The Brier Score evaluates how well a forecast matched what ultimately happened. It looks at the predicted probability of an event and compares it to the final outcome, scoring the result on a scale where lower values mean better accuracy. This makes it simple to judge how reliable a prediction was.
Prediction markets often use the Brier Score to assess how well their market probabilities performed over time. By examining past predictions and outcomes, analysts can see whether markets tended to be overconfident or underconfident. This helps identify strengths and weaknesses in forecasting behavior.
The score doesn’t just measure right or wrong — it measures how close the probability was to being correct. This makes the Brier Score especially useful for prediction markets data, where the goal is to track nuanced changes in belief rather than binary answers.
The Brier Score offers a clear, objective way to evaluate probabilistic forecasts. It helps teams understand how accurate their prediction markets data is and guides improvements in forecasting processes.
Prediction markets use the Brier Score because it rewards accurate, well-calibrated forecasts and penalizes inconsistent ones. The score reflects both confidence and correctness, giving a more complete picture than simple accuracy. It helps platforms compare different markets, detect bias, and evaluate long-term performance. Over many events, Brier Scores reveal how effective prediction markets truly are at forecasting outcomes.
The Brier Score is calculated by taking the squared difference between the predicted probability and the actual outcome (1 for “Yes,” 0 for “No”). Lower scores indicate better forecasts because the probability was closer to the truth. A perfect prediction gets a score of 0, while consistently poor predictions score higher. Analysts interpret these values to judge calibration, confidence, and overall forecasting quality. This makes the score a useful tool for reviewing prediction markets data.
Tracking Brier Scores over time helps analysts understand whether markets are improving or declining in accuracy. Patterns in the scores can highlight overconfidence, underconfidence, or structural issues in how markets are designed. Analysts can compare scores across categories to identify topics where forecasting is strong or weak. These insights support better decision-making and more reliable prediction markets data. Over time, Brier Score trends help strengthen overall forecasting performance.
A team reviews a year of internal prediction markets and calculates Brier Scores for each resolved event. They notice markets related to product timelines score well, while regulatory forecasts perform worse. These insights help them refine question design and improve future forecasting efforts.
Brier Score analysis depends on accurate historical probabilities and clear final outcomes. FinFeed's Prediction Markets API provides structured prediction markets data—including event probabilities, resolution results, and full price paths—that analysts can use to compute Brier Scores and evaluate long-term forecasting accuracy.
