The competitive landscape of prediction markets is becoming increasingly apparent. Companies like Kalshi and Polymarket, along with newer entrants, are enhancing their sports offerings. Meanwhile, established players such as DraftKings, Flutter, and Robinhood are investing in market-making capabilities, distribution, and exchanges.
A new sector is emerging behind these brands, populated by data suppliers, streaming services, specialist market makers, and technology firms.
A September report from Jefferies highlighted that sports have emerged as the “most important liquidity driver” in prediction markets, with combo and parlay-style contracts capturing a larger share of the activity. However, analysts warned that the financial viability of prediction markets relies on sustained liquidity, robust engagement, and consistent trading volume because they are primarily scale-driven businesses with modest revenue yields.
James Monk, founder of Catalist Sports, a sports data and streaming provider, knows this dependence firsthand. His company supplies ITF tennis data to both Kalshi and Polymarket and holds an exclusive sports-streaming agreement with Kalshi in the U.S. To be effective, they also need to provide data to the firms that are making markets on these events.
“If we just sold the data to Kalshi to list the markets but no one was placing liquidity, there’s no point in them listing the markets,” Monk explained. “We also need to supply the data to the market makers to inform their models.”
Catalist began with fewer than ten potential market makers from Kalshi and has since secured deals with nearly 20, while engaging with an additional 20. This growth mirrors the increasing variety and number of sports contracts available. ITF tennis, with over 60,000 matches each year that are generally not televised, is especially reliant on official data. Monitoring a tour spanning locations from Bogotá to Bali would be otherwise challenging, Monk pointed out.
Streaming has also evolved as part of the product offering. Interfaces for prediction markets are transforming from their former trading-driven formats to include options for streams and player propositions, resembling sportsbook bet builders.
“It was still very much a trading kind of UX,” he noted regarding Kalshi’s interface earlier this year. “The product offering has come a long way.”
Andrew Gonzalez, founder of ParlayX, a startup focused on prediction market infrastructure, emphasizes that the ability for smaller teams to provide liquidity is a defining aspect of the industry. “Anyone can be a market maker,” he stated, referring to the operation of two- or three-person shops in the sector.
Jefferies' analysts described market makers as the “liquidity backbone” of this ecosystem, responsible for posting viable bids and offers, managing inventory, and offering pricing especially when customer activity skews to one side.
The analysts estimated that a market operator could generate about $1.69 net from a $100 trade by successfully managing a one-cent spread. That said, operational risks persist, as fluctuating prices and unresolved inventory can nullify potential gains from spreads, rebates, and liquidity incentives.
Gonzalez pointed out that the operational infrastructure for these newcomers remains underdeveloped, especially when compared to equities. Equities can rely on prime brokers and standardized systems, a luxury not afforded to prediction markets.
“When it comes to prediction markets, none of that exists,” he remarked, stating that organizations are often forced to start from square one. The platforms are typically designed for individual users, not for trading groups that require different permissions and controls. Some teams, Gonzalez noted, still use a single shared login.
To address this, ParlayX is working on developing individual logins, delegated permissions, and subaccount capabilities. Other gaps persist, such as the need for unified execution across exchanges and standardized resolution processes. For example, a contract bought on Kalshi cannot simply be sold on Polymarket, even when both markets may seem to address the same outcome. This incompatibility adds to the risks for firms juggling trades across different platforms.
Liquidity in prediction markets tends to be self-sustaining. Market makers prefer platforms with reliable technology and strong order flows, and their involvement enhances pricing and execution for customers. According to Sahil Patel, founder of Aldrin AI, these relationships clarify Kalshi’s competitive position.
“A lot of market makers want to go where there’s liquidity,” Patel explained, underscoring the importance of platform stability and Kalshi’s commitment to the financial aspects of market maker partnerships. “I think Kalshi is a freight train that’s just kind of running away with it.”
Aldrin AI tracks product developments, advertising efforts, social media presence, app rankings, and trading volumes among prediction market operators. Patel stated that their goal is to correlate these indicators and illustrate how a product launch, supported by advertising, influences trading volume and market share.
Beyond the leading exchanges, numerous operators compete for relatively small portions of a rapidly expanding market. “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%.”
According to Jefferies, exchanges can retain around 65% of explicit transaction fees, with the remaining portion shared among clearinghouses, brokers, and liquidity providers. This reality suggests that more operators may opt to internalize aspects of their operational infrastructure.
For independent suppliers growing alongside these marketplaces, opportunities increase with every new exchange, contract, and market maker. While consumer-facing platforms draw users, the capacity for trading at scale hinges on the data, liquidity, and operational complexities developing in the background.
