Paradigm Exposes Double-Counted Volumes in Polymarket Trading Data—What’s Really Happening?

Paradigm just dropped a bomb on Polymarket's trading data—and it's not pretty. The crypto research powerhouse flagged what looks like a classic case of double-counted volumes. That's right: numbers that might be more inflated than a trader's ego after a lucky pump.

How the Double-Count Happens

Think of it like this: one trade gets logged twice on the books. It makes activity look hotter than it actually is—a mirage of liquidity that can sway everything from token prices to investor confidence. In a sector already wrestling with transparency, this kind of slip-up (or sleight-of-hand) cuts deep.

The Ripple Effect for Prediction Markets

This isn't just about bad math. For a prediction market like Polymarket, trust is the only real currency. When volume data gets fuzzy, the entire premise—using crowdsourced bets to forecast real-world events—starts to wobble. It raises the old, cynical finance question: if you can't trust the tape, what can you trust?

Paradigm's callout forces a harder look under the hood. It's a stark reminder that in crypto's wild west, even the slickest platforms need their numbers to add up—without creative accounting. The market's watching to see if this was a glitch or a glimpse of something shadier. Either way, the credibility meter just took a hit.

Slivkoff dissects Polymarket’s trade anatomy

The Paradigm research partner began by describing the on-chain data associated with each trade on the Polymarket platform. He pointed out that all the platform’s transactions follow a rigid template, which includes at most one group of matched Polymarket orders per Polygon transaction. 

Slivkoff further explained that each set of matched orders has at least one maker and precisely one taker. He also noted that trade transactions are submitted by approximately 50 EOAs affiliated with Polymarket, and that each transaction on the platform follows the same event sequence.

“Polymarket’s on-chain data is quite complex, and this has led to widespread adoption of flawed accounting methods.” 

–Storm Slivkoff, Research Partner at Paradigm 

According to Slivkoff, the accounting bug inflates both commonly used types of volume metrics for cash flow volume and notional volume, as well as the prediction market. He noted that the platform’s data has been confusing for crypto data analysts who find it difficult to untangle the many interacting layers using a block explorer. 

Slivkoff said this difficulty arises because trades on the platform can be either simple swaps or merges and splits, where both parties exchange opposing positions for cash. He also stated that the smart contracts present redundant events for tracking, which standard blockchain explorers often fail to distinguish clearly.   

Paradigm builds a simulator to illustrate trading volume behavior

Paradigm revealed that its team has built a simulator to illustrate how different trading metrics behave under at least eight trading types. The simulator calculates maker/taker balance changes, open interest changes, and various volume metrics for each trade type. 

Slivkoff further disclosed that the YES price and the number of traded contracts are the only two inputs required for the simulation. He also suggested that crypto data analysts can make copies of the spreadsheet and change the parameters to perform their own simulations. 

However, Slivkoff pointed out that analysts using this simulator should take note of a few invariants. He clarified that for each trade type, the maker and taker always take opposite positions. One is a long YES resolution, and the other is a short YES resolution. 

Slivkoff also noted that the maker and taker YES and NO deltas always have similar absolute values. However, he added that this is different from their USDC deltas, which can have differing absolute values.

The researcher also emphasized that split trades always increase open interest, while merge trades always decrease open interest. However, swap trades always leave open interest unchanged. 

Slivkoff noted that calculating both notional volume and cash Flow volume for swap trades is straightforward. He also observed that Polymarket’s OrderFilled sum presented a value that is twice the correct figure for both of these metrics. However, he emphasized that calculating these metrics for merge trades and split trades is more complex than for a conventional swap.

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