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Expert: Prediction market, sports betting structurally different

Jenny Ortiz-Bolivar
Written by Jenny Ortiz-Bolivar

Prediction markets have grown by transforming real-world events into tradable contracts, while sports betting has historically been framed as entertainment. As volumes increase and mainstream platforms integrate prediction products, regulators, investors, and operators are questioning where the boundary lies.

In an exclusive interview with SiGMA News, Alice Li, Investment Partner at Foresight Ventures, argued that the distinction is not merely semantic, but structural. From a regulatory and market design perspective, she said the focus should move away from a raw scale and towards how these markets function.

Regulators should focus less on absolute volume and more on market quality indicators,” Li said. “Healthy markets show steady liquidity growth, price movements that track real-world information, and broad user participation across diverse contracts.”

In contrast, she pointed to clear warning signs when markets tilt towards excess.

Warning signs include sudden volume spikes, persistent price dislocation from underlying events, high leverage usage, and concentration in short-term, high-risk markets,” she said. “An increase in fraud, manipulation, disputes, or hype-driven participation is often an early signal that speculation is outpacing informational value.”

Will prediction markets converge with sports betting?

The idea of convergence has gained momentum as sports-driven prediction contracts dominate volumes, and regulated platforms expand their reach. Yet Li is clear that overlap does not mean assimilation.

There will be overlap, but they remain structurally different products,” she told SiGMA News. “Sports betting is primarily entertainment-driven. Prediction markets are information-driven, designed to aggregate collective intelligence and price real-world uncertainty.”

This distinction, she explained, shapes how regulators view the sector, how platforms are built, and how data is ultimately used.

This distinction is reflected in their regulatory treatment, social utility, and scope,” Li said. “Prediction markets extend beyond sports into economics, policy, and corporate outcomes, and are increasingly positioned as decision-support tools rather than pure wagering products.”

Rather than a single merged industry, Li expects parallel evolution. “The two will likely evolve in parallel, with some hybrid middle ground,” she said.

According to The Prediction Market Duopoly: A Strategic Analysis of Kalshi and Polymarket report of Foresight Ventures published in December 2025, sports betting currently drives the majority of activity in prediction markets. “70 percent of the market’s volume is currently driven by sports betting, highlighting its crucial role in attracting mainstream users,” the report stated.

(Source: Foresight Ventures)

Explosive growth and the rise of a duopoly

The report frames prediction markets as a sector on track to rival the global betting industry. It benchmarks the total addressable market against “the $300 Billion global betting industry” and projects sharp expansion.

According to the report, the projected 2025 year-over-year growth was a “staggering 400 percent growth rate.” User growth is expected to follow a similar trajectory, with “the user base forecast to increase by 3-4x, growing from 4 million in 2024 to 15 million in 2025.”

Yet this growth is not evenly distributed. According to the report, “This explosive growth is almost entirely captured by a duopoly: Kalshi and Polymarket. Together, they command 99 percent of the market.

The report described these two platforms as representing two different visions for prediction markets: one rooted in regulatory compliance, the other in crypto-native infrastructure.

Kalshi embodies a compliant, top-down approach designed for mainstream integration, while Polymarket champions a permissionless, bottom-up model rooted in crypto-native principles,” the report authors wrote.

Regulation versus permissionless design

Kalshi and Polymarket differ sharply in how they approach users, regulators, and capital.

The report said that Kalshi operates as “a compliant, off-chain exchange with data accessible via API” and is “fully regulated by the CFTC (Commodity Futures Trading Commission) with licenses in all 50 states.” The report added that users face strict KYC requirements (SSN, bank account), while funding options include bank transfer, debit card, wire, and USDC.

Polymarket, by contrast, is described as a “permissionless, fully on-chain platform where markets and positions are public.” It requires “no KYC; a crypto wallet is the only requirement,” and operates on “USDC on the Polygon network.”

These differences extend to market focus. Kalshi’s volume is “sports (~90% of volume), especially the NFL,” while Polymarket historically concentrated on “Politics & Entertainment (formerly 80 percent during elections).”

The report argued that these contrasting strategies set the stage for a pivotal shift in market leadership.

The Robinhood effect and mainstream distribution

That shift came through what the report calls a “market-altering gambit”: Kalshi’s partnership with Robinhood.

The catalyst for this seismic shift was Kalshi’s integration with the retail brokerage giant Robinhood,” the report said. Beginning on 19 August 2025, Kalshi embedded NFL prediction markets directly into the Robinhood app, giving it access to “27.4 million funded accounts.”

The impact was immediate. “Market Share Reversal: In a matter of months, Polymarket’s dominant market share collapsed from ~95 percent to 32 percent, while Kalshi’s share skyrocketed from just 8 percent to 66 percent,” the report stated.

Volume and user numbers followed. “Kalshi’s annualised volume exploded from approximately $300 million to an estimated $40-50 billion,” while “Kalshi’s daily active users grew 20x to 75,000.”

The report said that this moment demonstrated that regulatory alignment combined with mainstream distribution could overpower crypto-native network effects.

It proved that in the battle for the U.S. market, compliant access to mainstream distribution channels could serve as a more powerful weapon than the permissionless network effects of the crypto world,” the report said.

Two strategies, two futures

Following the market reversal, the report outlined diverging long-term strategies.

Kalshi is described as executing “a ‘dimensionality reduction attack’ on the legacy sports betting industry.” Its federal CFTC licence allows it to operate nationwide, bypassing the state-by-state restrictions that limit traditional sportsbooks.

While traditional sports betting platforms are restricted by a patchwork of state-level laws. Kalshi operates under a federal licence,” the report said, unlocking markets such as California and Texas.

It also positions itself as more capital-efficient for users. “Kalshi operates like a financial exchange, offering a superior product with no withdrawal penalties or extra fees,” the report stated.

Polymarket, meanwhile, is pivoting away from retail monetisation towards institutional data. “The clearest signal of this strategic shift is the $2 billion investment from Intercontinental Exchange (ICE), the parent company of the New York Stock Exchange,” the report said.

This move reframes Polymarket’s activity as a data asset. “On-chain, real-time probability data generated by a global network of users” is positioned as a product for hedge funds, banks, and other institutions.

Li’s own view on data buyers aligns with this direction. “In the near term, financial institutions—particularly macro and quantitative funds—are the most likely buyers,” she said. “They have clear use cases, capital, and infrastructure to integrate probabilistic data into trading and risk models.”

She added that public sector adoption will be limited. “Government adoption will be slower and more selective, primarily through research units, think tanks, or pilot programmes rather than operational deployment,” Li told SiGMA News.

Structural hurdles to mainstream adoption

Despite growth and institutional interest, the report identified three barriers that could slow broader adoption.

The first is liquidity fragmentation. “Every new market is an isolated, independent liquidity pool,” creating inefficiencies and limiting depth.

The second is oracle reliability. The report contrasted Kalshi’s “centralised & manual” outcome resolution with Polymarket’s reliance on the UMA Protocol, noting that both approaches involve trade-offs. “The integrity of the entire system hinges on its ability to resolve outcomes accurately and resist manipulation,” the report said.

The third is user experience. Drawing on the failure of early platform Augur, the report noted that complexity, fees, and technical friction can block mass adoption.

To achieve mass adoption, the mandate is clear: the experience must be seamless and approximate a Web2 application,” it stated.

In all cases, success will depend on regulatory navigation, cultural relevance, and local distribution—not global scale alone.”

– Alice Li, Investment Partner at Foresight Ventures

Regional growth and localisation

Beyond the U.S., both Li and the report see opportunity shaped by local context rather than global scale.

Li identified three regions with strong potential. “The most likely regions are the EU, Southeast Asia, and Latin America,” she said. “In all cases, success will depend on regulatory navigation, cultural relevance, and local distribution—not global scale alone.”

The report argued that the U.S. market is effectively closed to new entrants, but global markets remain open to deep localisation.

Parallel paths rather than convergence

Li told SiGMA News that prediction markets are unlikely to merge into sports betting. While sports will continue to drive volume and user acquisition, the structural differences remain central.

As experts said, the likely outcome is not convergence into a single category, but coexistence: regulated exchanges competing with sportsbooks on efficiency, crypto-native platforms selling data to institutions, and hybrid models occupying the space in between.

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