A categorical market handles events with several clearly defined options. Instead of forecasting a number or answering yes-or-no, traders pick from categories—for example, which company will launch first, which movie will win Best Picture, or which policy will pass. Each category operates as its own outcome with its own trading activity.
As traders adjust their positions, the probabilities of all outcomes shift. If one outcome becomes more favored, its probability rises while the others fall to keep the total at 100%. This structure generates rich prediction markets data that reflects how beliefs move between competing alternatives.
Categorical markets simplify complex questions into an intuitive set of options. They help traders compare possibilities directly and reveal which outcomes gain or lose momentum. Over time, the market creates a clear probability landscape that shows how confidence changed leading up to the event.
Categorical markets offer a structured way to forecast multi-option events. They provide detailed prediction markets data that captures how expectations shift across all competing outcomes.
Prediction platforms use categorical markets to forecast events where multiple specific outcomes are possible. This avoids fragmenting liquidity across many separate binary markets. Traders get a unified place to express their beliefs, and the resulting prediction markets data is more coherent and easier to analyze. This structure also highlights how probabilities shift between options as information evolves.
In categorical markets, the sum of all probabilities must equal 100%. When traders buy shares in one outcome, its probability rises, and the others adjust downward proportionally. This dynamic shows how confidence redistributes across the entire set of possibilities. Analysts can watch these shifts to understand how sentiment evolves over time. The resulting prediction markets data becomes a clear record of how competing outcomes gained or lost support.
Analysts can see which options are gaining momentum, which ones stagnate, and how news affects the competitive field. They can track swings in sentiment as major announcements or rumors change expectations. Comparing probability paths across outcomes reveals patterns in forecasting behavior and uncertainty. These insights make categorical prediction markets data useful for decision-making, scenario planning, and trend analysis.
A prediction market tracks which film will win Best Picture at the Oscars. Each nominated movie represents an outcome in the categorical market. As award buzz, critic reactions, and early festival wins roll in, traders adjust their positions, causing probabilities to shift across the entire field of nominees.
