Incorrect Prediction
In prediction markets, forecasts are expressed as probabilities before an event resolves. A prediction is incorrect when the outcome it favored does not match the final outcome.
Incorrectness is not all-or-nothing. Assigning a very high probability to a losing outcome is more incorrect than assigning a low probability, even though both were wrong. Incorrect predictions can occur at different stages of a market. Early incorrect forecasts may reflect limited information, while late incorrect forecasts often point to overconfidence or misinterpretation.
When examined across many events, incorrect predictions reveal systematic patterns. Persistent errors can signal bias, slow learning, or structural weaknesses in the market.
For analysts, incorrect predictions are just as informative as correct ones. They help diagnose where and why prediction markets data fails to align with reality.
Why It Matters
Incorrect predictions define the limits of forecasting accuracy. Studying them helps improve interpretation, calibration, and trust in prediction markets.
What causes incorrect predictions in prediction markets?
Incorrect predictions can result from missing information, misleading signals, or behavioral bias. Overreaction to news, hype, or panic can push probabilities away from reality. Low liquidity and concentrated trading also increase error risk. These causes often appear clearly in prediction markets data.
Are incorrect predictions always a sign of poor forecasting?
No, incorrect predictions are expected under uncertainty. Even well-calibrated markets will sometimes be wrong. The key issue is frequency and confidence of errors. Repeated high-confidence incorrect predictions indicate deeper problems.
How are incorrect predictions analyzed in prediction markets data?
Analysts analyze incorrect predictions by comparing probability levels with final outcomes. High-probability failures are weighted more heavily in error metrics. Patterns across events reveal bias, miscalibration, or delayed learning. This analysis supports model improvement and risk assessment.
Real-World Example
On Polymarket, an outcome priced at 0.80 that ultimately does not occur represents a strongly incorrect prediction. Analysts study similar cases to see whether such errors are rare or recurring.
Incorrect Prediction and FinFeedAPI
FinFeedAPI’s Prediction Markets API provides prediction markets data needed to identify and analyze incorrect predictions. Analysts can align historical probability streams with final outcomes to measure error severity and patterns. This supports calibration analysis, performance evaluation, and forecasting improvement. The API enables consistent study of incorrect predictions across prediction markets.