Finding Mispriced Outcomes in Multi-Outcome Markets
Multi-outcome prediction markets — elections, awards, sports futures — are harder to price correctly. Here's where the edge hides and how to find it.
Why multi-outcome markets misprice
A binary market has one degree of freedom. Yes or No. The crowd usually gets this roughly right. A multi-outcome market — who wins the Democratic primary, which movie wins Best Picture, which team wins the World Cup — has dozens of contracts that must sum to 100%. They almost never do.
Take a hypothetical presidential primary with 8 candidates. If you add up every contract's Yes price, you'll often get a total between $1.05 and $1.15. That 5-15% overround means the market is collectively overpricing the field. But the overround isn't distributed evenly. Some candidates carry more excess than others — and that's where the edge hides.
The overround problem
An overround of $1.10 on 8 outcomes means an average of 1.25 cents of excess per contract. But in practice, the distribution is skewed. Favorites tend to be priced more efficiently because they attract the most volume and attention. Long shots — candidates trading at $0.03-$0.08 — carry disproportionate overround because:
- Low-probability events are harder to estimate accurately
- Thin liquidity means prices are stale
- Recreational bettors overpay for long shots (the favorite-longshot bias is real)
A candidate with a true probability of 2% might trade at $0.05. That looks small, but the market is pricing them at 2.5x their fair value.
How to find the edge
Start by adding up all the Yes prices. Use the multi-outcome calculator to normalize the probabilities — strip out the overround and see what each contract should cost in a fair market.
If the sum is $1.12 and one candidate trades at $0.45, their implied probability is 45%, but their fair probability (after removing overround proportionally) is about 40.2%. That's a meaningful difference. Now compare your own estimate. If you think the true probability is 43%, the raw price looks like a 2% edge, but after overround adjustment it's actually a 2.8% edge.
Example: 5-candidate market
| Candidate | Market Price | Normalized | Your Estimate |
|---|---|---|---|
| A | $0.45 | 40.9% | 43% |
| B | $0.30 | 27.3% | 25% |
| C | $0.15 | 13.6% | 14% |
| D | $0.12 | 10.9% | 12% |
| E | $0.08 | 7.3% | 6% |
| Total | $1.10 | 100% | 100% |
In this example, Candidate A is underpriced relative to your model (43% vs 40.9% normalized), while Candidate E is overpriced (6% vs 7.3%). Buy A, sell E — or at minimum, don't touch E.
Cross-platform arbitrage
When the same multi-outcome market exists on both Polymarket and Kalshi, the overround can differ. If Polymarket sums to $1.08 and Kalshi sums to $1.14, buying cheap contracts on one and selling expensive ones on the other can lock in risk-free profit. The arbitrage calculator handles the math.
Multi-outcome markets reward patience and arithmetic. The crowd gets the general picture right but misprices the details. That's your opportunity.
Before trading any of these markets, make sure you understand how platform fees affect your edge — a thin mispricing can vanish after Kalshi's winner fee.