Event Resolution Dataset
In prediction markets, forecasts remain active until an event is officially resolved. An event resolution dataset captures the confirmed outcome after all verification and settlement steps are complete.
This dataset typically includes the resolved outcome, resolution timestamp, and final market status. It marks the point where prediction markets data transitions from live signals to historical record. Event resolution datasets are essential for evaluating forecast accuracy. They provide the ground truth needed to compare predicted probabilities against real outcomes. They also support consistency across analysis. Once resolution is recorded, probabilities stop updating and can be used safely for backtesting and performance measurement.
For analysts, event resolution datasets anchor all retrospective work. They ensure that modeling, calibration, and behavioral analysis are based on finalized and reliable data.
Why It Matters
Without clear resolution data, prediction markets cannot be evaluated. Event resolution datasets make prediction markets data trustworthy and usable for long-term analysis.
What is an event resolution dataset in prediction markets?
In prediction markets, an event resolution dataset is a structured record of final outcomes. It specifies which outcome resolved as true and when resolution occurred. This data finalizes the market lifecycle. It serves as the benchmark for forecast evaluation.
How is an event resolution dataset used in prediction markets data analysis?
Analysts use event resolution datasets to measure forecast accuracy and calibration. Probabilities are compared against resolved outcomes to assess performance. The dataset also supports backtesting and bias detection. Without it, analysis lacks a reliable reference point.
Why are event resolution datasets important for prediction markets APIs?
Prediction markets APIs expose resolution data so systems can detect when markets are finalized. Event resolution datasets allow automated workflows to stop tracking live forecasts and begin evaluation. APIs make resolution status machine-readable and consistent across markets. This enables scalable historical analysis.
Real-World Example
On Kalshi, once an official data release confirms the result, the market resolves and the outcome is added to the event resolution dataset. Analysts then use that record to evaluate how forecasts performed.
Event Resolution Dataset and FinFeedAPI
FinFeedAPI’s Prediction Markets API provides structured event resolution datasets alongside live prediction markets data. Analysts can retrieve finalized outcomes, resolution times, and market status indicators. This supports accuracy measurement, backtesting, and long-term forecasting research. The API enables consistent access to resolution data across prediction markets.