Unanswered Questions Surrounding Prediction Markets

What prediction markets are and aren’t

Prediction markets are everywhere. After the conclusion of the 2024 US Presidential Election, it was expected that Polymarket and Kalshi’s respective volumes would fall off a cliff as they’d done in years past. But that hasn’t happened, and our goal for today is unpacking why they’ve remained relevant and what can be expected if these volumes continue inching higher over the next 2-3 years.

For reference, Kalshi and Polymarket have processed over $35 billion of volume according to @dunedata since inception, and a combined $17.5 billion of volume in 2025 so far.

This warrants a deeper analysis, considering this is no longer a niche form of speculation for hobbyists or data nerds, but a readily available option for anyone with an internet connection. But even this already measurable success is just the beginning, and depending on who you ask, a drop in the bucket when considering what’s to come.

There’s been an endless stream of surface level content flooding X every day for the past month, giving you lengthy analysis on anything you’d ever want to know about prediction markets and explaining how anyone can make money trading event outcomes with ease.

Rather than feeding you trade ideas or filling your brain with wishful thinking, we’ll give you a complete overview of the top prediction market platforms and how they’ve managed to get to where they are today, analyzing more niche areas of interest like market maker dynamics, distribution of volume and open interest across markets, historical accuracy across multiple venues, and many other foundational topics that will contribute to your better understanding.

At its most basic, a prediction market is a type of financial instrument that allows people to trade contracts tied to the outcomes of real world events, letting traders win if they’re right, or lose if incorrect.
That’s a very surface level explanation, and maybe you’re asking why this is any different from other types of investments, or what the innovation really is here.

People enjoy trading prediction market contracts not solely because of the payouts, but because of what they let you trade. Prior to the introduction of consumer-friendly prediction market interfaces, if you wanted to put your money on an event, an opaque OTC desk or random internet counterparty was the only avenue for placing this bet. Today, it’s easier than ever to read a headline, scoff at the journalist’s opinions, and throw $100 on the exact opposite outcome of whatever you just read.

Robin Hanson is considered by many to be the father of prediction markets, largely due to his robust body of work covering futarchy, idea futures, decision markets, and other related topics. Much of his work can be found here. Despite these illuminating pieces of writing existing, many of which date back to the early 1990s, prediction markets were confined to a small corner of academics and nerds, existing as more of a pipe dream than a feasible financial instrument.

It took years but some unlikely names laid the groundwork for Polymarket, Kalshi, and every other company alive today. To name a few notable experiments courtesy of Adjacent Research:

• Terrorism Futures were created by the Pentagon back in 2005

• Google ran their own internal prediction markets

• The CIA published a report analyzing prediction markets’ ability to enhance US intelligence PDF

It’s 2025, and prediction markets are a runaway success not because of what they are, but what they’ve been able to enable, and of course what could mean for global market structure.

What you can learn

Most of what you’ve heard about prediction markets is true, but there hasn’t been an honest attempt at mapping out the most important functions crucial to the continued success of these platforms. This report will answer the following and more:

• How do market makers and liquidity providers operate across Polymarket and Kalshi?

• What are these exchanges’ reward/incentive structures like?

• How much of a concern is market manipulation and what’s being done to mitigate this risk?

• Which markets are more likely to receive a higher distribution of incentives?

• Why haven’t parlays or leverage seen more traction?

• Can prediction markets predict the future more accurately than the best experts?

• What aspects of dispute resolution need to be fixed?

• How important is the CFTC and is a regulatory-first approach the path to mass adoption?

• Is it feasible to compete directly with sportsbooks on sports-related event markets?

• Why have dozens of crypto-native prediction market startups spawned, seemingly out of nowhere?

We’re taking a data-driven approach and pulling together dozens of hours of research to bring you the most comprehensive report on prediction markets to-date.

Before going any further, we must clarify some things.

This report primarily looks at usage across Polymarket and Kalshi as they are the most widely used venues. This is not to say that incumbents can’t be overtaken in the future, but rather their combined usage sums up the vast majority of prediction market activity >99% of notional volume & open interest, and analyzing their differences will shine the most light on the future of the space.

We aren’t here to tell you that prediction markets are or aren’t the next big thing. We’ll make use of dozens of sources across numerous independent publications and reports, use quotes liberally, and present the information to you while refusing to take a side.

It’s important for research to be unbiased and walk a tightrope between obvious favoritism and the objective truth. The truth?All of the above questions are a lot harder to answer and can’t be found just by looking at the data.

• Are prediction markets the next frontier for finance?

• Are prediction markets just a more obfuscated form of gambling?

• Are prediction markets only a side effect of humanity’s growing desire to speculate in new ways?

How prediction markets fit into a culture of speculation

Everyone likes to gamble. Whether it’s sports, scratch-off tickets, or slot machines, the global gambling market is massive and has continued to grow despite the reality that the house always wins. Since the legalization of sports betting in the United States in 2018, over $500 billion of volume has been done according to Legal Sports Report , generating tens of billions of dollars in revenue for states in the process.

You can’t watch a professional sports game without being shown moneylines, player prop bets, or betting-related predictions from TV analysts. Commercials frequently bombard you with propaganda distributed by these sportsbooks (namely FanDuel and DraftKings) directly, masking the toxic reality of gambling with endorsements from household names like LeBron James.

New York and New Jersey alone have made over $11 billion in tax revenue and the annual sum has grown exponentially in just seven short years. It’s unlikely sports betting is criminalized again given how lucrative it is to these states that allow it, though the negative externalities of widespread betting have not been considered.

We can make an assumption that gambling has more or less become an everyday occurrence for tens of millions of Americans over 30 million in 2023, presenting us with a world where gambling isn’t just allowed, but normalized.

Sports betting is the best comparison we have for prediction markets, and while it isn’t perfect, it will be used to present how and why prediction markets have become more popular. While prediction markets are structured with payouts resembling binary options, and sports betting is odds-based, the venn diagram of users converges and the thought process behind taking either of these bets can be viewed quite similarly.

If a devout Republican had read a headline in October 2020 that Donald Trump’s probability of winning the election sat at 40%, they may have previously scoffed and complained that mainstream media is out of touch with their personal worldview.

At this time - just five years ago - the aforementioned individual was more than likely unaware of prediction markets and wouldn’t have known they could stake their money on something as important as the US Presidential Election, but we live in a different world today.

In late 2024, this same individual could have placed $100 on Donald Trump shares at $0.40 and hoped to return 2.5x their money assuming a successful win in the election, or watched their money evaporate as Kamala Harris shares settled to $1.00, but the outcome isn’t the point.

The core innovation of prediction markets is their ability to turn human beliefs into bets, letting anyone put real dollars on an opinion they believe is correct. This is human nature and it’s one of the reasons sports betting has risen to notoriety so quickly. Sports is a major part of many Americans’ lives, especially young men, which by and large are making up a larger share of sports betting volumes every year .

Quick aside, I can name dozens of friends and fraternity brothers from college that discuss sports betting constantly and place bets every day of the week that doesn’t end in a Y. This doesn’t come as a means of shaming anyone, but an observation from a 23 year old noticing a trend amongst peers.

Young men live and breathe sports, and it isn’t a stretch to say professional sports is quite high up on these individuals’ lists of hobbies or pastimes. If you spend countless hours outside of work tracking pro sports developments, watching games, and arguing with friends over the outcomes, it’s just as likely for you to then feel the need to make money off of this.

This same individual that feels strongly about the Philadelphia Eagles might feel equally as strong on whether Jerome Powell will cut rates, but lack the knowledge or a simple outlet to precisely bet on this via other financial instruments. The average dude would never message a sports group chat asking what everyone thought about the jobs report, but this same guy might actively chat it up on Polymarket’s in-market chat rooms.

The logic of making money from something you feel you have an edge in can be extended from sports to prediction markets, except for one small detail: the TAM is nearly infinite.

Whether it’s politics, global events, or pop culture, there is a market for something on Polymarket, and there’s a market for every type of person.

Leading avenues for speculation

Crypto hasn’t been discussed yet, so let’s get into that, as it’s a pretty popular technology and quite notorious in discussions related to speculation.

Buying crypto is easier now than it’s ever been. For years you could have purchased Bitcoin (and other major assets) directly from Venmo, Robinhood, or even in the form of an ETF through your brokerage account, and while getting crypto on-chain is still a bit messy, it’s within reach in probably under ten clicks for someone that knows what they’re doing. This has led to significant growth of digital assets trading, proliferation of stablecoins, and the obvious eye-watering performance of assets like BTC and ETH over the last decade.

Across crypto’s largest exchanges, trillions of dollars of volume is done each month across a number of centralized and decentralized venues. The market for digital assets continues to resemble more sophisticated markets like equities or commodities, while the pool of traders and blockchain users only grows. We can make an argument that buying crypto, investing in crypto, or trading crypto on a shorter time frame is relatively easy and within reach for anyone with an internet connection, and that despite the utility accompanying many crypto assets, this is still an outlet for/form of speculation.

Going back to the point that prediction markets will become the next major avenue of speculation - similar to sportsbetting, 0DTE options, or memecoins - it’s easiest to compare the numerous frictions of retail user participation.

Signing up for a DraftKings or FanDuel account is quite easy and deposits can even be made directly through money transfer apps like Venmo or Cash App, making the temptation to just “place a small bet” all the more easy for tens of millions of Americans. We’ll discuss actual usage of these services quite soon, but DraftKings specifically has grown its annual revenue at a rate of 67% per year since 2020, with recent Q2 seeing over $1.5 billion in revenue. This growth also comes on the back of societal shifts, most notably the recent outflows of Las Vegas’ residents, businesses, and most importantly, its gamblers.

Robinhood has democratized access to stocks, crypto, and most importantly, options. You’ve probably heard of /r/wallstreetbets their absurd userbase, and the ridiculous amounts of money spent by retail traders on options contracts, specifically 0DTEs usually tied to memestocks.

But were you aware the actual volumes generated by retail option traders were quite high, with retail’s share of option trading peaking at roughly 48% via NYSE in 2023? That’s a ridiculous number, especially considering how complex the underlying mechanisms of a standard option contract can be, it’s a miracle so many willingly trade these.

Memecoin trading on Solana is still hot and, at one point, was one of the most lucrative ways to hit a 100-250x for relatively little effort (assuming you managed to hit one at all). Venues like Axiom and pump.fun still bring in millions of revenue each month, though participation has tapered off a bit since their peak.

Despite the drop-off, it’s undeniable that memecoins represent a more hands-off approach to financial freedom for those with high risk tolerance and a desire to make it as fast as possible. More than anything, memecoins were the first example of retail traders getting heavily involved on-chain, with social media platforms like TikTok showcasing the hundreds of thousands of newfound traders willing to burn money in search of a gem.

What do these three sources of speculation all have in common? The venues used to enable speculation allowed for almost anyone to access a new form of trading or gambling without needing to spend hours learning how it's done.

Negative externalities of prediction markets

PMs are not perfect, in fact they already have negative externalities. Similar to traditional gambling, there’s a certain ‘hook’ on prediction markets. And even more so than trad casinos because now PM users might feel like they have an ‘edge’ over some markets. Furthermore, participants are also able to directly influence some markets, opens an interesting pandora box on influencing these markets. On the softer side we have people throwing dildos at WNBA games and the guy who famously stroke the superbowl, got fined but made around ~374k on his 50k bet.

On the more obscure side of this Pandora box, every market involving actions of a certain person are de-facto assasination markets. Take this Charlie Kirk case as a good example. The assassin could theoretically bet on this market, and then go on and kill Charlie Kirk to collect a payout. This is why ‘permissionless’ prediction markets are so hard, they essentially enable an economy in which anyone can permissionlessly hire a hitman without very little repercussions.

The psychology of prediction markets

Prediction markets are still in their infancy, and more work is needed before everyone can be onboarded, but their biggest underlying strength is the lack of prior knowledge or education needed by their user base to immediately participate. This could be attributable to the broad subject matter available to trade, the ease of selling a position prior to resolution, or even the more streamlined probabilities distinct from clunky sports betting odds - but the reality is it’s not that difficult to understand this financial instrument.

Human nature is wanting to be right and proving that someone else is wrong in the process. Whether it’s arguing with a friend or coworker over something trivial, like debating the winner of the Monday Night Football game or as crucial as fighting for your client’s innocence in a court of law, many important human interactions take the form of a binary option.

I’m right, you’re wrong. You win, I lose. Double or nothing.

Part of what’s propelled prediction markets into the public zeitgeist is this idea that everyone likes being right, and everyone likes making money - a prediction market venue like Polymarket or Kalshi is able to satiate this innate human desire to be right and provide the tooling to enable mass speculation on previously un-investable ideas, beliefs, or even strategies. Maybe part of what makes prediction markets so appealing is the simplicity of it, or how it connects with your brain more than stock prices or Option Greeks do. Being able to launch Polymarket and bet whether earnings miss by 5% or 10% is not only simpler, it’s more intuitive.

And that’s where we are today. Prediction market odds have been displayed on national television programs like CNBC and CNN, sports betting is commonplace, and prediction market growth has continued even after the 2024 US Presidential Election, atypical of the volume drop seen in years prior.

It’s table stakes for an analyst to argue for continued growth in volume or DAUs but how do you underwrite mass speculation occurring at an unprecedented scale, the birth of everything markets, and the potential formation of the 21st century’s newest financial behemoths?

Let’s temper expectations with some much-needed criticism.

Reducible Errors has an excellent post full of necessary rebuttals, quantifying what it means for a market to be good and the set of characteristics a market must possess to attain this title, these being standardized products, heterogenous participants, low transaction costs, and many participants.

RE does a great job highlighting many of the most glaring issues surrounding prediction markets and explaining why these products won’t see institutional adoption anytime soon, but the post fails to recognize many of the more qualitative benefits.

This idea that prediction markets and their binary nature make for a poor hedging instrument is definitely true, though only if we’re assuming a user base consisting entirely of financial professionals and elite market makers.

Prediction markets exist in some type of superposition.

On one side you have a ravenous horde on X calling prediction markets the natural next step for all financial markets, while the other side - composed of professional sports sharps - remains convinced it’s just another form of gambling, albeit a more sophisticated version.

“ES trades so much because people use it to hedge. They're transferring risk they don't want, and they're perfectly willing to pay a (small) price for the service the market provides. It's this activity that gives rise to all the trading we see in financial markets.”

It’s true that a financial institution would much rather hedge with S&P 500 futures than some prediction market with less than $5 million in volume, the same can’t be said for a pair of college roommates living in a dorm trading their $50,000 portfolio of crypto and meme stocks.

Of course these two college roommates might know futures very well, and understand how opening and managing a position works, but it’s a lot easier to deposit $5,000 onto Polymarket and open a hedge without the hassle, especially considering how much simpler a prediction markets platform is compared to the average experience that comes with trading futures.

Saying that prediction markets in their current state are unfit for institutions is obvious, but suggesting a list of things that need fixing to accommodate for this is non-obvious, explaining why you’re much more likely to read a glowing or scathing review of prediction markets.

Aashish Reddy wrote that even if prediction markets might be comparatively worse next to other financial products, “other domains present informational needs that aren’t satisfied simply as a spillover from some other system” and their proliferation has been a clear signal the market values this addition, but hasn’t agreed upon what its final form will look like.

As you’re reading, keep these questions top of mind:

• What needs changed? (this is intentionally vague)

• How do these venues scale to billions in monthly volume? (much of the liquidity discussion will hinge around the feasibility of this)

• Are there any glaring barriers to mass adoption that haven’t been illuminated? (the final section will shine a light on some of the non-consensus views we possess)

Enjoy the report.

Dissecting order books and difficulties in market making

Liquidity is central to any market, and one of the most important topics of this report will be the discussion of market making or liquidity provision across major prediction market platforms, beginning with a look at Polymarket.

It’s stated plainly in the documentation, but every pair of event outcomes on Polymarket is fully collateralized by $1 of USDC on Polymarket, with an on-chain attestation occurring when two sides come to an agreement on the odds. For your information, there are multiple different types of markets you will find on a given prediction market platform.

Baheet dissected the typology of prediction markets in a recent X Article, explaining that the function of a platform depends on its specifications of five different categories: liquidity model, outcome resolution, market types, event creation mechanism, and underlying infrastructure, all of which will be discussed in detail. For now, we’re most interested in his definition of market types as it is foundational information.

The three types of markets you’ll find across platforms are binary markets, multi-outcome markets, and scalar markets.

Basic market making and market formation scenario

A basic example of an individual market seen on Polymarket could be two parties with opposing views on the winner of the 2026 NBA Finals, where the Miami Heat and Golden State Warriors are priced at $0.50 respectively, indicating each of these teams has a 50% chance of winning.

Putting aside the fact this matchup would be highly unlikely, the reality across most prediction markets is that a clear favorite could be priced at $0.47, a runner-up at $0.46, and numerous other candidates priced around $0.01 due to a much lower probability of winning. For the sake of simplicity, we’ll say this market was created a week prior to Game 1 of the NBA Finals, meaning only these two teams are in contention.

Polymarket used to offer AMM-based liquidity, meaning anyone that wanted to provide liquidity could deposit their USDC into the AMM and back both sides of the market, in this case Miami and Golden State outcome tokens. For context, an AMM is a type of decentralized exchange (DEX) design where smart contracts on a blockchain network manage bids and asks instead of humans directly placing these in an orderbook. AMMs have done wonders for crypto, but Polymarket’s brief stint is emblematic of the shortcomings this technology still faces.

Polymarket grew out of this model as LPs were essentially long on both sides of the market at equal portions, exposing them to inventory risk and led to market making being significantly more frustrating than it should be. Since then they’ve shifted to an order book model, the same type used by Kalshi and countless other exchanges.

A market is formed from an opinion and liquidity comes in afterwards, and regardless of how the liquidity is structured, this is the core foundation that brings event markets to life.

Liquidity must first be seeded by market makers who set the odds themselves. Let’s say a market maker was first and set an order to sell 1,000 Miami YES shares at $0.50 and buy 1,000 Miami YES shares at $0.48. This would be the first liquidity put forth towards the NBA Finals outcome, which a user can then fill. If they decided to fill the ask of our market maker and submit an order for 500 Miami YES shares, they’d effectively become long Miami with 500 shares and our market maker would now be short Miami with 500 shares.

Suppose another market maker comes in and wants to compete, this time submitting a buy order for 1,000 Miami YES shares at $0.49 and a sell order for 1,000 Miami YES shares at $0.51. Our orderbook would now look a bit tighter as additional liquidity has been added, meaning users could purchase another 500 shares from the original market maker at $0.50 before needing to pay $0.51 for the 1,000 shares of the second market maker.

From here, a prediction market operates like any other financial product, in the sense that more buys of the underlying moves the price up, pushing the other asset’s price down in the process. Depending on the outcome and how your inventory, you can either profit a little, profit more, lose a little, or lose it all. If Miami won the championship, anyone holding those shares would see their value increase to $1 each, yet if Miami lost, those shares would trade at $0.

This is the most simple explanation of liquidity provision that will be present in this report, so if you’re still a bit lost, you can read more within Polymarket’s documentation before continuing.

Kalshi’s market creation system is mostly similar as far as market making goes, except the collateralization side does not make use of USDC and is not conducted on blockchain rails.

For a market to be created on Kalshi, it must be launched by the team directly, a creator with exchange approval, or suggested by users for eventual review. Polymarket events can have mutually exclusive outcomes, of which these are all bundled together and collateralized to where $1 covers the entire set. This could be $1 encompassing the odds of five different teams to win the NBA Finals, or five different candidates to win an election.

On Kalshi, each outcome is its own binary (YES or NO) contract, and a market is created by bundling all of these binary contracts together and simply presenting it on the user interface as a single market. This doesn’t affect the end user as the 2028 US Presidential Election markets on Polymarket or Kalshi are displayed the same way, but under the hood, it is very different with a few major distinctions. A multi-outcome market on Kalshi is still functionally the same as a multi-outcome market on Polymarket, just composed under a different set of circumstances.

Because each market on Polymarket is fully collateralized by $1 of USDC, it follows that probabilities must always sum to 100%. Kalshi differs here, as when these binary contracts are stitched together, the probabilities might not sum up to a neat 100%, leading to arbitrage opportunities. This isn’t to say Kalshi’s markets aren’t fully collateralized - they are - but rather they are made up differently from Polymarket, and these structural differences contribute to various strategies used exclusively on Polymarket or exclusively on Kalshi.

Despite these examples making it simple, market making and liquidity provision on these platforms is much more complex and not nearly as black and white as we’ve described it so far.

Some metrics

Before analyzing market making dynamics, let’s observe more general metrics like volume, open interest, transaction count (where applicable), and DAUs to paint a picture of where these platforms are in the race for more users.

Of their $34 billion in volume inception, ~79% of this comes from Polymarket and ~21% from Kalshi. There hasn’t been a visible struggle between the two to attract market makers or retain liquidity, though as we’ll cover shortly, some of Kalshi’s recent tactics have put pressure on Polymarket’s once sizable lead in market share. More recently, Kalshi has seen its market share looking at weekly notional volume via Dune increase from 8% in January 2025 to just over 62% in September 2025.

Polymarket’s user count is visible due to it being a blockchain-based application, while Kalshi is a centralized application, meaning transaction count is a better metric for getting to the bottom of actual usage. Polymarket is sitting at 61 million cumulative transactions since inception compared to Kalshi’s 30 million, according to Dune. Polymarket has 2x the amount of transactions than Kalshi due to Polymarket having been around longer, while Kalshi has only entered the discussion more seriously relatively recently.

This isn’t discounting Kalshi, as we must note despite Kalshi lagging in cumulative transaction count, their recent growth has far outpaced Polymarket.

Going back three months (to June 1st 2025), Polymarket has averaged around 147,000 daily transactions, while Kalshi has seen an average of 142,000 - an improvement from their previous 2024 average of 24,000.

But if we look at more recent data and shift our focus to September exclusively, Kalshi has averaged 260,000 transactions through the first eighteen days of the month - Polymarket with 161,000 daily transactions - largely attributable to their prioritization of sports-related markets and growing sports-first mindset.

We’ve discussed that sports betting is a massive industry and is only continuing to grow, but it’s becoming apparent that this growth needs to be examined in light of Kalshi’s massive push into sportsbook territory and the implications this has for these two industries.

DraftKings is one of the largest online sportsbooks in the United States, and just happens to be a publicly traded company, giving us insight into their historical revenue.

All of this data was pulled from DraftKings’ investor relations pages , meaning we were unable to access more recent data since the start of the 2025-2026 NFL season, as this won’t be public until Q3. Regardless, the numbers show a potential trajectory or more realistic growth model for Kalshi, should it continue encroaching on traditional sportsbooks’ volume and users.





Regarding the encroaching, Kalshi has been quite active, with Adhi Rajaprabhakaran reporting that Kalshi submitted a new filing to the CFTC over a month ago on August 18th, requesting explicit permission to list player props and point spreads, a step beyond more traditional offerings of moneylines or futures contracts.

Kalshi’s hoop dreams

It’s easy to assume Kalshi is quite aware of the growing dominance of parlays and prop bets amongst sportsbooks, making a filing like this a no-brainer, and with roughly 80% or more of Kalshi’s weekly volume coming from sports alone, they are doing quite well. Here’s a screenshot of the September 18th matchup between the Miami Dolphins and the Buffalo Bills, showing live player props:

One of the more underrated benefits of using Kalshi as opposed to another sportsbook is the lack of any cashout penalties or fees. Closing out of a position on DraftKings might lose you 25-30% of your bet, especially if the game has already started. Cashing out on Kalshi depends on other factors like liquidity, current prices, and potential short term swings, but is significantly more frictionless than any alternatives.

We could write pages and pages regarding Kalshi’s push across the US to further sink its teeth into sports betting, but the decision to do so is best understood through this anecdote in the same post referenced above:

It’s common knowledge that if someone becomes too good at sports betting, they’re likely to get kicked off of the platform. This has led to the creation of many different workarounds over the years - quite an interesting rabbit hole - but is best highlighted by the counter to this extractive model: Kalshi and Polymarket have no incentive to ban their top traders, as they don’t operate as “the house.”

This is a rabbit hole in and of itself, considering a recent comment made by Kalshi’s Head of Corporate Development regarding their internal market making team (known as Kalshi Trading), particularly that “Market makers, including KT, compete for flow in an open, transparent and fair marketplace, and set their own prices as to what they are willing to pay for a contract, but are not able to dictate the broader market beyond that.”

It isn’t our place to confirm or deny whether KT or any other market makers receive preferential treatment, as this type of data isn’t accessible, but this conflict of interest has been brought to attention by many and its implications are best summarized here:

It could be assumed that sharps would slowly gravitate towards Kalshi instead of risking their livelihood on traditional sportsbooks, especially if market makers are receiving more favorable rebates or kickbacks in any form.

But has this already begun to happen? It’s tough to say.

Dustin Gouker of The Closing Line posted a weekend data dump on September 8th, highlighting Kalshi’s $300 million of volume, with a ridiculous 96% of that volume coming from sports, and 84% of that from football (NFL) alone. That isn’t just impressive for a prediction markets venue, but arguably on par with mid-tier sportsbooks.

This is not to say that Kalshi is immediately in contention with a name like DraftKings or FanDuel, or that all of these incumbents’ customers will move to Kalshi, but it’s hard to ignore Kalshi’s more favorable regulatory positioning with the CFTC which lets them market their product in all fifty states.

Considering sports betting is not legal in all fifty states - and the states that offer these services see a significant amount of annual revenue from this - it’s not hard to imagine why seven pro-gambling states have issued cease-and-desist orders to Kalshi in recent weeks. Benjamin Sturisky explained how Kalshi is able to skirt regulators and offer their services without repercussion:

Kalshi is moving fast and breaking things.

Another angle here is whether or not analysts could use Kalshi activity as a proxy for predicting DraftKings or FanDuel’s quarterly earnings. This is more than likely an original idea that you can only get on Valis Research. But we aren’t exaggerating, and believe there is some truth to this whacky proposal.

Incentivizing more liquidity inch-by-inch

We discussed that Polymarket and Kalshi have different strategies of seeding liquidity across new markets, but these platforms also differ in how market maker presence is maintained throughout a market’s life cycle, and their incentive structures are most emblematic of this.

We can examine the most basic level of competition through available data and take a closer look at liquidity incentives and targeted incentive distribution as these are the two most relevant drivers for incentivizing volume on both Kalshi and Polymarket.

Regardless of the market, there must be an incentive to participate, whether you are a market maker, a retail trader, or an institution taking a position on something. As discussed, market making zero-sum prediction markets is a difficult game and has frequently been brought up as the most important bridge to cross prior to the introduction of more users, more institutional trading activity, and of course, more volume.

Polymarket may have recently created this liquidity incentives dashboard, but it stands out as a nice initiative to draw in more amateur market makers with specific descriptions around what’s required of them and what’s exchanged in return.

The dashboard lists max allowable spread, USDC reward, the competitiveness of each individual market, and the existing prices. This is a good start and it’s clear that the markets being incentivized are clearly going to be hot at some point, but struggling to get off the floor and all trading with very low liquidity. From a very high level, here’s a basic description of how Polymarket determines liquidity rewards:

• Closer to average price = more earnings

• Reward amount depends on how “helpful” your orders are to others, in terms of size and pricing

• More competitive the limit orders are, more you can make

• Paid daily based on how much your orders add to the market

The actual formulas of these platforms will be covered very soon, but keep in mind it isn’t possible to track Kalshi’s market incentivization, though we can potentially extrapolate and come up with a mental model of what markets would be most incentivized if we ran Kalshi.

Looking at Polymarket’s developer docs, there’s a list of variables and their accompanying function - most interesting being the b-value and its role as an in-game multiplier.

We know that liquidity rewards can be juiced by either of these platforms, but it’s tough to say when this might be happening and what markets it’s occurring in. Despite this information gap, we can observe these platforms’ open interest metrics and come to a conclusion that neither has seriously juiced rewards enough to ice out their competitor.

As of September 17th, Polymarket and Kalshi had over $370 million of open interest across all of their markets, a sharp dropoff from the Q4 2024 peak of over $1 billion combined open interest, yet visibly stronger since the start of this year:

It should be noted that despite open interest down over 60%, this hasn’t stopped volume from approaching all-time highs: how does that work? In the case of the US Presidential Election, many were simply holding positions in Trump or Harris shares, rather than trading more short term markets back and forth. Volume was obviously high as well, but we’re seeing a different dynamic now where more markets are being traded and dispersion is common given nothing can compete with the significance of a presidential election outcome.

This distribution of open interest and consistent lead held by political markets can be explained by the obviously lofty implications tied to US elections, but also due to how widespread politically charged content is on social media like X, a platform that Polymarket and Kalshi frequently make use of for advertisements and news releases.

There’s an additional case to be made that political prediction markets are comparatively easier to market make due to the vast amounts of data available online, though this can’t be answered without speaking to market makers directly.

Not all liquidity is equal, and just because a platform has over $100 million of open interest, does not mean this platform has uniform liquidity, though this is more obvious with an example.

The 2024 US Presidential Election was - across all platforms - the most liquid and largest volume driver and has remained the peak of prediction market activity. At one time, there was over $150 million of open interest on this one market, representing a >50% market share considering Polymarket’s $300 million of OI as of October 29th last year.

Here’s what Polymarket’s open interest by category looks like today, referencing the dashboard created by @sealaunch_:

We see open interest is spread out across categories like crypto (neon blue), other (gray), business (light purple), sports (dark blue), and politics (red), but this massive skew towards a single market is not the norm. Even with politics maintaining a roughly 50% share of Polymarket’s open interest, there’s a healthy distribution amongst categories that aren’t sports.

How might these platforms go about incentivizing liquidity for non-political markets, or categories that are potentially more unpredictable and long-tail by nature?

Solutions to solving liquidity and why they’re necessary

Yoshi wrote a short article that offers up five unique ways of solving the liquidity problem of prediction markets, and the most interesting part is that each of these ideas targets a different side of the prediction market space. There’s a proposal to onboard more futures & options (F&O) market makers, a proposal to introduce a bonding curve similar to pump dot fun’s seeding of memecoin liquidity for long-tail prediction markets, and other basic proposals that seem pretty realistic and achievable, even in the short term.

You might be wondering why this is even a discussion: if the mechanisms for seeding liquidity or improving the distribution of liquidity are so obvious, why isn’t it being done?

This is a great question but it fails to recognize the state that Polymarket and Kalshi find themselves in. Remember how over 90% of Kalshi’s September 2025 volume has originated from sports? If you’re a platform that’s seen a significant increase in volume from a specific category, maybe you’re more inclined to incentivize this in the future.

Dustin Gouker posted on X that Kalshi is hiring two sports positions, one requiring “Extensive experience with sportsbooks – understanding how sportsbooks operate, common market types, settlement procedures, and the edge cases that arise in sports market operations” giving more credibility this is a direction they’re going in.

After all, if sports betting has done over $500 billion of volume since its legalization in the US and remains a go-to hobby for young men, why not go head-to-head with some of the largest sportsbooks out there?

You need market makers to not only feel compensated for their work - very difficult work considering these are zero-sum payout structures - but compensated to a degree they are unwilling to migrate their operations to another platform or competitor. If you think about it from a risk/reward perspective, incentives should be quite high.

Simply offering more incentives than the other guy is one way of maintaining liquidity, but keeping them around given the risks involved is more important, especially considering this undeniable reality of adverse selection.

A necessary counter to this comes from the fact that sports books are a market maker’s wet dream, given the allure of uninformed retail flow coupled with more predatory incentive structures present on sports books. It would be foolish to assume market makers wouldn’t accept a bit of inventory risk in exchange for massively uninformed retail flow (and favorable fee structures or rebates) on a platform like Polymarket or Kalshi.

Yoshi’s suggestion to onboard more F&O market makers is probably the clearest solution in the short term for sticky liquidity, due to the similarities in payout structure, but it’s also necessary considering the risk a non-professional liquidity provider might face in any given prediction market. With these MMs having a history of managing inventory risk and the skill it takes to accurately model volatility in a new market, their presence would potentially lead to tighter spreads but a level of sophistication that is yet to become widespread across these platforms./p>

This makes sense on paper but it’s worth noting that even if these MMs entered prediction market trading with the knowledge of other sectors, this wouldn’t translate 1:1 given the difficulties in hedging binary prediction market outcomes. F&O market makers can make use of countless strategies, tools, and feats of financial engineering to hedge anything, but doing this in the existing prediction markets space is quite challenging or nearly impossible.

Among the other liquidity proposals, Yoshi noted how bonding curves - made popular by pump.fun and their memecoin liquidity bootstrapping model - are a potential route for any platform looking to incentivize long-tail liquidity specifically. We noted how Polymarket began with an AMM model for market bootstrapping, before shifting to a primarily orderbook-based system: what if a subset of Polymarket’s more niche or risky market creation utilized a bonding curve like pump.fun?

This is an understandable solution given the obvious market making risk for any long-tail event (see: will aliens visit Earth by 2030?) but also benefits the platform as a whole, should more speculative-minded individuals wish to see larger potential payouts on Polymarket.

A really good example of this is the below market, in which approximately $241 were used to push the price of the contracts of this event from ~0.4 to ~0.9. Pretty interesting. For the unaware, refer to this tweet from the GTE team regarding their departure from MegaETH.

A smaller proposal offered was the potential for resolution-based incentives, or market maker incentives that only distribute (or vest) after the resolution of a given market. This would disincentivize wash trading and allow for a platform to incentivize early liquidity (via scaling reward distribution asymmetrically towards the start of a market), but would require manual oversight when it comes to which markets are best fit for this.

On the topic of wash trading or minimizing market maker shenanigans, Kalshi somewhat recently introduced a new incentive structure meant to encourage deeper, more consistent liquidity across all of its markets, a step in the right direction for solving this liquidity problem.

Kalshi’s new plan for liquidity defines three parameters, set in place for each market they designate as eligible. These are target size, discount factor, and time-period reward.

Using an example from Adhi, an aspiring market maker might offer up an order for 1,000 contracts on a market with a discount factor of 0.9 and a reward pool of $100.

These 1,000 contracts might be split between three separate price points (best bid, one cent away, two cents away) representing three unique payouts structures for this order. The math is pretty simple on paper, with a score calculated by multiplying the size by the discount factor taken to the power of distance from the best bid.

This simple calculation measures the reward rate for Kalshi’s market makers, explicitly prioritizing tighter liquidity and mitigating mercenary market makers by running a second-by-second snapshot.

It’s understandable that Kalshi would want to reward stickier liquidity, but what’s important is this revision makes it difficult to ascertain what systems are or aren’t in place at Kalshi, and their centralized nature makes it nearly impossible to get a glimpse into what’s going on behind the scenes.

The rumors of zero fees or rebates for select market makers has circulated more frequently as of late, though exact details of this are uncertain. SIG (Susquehanna International Group) was the first institutional market maker onboarded to Kalshi (in April 2024) and we aren’t here to debate the business acumen of these teams, but it would be ridiculous to assume SIG didn’t agree to this without some form of preferential treatment. If this sounds exclamatory, just reference all of the replies to this post as a sentiment check-in.

Even though as we know frontrunning is a big problem in this type of market, Polymarket and Kalshi both heavily incentivize market makers to provide two-sided liquidity, heavily penalizing those who don’t follow this rule.

Kalshi incentivizes market makers to provide two sided liquidity by soft slashing those who don’t quote one side. If a market maker decides to quote only on one side, the other side of the equation would be essentially zero’d out, giving a big incentive to provide liquidity on both sides of the orderbook. According to Kalshi’s docs market makers are only able to earn liquidity between $0.03 and $0.97. This makes sense as it disincentivizes gaming the market towards extreme prices.

Polymarket is even harsher at penalizing one sided liquidity. If you look at the equations below, providing one sided liquidity means that you’ll essentially get whatever points you accrued divided by 0.3333. Furthermore, you can only provide one sided liquidity when the contracts are trading between [0.1, 0.9] if you want to earn points outside that range, you have to provide two sided liquidity.

Conclusion of this is that Polymarket is much harsher at penalizing one sided liquidity, which is interesting because at the same time its platform is much more prone to insider trading because of lack of KYC. AKA, they should be more lenient with one sided liquidity provisioners as the chances of getting front runned by insiders are significantly higher on Polymarket vs Kalshi.

This is a shift for Kalshi, as their previous model consisted of fee rebates to incentivize market makers, yet this filing (PDF, via the CFTC portal) explains how the incentive system’s restructuring priorities volume and liquidity.

This document also confirms some type of axing of the previous rebate program but doesn’t give details, only stating: “For the avoidance of doubt, Kalshi hereby notifies the CFTC that Kalshi’s volume incentive program filed in October 2024, and Kalshi’s fee rebate program filed in January 2025, did not go into effect and are hereby terminated.”

Liquidity incentivization and the multiplier

The in-game multiplier appears to have been discontinued for a more discrete liquidity incentives program. AKA round numbers, probably to simplify liquidity provisioning for market makers. Only 32.3% of active markets have liquidity incentives.

Polymarket is spending approximately ~5m on their liquidity incentives program. Around 90.5% of markets being incentivized have 5 dollars of less a day in incentives. On the sportsbook, most markets have close to zero liquidity incentives (The median is 1).

However, some markets related to teams with high likelihood of making the superbowl final are being heavily incentivized with around $400 in liquidity/day. This indicates that Polymarket isn’t really trying to ‘vamp’ or compete with Kalshi for all sportsbook plays but only for the ones with the deepest liquidity (Super Bowl final). Probably because of regulations, Kalshi cannot really offer ‘Will X beat EPS this quarter’ because it’s too similar to a financial product. However, this is a pretty interesting market for Polymarket and is one they’re incentivizing pretty heavily in liquidity.

These markets open up close to the expiry date and sometimes can attract significant volume. COST is the most incentivized market right now. 69.5% of the markets with $100 or more in liquidity incentives per day are related to politics, it seems like Polymarket is more interested in competing for culturally relevant/politics rather than sports, which is the game that Kalshi is playing.

According to Kalshi’s API, currently 35 markets are being offered liquidity incentives, with all but two of these being sports markets. Kalshi has spent $8.75 million since inception on incentives and is averaging about $35,000 per day, nearly 2x the average daily spend of Polymarket. Quick aside, this site lists 37 active markets being incentivized, though overlap is significant so take both with a grain of salt.

Fortunately, we can more easily determine what Polymarket is doing.

There was a brief phase at the beginning of 2024 where Polymarket was paying out $1.7 per $100 of volume to its market makers highlighted by @defiance_cr here before returning to a more reasonable $0.025 per $100. It would be reasonable to assume this significantly boosted the platform’s volumes, as everyone appreciates a bit of extra money, but actually, this didn’t do much to grow their volumes, as you can see from the graphic below:

This chart shows Polymarket’s volumes actually grew significantly, after they’d shifted back to $0.025 per $100 of rewards, meaning LPs were relatively indifferent or potentially unaware of the significant earnings boost.

According to betmoar.fun, Polymarket has paid out close to $10 million in liquidity rewards since inception, and we’ve established that Kalshi’s data is opaque, though it can be assumed they’ve paid out a similar amount. Keep in mind that when we say that $10 million has been paid out, this comes entirely in the form of USDC (or other USD-backed stablecoins), not token rewards as neither platform has issued a token.

There is a short bit of analysis from Lucas of Adjacent that digs into the benefits should Polymarket launch a token, though we’ll save this brief discussion for later, but it’s worth mentioning that the launch of a token for Polymarket would significantly alter the race between them and Kalshi - stay tuned.

Regarding Kalshi - and we can’t say this is conclusive or what’s definitively occurring - it’s possible that reward distribution under the hood has remained unchanged, or only slightly increased, but more than anything might be comprised of a more favorable set of conditions for market makers, specifically these third parties or external trading partners like SIG.

But the liquidity problem goes deeper than that and concerns the immediate onboarding of more institutional market makers.

Why should you be concerned with adverse selection?

If you’re a US citizen looking to trade prediction markets and are unsure whether to use Polymarket or Kalshi, you can rest assured knowing the question has already been answered for you. As of writing this, the only way to use Polymarket in the US is by working around jurisdictional restraints and using a VPN, leaving you with Kalshi.

This isn't by design, as it isn’t like Polymarket decided on their own volition to not offer its product to over 300 million Americans, but a result of Polymarket not yet being in favor with the CFTC though actively working on it.

Kalshi gained its DCM (Designated Contract Market) status in 2020 and won a federal court decision allowing it to offer event contracts in the United States. Regulation and legal speak is boring but it plays quite an important role in many of the other topics discussed, especially market making.

Because of its current legal status, Polymarket has been unable to sign contracts or form agreements with external / third party market makers like Kalshi has: most notably their partnership with SIG, made public in April of last year.

Polymarket has also been unable to efficiently rid itself of any potential insider traders and adverse selection taking place within its markets, something Kalshi could theoretically do given KYC requirements. Due to these obstacles in its way, Polymarket hasn’t marketed itself in a way that Kalshi has, meaning their inability to defend against toxic flow has hindered the crucial process of onboarding more sophisticated market makers, those that are probably unwilling to subject themselves to even the possibility of trading in an environment with toxic flow.

Our main takeaway here is that the blanket statement “market making prediction markets is bad” might be true, but it’s rarely discussed as an immediate concern to these platforms’ growth. Despite the obvious challenges, it’s these irregularities and quirks of prediction markets that have propelled them to their current status, indicating there is potentially significant value in solving these problems as a third party or directly within organizations like Polymarket and Kalshi.

Questioning parlays and leverage

Discussing liquidity and the rabbit hole of market making was very important, as without it, we’d have no basis for arguing against the feasibility of parlays and leverage. And yes, they are very much really difficult to solve at scale given these current liquidity limitations.

Parlays are quite popular and have quickly become a major source of traditional sportsbooks’ revenue. Quarterly earnings reports don’t explicitly reveal what percentage of FanDuel or DraftKings’ revenue is coming from parlays and prop bets, but a 2023 blog via PGA Tour claimed that parlays represented nearly 70% of all NFL and NBA bets placed on FanDuel.

Anecdotally, almost everyone prefers the payout structure and lottery-type outcomes associated with a parlay, and there is a small but growing ecosystem of content creators across social media that offer parlay advice, specific bet setups, and other information targeting this bucket of sportsbook users.

Sportsbooks make it quite easy to build parlays within the app, offering specialized tooling that pools together disparate bets into an n leg parlay without the need to manually calculate odds. All parlays are different and ridiculous 10+ leg parlay posts are widespread across platforms like Reddit, with the implied odds on these basically falling off the screen.

We’ve discussed how sports betting and prediction markets have comparable user bases and outcomes, but when considering parlays, the platforms that facilitate both these forms of speculation are not equipped to deal with them in the same way.

Polymarket and Kalshi must fully collateralize all of the positions on their site, meaning that if a stated open interest of $150 million is listed for each of these platforms, all of that money is made up in active positions held by traders on the platform. Sportsbooks are different, as their users are betting against the house or other market makers that pay out depending on the outcome. This crucial difference affects the liquidity available on the order book of any given event market, as the money must be posted by a market maker or user, and these parties must deposit USD or USDC (in Polymarket’s case) to place this bet.

A sportsbook might see billions of dollars in volume each day, but not all of this money truly exists, especially in the case of parlays. If a user were to put $10 down on a 12 leg parlay with odds of +192876, guaranteeing a payout of $19,287, the market maker or counterparty on the other side of the trade wouldn’t need to immediately post these funds. In the case of a prediction market, the money must be there for resolution, making parlays quite difficult considering these long-tail outcomes.

The takeaway is that “small shifts toward the longshots explode collateral needs” making it almost impossible for existing market makers to accommodate even a small amount of parlay demand. Taking on even 5-10% of traditional parlay demand would make capital requirements skyrocket for market makers, an unnecessary burden to place on them considering the previously discussed difficulties of adverse selection, volatility, and inventory risk associated with prediction markets.

If a cash-backed prediction market platform wanted to offer parlays, Adhi listed three potential steps to take, each of which still representing a much more watered down version of sportsbook parlays:

• Sharply limiting stake sizes in the tail

• Funneling users into a small menu of pre-made parlays

• Getting permission to margin portfolios instead of ponying up for every worst‑case payout up front

No matter which direction taken, the end result is still a vastly less exciting parlay experience relative to sportsbooks’ “pick-your-own” model and would be void of typical lottery-style parlay outcomes that users crave. Kalshi has done parlays before but only on specific or pre-approved markets, and to our knowledge, Polymarket has not yet explored parlays of any kind. Kalshi’s foray into parlays so far can be categorized under the second option, pre-made parlays that are designated for users keen for more risk, but still a far cry from a traditional sports parlay.

One interesting aspect of Polymarket’s on-chain presence is the potential for a third party to offer up this infrastructure themselves, essentially creating a platform where users could trade and build parlays via on-chain Polymarket contracts, managing the risk themselves. It’s not clear whether an approach like this would be viewed kindly by the CFTC, and its existence would be more in-line with the origins of crypto’s DeFi industry than anything existing today. This would probably not be endorsed by Polymarket directly in any scenario, but it’s hard to imagine them not at least entertaining the idea.

Kalshi would be unable to do this, and if a third party tried to attempt the above example, it would resemble an OTC business more than another DeFi protocol, making it unlikely to gain traction and full of other regulatory risks.

This discussion shines light on the currently broken collateral system of prediction markets, where market makers must put up funds equivalent to the worst-case outcome, ignoring the obvious that two different resolutions are mutually exclusive and you don’t need to over-insure on a scenario that’s statistically (and logically) impossible.

When observing the fully collateralized nature of prediction markets from this angle, it doesn’t make nearly as much sense as it did before. The current state of market making would mean that if a five leg parlay was created between five separate markets on a platform, the worst-case of a trader hitting maybe +4528 odds in a victory would need to be insured against, even though our fearless speculator here might only be putting up $15 on the bet.

The incentives are simply not aligned for a market maker if they’re forced to put all of their capital stack to work on highly unlikely events instead of using it in other prediction markets separate from parlays, ones that would give them much greater capital efficiency. While this full-collateralization mandate is still in place, it’s hard to imagine parlays rolling out anytime soon, leaving us skeptical of their immediate role in competitive dynamics between both of these platforms.

While parlays might be the dominant method of boosting payouts in the sports betting world, leverage is the easiest way of doing this in crypto.

Earlier we displayed a graph of top crypto exchanges’ respective futures volumes, with monthly totals coming out to trillions in USD terms. Perpetual futures are popular due to their range of leverage, though the number ultimately depends on the exchange and the relative liquidity of an asset. Something like BTC, ETH, or SOL may offer up to 100x leverage, whereas a less liquid token would cap out at 3-5x leverage. This is significant but also poses great risk, even for experienced traders.

Implementing leveraged prediction markets would be tricky and arguably more complicated than the parlay structures just discussed. This relies on prior knowledge of market makers’ risk appetite and the challenging role of managing inventory risk considering one side of the market is bound to trade at $0 upon resolution. Market making in leveraged prediction markets would amplify these difficulties, and even if it would be great for speculative users, it is a completely uncharted territory for any existing counterparty.

Leverage has yet to be explored by either Polymarket or Kalshi, most likely because these platforms are still working out the kinks of spot market making, but potentially due to the extremely high 1,500% annualized volatility credit to @defiance_cr sof prediction markets.

Volatility is just one factor to consider, as the natural structure of a binary prediction market makes it that liquidity not so gradually dissipates from one side of the book as an outcome gets closer to resolution (100% or 0% probability). John Wang’s post discussing prediction market perps on Hyperliquid laid out potential solutions, like liquidation bands, leverage decay prior to expiry, rate limiting oracles, and pre-settlement auctions.

Scalar markets are still subject to binary resolution, even if these are ranges rather than binary YES or NO options. The issue doesn’t come from how a prediction market is presented, but how it concludes, and all of these resolve in the same, zero-sum way. Even if scalar markets present themselves as different due to their continuous nature, the price converges to one side and jump risk does exist, especially for markets that rely on a signal from a third party (like CPI prints).

It’s possible for safe leverage ratios to roll out over time, but nothing on the scale of crypto’s perpetual futures market would be possible, let alone outside of the top 25-30 markets on these platforms. Keep in mind that leverage for Solana memecoins has yet to be solved, despite the crypto industry’s fascination with perpetual futures - it’s simply a tough financial engineering problem and leveraged anything should be handled with the utmost precaution.

The leverage and parlays question is relevant because of claims that prediction markets just don’t pay enough or that locking capital up for three months or longer is simply bad R/R in a market environment like today’s. While more qualitative in nature, these claims are absolutely true, and it should not have taken this long to address the elephant in the room.

Coming from an industry where coins can spawn with a $150 million market cap and grow to nearly 15x sthat on billions of volume in a matter of three days, many of the prediction market payouts are comparatively weaker, though this shouldn’t come as a surprise. It’s a fairly recent phenomenon where parlays constitute a significant chunk of sports betting activity, partially due to a prior lack of accessibility, but potentially because of the a href="https://oldcoinbad.com/p/long-degeneracy" target="_blank" style="pointer-events:auto; position:relative; display:inline-block;">growing trend of rampant speculation amongst younger generations.

Regardless of how frequent this sentiment may appear, prediction markets are unfit to contend with the raw excitement present within sportsbooks - even Kalshi - until these improvements are made.

Forms of manipulation and historical accuracy analysis

Since we’re going to be discussing market settlement and accuracy of these markets prior to resolution, it’s best to provide a brief explainer on how these platforms actually handle dispute resolution and settlement in their own unique ways.

Brief aside on oracles

In Polymarket’s case, the UMA oracle manages settlement and dispute resolution in a way that requires arguably zero human oversight. The UMA protocol is an optimistic oracle and dispute arbitration system that brings in vast amounts of data on-chain, overseen by a committee of UMA token holders.

Polymarket’s most recognizable controversy stemmed from its “Will Zelensky wear a suit before July?” market which caused a stir due to the questionable attire worn by Zelensky and whether or not it could be deemed a suit. This specific market did over $242 million of volume before resolving to no, but led to significant debate across all parts of X, as it seemed pretty obvious Zelensky had worn some type of suit.

There are many other examples (nice list laid out by @aradtski here) unique to Polymarket, but the Zelensky problem stands out because it’s a resolution that everyone saw with their own eyes, yet failed to capitalize on given the authority of UMA’s oracle and Polymarket’s internal ruling. UMA is a crypto native protocol but has gained significant recognition primarily through its work with Polymarket.

“The Oracle Problem is not really the oracle problem, because crypto oracles were never designed for such things as world events” or put in simpler terms, UMA is simply unfit to manage the governance process of Polymarket in its current state, handling hundreds of millions of dollars each month.

Kalshi doesn’t face this issue as they have an internal team for market review and dispute resolution, and even if this work is slower than an oracle’s more automated approach, it has more than likely saved them headaches and bad press. Kalshi makes use of a dedicated markets team that handles internal review and dispute resolutions, though it’s worth noting they may utilize similar data sources as Polymarket, but are potentially more defensible against controversy despite this slower approach.

Resolution scandals could be considered manipulation, though ideologically they are more similar to a bug in the platform’s design. On the other hand, manual manipulation - done with intent - can be considered a feature and is achievable through a number of unique ways. There aren’t many proposals to improve existing oracles or any significant alternatives being worked on, but it’s something to keep your eye on if the more technical side of prediction market infrastructure interests you.

Manipulation, crime and some caveats

Historical accuracy of prediction markets was just discussed, but this concept must be revisited from a newer, non-academic angle. Whether or not you ascribe to the view that prediction markets are more accurate than mainstream media, polling data, or other sources of truth is besides the point: manipulation is not only measurable, but arguably already an issue.

We propose two different types of manipulation - social coercion and insider arbitrage -, as well as an explanation on how whales and large traders may or may not be contributing to this, with data to confirm our view.

Suppose there exists a high-ranking executive at Universal Music Group that wants to understand how the public feels about a new Drake album. Maybe internal polling and data suggests this album would do well, but they aren’t certain. They ask an employee in the hypothetical marketing or data science department to spin up a simple, multi-outcome market on Polymarket, titled something like “Drake’s first week album sales?” and offer up a small amount of liquidity.

Maybe the range of outcomes includes <1.5 million, 1.5 million-2 million, 2 million-2.5 million, >2.5 million. From here, you could pretty reasonably gauge consumer expectations around Drake not only as an artist, but from the perspective of an investment. The multi-outcome market isn’t just bored traders staking money on the success or failure of an album, but rather the aggregated beliefs of numerous market makers and potentially well informed music fans putting their money where their mouth is

Going back to the accuracy of prediction markets, it’s easy to argue even an illiquid market (or this hypothetical) gives more value to an opinion when the alternative is a set of random individuals polled with no methodology of gauging their internal views or knowing if they told the truth or not.

Let’s say previous hypothetical markets for artists like Taylor Swift, Olivia Rodridgo or Ed Sheeran had made headlines and been viewed tens of millions of times across platforms X, creating a mini-marketing wave of their own in the process.

Our hypothetical record label executive then decides that, in order to juice week one sales and drive a narrative that Drake’s new album is red hot, Universal Music Group will sway the orderbook in overwhelming favor of the highest week one sales, to a degree that anyone seeing it will be forced to take note of Drake’s upcoming album.

This will obviously cost the record label, but in this scenario, it’s a decent way of sparking buzz and creating headlines that has yet to be done by anyone else, and it’s probably cheaper than a corporate marketing campaign typically attached to an artist of Drake’s caliber.

If you’re a more astute reader, you’ll notice a problem with this hypothetical: if one entity singlehandedly purchases a large chunk of >2.5 million contracts, wouldn’t the market’s natural forces return this back to some sort of equilibrium and fair value?

This is not only correct, but it’s already been tested and proven to be the case, though market size for this hypothetical and the real world example are quite different.

Posted on X by @apralky (otherwise known as Yung Macro) in June of this year, this analysis pokes at the holes in Scott Alexander’s arguments, specifically the claim that whale skew is responsible for Polymarket’s inflated 2024 US Presidential Election odds assigned to Trump.

Scott wrote that money-based prediction markets priced Trump’s odds of winning far higher than non money-based prediction market platforms (like Metaculus ), seemingly caused by whales’ bets skewing the odds beyond “fair value.” Scott proposes a counter-argument, as well as a counter to the counter-argument, but we will only look at the objective truth.

Regardless of whether or not a whale skewed the odds from fair value (54% Trump win probability) to something 8-10% higher (62-64% Trump win probability), the reality is that this market did not correct the overzealous whale and return to a hypothetical fair value. In fact, Yung Macro’s data shows that the spread between money and non-money platforms did not collapse back to fair value and widened over time.

If traders believed a whale was skewing this market beyond reality, sellers would have taken advantage of this and brought the price lower over time, and the pre-whale price would have come back to the market. This did not occur, and it’s illogical to run with this narrative when the data says otherwise. Put simply, the market did its job and expressed the beliefs of traders even in the face of experts claiming otherwise.

This phenomenon was most observable in the distinction between Polymarket and the mainstream media’s coverage of the election, with CNN reporting how platforms like Polymarket saw something they didn’t. But it’s far more likely Polymarket itself didn’t see anything at all, rather the hivemind trading on it expressed their raw beliefs and backed them with dollars.

Without being overly reductive, we see this phenomenon in traditional finance occasionally, where a company might blow earnings estimates out of the water and pop in price +20/30% after hours, only to correct itself the next day.

This correction can come after a period of sobering up, when analysts have had more time to react to an entire earnings call, or it can simply occur after market makers and all other willing buyers and sellers have come to an agreement on the new fair value of this company, this usually being a price lower than its post-earnings beat peak.

Moving to the second defined form of manipulation, insider arbitrage (or insider trading), we actually have a good example that’s been debated extensively since coverage via Camilo on X.

At first glance it appears Kalshi was able to more accurately predict Tesla’s quarterly delivery numbers than @TroyTeslike (an individual specializing in quantitative Tesla and TSLA analysis) in addition to Wall Street’s estimates.

Is this an example where the wisdom of the crowd outperforms those who are paid to speculate on outcomes like this, or is this another case of whales skewing the fair value, leaving the market in a state of imbalance? Even worse, is it representative of someone with insider knowledge taking advantage of the lax regulatory nature of prediction markets and capitalizing without risk of legal penalty?

Keeping in mind our earlier point, that because of Polymarket’s current legal status, CFTC regulation is out of the question and it’s essentially not feasible to track down potential insiders, as they’re pretty much free to profit off of MNPI (material nonpublic information) as they wish. Kalshi could potentially do this, though there hasn’t been any release of information regarding this strategy, as it’s presumably a task handled by in-house legal teams.

We aren’t implying this is a definitive example of insider trading, but rather the mere possibility of its occurrence is a potential cause for concern and affects these platforms in the short term. Playing devil’s advocate, this is probably just a one-off example of some extremely sharp data science nerds/sleuths taking advantage of a more simplified interface.

It’s possible these individuals had positions in TSLA options expressing the same beliefs, yet sought out Kalshi as it could have been priced better at certain points prior to resolution. Additionally, some could argue insider trading is a method of making prediction markets more efficient, but the one-sided nature of insider information is less of an efficiency than it is an advantage for a certain subset of the orderbook.

Quick aside, Theia Research published a great write-up on the mathematics behind Brier scores and how these can be used in portfolio construction, accessible here.

Brier.fyi found that one month before close, 63% of markets are already within 30% of the correct resolution, or rather that prediction markets as a tool for predicting accuracy are within a +/- 30% range of the correct outcome, more than half of the time. That’s not bad!

This analysis was done across four platforms - Polymarket, Kalshi, Metaculus, and Manifold - to get the most diverse range of event outcomes (which we will explore), but also due to the nature of Metaculus and Manifold’s unique structures: they’re prediction market platforms that don’t use real money.

Manifold is comparable as it uses play money and targets a crowd similar to Metaculus, though at one point prioritized market creation more than curation of markets, leading to a user base once peaking at over 10,000 traders per day.

We see that Polymarket and Kalshi did the best here, but here’s some of the interesting takeaways from all of this data:

• This was measured across 942 matched markets

• Despite open interest being fairly equal on Polymarket and Kalshi, different categories saw different accuracy/performance, indicating an already evolving typology of prediction market traders from a cross-venue perspective

• Politics is still quite difficult to predict, despite this being the definitive most popular category outside of sports

Not to overuse it, but Brier’s analysis of the binary “Will a Republican win the 2024 US Presidential Election” market is quite interesting as we can view a breakdown of each venue’s accuracy at the following intervals: at market midpoint, time-weighted average, 30 days before close, and 7 days before close, as well as a relative score.

The graphic shows that each of these four platforms received at least a C- letter grade in the politics category, but the Republican binary market in question saw Polymarket scoring straight D-s across every interval, Kalshi scoring just a little bit better - but not great - yet both of these platforms receiving a final relative score of an A letter grade. How does that work?

Avoiding the accompanying logarithmic and spherical scores, this result is due to the relative score measuring the performance of a platform against the other platforms, meaning the D- letter grades of Polymarket and Kalshi were a lot better than the straight Fs seen on Manifold and Metaculus.

This is only a single example, and maybe cherry picking considering just how many orders of thinking go into something as consequential as the US Presidential Election, but give us some credit. It would be difficult to analyze every single market on Brier.fyi, but even this generalized data is still very useful to us, as it shows prediction markets aren’t perfect, yet maybe the sum of their indecisions add up to something.

This isn’t the only attempt to study prediction market accuracy - there’s a great Dune dashboard made by Alex McCullough dissecting Polymarket’s historical accuracy, and a post from @MovieTimeDev simplifying Alex’s data, though these are a bit different from Brier.fyi’s analysis.

The analysis found that 78.4% of the markets on Polymarket resolve to NO, which @MovieTimeDev said indicates “markets consistently overprice unlikely events” or rather Polymarket is generally inaccurate and too optimistic. This is true, but it’s also misleading.

78.4% of Polymarket’s markets resolving to NO might statistically mean markets are overpricing something (at any value greater than 0%, generalizing), but this is only due to a few factors that are non-negotiables for any existing prediction markets platform:

• Long-tail markets have weaker liquidity

• Traders feel the need to take a position when odds are low, locking in a better outcome

• The sheer amount of markets available on Polymarket and the more outrageous nature many of them have; related, @defiance_cr discussed that just 500 markets have done 82% of Polymarket’s volume, meaning that of the 40,569 markets analyzed in the above study, just 1.2% of them would have been a better data source considering their dominance

As we said earlier, the Brier score analysis is one of the best ways of measuring accuracy, and for a handful of events, prediction markets were actually quite useful. The data expressed above is still interesting enough in other ways, like the 95.3% accuracy measure four hours before resolution, but on its own it does not provide a conclusive answer as to whether or not highly liquid markets are more or less accurate than alternative sources of truth.

Where things are going and how to capitalize on this trend

One thing that hasn’t been discussed is the potential design space for net-new prediction market platforms, improved infrastructure/rails, and some of the social-first apps working in the space, so let’s take a look. Connor King put out a nice market map of everyone building crypto-native prediction market startups, defining categories like tooling, platforms, DeFi-focused, trading frontends, oracles / resolution, and niche markets.

We will not be reviewing these today, as it would take too much of our time, and truth be told it doesn’t directly impact the analysis of Kalshi and Polymarket.

Instead, we offer a simple framework for evaluating these startups, should you choose to invest in them, whether it’s through a private agreement or via liquid markets (token purchase).

It’s understandable that platforms like Limitless, Clearing Company, and Manifold are competing directly with the two main platforms (of which we’re quite familiar now), Polymarket and Kalshi. What isn’t so simple is deciphering where the rest of these applications fit in.

Some applications are building experiences more easily recognizable than others.

Many of these upstarts are optimizing for social engagement, discoverability, or a more user-friendly interface, all while offering structurally the same type of product users are already trading on major platforms, meaning competition is somewhat zero-sum.

What existing platforms could be planning

There’s an obvious goal of racing to become the TikTok of prediction markets or something similar for a more zoomer-oriented user base, but even this still feels out of reach and difficult to achieve. Polymarket and Kalshi have more reach than many assume , have already established outlets to reach retail audiences, and are frequently displayed on live television networks like CNN or CNBC.

On the topic of reach, the partnership between Robinhood and Kalshi sis one of the most consequential developments in recent fintech history. allows for Robinhood to officially “let users trade on the outcomes of NFL and college football games” and has been in effect since August 19th.

This comes after Robinhood and Kalshi’s prior partnership, allowing Robinhood users to trade contracts of Kalshi’s 2024 US Presidential Election, which saw over 2 billion contracts change hands according to this blog post released by Robinhood. We discussed Robinhood’s success at turning the average joe into an options trader, and now it seems they’re extending this to prediction markets through a continued and ongoing relationship with Kalshi.

This isn’t a shock, considering Robinhood’s 27.4 funded accounts aas of Q2 2025 and Kalshi’s pursuit for volume, but the jump seen from Kalshi raises an interesting question: just how much of this might be coming from Robinhood users directly, and is it even measurable prior to a quarterly earnings report?

We can’t know for sure, but it’s possible given the previous discussion of betters leaving sports books after getting the boot, the more readable odds of prediction markets, and the ease of placing a bet directly within Robinhood’s extremely simplified interface. Whether or not Robinhood continues to partner with Kalshi on markets outside of sports and politics is yet to be seen, but the initial success provides no reason to suggest this isn’t in the works.

It’s hard to make a claim for whether Polymarket or Kalshi outperforms more from here, but based on everything discussed, the view that both of these platforms can peacefully coexist for at least a few more years shouldn’t be that contrarian. They’ve done so already and are clearly operating with different short term goals in mind, and each of these platforms straddles a different side of the centralization v. decentralization spectrum.

Maybe Kalshi continues to prioritize sports-related markets and pursue competition against every traditional sportsbook. Polymarket can continue pushing for deeper liquidity on long-tail markets and improve its relations with regulator-friendly US-based trading desks over the next six months. These outcomes aren’t mutually exclusive, giving users of both platforms good reason to continue their trading activities regardless of headlines or external pressures.

Despite Kalshi’s sports betting ambitions and Polymarket’s push for trading earnings , both platforms are still walking down very similar paths:

• Elon-affiliated companies (X and xAI) have signed some form of partnership with Polymarket and Kalshi respectively.

• Both platforms continue pushing on the regulatory front, albeit under a different set of circumstances.

• The allure of more venture funding exists, and seems to be heating up despite both platforms announcing $200 million and $185 million funding rounds just three months ago.

We’ve discussed everything, but haven’t given you a playbook for capitalizing off prediction market growth in your own portfolios. Other than being more refined in your prediction market trades or developing a system to capitalize on the various forms of arbitrage, what are some ways you can get exposure to these platforms today?

The token discussion

Keeping in mind that what we are about to say is very speculative and potentially entirely inaccurate, this is arguably the best course of action for anyone unable to get access to Polymarket or Kalshi’s private rounds:

Just use the platforms.

In the case of Kalshi, the mental calculus is different considering they aren’t a blockchain-based application yet, though their team has recently expressed the intent to get more involved with crypto.

This isn’t meant to spark any rumors, though this image has been floating around X indicates some type of future reward:

Many fortunes have been made chasing airdrops in crypto, but none of these stories are more relevant than the case of Hyperliquid, which launched its token (HYPE) in Q4 of last year. The price performance of HYPE since late 2024 has made Hyperliquid’s airdrop distribution - roughly 31% of the total supply, or 310 million HYPE across seasons - arguably the largest wealth generating airdrop in crypto’s history, depending on who you ask. At the time of writing this, Hyperliquid is making about $1.2 billion in revenue (annualized) and trading at a $15.1 billion market cap ($55 billion fully diluted).

There are many reasons Polymarket would consider launching a token. First expressed by Lucas here, a percentage of token supply could be earmarked for boosting liquidity rewards, making Polymarket less reliant on paying out USDC reserves (roughly 6m USDC annually). If the rumors of Polymarket’s $10 billion term sheet are true, then Lucas’ estimate of 0.76% annual supply spend on incentives would mean over $76 million of liquidity incentives, assuming a token valuation of $10 billion. If Polymarket wanted to be more conservative and simply offer $25 million of annual incentives, this would only require 0.25% annual token spend.

The other obvious benefit is more in line with a traditional sportsbook’s strategy for user retention. Prior to Hyperliquid’s airdrop, a common strategy of point acquisition involved farming purposeful liquidations on leveraged positions, leading to greater point distributions each week. The outcome was that even net-negative PnL accounts came out on top with a positive ROI after accounting for profit made selling HYPE rewards.

The dynamic here is most similar to a sportsbook like DraftKings or FanDuel dishing out free bets or promos to their users - especially those with a history of loss - in order to keep them around on the platform. It’s possible that giving out even 0.3 - 0.5% of supply annually might cause poor price action, though this is a small expense and assuming Polymarket doesn’t earn any fees, it’s unsustainable to burn USDC reserves without any lever to bring money back into the system.

Data via @filarm on Dune shows that only 15% of accounts on Polymarket have a positive balance, while the other 85% has a negative balance (presumably an account that was zeroed out). It’s been found that just 3% of sportsbooks’ users are profitable long-term, and while this is a far-cry from Polymarket’s stronger 15% profitability rate, the strategy of airdropping net-negative accounts a greater share of token rewards would do wonders for user retention.

This is not to say that Polymarket would see the same level of success as Hyperliquid in revenue generation (the fee switch has yet to be turned on) or price performance, but structurally, the business of offering perps and prediction markets leads to a majority of your user base losing more on average, requiring some sort of artificial stimulus.

This is not to say that Polymarket would see the same level of success as Hyperliquid in revenue generation (the fee switch has yet to be turned on) or price performance, but structurally, the business of offering perps and prediction markets leads to a majority of your user base losing more on average, requiring some sort of artificial stimulus.

This was a lot of information and if you’ve made it this far, we hope you’ve learned a thing or two. This report can lead you down many different rabbit holes, but more than anything we hope it’s given you a new perspective on these markets. Prediction markets are great and even though parts of this report skewed towards the negative, it’s coming from a place of love. Even if these platforms aren’t yet ready to house the entire population’s appetite for speculation, there’s a case to be made that a large amount of it is onboarding in greater numbers each week.

If you liked this, please consider reposting it on X, sharing with a friend, or messaging us to discuss things further. Thanks for reading.