Statistical Forecasting Error
Statistical forecasting error looks at outcomes after the fact. It compares what the market expected with what reality delivered. A small error means the forecast was well calibrated. A large error means the market misjudged the likelihood.
In prediction markets, error is inevitable because uncertainty is real. Even a 90% probability can fail 1 out of 10 times. What matters is not avoiding error completely, but understanding its size, frequency, and pattern. On platforms like Polymarket, Kalshi, Myriad, and Manifold, this evaluation is done by comparing final probabilities with resolved outcomes across many markets. The results form a core part of prediction markets data analysis.
Over time, consistent patterns of error can reveal bias, overconfidence, or structural weaknesses. They can also show where markets perform especially well.
Why It Matters
Statistical forecasting error is how forecast quality is judged. It turns prediction markets data into measurable performance rather than intuition.
How is statistical forecasting error measured in prediction markets?
It is measured by comparing predicted probabilities to actual outcomes across many events. Common approaches look at average error, squared error, or likelihood-based scores. Using prediction markets data allows analysts to evaluate performance at scale, not just on single events.
Why doesn’t a wrong outcome always mean a bad forecast?
Because probabilities describe likelihood, not certainty. A low-probability outcome can still happen without the forecast being wrong. Statistical forecasting error looks at long-term patterns, which is the correct way to judge prediction markets data.
What can analysts learn from forecasting error patterns?
Analysts can identify systematic bias, such as persistent overconfidence or underreaction. They can also compare different market designs, time horizons, or event types. These insights help improve how prediction markets data is interpreted and used.
Real-World Example
A set of election markets on Polymarket closes with probabilities around 70% for winning candidates. Most resolve as expected, but some do not. By measuring statistical forecasting error across all markets, analysts assess whether the probabilities were well calibrated overall.
Statistical Forecasting Error and FinFeedAPI
Analyzing forecasting error requires historical probabilities and resolved outcomes. FinFeed's Prediction Markets API provides structured prediction markets data so developers and analysts can calculate forecasting error, evaluate calibration, and improve forecasting models.