Fill Probability
Fill probability depends on market conditions at the moment an order is placed. If liquidity is deep and trading is active, orders are more likely to execute quickly. If liquidity is thin or prices are moving fast, orders may sit unfilled or only partially fill.
In prediction markets, fill probability changes constantly. It is influenced by bid–ask spreads, order flow imbalance, execution priority, and price impact. On platforms like Polymarket, Kalshi, Myriad, and Manifold, fill probability is visible indirectly through how often orders execute at the displayed price versus slipping or failing to fill. In prediction markets data, low fill probability often shows up during volatile periods or right after breaking news.
Understanding fill probability helps traders and analysts distinguish between displayed prices and executable prices.
Why It Matters
Fill probability affects whether market prices are actionable. It helps determine how reliable prediction markets data is for real-time trading and analysis.
How does fill probability affect trading in prediction markets?
A low fill probability means that the quoted price may not be achievable. Traders may need to accept worse prices or wait longer. This creates a gap between visible probabilities and actual execution, which analysts must consider when interpreting prediction markets data.
What factors reduce fill probability?
Rapid price movement, wide bid–ask spreads, low liquidity, and high competition for execution all reduce fill probability. During these moments, many traders act at once, and only a subset get filled at favorable prices. These conditions are reflected in prediction markets data as volatility spikes and short-lived prices.
How can analysts use fill probability as a signal?
Low fill probability often signals uncertainty or stress. It can indicate that prices are unstable or that new information is being absorbed unevenly. Analysts who track fill likelihood alongside prices gain a clearer view of market confidence and execution risk within prediction markets data.
Real-World Example
After a sudden policy announcement, a Polymarket market moves rapidly. Traders placing buy orders at the old price see many orders go unfilled as probabilities jump, revealing a low fill probability during the information shock.
Fill Probability and FinFeedAPI
Analyzing fill probability requires detailed timing, pricing, and liquidity context. FinFeed's Prediction Markets API provides structured prediction markets data—time-stamped prices, volume and OHLCV — that developers can use to estimate fill probability, assess execution risk, and better interpret real-time forecasts.