Predictive Power Score
A predictive power score is a performance metric, not a forecast itself. It looks backward and evaluates how closely predicted probabilities matched real outcomes across many events. Higher scores indicate stronger forecasting ability, while lower scores suggest noise or bias.
In prediction markets, predictive power emerges from repeated accuracy. Markets that consistently price outcomes well earn higher scores when evaluated across resolutions. On platforms like Polymarket, Kalshi, Myriad, and Manifold, this assessment is based on historical prediction markets data rather than isolated wins or losses. One correct call means little; consistent calibration matters more.
Predictive power scores help separate skill from luck. They focus on patterns, not anecdotes, and reward forecasts that assign probabilities appropriately over time.
Why It Matters
Predictive power scores quantify forecast quality. They allow analysts to compare prediction markets data objectively and identify which signals are most reliable.
How is a predictive power score calculated in prediction markets?
It is calculated by comparing forecast probabilities with actual outcomes across many events. Methods may use scoring rules, calibration analysis, or error aggregation. Using prediction markets data ensures the score reflects real, incentive-backed forecasting rather than opinions.
Why is predictive power different from accuracy?
Accuracy looks at whether an outcome was right or wrong. Predictive power looks at how well probabilities were assigned. A market that predicts many 70% events correctly over time has higher predictive power than one that swings between extremes. This distinction is crucial when evaluating prediction markets data.
How do analysts use predictive power scores?
Analysts use them to rank markets, compare forecasting approaches, and test models. Scores also help identify bias, overconfidence, or systematic error. This makes predictive power scores a core tool for working with prediction markets data at scale.
Real-World Example
An analyst evaluates hundreds of resolved Polymarket markets and assigns predictive power scores based on how close final probabilities were to actual outcomes. Markets with stable calibration and fewer large errors rank higher than those with frequent mispricing.
Predictive Power Score and FinFeedAPI
Calculating predictive power scores requires historical probabilities and resolved outcomes. FinFeed's Prediction Markets API provides structured prediction markets data—including probability histories, final prices, and resolutions—so developers and analysts can compute predictive power scores, compare forecasting performance, and improve evaluation frameworks.