Prediction Quality
In prediction markets, prediction quality captures how well forecasts represent real uncertainty. It considers probability accuracy, confidence, stability, and responsiveness to information.
High-quality predictions are not just correct. They are supported by liquidity, broad participation, and consistent belief updates over time.
Low-quality predictions may still be correct by chance. They often show weak support, high volatility, or behavior driven by hype or thin trading.
Prediction quality varies across markets and over time. The same market can produce high-quality forecasts in one phase and low-quality signals in another.
Why It Matters
Prediction quality determines how much trust to place in a forecast. It helps users avoid overreliance on probabilities that lack strong support.
What factors determine prediction quality in prediction markets?
Prediction quality depends on liquidity depth, number of participants, price stability, and learning behavior. Clear event definitions and reliable resolution also matter. Markets that absorb information smoothly tend to show higher quality. These factors are observable in prediction markets data.
How is prediction quality different from forecast accuracy?
Forecast accuracy measures whether a prediction was right after resolution. Prediction quality evaluates how trustworthy the forecast was before resolution. A low-quality forecast can be correct by luck, while a high-quality forecast can still fail. Quality focuses on signal strength, not just outcomes.
How do analysts assess prediction quality using prediction markets data?
Analysts assess prediction quality by combining probability paths, confidence signals, liquidity, and historical performance. Stable prices with strong participation score higher. Volatile or thin markets score lower. This multi-signal approach improves evaluation and modeling.
Real-World Example
On Polymarket, two markets may both price an outcome at 0.70. The one with deep liquidity and steady trading has higher prediction quality than one driven by a few recent trades.
Prediction Quality and FinFeedAPI
FinFeedAPI’s Prediction Markets API provides prediction markets data needed to assess prediction quality. Analysts can combine probability prices, volume, liquidity signals, and resolution outcomes to evaluate forecast reliability. This supports quality scoring, market filtering, and model calibration. The API enables consistent prediction quality analysis across prediction markets.