Meet the invisible sportsbook powering the prediction market boom
Strip away the language of contracts, order books and event trading and Bernard Marantelli has a blunt description of what is happening inside US sports prediction markets. “Kalshi is a sportsbook that’s just not allowed to have an in-house risk team,” says the founder of White Swan Data, one of the specialist firms now making markets on prediction exchanges.
The difference is all about the risk function. A sportsbook employs traders to price bets and manage its exposure. An exchange provides an API through which firms compete to quote prices and supply liquidity.
“Here’s an API. Bernard and 88 other people can market-make all these request-for-quotes (RFQs),” Marantelli says. “Some people might come in and just do esports because they’re esports experts. Others do everything. Some focus on same-game parlays. But it’s a sportsbook.”
The institutional layer is largely invisible to customers presented with a P2P proposition. Retail users may technically trade against one another, but the depth required by a mass-market product cannot be supplied by occasional customers alone. Professional firms must be prepared to quote continuously and commit substantial capital.
White Swan accounts for 40% of activity on some secondary exchanges
Marantelli says the London-based White Swan is a significant market maker on several secondary exchanges, accounting for as much as 40% of activity on some platforms. Its particular focus is the RFQ, parlay market.
“Just better margins,” he says of that decision. “I think it’s more defendable. It’s the area that fewer people can do well. So I think it’s more defendable margin, more ability to get long-term contracts and beneficial positions.”
Singles can be profitable, but parlay pricing requires the market maker to calculate the correlations between multiple outcomes and respond dynamically to individual requests. It is a skill set built over years in the sharper regions of the existing sports betting ecosystem.
Marantelli identifies White Swan and Susquehanna as two firms operating at industrial scale in parlays, with Jump Trading, Mojo and DL Trading among the possible leading group. Below them are numerous smaller syndicates, some managing between $5 million and $10 million, alongside sports-specific specialists.
Moving rapidly into the US
Enda Kendrick, chief executive of service provider Veltium, similarly says the largest UK and European sharp-betting groups have moved rapidly into US prediction markets. He believes there are also more than 100 smaller operations, ranging from individual traders to teams of around 10, interested in entering the regulated US market.
Yet the presence of professional counterparties complicates the customer-facing idea that prediction markets merely allow users to trade opinions with one another. As Kendrick puts it, two ordinary customers are not going to place $10 million or $20 million behind the Philadelphia Eagles. Markets at that scale require institutions.
For Marantelli, the exchange format could also cause some customers to lose money faster than they would with a conventional sportsbook. The ability to enter and exit positions creates a perception of flexibility, but that optionality can encourage users to commit more of their bankroll.
A customer might buy a team at 55 or 56 cents expecting the price to rise to 58 or 59 cents, he explains. If it falls to 45 cents instead, the trader may refuse to accept the loss and continue holding the position.
Faster, faster, kill, kill
“People will lose money faster on exchanges for lots of reasons,” Marantelli says. “It inherently increases spend, volatility, lots of things. And you’re playing against a sharper audience than you’re playing against at the DraftKings sportsbook.”
He compares the effect with sportsbook cash-out features, which gave customers more apparent control over their bets but may also have encouraged greater spending. The crucial difference is that an exchange customer can be facing a specialist whose entire business is identifying inaccurately priced contracts.
Kendrick sees a warning in the history of betting exchanges. In their early growth phase, there was sufficient retail liquidity for numerous market makers to profit. As that retail pool weakened, the sharper firms increasingly found themselves trading against one another.
His analogy is a poker table at which the weaker participants sustain the game. If those players disappear, the fourth-best professional at the table can suddenly become a loser because only the three strongest remain.
The US addressable market is vastly larger and customer recruitment remains strong. Marantelli says Kalshi increased its number of clients fivefold during the World Cup, while White Swan predicts that NFL prediction markets could generate between $5 billion and $7 billion of liability in a single week.
But he acknowledges the possibility that faster customer losses could eventually test the sustainability of the model.
“They lose quicker, dry up quicker, recruitment or re-recruitment,” he says. “If the recruitment of players dries up, then what are you going to do? Definitely there can be components like that.”
For now, the growth provides room for multiple market makers. Marantelli expects margins to “stay good during the growth period” before contracting as competition intensifies. The more complex RFQ and parlay markets may offer the best protection against that compression.
The result is an emerging ecosystem that looks less like millions of customers casually trading predictions with one another and more like an outsourced sportsbook trading room. Exchanges own the platform and recruit the customers; specialist firms price the risk and provide the money needed to make those markets function.
As Marantelli says: “Let’s call a spade a spade.”