that analysts can use to compare live forecasts with historical baselines and strengthen calibration.
