Implied Probability in Prediction Markets: How to Read a Price

On Kalshi or Polymarket, a contract price is a probability. That is not a metaphor. If a YES contract for 'Will the Fed cut rates in June?' trades at $0.34, the market is collectively saying there is a 34% chance of a cut. Everything else in prediction market trading follows from that single fact.

The Mechanics: Why $1 Is the Anchor

Every binary prediction market contract settles at exactly $1.00 if the event happens and $0.00 if it does not. That hard boundary is what ties price to probability. If you buy a YES contract at $0.62, you risk $0.62 to win $0.38 on a correct call, netting $1.00 at settlement. If the event does not happen, you lose the $0.62.

The NO side mirrors this exactly. A YES contract at $0.62 implies a NO contract at $0.38, because the two sides of the same market must sum to $1.00. That accounting identity, YES price + NO price = $1, holds by construction. A market maker who prices YES at $0.62 and NO at $0.62 would be giving away free money, so competition forces the sum to $1.

Kalshi makes this explicit in its order book. Contracts are quoted from 1 cent to 99 cents, and when you buy a YES at 30 cents, the 70 cents put up by the other side (the NO buyer) sits with Kalshi until resolution, and $1 goes to whichever side called it correctly.

Converting Price to Odds and Back

Implied probability and decimal odds are the same number expressed differently. The conversions are straightforward:

# Price (cents) to implied probability
implied_prob = price / 1.00          # e.g. 0.34 -> 34%

# Implied probability to decimal odds
decimal_odds = 1 / implied_prob       # e.g. 1/0.34 -> 2.94x

# Decimal odds to American odds
# For odds > 2.0  (underdog):
american_odds = (decimal_odds - 1) * 100   # e.g. +194
# For odds < 2.0  (favorite):
american_odds = -100 / (decimal_odds - 1)  # e.g. -167

# Expected value of a YES trade
ev = (implied_prob * (1 - price)) - ((1 - implied_prob) * price)

A YES contract at $0.34 has decimal odds of 2.94 and American odds of +194. That means you win $1.94 per dollar risked if the event occurs. A contract at $0.70 has American odds of -233, meaning you must risk $2.33 to profit $1.00. Both frames carry the same information; which you prefer depends on your background.

Fees Widen the Gap Between Price and True Probability

The raw price is a clean probability only before fees. Both Kalshi and Polymarket use a probability-weighted fee formula, fee = multiplier x shares x price x (1 - price), that peaks at $0.50 contracts (50% implied probability) and decreases as contracts approach 0 or 1. On Kalshi, the taker fee multiplier is 0.07, so the peak fee at the 50-cent level works out to about 1.75 cents per contract. On Polymarket, fees vary by market category: the peak taker rate at 50/50 is 1.80% for crypto markets, 1.50% for economics, 1.00% for politics and finance, and 0.75% for sports. Geopolitics markets on Polymarket are currently fee-free. The international polymarket.com and the US-regulated exchange (via QCX, LLC) also have separate fee schedules.

In practice this means mid-probability contracts are the most expensive to trade as a taker, regardless of platform. A YES at $0.50 costs more in friction than a YES at $0.10 or $0.90. This matters when you compare a market price to your own probability estimate: your edge needs to exceed the round-trip fee cost, not just the bid-ask spread.

Spotting a Mispriced Implied Probability

A contract is mispriced when you believe the true probability differs from the market price by more than the cost to trade it. Spotting that requires an independent probability estimate. Here are the main sources of mispricing:

• Stale prices: In thin markets, prices update slowly after news. A contract at $0.30 that should be $0.55 after a data release is a simple speed-of-information gap.
• Liquidity distortion: Wide spreads can make an implied probability look tradable when it is not. Buying YES at the ask and selling at the bid can be expensive when depth is shallow. The midpoint of the spread is a better estimate of true implied probability than either side alone.
• Favorite-longshot bias: Prediction markets show the same systematic pattern documented extensively in traditional betting markets: low-price contracts (longshots) tend to win less often than their price implies, while high-price contracts (favorites) tend to outperform their implied probability. Buyers at the tails generally face worse expected value than the price alone suggests. The size of this effect varies by market and liquidity.
• Correlated markets: If two markets share an underlying driver (same game, same Fed meeting), mispricing in one often means mispricing in the other. Comparing implied probabilities across related contracts can surface inconsistencies.
• Reference class neglect: Traders anchor on narrative rather than base rates. A contract for a 'historic' event at $0.25 may look cheap, but if the base rate for that type of event is 8%, $0.25 is expensive.

A Worked Example

Suppose a Kalshi contract for 'CPI below 3% in October' trades at $0.58 YES. Your model, built from Cleveland Fed inflation nowcasts and recent core PCE readings, puts the probability at 72%. The gap is 14 percentage points. You estimate round-trip fees and spread cost roughly 2 cents. Your net expected edge is about 12 cents per dollar of notional, well above zero. That is a trade worth sizing.

If your model put the probability at 61%, the edge shrinks to 3 cents before fees. After fees it may be negative. That is not a trade worth taking, even though the direction is the same.

Why This Market Is Worth Paying Attention To

Combined monthly trading volume on Kalshi and Polymarket's international platform rose from under $5 billion in September 2025 to roughly $24 billion in April 2026, according to a Pew Research Center analysis of data from The Block. Both platforms now operate under CFTC oversight as Designated Contract Markets. US persons can trade legally on both. Polymarket US launched in December 2025 after the company acquired QCEX, a CFTC-licensed exchange and clearinghouse, and is available in most US states. Kalshi has been CFTC-regulated since 2021 and trades across all 50 states. The international polymarket.com site is a separate product that blocks US users.

With that kind of capital flowing through these order books, the implied probabilities in active markets are reasonably informative. In liquid markets like major election calls or Fed rate decisions, spreads are tight and prices update fast. In thinner markets, the gap between price and true probability is wider, and that is where careful traders find opportunity.

Automating the Edge

Manually scanning markets for probability gaps is slow. Banger (bangertrades.com) lets you write a Python strategy that pulls live order book data, calculates your own probability estimate, and routes limit orders when the gap exceeds your threshold. You can paper-trade it first with banger run strategy.py --paper against the live book before committing real capital. A declarative risk envelope (per-trade cap, daily loss stop, max open positions) keeps any single mispriced trade from becoming a portfolio event. Banger never holds your funds; you keep your own Kalshi or Polymarket API keys.

The core skill is still the same: estimate the true probability, compare it to the market price, and only trade when the gap is real and large enough to survive fees. The code just handles the execution.