Original title: Prediction Markets: Bottlenecks and the Next Major Unlocks
Written by: Mikey 0x, 1kx
Compiled by: Tia, Techub News
Prediction Markets: Bottlenecks and the Next Big Breakthrough
Augur is the first on-chain prediction market and one of the earliest applications launched on Ethereum. Its vision was to allow anyone to bet on anything at any scale. Due to many problems, Augur's vision was not realized. The project has been discontinued due to lack of users, poor settlement user experience and high gas fees. However, blockchain development has achieved enough breakthroughs: block space is cheaper and order book design is more efficient. Existing innovations have consolidated the permissionless and open source nature of cryptocurrencies, allowing for the establishment of an unconstrained global liquidity layer, which provides a breeding ground for providing liquidity provision, market creation or betting to become a market participant.
Polymarket is the emerging market leader with around $900 million in volume to date, while SX Bet has amassed $475 million to date. Still, there is plenty of room for growth (if you compare it to the sheer size of sports betting, a subcategory of traditional prediction markets). In the U.S. alone, sports betting processed more than $119 billion in transactions in 2023. This volume is even more pronounced if you take into account the volume of physical and online sports betting in all other countries, as well as other types of prediction markets such as politics and entertainment.
This article aims to analyze how prediction markets work, the current bottlenecks that need to be addressed for further mass adoption, and some of the ways we think they can be addressed.
How do prediction markets work?
There are many ways to design prediction markets, most of which can be divided into two categories: order book models and centralized AMM (automated market maker) models. Our argument is that order book models are the superior design choice because they allow for better price discovery, maximum composability, and ultimately scaled trading volumes.
For the order book model, each market has only two possible predefined outcomes: "yes" and "no". Users trade these outcomes in the form of stocks. At the time the market settles, the correct stock is worth $1.00 and the incorrect stock is worth $0.00. Before the market settles, the price of these stocks can range from $0.00 to $1.00.
In order for stocks to be traded, there must be liquidity providers (LPs); in other words, they must provide bid and ask prices (quotes). These LPs are also called market makers. Market makers provide liquidity in exchange for a small profit on the spread.
Example of a specific market: If something has an equal probability of happening, such as a coin toss landing heads, then both the Yes and No stocks should theoretically trade at $0.50. However, like any financial market, there tends to be a spread, which allows for slippage. If I want to buy the Yes stock, my strike price may end up being closer to $0.55. This is because my counterparty (liquidity provider) is intentionally overestimating the true odds to gain potential profit. The counterparty may also be selling the No stock for $0.55. The $0.05 spread on each side is the reward that the liquidity provider gets for providing the quote. The spread is driven by implied volatility (expectation of price movement). Prediction markets essentially guarantee realized volatility (actual price movement), simply because the stock must eventually reach a predetermined value of $1 or $0 by some predetermined date.
To illustrate the market maker scenario for the coin toss prompt above:
The market maker sells 1 share of Yes at $0.55 (which is equivalent to buying 1 share of No at $0.45)
The market maker sells 1 share of No at $0.55 (which is equivalent to buying 1 share of Yes at $0.45)
The market maker now has 1 share of “yes” and 1 share of “no”, paying a total of $0.90
Regardless of whether the coin comes up heads or not, the market maker will redeem $1.00, thereby earning a $0.10 spread
The other main way to solve prediction markets is through AMMs, which are used by Azuro and Overtime. An analogy in DeFi is GMX v2. Funds are pooled together to act as the sole counterparty to the platform's traders, and the pool relies on external oracles to price the odds.
What bottlenecks currently exist in the prediction market?
Prediction market platforms have been around and known for a long time, and if the product had true market fit, escape velocity would have occurred long ago. The current bottleneck can be attributed to the simple conclusion that there is a lack of interest on both the supply side (liquidity providers) and the demand side (bettors).
On the supply side, problems include:
Lack of liquidity due to volatility: Polymarket’s most popular markets tend to be those where the concept is novel and relevant historical data is scarce, making it difficult to predict the outcome and accurately price it. For example, it is difficult to predict whether a CEO (such as Sam Altman) will return to his post after rumors of possible mishandling of AGI circulate, as there are no past events that closely correlate to this situation. Market makers will offer wider spreads and less liquidity in uncertain markets to compensate for implied volatility (i.e. the wild price action in the Sam Altman CEO market, where consensus flipped 3 times in less than 4 days). This makes it less attractive to whales who want to place large bets.
Lack of liquidity due to too few professional participants: Despite hundreds of market makers earning rewards on Polymarket every day, many long-tail markets lack liquidity simply because there are not enough participants with expertise who want to make markets. Example markets include “Will celebrity x be arrested or charged for y?” or “When will celebrity x tweet?” This will change over time as more prediction market types are introduced, data becomes richer, and market makers become more professional.
Information asymmetry: Since the existence of makers allows any taker to buy and sell at any time, when takers obtain favorable information, they have the advantage of making positive EV bets. In the DeFi market, this type of taker can be called a toxic flow. Arbitrageurs on Uniswap are a typical example of toxic takers because they use their information advantage to continuously extract profits from liquidity providers.
In one Polymarket market, “Will Tesla announce a Bitcoin purchase before March 1, 2021?”, a user bought $60,000 worth of Yes shares at odds of about 33%. This market was the only one the user had ever participated in, and it can be assumed that the user had favorable information. Leaving aside the legal issues, the market maker providing this quote could not have known that the taker/bettor had this favorable information at the time, and even if the market maker initially set the odds at 95%, the taker might still bet because the true odds were 99.9%. This results in the market maker inevitably losing money. In prediction markets, it is difficult to predict when toxic flow will occur and at what scale, making it even more difficult to provide tight spreads and deep liquidity. Market makers need to price the risk of toxic flow occurring at any time.
On the demand side, the main issues are:
Lack of leverage tools: Without leverage tools, prediction markets are far less attractive to retail investors than other crypto speculation tools. Retail investors want to make "huge fortunes", which is more likely to be achieved on memecoin, while betting on capped prediction markets is much less likely. For example, early bets on $BODEN and $TRUMP bring much greater upside than betting on Biden or Trump to win the presidency.
Lack of exciting short-term markets: Retail bettors are not interested in bets that will be settled in a few months, a conclusion that can be confirmed in the sports betting world, where a large amount of retail trading today occurs on live betting (ultra-short term) and daily events (short term). At least not yet, there are not enough short-term markets to attract a mainstream audience.
Is there any way to solve these problems? How can we increase production?
On the supply side, the first two problems (illiquidity due to volatility and illiquidity due to fewer experts) will naturally decrease over time. As the size of individual prediction markets grows, the number of professional market makers and those with higher risk tolerance and capital will also increase.
But rather than waiting for these issues to abate over time, the lack of liquidity is addressed head-on through a liquidity collaboration mechanism originally invented in the DeFi derivatives space. The idea is to allow stablecoin depositors to earn yield through a vault, which deploys market-making strategies in different markets. This vault will act as the primary counterparty to traders. GMX was the first protocol to do this through a pooled liquidity provision strategy that relies on oracles for pricing, while Hyperliquid was the second to deploy a native vault strategy, but with the distinction that liquidity is provided on CLOBs. Both vaults have been profitable over time because they are able to act as counterparties to the majority of non-toxic flows (retail users who tend to lose money over time).
Hyperliquid’s vault PNL continues to grow
Native vaults allow protocols to easily bootstrap liquidity on their own without relying on other vaults. They also make long-tail markets more attractive; one of the reasons Hyperliquid has been so successful is that newly listed perpetual assets contain a lot of liquidity from day 0.
The challenge of building a vault product for prediction markets is preventing toxic flows. GMX prevents this by charging high fees on its trades. Hyperliquid implements a market maker strategy with wide spreads, a 2 block delay on taker orders to give market makers time to adjust their quotes, and prioritizes market maker order cancellations within one block. Both protocols create an environment where toxic flows cannot enter because they can find better price execution elsewhere.
In prediction markets, toxic flows can be prevented by providing deep liquidity at wider spreads, selectively providing liquidity to markets that are less susceptible to information advantages, or hiring sharp strategists who can gain information advantages.
In effect, the local vault can deploy $250,000 of additional liquidity, buying at 53 cents and selling at 56 cents. A wider spread helps increase potential vault profits because users will bet while accepting worse odds. This is in contrast to quotes at 54 and 55 cents, where counterparties may be arbitrageurs or savvy individuals looking for a good price. This market is also relatively less susceptible to information asymmetry issues (there is less inside information and insights, which are often released to the public relatively quickly), so the expectation of toxic flows is lower. Vaults can also use information oracles to provide insights into future line moves, such as pulling odds data from other betting exchanges or collecting information from top political analysts on social media.
The result is greater liquidity for bettors, who are now able to place larger bets with less slippage.
There are several ways to solve or at least reduce the information asymmetry problem. The first few are related to order book design:
Gradual Limit Order Book (GLOB): One way to combat toxic flow is to increase the price based on the speed and size of the orders. If buyers are sure something is going to happen, then a reasonable strategy is to buy as many shares as possible at a price below $1. Additionally, if the market eventually gets favorable information, it is also wise to buy quickly.
Contro is implementing this GLOB idea.
If the Tesla $BTC market were to occur in a GLOB model, the taker would have to pay much higher than 33% for each “yes” share because of the “slippage” that occurs given the combined velocity (a slice) and size (huge) of the orders. He would still make a profit regardless of the slippage because he knows his “yes” shares will end up being worth $1, but this at least covers the market maker’s losses.
One could argue that if takers execute a DCA strategy over a long period of time, they could still suffer very little slippage and pay closer to 33% of the stock price for every “yes” share, but in this case, it at least gives the market maker some time to withdraw its quote from the order book. Market makers may withdraw quotes for several reasons:
It suspects there is toxic traffic because there are a lot of taker orders coming in
It is certain that there is a toxic flow because it checked the taker's profile and found that he had never placed a bet before.
It wants to rebalance its treasury, no longer wanting to be too one-sided, it needs to consider how much “no” stock it is accumulating based on how much “yes” stock it is selling - maybe the market maker initially has $50,000 worth of orders on the sell side, accounting for 33%, and $50,000 worth of orders on the buy side, accounting for 27% - its initial goal is not to have a directional bias, but to remain neutral so that it can earn profits through symmetrical liquidity provision
Winners’ Take: In many markets, some of the profits are redistributed to those with favorable information. The first example is peer-to-peer web2 sports betting, especially Betfair, where a fixed percentage of the user’s net winnings is redistributed to the company. Betfair’s take really depends on the market itself; in Polymarket, it may make sense to charge a higher net winning take for newer or long-tail markets.
This concept of redistribution also exists in DeFi in the form of order flow auctions. Bots running in the background extract value from information asymmetry (arbitrage) and are forced to return the proceeds to the people who participate in the transaction, who may be liquidity providers or users who make transactions. To date, there have been many PMFs in order flow auctions, and CowSwap is pioneering this category with MEVBlocker.
Static or dynamic taker fees: Polymarket currently does not charge taker fees. If implemented, the proceeds would be used to provide liquidity rewards to markets with high volatility or susceptible to toxic flows. Alternatively, higher taker fees could be set in long-tail markets.
On the demand side, the best way to address the lack of upside is to create a mechanism that allows for upside. In sports betting, multiple bets are becoming increasingly popular with retail bettors because they offer the opportunity to "win big." A multiple bet is a bet that combines multiple individual bets into a single bet. For the multiple bet to win, all individual bets must win.
One user won over $500,000 with an initial bet of just $26
There are three main ways to increase the number of users of crypto-native prediction markets:
Parlays
Perpetuals
Tokenized leverage
Multiple bets: Technically, this is not feasible on Polymarket’s books, as bets require upfront capital and the counterparty is different for each market. In practice, the new protocol can fetch odds from Polymarket at any given point in time, price the odds for any parlay bet, and act as the sole counterparty for the parlay.
For example, a user wants to bet $10 on:
If placed individually, these bets have limited returns, but when they are combined into multiple bets, the implied return soars to about 1:650,000, which means that if every bet is correct, the bettor can win $6.5 million. It is not difficult to imagine how multiple bets can earn PMF among cryptocurrency users:
The cost of participation is low, you only need to invest a small amount of money to win a lot
Sharing a multiple bet slip can quickly go viral on crypto Twitter, especially when someone wins a jackpot, creating a feedback loop with the product itself
There are challenges in supporting multiple bets, namely counterparty risk (what happens when multiple bettors win large multiple bets at the same time) and odds accuracy (you don’t want to offer bets that underestimate the true odds). Casinos have solved the challenge of offering multiple bets in the sports world, and it has become by far the most profitable part of sports betting. Even if some bettors get lucky and win big, the profit margin is about 5-8 times higher than offering single market bets. Another added benefit of multiple bets is that there are relatively fewer toxic streams in multiple bets compared to single game markets. The analogy here is: why would a professional poker player who lives and dies by expected value put money into the lottery?
SX Bet is a web3 sports betting application chain that has launched the world's first peer-to-peer multiple betting system, which has reached $1 million in multiple betting volume in the past month. When a bettor "requests a multiple bet", SX creates a private virtual order book for the multiple bet. Programmatic market makers listening through the API will have 1 second to provide liquidity for the bet. It will be interesting to observe the increase in liquidity and appeal of non-sports multiple bets.
Perpetual Prediction Markets: This concept was briefly explored in 2020 when FTX offered perpetual contracts for the US election results. You could go long the price of $TRUMP, which would redeem $1 per share if he won the US election. As the odds of Trump actually winning changed, FTX had to change margin requirements. Creating a perpetual mechanism for a market as volatile as a prediction market presents a lot of challenges with collateral requirements, as the price could be $0.90 one second and $0.10 the next. As a result, there might not be enough collateral to cover the losses of someone who did the wrong thing. Some of the order book designs explored above can help compensate for the fact that prices can change so quickly. Another interesting thing about the FTX $TRUMP markets is that we can reasonably assume that Alameda is the primary market maker for these markets, and without locally deployed liquidity, the order books would be too thin to generate much volume. This highlights the value of a native liquidity vault mechanism for prediction market protocols.
Both LEVR Bet and SX Bet are currently working on the perpetual sports betting market. One benefit of sports leverage is that “yes or no” stocks will see smaller price swings, at least in most situations. For example, a player making a basketball shot might increase the team’s chance of winning from 50% to 52%, since the average team might make 50 shots per game. A 2% increase on any one shot is manageable from a liquidation and collateral requirements perspective. Offering perpetual contracts at the end of a game is another matter, since someone might make the “game-winning shot” and the odds could change from 1% to 99% in half a millisecond. One potential solution is to only allow leveraged betting up to a certain point, since any event after that would change the odds too much. The viability of perpetual sports betting also depends on the sport itself; a hockey goal changes the expected outcome of a game far more than a basketball shot.
Tokenized leverage: Lending markets that allow users to borrow against their prediction market positions (especially long positions) could increase volume for professional traders. This could also lead to more liquidity, as market makers can borrow against positions in one market to trade in another. Until there are products like these (such as those that Eigenlayer is attracting), tokenized leverage may not be an interesting product for retail bettors. The overall market may be too immature for such an abstraction layer to exist right now, but eventually these types of circular products will definitely appear.
Beyond the pure supply and demand side of things, there are other nuanced ways to increase adoption:
From a UX perspective: Switching settlement currency from USDC to a yielding stablecoin will increase participation, especially in long-tail markets. This has been discussed a few times on Twitter; holding a market position that expires at the end of the year has a large opportunity cost (e.g. betting on Kanye West to win the presidency earns 0.24% APR, while betting on AAVE earns 8% APR).
Additionally, the degree of gamification aimed at improving retention can really help attract more users in the long run. Simple things like “daily betting streak” or “daily contests” work well in the sports betting industry.
Several industry-level trends will also increase adoption in the near future: the combination of growing virtual and on-chain environments will unlock entirely new speculative demand, as the number of short-term events will eventually be infinite (think AI/computer-simulated sports) and the level of data will be abundant (making it easier for market makers to price outcome odds). Other interesting crypto-native categories include AI gaming, on-chain gaming, and general on-chain data.
Accessible data will lead to increased levels of betting activity from non-humans, more specifically autonomous agents. Omen on Gnosis Chain is pioneering the idea of AI agent bettors. Since prediction markets are a game where the outcome is already determined, autonomous agents can become increasingly skilled at calculating expected value, potentially with far greater accuracy than humans. This reflects the idea that it may be more difficult for AAs (autonomous agents) to predict which memecoins will become popular because there is a more "emotional" element to what makes them successful, and humans are currently better at feeling emotions than AAs.
All in all, prediction markets are a fascinating user product and design space. Over time, the vision of allowing anyone to bet on anything at any scale will become a reality. If you are building in this space, whether it is a brand new protocol, a liquidity collaboration platform, or a new leverage mechanism, please get in touch! I’d love to provide feedback.