What is realized edge?

For each resolved position, take the outcome (1 if the side the wallet held resolved true, 0 if not) and subtract the entry price the wallet paid for that side. Average those differences across all resolved positions. The result, in probability points, is the realized edge: how much more often the wallet's entries resolved true than the prices it paid implied. It is always delivered with a 95% confidence interval and the resolved-position count.

How can a 74% win rate come with a negative edge?

Because the entry prices implied more than 74%. A real example from our published board, read fresh on 2026-07-10: 35 wins and 12 losses on 47 resolved positions is a 74% win rate, but the realized edge is -2.5 probability points with a 95% interval of [-7.9, +1.9]. The wallet paid prices that implied roughly 77 wins per 100 and delivered about 74. Winning often is not the same as beating the price.

Why measure against the entry price instead of profit?

The entry price is the market's own stated probability at the moment of the trade, fixed before the world resolves the question. Measuring against it asks the only question that separates judgment from luck: did this wallet's chosen sides resolve true more often than the prices said they would? Dollar profit mixes that judgment with position sizing, fat-tailed outcomes, and one-off windfalls.

Entry price and realized edge

The answer first

Realized edge is the average of resolved outcome minus entry price, across a wallet's resolved positions, in probability points. A prediction-market price is a probability: paying 74 cents for a Yes share is the market saying 74%. If your entries resolve true more often than the prices you paid implied, your edge is positive. If not, it is negative, no matter how often you won.

The intuition, with one worked example

Here is a real read from our published top-50 board, run fresh through the analyzer on 2026-07-10. The wallet, Zarvantis, shows 35 wins and 12 losses on 47 resolved positions. That is a 74% win rate, and it looks like a sharp record. The realized edge on those same 47 positions is -2.5 probability points, 95% CI [-7.9, +1.9]. A 74% win rate is not edge if the entry prices implied more than 74%. This wallet paid prices that implied roughly 77 wins per 100 positions and delivered about 74. The record wins often and still runs slightly behind the prices it paid, and the interval includes zero, so the verdict is not separable from luck.

The actual method

For each resolved binary position the wallet held, the public settlement record gives two quantities:

The entry price: the volume-weighted average price the wallet paid for the side it held. This is set by the order book strictly before the market resolves.
The outcome: 1 if that side resolved true, 0 if it did not.

The per-position edge is outcome minus entry price. The per-wallet edge is the mean across resolved positions. The design is leakage-free by construction: the predictor (the price) is fixed before the world settles the question, and no profit-derived quantity appears anywhere in the measurement. A guard check confirms the entry-price level is not coupled to the edge statistic (Spearman 0.231 across the 3,871-wallet headline cohort, well under the 0.6 screen). The edge is never delivered alone: the 95% interval (lesson 3) and the resolved-position count travel with it in the same record.

Where you see this on the site

The free wallet analyzer leads with this number for any Polymarket address. The wallet board carries it per row, the embeddable skill badge quotes it with its interval, and the API returns it as realized_edge with the interval required by schema. The deeper glossary page is /learn/realized-edge.

What this does NOT mean

Realized edge is not profit. A wallet can have a positive edge and a small account, or a negative edge and one huge lucky win. It is not a forecast: the edge describes resolved past positions and says nothing binding about the next one. And a positive point estimate alone is not a skill verdict. The verdict comes from the interval, the sample size, and the concentration checks in the next two lessons, which exist precisely so a flattering number cannot outrun its own evidence.

Next lesson: confidence intervals

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