Over/Under
In prediction markets, Over/Under markets are used when the outcome can be measured on a numeric scale. Instead of predicting a specific result, participants decide whether the final value will exceed or fall short of a set number.
The threshold is defined when the market is created. Examples include counts, totals, percentages, or measurable performance metrics.
Over/Under markets simplify complex outcomes into a single comparison. This makes them easier to trade and interpret, especially when exact prediction is difficult. Probabilities in these markets reflect collective belief about where the final value will land relative to the cutoff. Small changes in information can shift probabilities as expectations move closer to or further from the threshold.
For analysts, Over/Under markets produce clean prediction markets data. They are well suited for studying confidence, volatility, and reaction to incremental information.
Why It Matters
Over/Under markets translate uncertain numeric outcomes into clear forecasts. They help users express expectations without needing precise estimates.
What does Over/Under mean in prediction markets?
In prediction markets, Over/Under means predicting whether a final numeric outcome will be above or below a predefined level. The market resolves based on that comparison alone. This format reduces complexity while preserving meaningful uncertainty. It is commonly used for measurable events.
How do Over/Under markets affect prediction markets data?
Over/Under markets often show sensitivity near the threshold value. As new information arrives, probabilities can shift quickly when expectations approach the cutoff. This creates distinct volatility patterns in prediction markets data. Analysts can clearly observe belief convergence around the threshold.
Why are Over/Under markets important for prediction markets APIs?
Prediction markets APIs expose Over/Under markets with clearly defined numeric resolution rules. Analysts can track probability movements relative to the threshold over time. This supports quantitative modeling, sensitivity analysis, and forecast evaluation. APIs make Over/Under market data easy to process at scale.
Real-World Example
On Kalshi, an Over/Under market may predict whether inflation will be above or below a specific percentage. Traders express expectations without forecasting the exact value.
Over/Under and FinFeedAPI
FinFeedAPI’s Prediction Markets API provides prediction markets data from Over/Under market structures. Analysts can analyze probability shifts as expectations move toward or away from numeric thresholds. This supports trend analysis, confidence modeling, and performance evaluation. The API enables consistent analysis of Over/Under dynamics across prediction markets.