The fish are the product

What Kalshi’s parlay experiment teaches us about exchange economics

In any exchange, the fundamental dynamic is simple: takers pay fees to trade immediately, while makers earn positive-EV and rebates for posting standing orders. But Wall Street’s traditional metrics miss what’s actually driving value in prediction markets.

Take Spruce Point’s widely circulated short report on DraftKings. They tried to calculate Kalshi’s “hold” – the traditional sportsbook metric of revenue divided by handle. But applying sportsbook metrics to exchanges misses the point.

Unlike DraftKings, Kalshi doesn’t take the other side of bets – it facilitates trading between users¹, creating fundamentally different economics.

Not unlike my high school mathematics exploits, Spruce Point got to the right answer with the wrong work.

The fish are the product, and on parlays, the fish are the takers.

When DraftKings reports an 8% hold, that’s their cut of the fish flow – the systematic transfer of wealth from recreational bettors to the house. When Kalshi reports fee revenue of ~1%, that’s just their toll for facilitating a much larger transfer. The other 7% flows directly from takers to makers, invisible in Kalshi’s revenue but very real in economic terms.

The real product is the negative-EV flow itself, or the aggregate losses of takers that are the lifeblood of the entire ecosystem. This is what Citadel is paying Robinhood for—the infamous PFOF, or Payment for Order Flow— or the privilege of feasting on their customer base, of being the Brown Bear in the Alaskan river. That flow of losses becomes maker profits and exchange fees².

Until now, nobody was actually measuring the fish on prediction markets. Traditional sportsbooks capture it all as revenue, making their unit economics opaque. Kalshi isn’t calculating and publishing these figures themselves either.

But prediction market parlays offer something unique: a controlled environment where we can finally cleanly quantify the fish in sportsbook terms. We can measure, with precision, exactly how much takers lose and where that money flows – not just to the exchange, but to every participant in the ecosystem.

The results reveal the true economics of modern prediction markets – and why comparing Kalshi’s fees to DraftKings’ hold is like comparing the NYSE’s revenue to Jane Street’s.

The perfect laboratory

Unlike most prediction markets where the same wager can be placed and unwound multiple times, Kalshi’s parlay structure makes round-trip trading nearly impossible. You can’t easily buy a parlay ticket and then sell it back – the combinatorial complexity and thin liquidity make it economically unfeasible³.

This constraint is actually a feature for our analysis. Every dollar of parlay volume represents pure directional betting – handle is simple to calculate directly. No need to wrestle with the “volume” number. There’s no noise from arbitrageurs, no day-trading activity, no sophisticated players gaming the system. Just users making directional bets and holding them to expiration.

For this analysis, I examined all parlay trading on Kalshi from September 29, 2025 (when parlays launched) through last night. The dataset includes every fill, with maker/taker flags, execution prices, fees paid, and settlement outcomes.

This data and technique lets us calculate the total economic transfer in the system⁴.

Finally, a “hold rate” for prediction markets

For the first time, we can calculate a true “hold rate” for prediction markets – the percentage of money that flows from takers to the broader ecosystem.

The formula mirrors traditional gambling metric: (Taker P&L × -1) / Taker Cost Basis, where the Taker P&L is the inverse of the sportsbook revenue, and Taker Cost Basis represents total “handle” wagered.

Across seven days of Kalshi’s parlay trading, we saw $5.6M of volume, $806k of “handle” and an overall hold rate of 11%.

What’s striking is that even in this transparent, competitive environment – where anyone can make markets or take liquidity, spreads are visible, and there’s no house edge – takers systematically lose. The exchange structure doesn’t eliminate the fundamental dynamic: less-informed traders transfer wealth to more-informed ones.

The data spans all parlay trades during since the launch of the product on Monday, capturing both high and low volatility periods. On the first day, the takers actually won in the aggregate. But as more data flowed in, the story is clear: the exchange and the makers are having a field day.

The bigger picture

The parlay data quantifies what Spruce Point missed: the total fish flowing through this platform.

The vertically integrated sportsbook model – where they’re simultaneously the broker, exchange, clearinghouse and market maker — creates operational bloat that obscures the core value transfer.

Kalshi’s parlay data proves exchanges can facilitate the same economic transfer – recreational bettors losing to sharps – without the overhead. No dealing with a state-by-state patchwork regulations. No cat-and-mouse game with profiling and limiting. Same fish, same flow, 90% less infrastructure and 10x the potential distribution through brokers in every country that allows futures trading.

Now that we can actually measure the fish, we can start asking whether the current ecosystem is catching them efficiently. Spoiler alert: it’s not.

1. And yes, one of the users is in fact Kalshi Trading, a market making arm. But they are just one, and trying to construe this as some sort of deceitful and secretive way to be the house is a tired tack. If that was the goal, it’d have been so much easier to do a privileged-market-maker sweepstakes model like Novig.

2. And soon, liquidity rewards and volume rebates to the traders. Kalshi is going to make it rain on the market makers to lock in the liquidity to their platform.

Kalshi’s new liquidity incentives could be the dawn of the garage-band market maker

3. For now! Imagine a world where parlay contracts are traded around through RFQ or orderbook in the same way Credit Default Swaps are traded. The size of the individual contract will be too small for a human to care about it directly, the volume will be enough in the aggregate that an automated system of algorithmic pricers will be flipping the risk back and forth.

It is technically possible that someone bought a parlay and then cashed it out, but I investigated this and if there is any effect on the calculus it is vanishingly small because it almost never happens.

4. And it scales to the rest of the markets on Kalshi, the metric works regardless of the market microstructure. I’ll be writing more about this in an upcoming post!