The tech providers behind the prediction markets boom

The consumer battle in prediction markets is increasingly visible. Kalshi, Polymarket and newer entrants are expanding their sports products, while DraftKings, Flutter, Robinhood and a host of others are investing in exchanges, distribution and market-making capabilities.

In fact, behind those brands, a whole new sector is taking shape. Data and streaming suppliers, specialist market makers and technology companies are quickly invading the space.

The investment banking and capital markets firm Jefferies said in a September report that sports had become prediction markets’ “most important liquidity driver”, with combo and parlay-style contracts accounting for an increasing share of activity. But the analysts cautioned that prediction markets are scale businesses with relatively low revenue yields, leaving their economics dependent on sustained liquidity, engagement and trading activity.

Data meets liquidity

James Monk, founder of sports data and streaming provider Catalist Sports, has witnessed that dependence directly. Catalist supplies ITF tennis data to Kalshi and Polymarket and has an exclusive US sports-streaming agreement with Kalshi.

The company must also provide data to the firms making markets on those events.

“If we just sold the data to Kalshi in order to list the markets but no one was coming in and placing liquidity, there’s no point in them listing the markets,” Monk said. “We also need to supply the data to the market makers to inform their models.”

Catalist initially received a list of fewer than 10 potential market makers from Kalshi. It has since completed agreements with close to 20 and is engaging with approximately another 20.

That growth reflects the expanding number and variety of sports contracts. ITF tennis is particularly dependent on official data because its more than 60,000 annual matches are not generally televised. Unofficially monitoring a tour moving between locations such as Bogotá and Bali would be difficult, Monk noted.

Streaming is also becoming part of the product. Monk said prediction market interfaces had moved beyond their earlier trading-led presentation to incorporate streams, player propositions and combinations resembling sportsbook bet builders.

“It was still very much a trading kind of UX,” he said of Kalshi at the beginning of the year. “The actual product offering has come a long way.”

The new market makers

Andrew Gonzalez, founder of prediction market infrastructure startup ParlayX, believes the ability of small teams to provide liquidity is one of the sector’s defining features. “Anyone can be a market maker,” he said. “You have these two- or three-man shops.”

Jefferies described market makers as the ecosystem’s “liquidity backbone”. They post executable bids and offers, manage inventory and provide prices when customer activity is heavily weighted to one side.

The analysts estimated that an operator capturing a one-cent spread and managing its exposure successfully could generate net economics of approximately $1.69 on a $100 trade. Returns are not guaranteed: adverse price movements and unresolved inventory can offset or exceed income from spreads, rebates and liquidity incentives.

The operational infrastructure available to these new firms, however, remains underdeveloped. Gonzalez contrasts prediction markets with equities, where trading businesses can use prime brokers, clearinghouses and standardised systems such as FIX.

“When it comes to prediction markets, none of that exists,” he said. “Everyone that’s building in the prediction market space mostly starts from ground zero.”

Platforms were generally constructed for one person operating one account, rather than trading organisations requiring separate permissions and controls. According to Gonzalez, some teams still share a single login.

“Whether it’s a 10-person or 100-person fund, they log in through the same Google email and share the same login credentials, which makes no sense,” he said.

ParlayX is developing individual logins, delegated permissions and subaccounts for such teams. Other gaps include unified execution across exchanges, prime brokerage and common resolution standards.

A contract purchased on Kalshi cannot simply be transferred and sold on Polymarket, even where the two markets appear to cover the same outcome. Each exchange may also define and resolve its contracts differently, creating an additional risk for firms trading across venues.

Following the liquidity

Liquidity can consequently become self-reinforcing. Market makers gravitate towards platforms offering dependable technology and substantial order flow, while their participation improves pricing and execution for consumers.

Sahil Patel, founder of competitive intelligence provider Aldrin AI, said those relationships help explain Kalshi’s position.

“A lot of market makers want to go where there’s liquidity,” he said, adding that platform stability and Kalshi’s investment in the financial side of its market-maker relationships were also important. “I think Kalshi is a freight train that’s just kind of running away with it.”

Aldrin monitors product changes, advertising, social media activity, app-store rankings and trading volume across prediction market operators. Patel said the objective is to connect those indicators and show how a product launch supported by advertising affects volume and market share.

Below the largest exchanges, he sees numerous operators competing for relatively small shares of a fast-growing category. “If you get 1% of this market, I think it’s a huge opportunity,” Patel said. “There are a lot of people fighting to get 1%.”

Jefferies estimates exchanges can retain approximately 65% of explicit transaction fees, with the balance distributed across clearinghouses, brokers and liquidity providers. It therefore expects more operators to bring parts of the infrastructure in-house.

For the independent suppliers growing alongside them, however, the opportunity expands with every new exchange, contract and market maker. The consumer-facing platforms may attract the users, but their products cannot trade at scale without the data, liquidity and operational machinery developing behind the screen.