Event Probability Logs
In prediction markets, probabilities shift whenever trades occur or new information is absorbed. Event probability logs capture each of these changes as time-stamped records. Rather than showing only the latest probability, the logs store every update in sequence. This includes small adjustments, sharp moves, and long periods of stability.
Event probability logs make market behavior transparent. They show exactly when beliefs changed and how fast the market reacted to signals. These logs are essential for understanding dynamics that snapshots cannot reveal. Patterns like overreaction, delayed response, or gradual convergence only appear when the full probability history is available.
For analysts, event probability logs turn prediction markets data into an audit trail. They support detailed review, modeling, and behavioral analysis across the entire event lifecycle.
Why It Matters
Without probability logs, important context is lost. Event probability logs allow users to evaluate how forecasts evolved, not just where they ended.
What are event probability logs in prediction markets?
In prediction markets, event probability logs are time-ordered records of probability updates for an event. They document how expectations changed as information arrived. This allows analysts to study forecasting behavior in detail. Logs provide transparency and traceability.
How are event probability logs used in prediction markets data analysis?
Event probability logs are used to analyze reaction speed, volatility, and belief shifts. Analysts compare probability paths against news and outcomes to detect bias or overreaction. Logs also support backtesting and calibration studies. They are a core input for advanced forecast analysis.
Why are event probability logs important for prediction markets APIs?
Prediction markets APIs provide access to the time-stamped data needed to build event probability logs. APIs allow analysts to retrieve complete probability histories programmatically. This supports automation, large-scale analysis, and consistent data collection. APIs make probability logging reliable and scalable.
Real-World Example
On Polymarket, an analyst may review event probability logs for an election market to see how probabilities moved after debates, polls, and breaking news. The logs reveal exactly when major belief shifts occurred.
Event Probability Logs and FinFeedAPI
FinFeedAPI’s Prediction Markets API provides prediction markets data suitable for constructing event probability logs. Analysts can retrieve time-series probability updates for individual events. This supports behavioral analysis, backtesting, and forecast evaluation. The API enables consistent probability logging across prediction markets.