A range market breaks a numeric outcome into several intervals—for example, “0–10,” “10–20,” or “20–30.” Traders buy and sell shares tied to each range based on where they think the final number will end up. This setup allows markets to forecast continuous outcomes without requiring a precise single-value prediction.
As traders shift their positions, probabilities for each range update in real time. If traders believe an event is trending higher, upper ranges gain probability while lower ones lose it. This creates clear prediction markets data that shows how confidence moves across different parts of the outcome spectrum.
Range markets are useful for events where the exact result is uncertain but likely to fall within a band. They simplify forecasting by dividing complex numeric predictions into manageable segments. Over time, the market produces a probability distribution across all ranges, giving analysts a structured view of expectations and uncertainty.
Range markets make forecasting easier by transforming continuous outcomes into interpretable intervals. They produce organized prediction markets data that shows how expectations cluster around certain ranges, improving analysis and decision-making.
Platforms use range markets to forecast numeric outcomes without requiring pinpoint precision. They allow users to express beliefs in broader intervals, which encourages more participation and reduces prediction pressure. Range markets also help concentrate liquidity across a few well-defined buckets instead of a single scalar number. The resulting prediction markets data becomes easier to analyze and compare across events.
Probabilities shift as traders buy and sell shares in specific ranges. If traders expect a higher final value, upper ranges gain probability while lower ones decline. When uncertainty increases, probabilities may spread more evenly across ranges. These movements create a dynamic picture of expectation and sentiment. Analysts can study these shifts to identify emerging trends in the prediction markets data.
Range markets reveal how confident traders are about different segments of a possible outcome. Analysts can see where probability is concentrated and how sentiment adjusts when new information appears. Comparing ranges over time highlights swings in expectations and uncertainty levels. This structured prediction markets data is especially useful for risk assessment and scenario planning.
A prediction market tracks the expected opening weekend box office for a major Oscar contender. The market divides outcomes into ranges such as “$0–20M,” “$20–40M,” and “$40–60M.” As early reviews and buzz develop, traders shift probability toward the range they believe best reflects the film’s prospects.
Range markets generate probability distributions across several intervals, creating rich and structured datasets. FinFeed's Prediction Markets API provides prediction markets data for each range—allowing developers to analyze how expectations move between intervals, build visualization tools, and integrate range-based forecasts into research workflows.
