How we model fair value

The short version: it is a weighted average of two real order books, not a forecast. Everything below is what the code actually does.

    Why this page exists. Any site can print a number and call it fair value. If we are going to tell you a market is mispriced, you are entitled to know precisely how we decided that, and precisely where the method breaks down. Both are below.
  

The number itself

Fair value is a liquidity- and recency-weighted consensus of the price on each venue that lists the same contract. There is no machine learning, no news model, no black box. A deeper, fresher book pulls the number toward its price; a stale or thin one barely moves it.

Neither venue is allowed to dominate the result. Left unweighted, the venue with consistently deeper books would define fair value outright, so the other venue would appear permanently mispriced and every edge would point the same direction. That is an artefact of the maths, not an opportunity, and the model is constrained to prevent it.

The edge on a venue is simply fair value − that venue's price. Positive means it looks under-priced. We quote it against the real ask, not the midpoint, because the midpoint is a price nobody can trade at.

What we refuse to call an edge

Most apparent cross-venue arbitrage is not real. Four filters remove it, and each exists because it burned us first.

1. The two contracts must settle identically

Near-identical titles routinely resolve on different criteria: map winner versus match winner, "will X run" versus "will X win", differing tie and void rules, different sources of truth. Every candidate pair is machine-checked against both venues' resolution criteria before it can produce an edge. A pair that has not been checked is labelled not checked and never labelled safe.

To date: — candidate pairs checked, — confirmed to settle the same way. That ratio looks brutal because candidates come from a similarity sweep, so most were never plausible matches. The useful figure is that even among pairs whose titles look almost identical, a clear majority still do not settle the same way.

2. Neither leg may already be closed

The two venues do not mean the same thing by a market's close time, and the difference is not visible in the data unless you go looking for it. Compare them naively and you end up holding a live price against one that stopped moving hours earlier, which produces a gap that looks enormous and cannot be traded. Detecting and excluding those was the single largest correction we have made to our own scanner. Every group is checked for it twice before you see it.

3. Both books must carry real volume

An illiquid book holds a stale quote, and a stale quote produces the largest apparent edge, so without a floor the most eye-catching row on the page is reliably the least real one. Legs below a liquidity floor are excluded outright, and books that are thin but still tradeable are flagged as such rather than quietly ranked alongside the rest.

4. The model must be confident

Confidence combines how tightly the venues agree, how much volume stands behind them, and how many independent prices exist. Groups below the threshold are dropped rather than shown with a caveat attached. Extreme longshots are excluded as well, since at those prices an apparent gap is usually a fee and tick-size artefact rather than an opportunity.

What this cannot tell you

    It is a consensus, not a forecast. Fair value describes where two order books collectively sit. If both venues are wrong about the world, our fair value is wrong with them, confidently. It is a measure of relative mispricing between venues, not a prediction of the outcome.
  

Two venues is a small sample. With only Kalshi and Polymarket listing a contract, "consensus" means two opinions. Treat a 1–2% gap accordingly.
Fees and slippage are not included. The edge shown is gross. Exchange fees, the spread you actually cross, and what the second leg fills at all come out of it.
Prices refresh every few minutes, not continuously. Anything faster-moving than that is not a market this tool is useful for.
Verification is automated. Resolution criteria are checked by model, not by a lawyer. It is far better than eyeballing titles and it is not infallible.
A small edge count is the honest outcome. Each filter removes far more than it keeps. Right now the engine is showing — live cross-venue edges across — tracked markets. We would rather show you a handful that survive scrutiny than fifty that fall apart at execution.

Current engine state

Loading live figures…

These numbers are read live from the engine each time this page loads. They are never typed into the page by hand, because a hardcoded accuracy claim becomes a false one the moment the data moves.

Found something wrong?

If a pair is matched that should not be, or an edge survives that clearly should not, we want to know. That is the failure mode we care most about. Tell us.