Forecast Error
In prediction markets, forecasts express uncertainty as probabilities before an event resolves. Forecast error appears once the outcome is known and the forecast can be evaluated.
Error is not binary. A forecast that assigned 90% to an outcome that did not occur has a larger error than one that assigned 55%, even though both were wrong. Forecast error can be analyzed at different moments in time. Early forecasts, late forecasts, and final prices can each produce different error profiles.
Systematic forecast error reveals patterns. Persistent overconfidence, underreaction, or late corrections often show up when errors are aggregated across events.
For analysts, forecast error is a core diagnostic tool in prediction markets data. It helps assess calibration, learning, and market efficiency over time.
Why It Matters
Forecast error shows how accurate prediction markets really are. Understanding it helps users judge reliability and improve forecasting models.
How is forecast error measured in prediction markets?
Forecast error is measured by comparing predicted probabilities with resolved outcomes. Common approaches include squared error, absolute error, or log-based scoring. Different metrics emphasize different types of mistakes. The choice depends on whether confidence or direction matters more.
Why do prediction markets show recurring forecast error patterns?
Recurring forecast errors often come from behavioral bias or structural limits. Markets may overreact to news, underweight slow information, or converge too late. Liquidity constraints and attention cycles also play a role. These patterns become visible only across many events.
How can forecast error be reduced in prediction markets data analysis?
Forecast error can be reduced by weighting forecasts using confidence signals, liquidity, or timing. Analysts often downweight unstable or thin markets. Incorporating historical error patterns improves models. Error-aware analysis leads to more reliable conclusions.
Real-World Example
On Polymarket, an outcome priced at 0.85 that ultimately fails produces a large forecast error. Analysts compare similar cases to see whether high-confidence errors are rare or systematic.