Beginner Guide

Favorite-Longshot Bias in Prediction Markets

Why markets often overprice unlikely outcomes and underprice likely ones, and what that means for readers and traders.

By Top Prediction Markets EditorialReviewed July 26, 20263 min read

Answer first

The favorite–longshot bias is a pattern where markets assign relatively too much probability to longshots (low-probability outcomes) and too little to favorites (high-probability outcomes). It appears for behavioral, informational, and structural reasons and shows up most clearly in low-liquidity or fixed-odds settings. For readers and traders, the key is to compare market prices to objective or aggregate estimates, watch liquidity and fees, and avoid simple arbitrage attempts without accounting for costs and uncertainty.

What it means

In simple terms, the favorite–longshot bias describes a systematic mismatch between market prices and outcomes: longshots (events with small chances) tend to be priced as if they are more likely than they actually are, while favorites (events with large chances) are priced as if they are less likely than they actually are.

Here's the basic idea: if a Yes contract costs 8¢ (implying an 8% chance) but the event actually happens only 2% of the time in comparable situations, that longshot is overpriced. Conversely, if a favorite is priced at 62¢ but should win 70% of the time, that favorite is underpriced.

Why it matters

The key thing to know is that the bias affects how you read prices and what you expect from a market.

  • Mispriced longshots and favorites change which bets appear attractive and which do not.
  • The bias is not the same across all markets: it’s stronger where liquidity is low, information is sparse, or bettors face fixed-odds payoff structures.
  • For readers, recognizing the bias helps set expectations when comparing market-implied probabilities to polls, models, or other forecasts.

How it works

  1. Behavioral causes. People tend to overreact to small probabilities. That leads to relatively more money on longshots and relatively less on favorites. Psychological patterns include probability distortion (overweighting tiny chances) and risk-seeking for low-probability payoff scenarios.

  2. Informational gaps. Favorites are often the visible, well-discussed outcomes; longshots can attract speculative or emotional interest. When information about outcomes is uneven, prices can skew because fewer informed traders correct the mispricing on longshots.

  3. Market structure and frictions. Fees, transaction costs, limits on trade size, and payout formats change incentives. Pari-mutuel pools (where bettors split a fixed pot) and fixed-odds markets amplify the effect because payoffs for winners rise when many people back a longshot.

  4. Liquidity and sample size. Low trading volume makes it easy for a few bets to move prices. In thin markets, realized outcomes are noisy, so comparing price to outcome across a small sample can look biased even if the underlying process is random.

  5. House edge and overround. In many commercial betting settings, bookmakers build a margin into prices. That margin interacts with bettors’ preferences and can make longshots look especially poor value on average.

A simple example

If a Yes contract costs 62¢ and pays $1 if the event happens, buying one contract costs $0.62. If the event happens, the contract pays $1, so the gain before fees is $0.38. If the event does not happen, the contract expires at $0, so the loss is $0.62.

Suppose an analyst estimates the event’s true chance at 70% (0.70). Expected value (EV) per contract = 0.70 × $0.38 − 0.30 × $0.62 = $0.266 − $0.186 = $0.08. That positive EV indicates the favorite is underpriced relative to that estimate.

A simple longshot example: if a Yes contract costs 8¢ and an objective estimate of the chance is 2% (0.02), buying one contract costs $0.08. If the event happens, you gain $0.92; if it fails, you lose $0.08. EV = 0.02 × $0.92 − 0.98 × $0.08 = $0.0184 − $0.0784 = −$0.06. That negative EV shows the longshot is overpriced relative to the estimate.

These calculations assume you can trust the analyst’s probability estimate and ignore fees, limits, and execution frictions that often remove simple profit opportunities.

Common mistakes

Treating market price as a perfect probability

Markets give an implied probability, but that number reflects traders’ preferences, liquidity, and fees as much as information. Don’t assume the market price equals the true chance.

Assuming the bias is uniform across markets

The favorite–longshot bias is stronger in some settings (horse racing, low-liquidity political submarkets) and weaker in others (high-volume, well-informed exchanges). Check the market context before generalizing.

Trying to arbitrage without accounting for costs

Small systematic mispricings can look exploitable, but fees, bid-ask spreads, trade limits, and the risk of being wrong on your probability estimate often eliminate practical profit. Ignoring these frictions is a common mistake.

Related concepts

Frequently asked questions

What exactly is the favorite–longshot bias?

It’s a pattern where low-probability outcomes (longshots) receive relatively more money and are priced too high, while high-probability outcomes (favorites) receive relatively less and are priced too low, compared with objective or realized frequencies.

Does the bias always mean favorites are profitable to buy?

Not always. Apparent mispricing can be removed by fees, limits, execution risk, and uncertainty in your own probability estimates, so a favorite that looks cheap may still be risky after costs.

Where is the bias strongest?

It tends to be strongest in low-liquidity markets, pari-mutuel pools, and settings with asymmetric information or emotional betting. High-volume, well-informed markets usually show a smaller effect.

Can the bias be used to forecast elections or outcomes?

Markets with enough liquidity and information are useful forecasts, but the bias warns you to treat small-probability prices with caution. Compare market prices to polls, models, and aggregated information before drawing conclusions.

Are prediction markets legal?

Rules vary by location and platform. See our dedicated guide on whether prediction markets are legal in the US.

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