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Pew’s $53 Billion Prediction-Market Figure Is Notional Volume
Pew counts each traded contract at $1, not its execution price. What the $53 billion figure measures—and what it cannot tell you about forecasts.

Pew’s $53 billion figure measures traded contracts at their $1 face value—not the dollars traders paid for them. Its September 23, 2026 analysis reports combined monthly global volume on Kalshi and Polymarket rising from $25.66 billion in May to $52.99 billion in July. Polymarket includes both its international and US platforms. The measure is notional taker volume, according to Pew’s methodology and data.
That is roughly a 106.5% increase, calculated from Pew’s published totals. It establishes a sharp rise in contract turnover under the same counting convention. It does not establish that $53 billion of new money entered these platforms, or that their forecasts became more accurate.
How notional taker volume works
Pew counts each contract traded by a taker at $1, the contract’s value if it wins, rather than its execution price. A taker executes against an existing order; a maker supplies the resting order. Counting the taker side avoids adding a second volume entry merely because the same matched trade also has a maker.
For the $1 contracts in Pew’s analysis:
Notional taker volume = contracts traded by takers × $1.
Here is a hypothetical example, excluding fees:
| Taker purchase | Execution-price value of purchase | Pew-style notional volume |
|---|---|---|
| 100 YES contracts at 10¢ | $10 | $100 |
| 100 YES contracts at 50¢ | $50 | $100 |
| 100 YES contracts at 90¢ | $90 | $100 |
Equal contract counts produce equal notional volume despite very different purchase costs. Notional is therefore useful for comparing the quantity traded without letting contract prices determine the volume measure. It is not a measure of the taker’s cash outlay.
Nor can you recover execution-price turnover from the $52.99 billion total alone. That requires quantities and actual prices for the trades, including whether the taker traded YES or NO. Multiplying aggregate notional by one headline YES price would not do it.
Turnover is not money still invested
Volume accumulates through time. Open interest measures contracts that remain open—a distinction also made in Kalshi’s trading glossary.
If a hypothetical block of 100 contracts changes hands five times, those transactions contribute $500 of notional volume. They need not represent 500 contracts outstanding at the end, five independent forecasts, or five deposits of fresh money.
This also makes a direct comparison with sportsbook handle, the total dollar amount wagered, problematic. Pew’s prediction-market measure uses payout face value and includes secondary trading. A dollar sign on both figures does not make them interchangeable measures of money wagered.
What the sports surge establishes
Pew attributes most of the May-to-July increase to sports. Its category table shows combined sports notional volume rising from approximately $18.02 billion to $42.01 billion. Calculated from those figures, sports contributed about 88% of the increase in total volume and accounted for about 79% of July’s total. These are shares of notional turnover, not shares of users or deposited capital. Source: Pew’s category table.
Pew’s category analysis stops in July because of changes in the availability of Kalshi combo category data. That limits later topic comparisons; it does not mean aggregate volume disappeared. Pew separately reports approximately $47 billion in combined August volume. See what missing combo category data means.
Platform coverage matters too. Pew combines Polymarket International and Polymarket US, while The Block’s volume-market-share chart displays Kalshi, Polymarket and Polymarket US separately. Align those groupings before comparing platform shares.
What a forecaster should check instead
Aggregate volume describes activity, not forecast quality. For a specific probability estimate, inspect the contract’s resolution criteria, timestamped bid and ask, available depth, and fees. For historical accuracy, compare probability snapshots with resolved outcomes across a defined sample—not platform-wide turnover. Our guide to Brier score versus calibration explains the distinction.