Original article | @marvellousdefi_

Compiled by Odaily Planet Daily ( @OdailyChina )

Translator | Dingdang ( @XiaMiPP )

Editor's Note: Prediction market protocols are emerging one after another, from Polymarket, Kalshi, to Zeitgeist, all attracting significant capital and user influx. On the surface, prediction markets appear to be a natural "future information aggregator," enabling both betting on event outcomes and capturing the wisdom of the crowd. (Related: " The Evolution of Prediction Markets in 2025: From the Polymarket Rush to the Compliant, Socially Embedded Explosion of Kalshi ")

But the problem is that not every prediction market is worth your time or money. So how do we judge whether a prediction market project is valuable?

If you’re not familiar with the concept of prediction markets, they are a type of exchange where participants bet on the outcomes of future events, and they are becoming increasingly popular in the crypto and financial sectors.

However, not all prediction markets are created equal. Evaluating whether a platform is “worthwhile” (in terms of investing time and money) depends on a combination of three key factors:

• Market Design
• Economic environment
• User-related factors

Each of these factors is crucial, determining whether a prediction market can provide accurate predictions, sufficient liquidity, and a trustworthy trading experience. Let’s break them down one by one.

1. Market Design: Structure, Mechanisms, and Clarity

Market design explores the structure and operation of prediction markets—including trading mechanisms, contract rules, and how outcomes are determined . I believe a good design must both properly align incentives and ensure smooth market operation.

i) Trading Mechanism

Different prediction markets use different methods for matching trades. Some platforms (like @Polymarket , @Kalshi ) use order books, while others (like @ZeitgeistPM ) use automated market makers (AMMs) such as LMSR.

Overview of common models:

• Order Book (CDA): Efficient when liquidity is sufficient, but performs poorly in a quiet market.
• CPMM (x*y=k): Simple, but with large slippage at price extremes.
• LMSR: loss is bounded and probability is normalized, but parameter sensitive.
• DLMSR/pm-AMM: A newer model designed to address liquidity and slippage issues.

ii) Contract type and clarity

A good market must have clearly defined contracts and settlement criteria . Typically, contracts are categorized as binary options (yes/no outcomes, paying $1 if an event occurs), or multiple-outcome contracts or scalar contracts (with tiered payouts based on the numerical outcome).

It is important to note that the problem being bet on must be clear and verifiable. Research indicates that “clearly defined problems with clear settlement criteria” are key factors for efficient prediction markets.

Because if the questions are vague or the results are subjective, traders cannot trust that their bets will be settled fairly.

iii) Result Adjudication and Oracle

The design must ensure the trustworthiness of the outcome. Traditional prediction markets rely on the platform or a third party to adjudicate and pay out prizes, while crypto prediction markets use oracles (on-chain trusted data sources) to input real-world outcomes into smart contracts.

Not familiar with crypto oracles? You can refer to this: https://x.com/marvellousdefi_/status/1812810604454789141

For example, @Polymarket uses @UMAprotocol to provide real-world data (such as election results) to complete market settlement.

A robust arbitration mechanism prevents disputes and manipulation, thus maintaining market integrity. Therefore, when evaluating a platform, consider:

• Does it have a reliable oracle or arbitrator?
• Are disputes likely to arise? If so, how are they handled?

iv) Fees and technical design

Excessively high transaction costs or slow systems can directly “kill” the platform’s usability.

Looking back at early decentralized markets, such as Augur (a pioneer on Ethereum) launched in 2018, it struggled to gain mainstream adoption because users faced high gas fees, insufficient liquidity, and a poor experience.

Therefore, you need to consider which chain your product will be deployed on, for example: @GroovyMarket_ on @SeiNetwork , @Polymarket on @0xPolygon , @triadfi on @solana , etc.

What these platforms have in common is that their chosen blockchains guarantee lower transaction fees and faster transaction speeds, along with a simplified user interface. For example, Polymarket, built on Polygon (an Ethereum sidechain), uses USD stablecoins for transactions, offering a fast and stable experience while preventing users from directly exposing themselves to cryptocurrency price fluctuations. It also charges 0% transaction fees, making transactions virtually frictionless. This design significantly enhances the user experience, making first-generation platforms seem clunky by comparison.

In addition, you also need to evaluate the various fees charged by the platform: market creation fees, transaction fees, deposit/withdrawal fees, profit handling fees, etc.

In summary: Whether a prediction market design is “worthwhile” depends on whether it has a clear and fair structure: an efficient trading mechanism, sufficient liquidity, transparent rules, and a credible settlement method.

Poor design (slow transactions, unclear rules, or distrusted adjudicators) can kill a market before it even gets off the ground.

2. Economic factors: liquidity, pricing, and incentives

I believe that even the best design requires economic support. Key economic factors determine whether a prediction market can effectively aggregate information and appropriately reward participants. Let's examine each of these factors.

i) Liquidity and market depth

Liquidity refers to the presence of sufficient active trading and funds in the market to allow traders to buy and sell at reasonable prices without incurring significant slippage. Liquidity has long been one of the most important considerations.

Research has found that the effectiveness of prediction markets relies on "sufficient market liquidity" and a large trading community. If only a few people are trading, prices may fluctuate wildly or stagnate, failing to reflect true probabilities. Therefore, a balance is necessary.

Look for platforms with high trading volume or liquidity pools. For example, Polymarket has become the largest decentralized prediction market, holding approximately 94% of the market share in 2024 and processing over $8.4 billion in bets, even with the emergence of new competitors this year.

This massive liquidity, especially during major events like the US election, means there’s deep market support behind the odds, reducing the likelihood of price manipulation by a single user.

ii) Pricing accuracy (information aggregation)

The core concept of prediction markets is that market prices reveal the public’s collective beliefs about the probability of an event. When economic conditions are sound—that is, when there are a large number of informed, invested traders—market prices become highly accurate forecasts.

In fact, mature markets often outperform polls and experts:

• The Iowa Electronic Markets outperformed professional pollsters 74% of the time in their election predictions.

• Google's internal prediction market is more accurate than the company's experts.

These are all well-known cases supporting the power of prediction markets.

However, if the market is too small or dominated by uninformed bets, prices are less reliable.

Therefore, always examine its historical performance:

• Does the platform have examples of times where its odds predictions were correct when other predictors were wrong?

Notably, Polymarket’s odds were closely watched during the 2024 US election, even outperforming traditional polls and attracting the attention of figures like Elon Musk.

iii) Incentive alignment

Economic design should also consider how traders are rewarded and the cost of participation. Low or zero fees are a significant advantage, as high fees discourage frequent trading or arbitrage activities, which are crucial for ensuring accurate prices.

Platforms like Polymarket don’t charge transaction fees, while some even subsidize participation through token rewards or earnings. Furthermore, some platforms reward information discovery, such as offering bonuses or reputation points to the best predictors, to encourage knowledgeable participants.

I believe that a healthy prediction market economy will allow traders to profit from correcting incorrect odds, so that attempts to manipulate prices will generally be self-correcting. For example, if someone makes an irrational bet, others have an economic incentive to take the opposite side, thereby bringing the price back to a rational level.

But be careful: if the market is small, wealthy manipulators can still swing the odds in the short term, so scale still matters.

iv) Risks and regulatory costs

Another economic consideration is risk—not just the risk of losing money, but also counterparty and regulatory risk. In crypto prediction markets, smart contract security is crucial (because funds are held in escrow by the code). On centralized platforms, you rely on the solvency and integrity of the company.

It’s important to note that regulatory crackdowns can come with significant costs. For example, Polymarket was fined $1.4 million by the U.S. Commodity Futures Trading Commission (CFTC) for operating an unregulated event market and was forced to block U.S. users.

During this period, liquidity in some markets declined. Similarly, some countries banned prediction markets outright. By the end of 2024, France, Singapore, and Thailand had all blocked access to Polymarket. In practice, these actions have an impact on platform economics (reducing user base or forcing them to bear compliance costs).

Therefore, a "worthy" market must have a stable legal basis or emergency plan. Otherwise, participants may face the risk of sudden closure or inability to withdraw cash.

In summary: The economic foundation of a prediction market must ensure sufficient "real money" participation and smooth transactions. The best markets will have a large number of active users, low transaction costs, and incentives for accurate predictions.

3. User and Community Factors: Engagement, Trust, and Experience

I also tend to start with user-related factors, which are essentially the human side of the market, because the effectiveness of a prediction market depends on its users and community.

Key points to assess include:

i) Scale of participation

Prediction markets rely on scale. The more participants there are, the more efficient the market becomes. A large and active user base means more diverse information and perspectives are introduced.

ii) Diversity of viewpoints

Diversity is crucial. If all traders were thinking the same way (or colluding), the market would be unable to aggregate independent information.

Therefore, the following indicators should be paid attention to:

• Number of active users
• Number of bets and number of open contracts

In general, platforms with thousands of active traders are far more robust than those with only a handful of users. Participants with diverse information backgrounds are the key driver of market prediction accuracy.

For example, despite being fully decentralized, Augur had few early adopters, limiting its effectiveness.

In contrast, Polymarket quickly gained a critical mass of users by offering markets on trending topics (elections, sports, crypto prices) and simplifying onboarding (no KYC required globally, simple web interface). This significantly enhanced the “wisdom of the crowd” effect.

iii) User experience and accessibility

Even for crypto-native users, experience is important. Platforms that are overly complex or require tedious wallet setup will discourage users.

Emerging prediction markets generally emphasize a smooth onboarding experience: a simple interface, intuitive charts, and clear odds display can attract more users and thus improve market quality.

On the other hand, if the operation is cumbersome (such as having to manually purchase and stake a certain token, or waiting too long for transaction confirmation), ordinary users may feel that it is "not worth it."

Therefore, you need to consider:

• Is it convenient to recharge on the platform?
• Is it supported on mobile devices?
• If something goes wrong, is there customer service or community help?

iv) Community reputation and trust

Since real money is involved, trust is paramount. Sources of trust include transparency (open source code, audited contracts, credible investors), and a track record of fair operations.

Check to see if the platform has a history of scandals or denials of payments.

Decentralized marketplaces like Polymarket, where funds aren't controlled by a centralized institution, are considered "trustless." Others, like Kalshi, build trust through full regulatory compliance. For example, in 2024, Kalshi became the first exchange to obtain CFTC regulation and legally offer event contracts in the United States, winning a lawsuit that allowed election betting.

Such regulatory endorsement enhances credibility, indicating that users can trust it to operate within a legal framework.

On the contrary, platforms that operate in a gray area are dangerous signals. Either they are fully decentralized and their code is auditable, or they are fully compliant.

v) User motivations and behaviors

Another human factor is: Why do users participate? Is it for interest, profit-seeking trading, or expert risk hedging? I believe that a community of professional forecasters (academics or experts in related fields) may bring higher-quality insights.

A platform’s culture—whether it’s a gambling atmosphere or a serious prediction tool—will also influence whether it’s suitable for your purposes. When deciding whether a market is worth using, observe the community’s:

• Is it active and serious?
• Is there diversity of opinion?

"Having a diverse and actively engaged user base" has been shown to be an important factor in the success of prediction markets.

I believe a constructive community drives meaningful, correctly settled markets, while a poor community is likely to be filled with obscure or troll-like questions.

In summary: The user factor ultimately comes down to the quantity and quality of participation. A platform with a large, diverse, and trusted user base is more likely to provide a valuable experience. If a market is largely unused or the community is toxic, then no matter how advanced the technology, it's not worth getting involved. After all, prediction markets are a form of "crowdsourcing"—without the crowd, there's no outsourcing.

Final Thoughts

When evaluating prediction markets, always return to three core points:

• Market Design
• Economic feasibility
• User factors

A platform with reasonable mechanisms, sufficient liquidity, and an active and trustworthy community is more likely to provide valuable experiences - including both profit opportunities and accurate predictions.