How To Calculate Edge On Polymarket AI Markets

A binary prediction market price is an implied probability, but it is not automatically a fair probability. Your edge exists only if your own estimate is better than the market price after costs, liquidity, and resolution risk.

For AI markets, your fair probability should come from concrete evidence: benchmark changes, company announcements, model release artifacts, funding news, and market liquidity.

If a yes share costs 0.51 and your fair probability is 0.62, the expected return on cost is positive because your estimate is above the market-implied price.

If your fair probability is below the yes price, the calculator should show negative expected value. That does not prove the market is right; it proves your estimate does not support a yes position at that price.

Kelly sizing can become aggressive when the estimated edge is large. Odsage caps Kelly output at 25% because AI markets can have thin liquidity, fast news cycles, and ambiguous resolution details.

Many traders use half Kelly or smaller. The purpose of the calculator is to expose risk, not to tell users what to stake.

A market can look attractive in probability terms but still be difficult to enter or exit. Liquidity, spread, and order-book depth matter.

Resolution risk is also central in AI markets. Model names, release definitions, benchmark criteria, and company wording can all affect whether a market resolves yes.

Start from a market page, read the current price and resolution context, check the related daily news, then use the calculator with your own fair probability.

Save the market to your watchlist if the edge depends on future news. The point is to build a repeatable research workflow, not to react to every headline.