Author: Kunal Doshi, Blockworks Research; Compiler: Felix, PANews

This report analyzes Kalshi's rapid growth and the evolving structure of the sports prediction market and provides a comparative assessment of its pricing, liquidity and valuation against traditional sports betting companies. Furthermore, this article will assess whether these markets offer viable trading opportunities and what is driving their popularity.

Prediction markets have rapidly evolved from a niche product to an important and fundamental financial tool. While its early mass adoption was driven by the 2024 US election, these markets have since expanded into new categories, with sports events becoming one of the largest and fastest-growing segments. The shift comes as the $167 billion U.S. sports betting market continues to grow, but its structure remains disadvantageous to participants due to built-in margins and bookmaker-controlled pricing.

In contrast, prediction markets operate in the form of peer-to-peer trading. Prices reflect probabilities set by participants rather than odds set by bookmakers. The pricing mechanism is more transparent and similar to financial markets. In financial markets, price discovery is driven by information flows and participant behavior.

Kalshi’s growth and market composition

Kalshi has experienced rapid growth over the past year, with total monthly notional trading volume increasing 80-fold from $180 million in early 2025 to $14.4 billion in March 2026. Average monthly transaction volume growth is 40%, reflecting increased user engagement and increased trading activity.

The sports market has been a major driver of this growth, currently accounting for 68% of total transaction volume. Deal volume in sports grew from $540 million in July 2025 to $9.9 billion in March 2026, an 18x increase in three quarters. Kalshi has a solid position in the space, accounting for approximately 70% of sports prediction market trading volume.

At the same time, the composition of transaction volume has also begun to diversify. Sports’ share has declined from a peak of 90% in September 2025 as other categories such as cryptocurrency, entertainment and elections gain traction. This diversification reduces reliance on a single category and helps achieve more stable long-term growth.

In sports transactions, transaction volume is significantly affected by seasonality and is driven by major events. Basketball accounts for 44% of sports volume, followed by football (28%) and tennis (10%). Football drove a sharp increase in deal volume between August and September 2025, surging from $640 million to $2.6 billion, before dialing back after the Super Bowl.

Basketball has topped the list of transactions since October, with the recent NCAA men's basketball tournament generating $1.5 billion in actual volume and $3.3 billion in nominal volume. That’s comparable to the estimated $3.3 billion Americans will bet on during March Madness in 2026, underscoring the growing role of prediction markets.

Pricing accuracy and information efficiency

Prediction markets are designed to reflect true probabilities. To assess this, this article compares the implied probabilities two days before the race with the actual results. In an efficient market, a team with 70% odds should have about a 70% win rate.

Focus on the basketball and NFL markets, which together account for 72% of Kalshi Sports transaction volume. The analysis is based on 3,100 games involving a notional trading volume of $8.5 billion.

Two days before the start of the game, the Kalshi price is a strong indicator for predicting the outcome of the game. On average, the implied probabilities deviated from the actual outcome by 5.5 percentage points. Of the four leagues analyzed, NCAA college football had the highest prediction accuracy.

This shows that pre-match odds are a strong indicator of final results. Kalshi prices are not only reactive but also contain important pre-match information. This accuracy likely reflects the presence of informed and model-driven traders who aggressively price probabilities based on available data. As a result, market prices begin to move closer to the true outcome long before the game begins, reinforcing the role of prediction markets as efficient information aggregation mechanisms.

Liquidity, trading volume distribution and market maker behavior

It is crucial to analyze the distribution of trading volume across sports market cycles. Volume distribution provides insight into when valuable information enters the market, how price discovery evolves, and who is driving moves. For traders, this highlights when markets may be mispriced and when they are effectively priced. For market makers, it provides a basis for setting spreads, managing inventory risk, and choosing when to quote aggressively or passively.

As expected, trading volume during the game accounted for approximately 85% of the total trading volume, as real-time events continuously update each team's probability of winning. About 3% of trading volume occurs in the last 30 minutes, while almost zero trading volume occurs in the last 10 minutes, when the outcome of the game is largely determined. The absence of a late surge in trading volume suggests that most participants will hold their positions until the end of the game rather than actively closing them.

Pre-match trading activities are concentrated in two time periods. Trading volume between 1 and 48 hours before the game accounted for 10.4% of total trading volume, while trading volume in the last hour before the game accounted for 4%. Prior to this, trading activity was minimal at 1.3%. This suggests that approximately 48 hours before the event, the market transitions from a low-liquidity, low-information environment to a more active price discovery phase, when participants with information or who are driven by models begin to take positions. The surge in last-minute volume likely reflects the consolidation of last-minute information such as lineups and news.

Based on this distribution, evaluate how market makers adjust their quotation behavior. In-depth data is available starting on March 25, so the analysis focuses on March Madness games because of the spike in game trades during that period.

The spread was wide at first, but narrowed to around 1.11% about 48 hours before the start of the game and stayed near the 1% floor until the end of the game. This coincides with an increase in trading volume, suggesting that market makers were more active and competitive during this period.

In the 48-hour window before the game, market depth and trading volume increased 19 times in parallel. However, as the game progressed, depth dropped by 76% despite greater trade activity. This suggests that market makers are reducing pending order sizes while maintaining tight spreads, most likely to manage the risk of adverse selection faced when probabilities update rapidly in real-time matches.

The average contract depth per market during the tournament was 163,000 contracts, compared with a pre-tournament peak contract depth of 730,000 contracts. At a typical price of close to $0.50, this means each party has approximately $4,100 of executable liquidity at the time the trade is struck. This is sufficient for small retail trades, but becomes a limitation for larger institutional orders.

For institutional players investing $5,000 or more per position, execution capabilities during the competition are limited as orders either impact price or must be spread across multiple markets. Therefore, for larger traders, the best execution window is 48 hours to 1 hour before the event, when contract depth is more than 4x what it was during the event and spreads remain tight.

Comparison with traditional sports betting and arbitrage dynamics

Forecast market prices must accurately reflect real-time competition conditions in order to attract sophisticated traders and institutional funds. To assess this, this article compares live odds between Kalshi and FanDuel (traditional betting platforms) on two high-volume games.

Michigan vs. Arizona, NCAA Tournament

In the 72 hours leading up to the game, Kalshi and FanDuel predictions were very close, with Arizona's implied win rate on both platforms around 48%. About 18 hours before the game, FanDuel moved Arizona's win rate to nearly 50 percent, and Kalshi did the same about seven hours later. The average pre-match difference was 0.54%, and the pre-match predictions were highly consistent.

After the competition started, prices moved almost simultaneously, with a correlation of 0.9962. This shows that within 5 minutes there is no significant lead or lag between the two platforms. However, there is a persistent pricing gap between the two, with the median difference in play being 2.25%.

This difference mainly stems from the difference in platform structure. FanDuel has a built-in margin (or margin) of about 4.5%, which works out to about 2.25% per side. On the Kalshi platform, contracts trade in 1% increments with a minimum spread of 1%. For the taker in an arbitrage trade, the actual cost of crossing the spread is about 0.5%, making the total trading friction about 2.75%. Therefore, traders must overcome these combined costs to make arbitrage opportunities profitable.

In reality, the price difference only slightly makes up for these costs. The median price difference across matches was 2.59%, with 8 out of 20 five-minute periods (40%) exceeding the cost threshold. While the price differential briefly reached 3.64% in the second half as FanDuel was slower to repricing Michigan's growing lead, those opportunities didn't last long enough to support a solid arbitrage strategy. Additionally, Kalshi’s lack of in-game liquidity further limits the realization of effective scale arbitrage.

It's worth noting that the price differential in the second half was directional, with FanDuel consistently pricing Arizona higher than Kalshi as Michigan extended its lead. This suggests that Kalshi is able to integrate in-game information more quickly during periods of high-momentum play.

Buffalo Bills vs. Denver Broncos, NFL Playoffs

There was no immediate convergence in pre-game pricing. Five days before tipoff, the two platforms' predictions differed by 5.7%, with FanDuel predicting Buffalo's win rate at 53.7% and Kalshi at 48%. Over the next week, both platforms' predictions gradually adjusted, with no one consistently leading. By tipoff, both platforms had Buffalo's win rate predicted to be closer to 48%. The average pregame difference was 1.40%, more than twice the size of similar cases in the NCAAB (National College Football League), indicating a weaker early consensus.

After the match started, prices were closely linked again, with a correlation of 0.9738. The game was an intense game that featured multiple lead changes and wild swings in winning percentage, including a 74% win rate for Buffalo late in overtime, but ultimately the Denver Broncos won the game.

The median difference between games was 1.61%, and the mean was 2.40%. Unlike the NCAAB example, there is no obvious pattern in the direction of the gap in this game, with the two platforms alternately leading and trailing during the game.

At the 2.75% cost threshold, 13 of the 45 five-minute periods (29%) saw spreads large enough to trade. These opportunities are concentrated during periods of severe price volatility, with spreads reaching up to 8.8%, but of shorter duration. Similar to the NCAAB example, insufficient in-game liquidity on the Kalshi platform limits effective transaction size and reduces the practical feasibility of arbitrage.

What does this mean for market participants

Kalshi’s odds align closely with traditional sportsbooks, with both platforms integrating game information in near real-time. The consistency between pre-match and in-play odds is high, although there are still some subtle and persistent differences. This highlights the advantages of Kalshi Markets in aggregating information and generating accurate probabilities.

FanDuel's odds are net of commissions, which are equivalent to approximately 2% of the cost of entry per side. On the Kalshi platform, the handling fee will change according to the contract probability, with the highest absolute value at the probability level of about 50%. For takers, the handling fee is about 3.50%, and for placers, the handling fee is about 0.88%. Since most floor trades occur within the 40% to 60% probability range, actual transaction costs on the Kalshi platform range from 2.8% to 4.2%, which is comparable to, and in some cases even higher than, traditional sports betting platforms, especially for takers.

These higher costs, combined with thinner in-play liquidity, mean that larger, more sophisticated bettors are likely to continue to prefer traditional bookmakers, where pricing is often negotiable and large trades can be executed more efficiently. To become more competitive, Kalshi needs to lower fees and strengthen liquidity incentives, especially during in-play trading periods, when trading activity is high but depth is declining.

However, these limitations may improve over time. Increasing competition from platforms like Polymarket should drive changes to fee structures and liquidity incentives. In the sports betting market, Polymarket’s taker fee is 0.03, which is lower than Kalshi’s 0.07, while Polymarket also offers zero fees and 25% fee rebate for makers. Additionally, Polymarket has announced a $5 million sports liquidity incentive program that will launch in April 2026 and is designed to encourage tighter quotes and deeper order books.

While Kalshi also offers a liquidity incentive plan, its underlying fee structure is still higher. As competition among platforms increases, it should drive fee compression and improve liquidity conditions across the market.

This, in turn, creates a virtuous cycle. Improved liquidity attracts more traders, increased trading volume attracts more market makers, and increased market making activities further deepen liquidity and improve execution quality.

Over time, as market depth improves and execution becomes more efficient, expect more sophisticated capital to participate, including sports-focused hedge funds and reinsurance-like players. Early signs of this trend are emerging, with Kalshi expanding into the reinsurance market through partnerships with the likes of Game Point Capital.

In addition to transactions, the probability data on the Kalshi platform has also begun to show information value. In sports markets, clubs, agents or reinsurers can use market-implied probabilities to hedge contract risks, such as performance bonuses tied to player or team results.

Kalshi’s structural advantages

Although pricing mechanisms are similar to traditional sportsbooks and transaction costs are sometimes higher, Kalshi has been able to expand rapidly because of several structural advantages it has over traditional sportsbooks.

First of all, prediction markets use a peer-to-peer trading model, where traders trade with each other rather than betting against bookmakers with built-in advantages. This structure allows Kalshi to be regulated by the U.S. Commodity Futures Trading Commission (CFTC) as a designated contract market, allowing it to operate in all 50 states. By comparison, with major U.S. sportsbooks like DraftKings and FanDuel only offering their services in select states, and platforms like Pinnacle and Betfair still not available in the U.S., Kalshi has a much wider addressable market.

Secondly, traditional sports betting companies will suppress the capital investment of profitable users by limiting the amount of bets, thereby reducing the large-scale investment of bettors who continue to make profits. These limits are often dynamic, and profitable users may face reduced bets or account restrictions. This creates a structural cap on earnings. Kalshi has no such restrictions and instead benefits from the participation of more sophisticated traders who help improve price accuracy and overall market efficiency.

Thirdly, Kalshi's interface directly presents results in probability rather than odds, making pricing more intuitive and easy to understand. This aligns more closely with the way institutional players assess expected value and risk. In addition, Kalshi supports continuous trading, allowing users to dynamically open and close positions throughout the entire cycle of the competition. While sports betting sites may offer early cash-out features, these features are not universally available and are often limited at critical times.

Despite this, Kalshi's mobility wasn't always consistent throughout the game. Based on communications with market makers, liquidity may drop during critical moments (such as shots or key moves), with some liquidity providers temporarily withdrawing quotes while waiting for settlement. However, users can still close positions during these periods, but execution may experience some slippage.

Valuation Assessment

Given the above, the key question is how Kalshi's valuation compares to traditional sports betting operators.

Use Kalshi's fee calculation formula: 0.07 × number of shares × stock price × (1 − stock price) to estimate the revenue it generates in each market. This formula does not include certain fee-free markets, differences in market maker fees, etc. Additionally, the formula does not include trading volume through partners such as Robinhood, which is subject to revenue sharing.

Based on this, Kalshi had revenue of approximately $263 million last year and is expected to reach $1.3 billion by 2026, growing at an average monthly rate of 24%. About 80 to 90 percent of revenue comes from the sports betting market. Based on its $22 billion valuation from its last funding round, that implies a forward price-to-sales ratio of about 16.9 times in 2026.

In comparison, traditional sports betting operators such as DraftKings (DKNG) and Flutter Entertainment (FLUT) have much higher absolute revenues, expected to reach $6.1 billion and $16.4 billion, respectively, by 2025.

Nonetheless, their market caps are $11 billion and $17.9 billion, respectively, meaning their valuation multiples are significantly lower. Historically, these companies have typically traded at price-to-sales ratios between 2x and 4x.

The valuation gap reflects differences in the market's positioning of Kalshi. While its basic purpose is similar, based on analysis of event results, Kalshi's valuation is closer to that of an exchange than a sportsbook. This is largely due to its peer-to-peer trading structure, regulation by the U.S. CFTC, and its ability to expand into areas such as cryptocurrency, macroeconomics, and political markets. These factors mean a larger addressable market and higher long-term growth potential.

Kalshi's growth prospects further support its premium. While DraftKings and Flutter's revenue continues to grow, growth has slowed significantly in recent years. Kalshi, by contrast, is growing at a staggering pace, with its monthly growth rates exceeding the annual growth rates of these legacy operators.

Valuation differences across the industry also reflect broader market dynamics. Gambling companies tend to trade at lower valuation multiples due to regulatory risks, taxes and margin pressures. By comparison, valuation multiples on exchanges like Coinbase and Robinhood are higher, suggesting investors are valuing Kalshi similarly.

This market positioning is consistent with its market structure, in which participants trade with each other rather than betting against the banker. However, there are some risks. Traditional sportsbooks are actively exploring entering prediction markets, which could erode Kalshi’s early lead. Recent regulatory pushback from these operators may reflect their attempts to delay the arrival of competition while they build their own products.

In addition, it is estimated that a significant portion of Kalshi's trading volume comes from distribution partners such as Robinhood. Control over end users is a key driver of long-term value, and any move by partners to launch competing products could impact Kalshi's growth trajectory.

Summary

Kalshi has become the leading sports prediction market platform, with prices closely tracking real-time dynamics and getting closer to true probabilities before an event begins. This pricing accuracy and information efficiency make it a reliable alternative to traditional sportsbooks.

While higher fees and limited on-market liquidity currently limit trade execution, especially on larger trades, these issues are expected to improve as competition drives fees down and liquidity incentives increase. As market depth increases, higher trading volumes, smaller spreads and more active market-making activities will form a virtuous cycle, further improving market quality.

Sports events are a major driver of trading volume and user engagement, and Kalshi’s exchange-like structure and ability to attract participants has laid a solid foundation for its continued growth. Its valuation reflects this market position. But maintaining this high valuation will depend on its ability to effectively expand its liquidity scale and maintain its role as a core hub in the sports event market.