Fair value probability captures the idea of what the market should be pricing if all available information were processed correctly. It’s the probability that reflects the event’s real-world likelihood—not distorted by noise, low liquidity, bias, or manipulation attempts. In active markets, traders push prices toward this fair value as they react to news, data, and shifting expectations.
On prediction platforms like Polymarket, Kalshi, Myriad, and Manifold, fair value probability emerges when informed traders correct mispricing and markets absorb information efficiently. As markets move closer to equilibrium, the probability stabilizes around a value that represents the crowd’s best, most accurate estimate. This value becomes meaningful prediction markets data because it shows the consensus belief after the market digests all available signals.
Fair value probability helps separate temporary distortions from true expectations, giving analysts a clearer view of real sentiment.
Fair value probability is essential for interpreting prediction markets data correctly. It indicates when a forecast reflects real information rather than noise, making forecasts more reliable for analysis and decision-making.
Prediction markets aim for fair value because accurate pricing improves forecasting performance. When probabilities converge toward the true likelihood, markets become more informative and trustworthy. Traders rely on fair value probabilities to make decisions, and analysts use them to study how well markets process information. Clean prediction markets data depends on markets reaching this equilibrium.
Traders correct deviations by buying undervalued outcomes and selling overvalued ones. Informed participants act quickly when new information appears, pushing the probability closer to its fair value. As this process repeats, noise-based movements are filtered out, and the market stabilizes. Analysts reviewing prediction markets data can often see this convergence pattern as markets “settle” around the fair value.
Analysts can identify periods when markets diverge from fair value—often due to low liquidity, information latency, or mispricing signals. Comparing actual price paths with estimated fair value helps uncover inefficiencies and evaluate market accuracy. These insights strengthen interpretation of prediction markets data and improve forecasting models.
On Polymarket, a market forecasting the approval of a major regulatory decision saw early swings due to speculation. As official statements and credible reporting emerged, the probability gradually converged toward a stable number that reflected the true underlying likelihood—its fair value probability.
Estimating fair value probability requires high-quality, time-stamped forecast data. FinFeed's Prediction Markets API provides probability histories, liquidity metrics, and outcome data that help developers analyze price efficiency, detect deviations from fair value, and model how markets converge toward accurate forecasts.
