Base rate neglect happens when traders overlook the historical likelihood of an event and instead react strongly to fresh signals, headlines, or emotionally charged developments. Instead of starting with a realistic baseline, they treat new information as more important than it actually is. This can pull prediction market probabilities away from what long-term data would support.
On platforms like Polymarket, Kalshi, Myriad, and Manifold, base rate neglect often appears in political, economic, and crypto-related markets. A small news update can cause sudden jumps in probability even when the historical base rate suggests little has changed. These divergences show up clearly in prediction markets data as rapid, sentiment-driven movements that disconnect from long-term patterns.
Understanding base rate neglect helps analysts recognize when markets are reacting to noise rather than meaningful information.
Base rate neglect can distort prediction markets data, making probabilities less reliable. Recognizing it helps analysts separate stable historical patterns from short-term emotional reactions.
It appears because traders tend to overweight fresh or dramatic updates, even if the historical likelihood of the event hasn’t changed much. When participants react strongly to eye-catching signals instead of statistical norms, probabilities shift too far. These patterns become visible in prediction markets data during fast-moving news cycles.
It pushes probabilities away from historically grounded expectations. Markets become more volatile and more sensitive to noise, which can reduce calibration and accuracy. Analysts must adjust interpretations to account for this bias to ensure prediction markets data reflects real information rather than sentiment swings.
Analysts can identify when markets are overreacting, highlight mispricing signals, and see where sentiment diverges from long-term trends. They can compare current probabilities to historical baselines to evaluate whether expectations are realistic. This helps improve how prediction markets data is used in modeling and decision-making.
A Polymarket election market jumps sharply after a single viral news story, even though the long-term base rate for similar elections shows little impact from comparable events. Analysts note the divergence and classify it as base rate neglect driven by short-lived media attention.
Identifying base rate neglect requires comparing live probabilities with historical benchmarks. FinFeed's Prediction Markets API provides structured prediction markets data—historical outcomes, probability histories, orderbooks, and OHLCV that analysts can use to detect when markets diverge from long-term base rates.
