Lesson 02·Foundations·7 min read

How Prediction Market Prices Map to Probabilities

Binary contract prices translate directly into implied probabilities. Here's how to read them — and where the simple translation breaks down.

Every binary prediction contract pays $1 if the event happens and $0 if it doesn't. If the market is pricing the contract at $0.62, traders collectively believe the event has roughly a 62% chance of happening. This direct mapping — price equals probability — is what makes prediction markets so legible. It's also why journalists cite them as odds even when the underlying trading is thin.

The basic math

Expected value = (probability of yes) × $1 + (probability of no) × $0. If the market price equals expected value, the price IS the probability. A risk-neutral trader who thinks the true probability is 70% will buy any Yes contract priced below $0.70 and sell any priced above. The bid/ask spread is where market-makers collect a small edge for providing continuous liquidity.

For mutually exclusive outcomes (Yes and No), the two prices should sum to exactly $1 in a frictionless market. In practice they sum to slightly less than $1 because of spread — the difference is the market-maker's edge.

Where the translation gets noisy

Fees, spread, and time value all push price away from the true probability. On Kalshi, a fee on profitable closes means a fair Yes price is slightly above the true probability for Yes-buyers, because you'll pay a fee on any winning trade. On Polymarket, a wide bid/ask spread on illiquid markets means there's no single "price" to read — the mid is a rough guess.

Long-dated markets also discount for opportunity cost — capital tied up for a year should earn a return, so contracts on far-future events trade below their true probability by roughly the risk-free rate. If T-bills yield 5% and a market resolves in a year, a $0.50 contract implies about a 52.5% true probability once you account for the time value of money.

Worked example: reading an aggregated headline

Suppose a news article reports "Polymarket gives Candidate X a 63% chance to win." Peek at the actual order book. If Yes trades at $0.62/$0.64 with $2M of depth on each side, the 63% is a reasonable midpoint and there's real conviction behind it. If Yes trades $0.55/$0.71 with $50k of depth, the 63% headline is essentially made up — the market is thin enough that a single $10k order could move the price 5 cents.

The spread tells you how confident the market is in its own number. A market quoting 60/64 is far less certain than one quoting 61.8/62.2. Journalists rarely include the spread; sophisticated readers always check.

Reading aggregated odds

When media outlets cite "a 62% chance according to Polymarket," they're reading the midpoint of the bid/ask on the flagship market. That's a fine first approximation for headline purposes but it hides the depth and the spread. For serious use, look at the actual book, or better, look at time-weighted average price over the last day to smooth out noise.

Common pitfalls

Don't over-interpret a $0.90 price as certainty — a 90% market still fails 10% of the time. Don't compare prices across platforms without checking the resolution criteria; a $0.60 Kalshi contract and a $0.62 Polymarket contract may be pricing subtly different events. Don't confuse implied probability with your own probability — that's the source of your edge, or your loss.

When to trust prices as probabilities

Trust them most on deep, liquid, actively-traded markets with unambiguous resolution — flagship elections, Fed decisions, major sports. Trust them less on thin markets, novelty markets, and markets with subjective resolution. As a rule of thumb: if a $10,000 order would move the price by more than a cent, treat the current price as a rough estimate rather than a precise probability.

Frequently asked

Does a $0.50 contract mean a coin-flip?
Usually yes — but check liquidity. A $0.50 mid on a thin market may just mean nobody has formed a strong view. Wide spreads at $0.50 signal uncertainty about the probability itself, not necessarily belief in a true 50/50 outcome.
Why do Polymarket and Kalshi sometimes show different probabilities?
Different user bases, fee structures, and access restrictions cause persistent gaps of a few cents. Arbitrageurs narrow them but rarely close them entirely. Larger gaps (5+ cents) often signal a resolution-criteria difference — read both contracts before assuming they're pricing the same event.
How do fees change the price-probability mapping?
On Kalshi, fees on profitable closes mean the fair Yes price is slightly above the true probability, because you'll owe a fee on the win. On Polymarket, zero trading fees mean price is a cleaner probability read, though spread still adds noise.
What is the risk-free rate adjustment?
Capital tied up in a long-dated contract can't earn interest elsewhere. Rational traders discount future payouts by roughly the risk-free rate — so a year-out contract trades below its true probability by that rate. At 5% risk-free, a $0.50 mid implies about a 52.5% true probability.
Is the market always right?
No. Markets can be biased by concentrated capital, thin liquidity, or partisan retail flow. But over many markets and time, they beat expert forecasts. Individual mispricings are the source of every trader's edge — spotting them consistently is the hard part.
How do I calculate my expected value on a trade?
EV = (your estimate of probability × $1) − (price you paid). If you buy a Yes contract at $0.55 and estimate the true probability at 70%, your EV per contract is $0.70 − $0.55 = $0.15. Over many trades with a positive-EV edge, profits compound.
What is 'the spread' in a prediction market?
The spread is the distance between the best bid (highest buy price) and best ask (lowest sell price). Tight spreads (1–2 cents) signal deep liquidity and confident pricing. Wide spreads signal thin liquidity or uncertainty. Never trade a market without checking the spread first.
Do prediction market prices predict the future?
They predict the collective view of traders willing to bet on the future. That view is often more accurate than expert forecasts, but not infallible — Brexit and Trump 2016 are canonical examples of markets that priced correct-in-hindsight outcomes as unlikely.

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