Expected Outcome Value
Expected outcome value answers a simple question: what is the average result if the same situation played out many times? Instead of focusing on one outcome, it weighs every possible outcome by how likely it is. This turns uncertainty into a single, comparable number.
In prediction markets, expected outcome value is derived from the full outcome distribution. Traders implicitly compute it when deciding whether a price is attractive. On platforms like Polymarket, Kalshi, Myriad, and Manifold, this value evolves as probabilities shift across outcomes. In prediction markets data, changes in expected value often explain why traders enter or exit positions even when the most likely outcome hasn’t changed.
Expected outcome value is especially useful when outcomes have different magnitudes, ranges, or payoffs. It helps compare scenarios that are not simply yes-or-no.
Why It Matters
Expected outcome value connects probability with impact. It makes prediction markets data more useful for decision-making, risk assessment, and comparison across events.
How is expected outcome value calculated in prediction markets?
It is calculated by multiplying each possible outcome by its probability and summing the results. The probabilities come directly from market prices. Using prediction markets data ensures the expected value reflects real-time collective belief rather than assumptions.
Why can expected outcome value change even when the top outcome stays the same?
Because probability can shift within the distribution. Small increases in unlikely but high-impact outcomes can raise or lower the expected value without changing the most likely result. This nuance is visible only when analyzing full prediction markets data.
How do analysts use expected outcome value?
Analysts use it to compare scenarios, evaluate risk-adjusted expectations, and track how forecasts evolve beyond headline odds. It is especially helpful for markets with ranges, thresholds, or continuous outcomes. This makes expected outcome value a key metric derived from prediction markets data.
Real-World Example
A Kalshi market predicts the range of an economic indicator. Even though the most likely range stays the same, probability shifts toward higher values. The expected outcome value rises, signaling a meaningful change in market expectations.
Expected Outcome Value and FinFeedAPI
Calculating expected outcome value requires full outcome-level probabilities. FinFeed's Prediction Markets API provides structured prediction markets data—including outcomes, probabilities, and historical data —allowing developers and analysts to compute expected values, track changes over time, and build decision-support tools.