Scalar market — Definition | Polymarket Trade
A scalar market resolves to a continuous numeric value within a specified range. Rather than predicting YES or NO, traders bet on where a measurable outcome like temperature or inflation will fall on a numeric scale.
In depth
A scalar market is a prediction market that resolves to a specific numeric value within a pre-defined range, rather than collapsing to discrete outcomes like YES or NO. The term "scalar" refers to a single-dimensional numeric quantity—think of it as a point on a number line. Where a binary market asks "Will this happen or not?", a scalar market asks "What will the value be?" These markets are designed to capture predictions about continuous variables: temperature, inflation rates, stock prices, unemployment figures, or any measurable phenomenon that can be expressed as a number within a bounded range.
The concept of scalar markets emerges from prediction markets theory and financial forecasting, which recognized that not all real-world questions fit neatly into binary or multi-category frameworks. Prediction markets grew out of financial derivatives, where traders understood that prices themselves carry information about uncertain future events. Scalar markets extend this insight: instead of asking people to vote on discrete categories, you can ask them to predict a precise numeric outcome. This approach is particularly valuable for economic and scientific forecasting, where policymakers need granular information about expected values rather than just probability ranges. The U.S. Department of Defense's IARPA program and platforms like PredictIt have experimented with scalar markets for years. On Polymarket, most markets have historically been binary (YES/NO) or categorical, but scalar markets represent a natural extension when the underlying question is inherently numeric.
On Polymarket, scalar markets are less common than binary markets but do appear for inherently numeric questions. When you encounter one, the interface presents a numeric range rather than outcome buttons. For example, a scalar market might ask "What will the S&P 500 close at on December 31, 2025?" with a range from 4,000 to 6,000. Unlike binary markets where your position is either profitable or not, scalar markets settle to the exact value. Your profit or loss depends on how close your prediction was to the actual outcome. The market's price reflects the collective belief about where that value will land—the mid-price represents the consensus estimate. Traders buy shares if they expect the outcome higher than the current price and sell if they expect it lower. Scalar market liquidity can be lower than binary markets since the outcome space is infinite rather than a few distinct options.
One common misconception is that scalar markets are automatically "more accurate" than binary markets. In reality, they are different tools suited to different questions. Binary markets excel at yes/no questions and develop deep liquidity because traders can take simple long or short positions. Scalar markets shine when the specific numeric value matters—such as inflation forecasting, where knowing the consensus estimate is more valuable than knowing whether it will exceed a threshold. Another pitfall is underestimating basis risk: you might be directionally right but wrong on magnitude. If you expect a temperature higher than the market's 72°F consensus but it turns out to be 70°F, your position loses money despite being directionally correct. Additionally, scalar markets often suffer from thin liquidity and wide spreads, making entry and exit difficult. Resolution requires a precise, verifiable data source—disputes over exact values can create complications.
Scalar markets sit at the intersection of several prediction market concepts. Binary markets, the most common type, require predictions to collapse to one of two outcomes. Categorical markets extend this to multiple discrete choices. Scalar markets operate on a continuum. Related is the "spread market," where traders bet on ranges (e.g., "between 70 and 75?"), approximating scalar outcomes through discrete buckets. The concept of continuous outcomes ties directly to probability distributions: instead of assigning a single probability, you assign probabilities across a range of values. Understanding scalar markets deepens your grasp of how prediction markets capture different kinds of uncertainty and how collective intelligence emerges from granular numeric predictions rather than simple binary choices.
Example
Imagine Polymarket hosts a scalar market: "What will the official US inflation rate be for Q1 2026?" with a range from 1.5% to 4.5%. The current market price is 3.1%, indicating collective belief that inflation will be around 3.1%. If you believe it will be higher (say, 3.7%), you buy shares at 3.1%. When the government releases 3.6%, your position profits because the actual outcome (3.6%) exceeds your entry price (3.1%), even though it falls short of your forecast.