Written by: Marvellous
Translated by: AididiaoJP, Foresight News
Introduction:
Prediction markets are trading platforms where participants bet on the outcomes of future events, and they are becoming increasingly popular in the cryptocurrency and financial sectors.
However, not all prediction markets are the same. Whether a particular platform is "worth" your time or money depends on a combination of the following three key factors:
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Its market design
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Economic environment
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User-related factors
These factors are crucial in determining whether a prediction market can provide accurate forecasts, sufficient liquidity, and a trustworthy trading experience.
Market Design: Structure, Mechanisms, and Clarity
The concept of market design explores the structure and operation of prediction markets, including trading mechanisms, contract rules, and outcome determination methods. A good design must align incentives and ensure smooth market operation:
Trading Mechanisms:
Prediction markets use different mechanisms to match trades. Some, such as @Polymarket and @Kalshi, use order books, while others like @ZeitgeistPM use automated market maker models, such as LMSR.
Model overview:
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Order book: Highly efficient under high liquidity but performs poorly in illiquid markets.
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Constant Product Market Maker (CPMM, x*y=k): Simple, but high slippage in extreme cases.
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Logarithmic Market Scoring Rule (LMSR): Limited loss and probability normalization, but sensitive to parameters.
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Dynamic LMSR (DLMSR) or pm-AMM: New models addressing liquidity and slippage issues.
Contract Types and Clarity:
A well-designed market must have clearly defined contracts and outcome determination standards. Contracts are usually binary options (yes/no outcome, pays $1 if the event occurs), but can also be multi-outcome or scalar contracts (payout varies based on numerical result).
Note: The questions being bet on must be unambiguous and the outcomes verifiable. Research indicates that "questions with clear determination standards and well-defined terms" are key factors for effective prediction markets.
This is because if market questions are vague or outcomes are subjective, traders will lack confidence, fearing their bets may not be judged fairly.
Outcome Determination and Oracles:
The design must ensure people trust the way outcomes are determined. Thus, traditional prediction markets rely on platform operators or third parties to announce results and pay out rewards, while crypto-based prediction markets use oracles to input real-world outcomes into smart contracts.
For example, @Polymarket uses @UMAprotocol to provide real-world data for market resolution.
A sound resolution mechanism can prevent disputes and manipulation, thereby maintaining market integrity. Therefore, when evaluating a platform, consider:
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Does it have a reliable oracle or arbitrator?
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Is there a possibility of disputes? If so, how are they handled?
Fees and Technical Design:
High transaction costs or slow systems can kill a platform’s usability.
Recall the early decentralized markets, such as Augur (launched as a pioneer on Ethereum in 2018), but users faced high gas fees, low liquidity, and poor user experience, which hindered them from gaining mainstream attention.
Therefore, you should consider which chain the product is deployed on. For example, @GroovyMarket_ launched on @SeiNetwork, @Polymarket on @0xPolygon, and @triadfi on @solana.
The platforms I mentioned have one thing in common: the chains they are built on ensure lower transaction fees and faster trading speeds.
And by simplifying the user interface. For example, Polymarket is built on Polygon (an Ethereum sidechain) and uses USD stablecoins for trading, providing a fast and stable trading experience without exposing users to volatile crypto prices. It also charges 0% trading fees, making trading frictionless. Compared to first-generation platforms, such design choices greatly enhance usability.
In addition, you need to assess the fees charged by these platforms (market creation fees, trading fees, deposit/withdrawal fees, profit fees, etc.).
In short, if a prediction market’s design can provide a clear and fair structure: an efficient trading mechanism with ample liquidity supply, transparent rules, and trustworthy outcome determination, then it is worthwhile.
Poor design (slow trading, unclear rules, or untrusted outcomes) may be directly rejected by the market.
Economic Factors: Liquidity, Pricing, and Incentives
I believe every excellent design needs an economic model to succeed, because key economic factors will determine whether a prediction market can effectively aggregate information and appropriately reward participants.
Liquidity and Market Depth:
The concept of liquidity explains that there must be enough active trading and capital in the market so that traders can buy and sell at fair prices without experiencing huge slippage.
For a long time, sufficient liquidity has been an absolutely critical consideration.
Research has found that the effectiveness of prediction markets depends on "sufficient market liquidity" and a large number of traders. If only a few people are trading, prices may fluctuate wildly or stagnate, failing to reflect true probabilities. Therefore, a balance must be achieved.
Look for platforms with high trading volume or liquidity pools. For example, Polymarket has become the largest decentralized prediction market, accounting for about 94% of total market trading volume in 2024, handling over $8.4 billions in bets, despite new challengers emerging this year.
Such massive liquidity (especially during major events like the US elections) means its odds have enough market depth to support them, making it harder for any single user to manipulate prices.
Accurate Pricing (Information Aggregation):
The core idea of prediction markets is that market prices reveal the crowd’s collective belief about the probability of events. Therefore, when the economic mechanism is sound, i.e., many informed traders with capital participate, market prices become very accurate predictions.
In fact, well-functioning markets outperform polls. Recall:
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The Iowa Electronic Market’s election predictions beat professional polling agencies 74% of the time.
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Google’s internal prediction market made more accurate forecasts than company experts.
However, if market liquidity is insufficient or dominated by uninformed bets, prices may be less reliable.
Therefore, always consider the platform’s track record:
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Does the platform have examples where its odds correctly predicted outcomes when other forecasters failed?
Notably, during the 2024 US election, Polymarket’s odds were closely watched and even outperformed traditional polls, attracting the attention of figures like Elon Musk. This is an important area to consider.
Incentive Alignment:
Economic design should also cover how traders are rewarded and the cost of participation. Low or zero fees are a huge advantage, as high fees hinder frequent trading or arbitrage, both of which help keep prices accurate.
Platforms like Polymarket charge no trading fees, and some other markets even subsidize participation through token rewards or yields. Additionally, some markets reward information discovery, such as offering prizes or reputation to top predictors, to encourage knowledgeable participants.
A healthy prediction market economy will make it profitable for traders to correct mispriced odds, so attempts to manipulate prices are usually self-correcting. For example, if someone bets irrationally, others have an economic incentive to take the opposite position and push prices back to rational levels. If a market is very small, a wealthy manipulator may temporarily influence odds, so scale becomes important again.
Risk and Regulatory Costs:
Another economic consideration is the risks involved, not just the risk of losing a bet, but also counterparty risk and regulatory risk. In crypto prediction markets, smart contract security is crucial (since funds are held by code).
On centralized platforms, you rely on the company’s solvency and integrity.
Note that regulatory crackdowns can impose costs at any time. For example, after Polymarket settled with the US Commodity Futures Trading Commission (CFTC) (fined $1.4 millions), it had to geoblock US users because it was operating unregulated event markets.
During this period when US users were excluded, liquidity in certain markets reportedly declined. Similarly, some countries completely ban prediction markets.
By the end of 2024, France, Singapore, and Thailand all blocked access to Polymarket. In practice, these factors can economically impact a platform (reducing its user base or forcing compliance costs).
Therefore, a "worthwhile" market should have a stable legal foundation or contingency plans. Otherwise, participants face the economic risk of sudden shutdowns or inability to cash out.
Essentially, the economics of prediction markets must ensure enough stakeholders and smooth trading. The best markets will have ample participation, low transaction costs, and mechanisms that incentivize accurate predictions.
User and Community Factors: Engagement, Trust, and Experience
Regardless, I like to consider user-related factors, which are essentially the human side of the market, because the effectiveness of prediction markets depends on their users and the surrounding community.
Therefore, key points to assess include:
Scale of Participation:
Prediction markets rely on scale. The more individuals participate, the more effective they are. A large and active user base means diverse information and perspectives are brought to the table.
Diversity of opinion is crucial
If all traders think alike (or collude), the market cannot aggregate independent information. Therefore, it’s important to focus on the following metrics:
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Number of active users
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Number of bets placed
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Open interest, etc.
Overall, a platform with thousands of actively participating traders is much more robust than one with only a handful of users. Actively engaged participants with diverse informational backgrounds are one of the key drivers of prediction market accuracy.
For example, Augur is fully decentralized, but its early versions had very few active users. Despite its technical novelty, this limited its effectiveness.
In contrast, Polymarket achieved critical user scale by offering markets on popular topics (elections, sports, crypto prices) and making onboarding easy (global, no KYC, simple web interface). This scale of participation greatly enhances the "wisdom of the crowd" effect.
User Experience and Accessibility:
Even for crypto-native users, user experience matters. Platforms that are too complex or require complicated wallet setups will scare off users.
Pay attention to emerging prediction markets that focus on smooth onboarding experiences, as clean interfaces, useful charts, and clear odds displays will attract more users, which in turn improves market quality.
On the other hand, cumbersome processes (e.g., requiring manual acquisition and staking of specific tokens to bet, or long waits for transaction finality) may make traders feel the market is not worth the effort.
Therefore, always consider how easy it is to use a platform.
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Can you conveniently deposit funds?
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Does it support mobile devices?
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If problems arise, is there customer support or community help?
Reputation and Community Trust:
Since real money is involved, trust is crucial. Trust can come from transparency (open-source code, audited contracts, or reputable backers) or from a track record of fair operation.
Therefore, check whether the platform has had any scandals or failed to pay out. Community-operated and decentralized markets like Polymarket seem trustless, while others like Kalshi build trust through full regulation and compliance. As we saw in 2024, Kalshi became the first CFTC-regulated exchange in the US to offer legal event contracts, even winning a lawsuit to offer election betting.
This regulatory stamp of approval gives credibility and signals to users that they can trust the platform to operate within legal boundaries.
Meanwhile, platforms operating in gray areas are a red flag. Either you are decentralized and your code is audited, or you are fully regulated.
User Incentives and Behavior:
Another human factor is why users participate. Are they amateur gamblers, profit-seeking traders, or domain experts hedging risk? I believe markets with strong communities of forecasters may generate better insights.
A platform’s culture—whether it feels more like a gambling environment or a serious forecasting tool—will affect whether it suits your purposes. When deciding if a prediction market is worth using, evaluate the community:
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Is it active and serious?
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Do they have opposing viewpoints?
The presence of "actively engaged participants with diverse information" is one of the key factors for prediction market success.
I believe a constructive community will support meaningful markets with properly determined outcomes, while a poorly managed community may indulge in ill-defined markets.
In short, user factors boil down to the number and quality of participants. Therefore, a platform with a large, diverse, and actively engaged user base that has earned their trust is more likely to provide a valuable experience.
If a market has almost no users or community, no matter how good the technology behind it, you might want to avoid it. After all, prediction markets are a form of crowdsourcing, which means that without a "crowd," there’s nothing to participate in.
Final Summary:
When evaluating a prediction market, always return to the three core considerations:
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Market design
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Economic viability
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User factors
A platform with sound mechanisms, ample liquidity, and a vibrant, trustworthy community is more likely to provide value in profitable trading opportunities and accurate predictions.
