Prediction markets went from roughly $9 billion in combined global trading volume in 2024 to more than $40 billion across Kalshi and Polymarket in 2025. That figure gets quoted constantly. It is also assembled from numbers the two platforms do not define the same way.
Anyone trying to size this sector, compare venues or model where liquidity is actually sitting runs into the same wall that DeFi analytics hit five years ago. The headline metric is easy to publish and hard to trust.
Three Ways Volume Gets Counted
Start with what a platform means when it says volume.
Notional volume counts the full face value of every contract traded. A contract that settles at $1 and trades at 30 cents can be counted at $1 of notional or 30 cents of cash traded, and those produce very different totals for identical activity.
Matched volume counts only trades that actually cleared against a counterparty. Some venues report order flow that includes activity never matched, which inflates the figure without inflating the market.
Then there is double counting on the two sides of a trade. A buyer and a seller create one trade. Count both legs and you have doubled your market overnight, and different venues make different choices here.
None of this is unique to prediction markets. It is the same argument crypto exchanges had about wash trading and reported volume for most of the last cycle, and it took independent trackers years to force convergence on a definition.
The Same Problem in DeFi Lending
Lending protocols have their own version. Total value locked is the metric everyone quotes and one of the least informative available.
TVL tells you what has been deposited. It says nothing about how much of that capital is being borrowed, which is the number that determines whether a supply rate is real or theoretical. A protocol with $2 billion in TVL and 20% utilization is a very different business from one with $600 million and 85% utilization, and the second one is paying its lenders considerably more.
Supply rates quoted without a utilization figure alongside them are close to meaningless. So are borrow rates quoted without the reward token emissions that offset them, because a 9% borrow cost paid down by 6% of incentive tokens is a 3% borrow cost until the emissions schedule changes.
This is the specific gap the analytics layer is meant to fill. The team at DeFi Rate, a site specializing in tracking live data across prediction markets and DeFi sectors, normalizes figures so that volumes, rates and market activity can be compared like for like across platforms rather than taken at each venue’s own definition. That normalization step is the whole value. Raw platform data is not wrong so much as incompatible.
Why Cross-Venue Comparison Is Getting More Valuable
Two developments make this more than a data hygiene issue.
The first is that price divergence between venues is now large enough and frequent enough to matter. The same event contract routinely trades at different prices on different platforms. Those gaps close through arbitrage, and identifying them requires a normalized view of both sides. A trader looking at one venue’s screen sees a price. A trader looking at a normalized cross-venue feed sees a spread.
The second is that prediction market pricing has become a sentiment input for crypto markets generally. Contracts on Federal Reserve decisions, regulatory outcomes and protocol events now carry enough liquidity that their implied probabilities move alongside token prices. Reading those probabilities correctly means knowing which markets are deep and which are three traders and a wide spread.
Liquidity depth is the filter. A market with meaningful two-sided volume produces a price worth acting on. A thin market produces a number that looks like consensus and is not.
The Regulatory Variable
Sizing this sector also means accounting for a legal position that has not settled.
The Commodity Futures Trading Commission claims exclusive federal jurisdiction over event contracts traded on registered designated contract markets. State gaming regulators argue sports-related contracts are gambling under state law. Courts have split. The Third Circuit favored federal preemption in KalshiEX LLC v. Flaherty in April 2026, while district courts in Nevada and Utah reached the opposite conclusion. Ninth and Tenth Circuit appeals are outstanding.
The CFTC proposed amendments to its event contract regulations in June 2026. Law firm analysis such as Norton Rose Fulbright’s summary of the preemptionÂ
and enforcement position is useful for tracking where this goes, and the CFTC’s own event contracts page carries the rulemaking record.
That matters for the numbers because a large share of prediction market volume is sports. Sports contracts made up around 80% of Kalshi’s 2025 volume and 39% of Polymarket’s. A regulatory outcome that restricts sports event contracts would take a substantial bite out of the sector’s headline volume without changing anything about the underlying technology.
What to Check Before Quoting a Figure
Ask whether the volume is notional or cash traded. Ask whether it counts both legs. Ask what proportion sits in sports contracts, because that share carries specific regulatory risk the rest does not.
For lending data, pair every rate with a utilization figure and separate base yield from token emissions.
And prefer a source that states its methodology over one that publishes a bigger number. The second cycle of any data category is where definitions get standardized, and prediction markets are entering theirs now.
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