Prediction Market Guide: How to Select the Most Promising Platform?

Author: Marvellous

Compiled by: AididiaoJP, Foresight News

Original title: If the next big trend comes from prediction markets, how should we choose the most promising platforms?

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 finance sectors.

However, not all prediction markets are the same. Whether a particular platform is "worth" investing your time or money depends on a comprehensive consideration of the following three key factors:

• Its market design
• Economic environment
• User factors related to it

These factors are crucial in determining whether a prediction market can provide accurate forecasts, sufficient liquidity, and a reliable trading experience.

Market Design: Structure, Mechanism, 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 the smooth operation of the market.

Trading Mechanism:

Prediction markets use different mechanisms to match trades. Some, like @Polymarket and @Kalshi, use order books, while others, like @ZeitgeistPM, use automated market maker models such as LMSR.

Model Overview:

• Order Book: Highly efficient under high liquidity, but performs poorly in illiquid markets.
• Constant Product Market Maker (CPMM, x*y=k): Simple, but high slippage in extreme cases.
• Logarithmic Market Scoring Rule (LMSR): Loss is limited and probabilities are normalized, but it is sensitive to parameters.
• Dynamic LMSR (DLMSR) or pm-AMM: A new model that addresses liquidity and slippage issues.

Contract Type and Clarity:

A well-designed market must have clearly defined contracts and outcome determination criteria. Contracts are typically binary options (yes/no outcomes, paying $1 if the event occurs), but they can also be multi-outcome or scalar contracts (payments vary based on numerical results).

Please note: The questions for betting must be clear and unambiguous, and the results must be verifiable. Research indicates that "questions with clear criteria for judgment and well-defined definitions" are key factors for effective prediction markets.

This is because if market issues are vague or the results are subjective, traders will lack confidence and worry that their bets will not be fairly judged.

Result Determination and Oracles:

Design must ensure that people trust the way results are determined. Therefore, traditional prediction markets rely on platform operators or third parties to announce results and pay out prizes, while prediction markets in the crypto space use oracle to input real-world results into smart contracts.

For example, @Polymarket utilizes @UMAprotocol to provide real-world data for market judgments.

A sound judgment mechanism can prevent disputes and manipulation, thereby maintaining market integrity. Therefore, when evaluating a platform, please consider:

• Does it have reliable oracles or arbitrators?
• Is it possible for disputes to arise? If so, how will they be handled?

Costs and Technical Design:

High transaction costs or slow systems can stifle the usability of the platform.

Reflecting on the early decentralized markets, such as Augur (launched as a pioneer on Ethereum in 2018), users faced high gas fees, low liquidity, and poor user experience, which hindered their mainstream attention.

Therefore, you should consider which chain to deploy the product on, for example, @GroovyMarket_ launching on @SeiNetwork, @Polymarket on @0xPolygon, and @triadfi on @solana.

The platforms I mentioned have a common point: the chains they build ensure lower transaction fees and faster transaction 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 the volatility of cryptocurrency prices. It also charges 0% trading fees, making transactions frictionless. Compared to first-generation platforms, such design choices significantly enhance usability.

In addition, you need to assess the fees charged by these platforms (market-making fees, trading fees, deposit/withdrawal fees, profit fees, etc.).

In summary, if the design of a prediction market can provide a clear and fair structure: an efficient trading mechanism with sufficient liquidity supply, transparent rules, and reliable result determination, then it is worthwhile.

Poor design (slow transactions, unclear rules, or untrusted outcomes) may be directly rejected by the market.

Economic Factors: Liquidity, Pricing, and Incentives

I believe that every excellent design needs an economic model to succeed, as key economic factors will determine whether a prediction market can effectively aggregate information and reward participants accordingly.

Liquidity and Market Depth:

The concept of liquidity explains that there needs to be sufficient active trading and capital in the market so that traders can buy and sell at fair prices without experiencing significant slippage.

For a long time, sufficient liquidity has absolutely been a crucial 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 dramatically or stagnate, failing to reflect true probabilities. Therefore, a balance needs to 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 the total market trading volume in 2024, processing over $8.4 billion in bets, despite new challengers emerging this year.

Such massive liquidity (especially during significant events like the U.S. elections) means that its odds have enough market depth to support them, making it more difficult for any single user to manipulate prices.

Accurate Pricing (Information Aggregation):

The core idea of prediction markets is that market prices reveal the collective beliefs of the crowd about the probabilities of events. Therefore, when the economic mechanism is sound, meaning that there are many informed traders with capital participating, market prices will become very accurate predictions.

In fact, a well-performing market outperforms opinion polls. Recall:

• The Iowa Electronic Market's election forecasts outperformed professional polling organizations 74% of the time.

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

However, if the market lacks liquidity or is dominated by uninformed bets, the prices may be less reliable.

Therefore, always consider the platform's track record:

• Are there examples on the platform where the odds correctly predicted the outcome when other predictors failed?

It is worth noting that during the 2024 U.S. election, the odds on Polymarket 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 earn rewards and the cost of participation. Low fees or zero fees are a huge advantage, as high fees can hinder frequent trading or arbitrage, which helps maintain price accuracy.

Platforms like Polymarket do not charge trading fees, and some other markets even subsidize participation through token rewards or profits. Additionally, some markets can reward information discovery, such as offering prizes or reputation to the best 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 often self-correcting. For example, if someone irrationally bets, others have an economic incentive to take the opposite position, pushing the price back to a rational level. If a market is very small, a wealthy manipulator may temporarily influence the odds, so scale becomes important again.

Risk and Regulatory Costs:

Another economic consideration is the risks involved, not only the risk of betting failure but also counterparty risk and regulatory risk. In crypto prediction markets, the security of smart contracts is crucial (as funds are held by code).

On centralized platforms, you rely on the company's solvency and integrity.

Please note that regulatory crackdowns can bring costs at any time. For example, Polymarket had to geo-block U.S. users after reaching a settlement with the Commodity Futures Trading Commission (CFTC) (fined $1.4 million) due to its operation of an unregulated event market.

During this period when American users were excluded, liquidity in certain markets is presumed to have decreased. Similarly, some countries have completely banned prediction markets.

By the end of 2024, France, Singapore, and Thailand have all blocked access to Polymarket. In fact, these factors can economically impact a platform (reducing its user base or forcing compliance costs to be incurred).

Therefore, a "worthy" market should have a stable legal foundation or contingency plan. Otherwise, participants will face sudden shutdowns or economic risks that cannot be realized.

Essentially, the economy of prediction markets must ensure that there are enough stakeholders and smooth transactions. 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 essentially represent the human aspect of the market, because the effectiveness of predicting the market depends on its users and the surrounding community.

Therefore, the key points that need to be evaluated include:

Participation Scale:

The effectiveness of prediction markets relies on scale. The more individuals that participate, the more effective they become. A large and active user base means a diversity of information and perspectives is brought to the table.

Diversity of perspectives is crucial.

If all traders have the same thoughts (or collude), the market cannot aggregate independent information. Therefore, it is important to pay attention to the following indicators:

• Active Users
• Quantity that has been bet
• Open Contracts etc.

Overall, a platform with thousands of actively participating traders is much more robust than one with only a few users. Actively engaged participants with diverse informational backgrounds are one of the key driving forces that make prediction markets accurate.

For example, Augur is completely decentralized, but its early version had very few active users. Although the technology is innovative, it limited its effectiveness.

In contrast, Polymarket has gained a critical scale of users by providing markets on popular topics (elections, sports, cryptocurrency prices) and making entry easy (no KYC required globally, simple web interface). This scale of participation greatly enhances the "wisdom of the crowd" effect.

User Experience and Accessibility:

User experience is important even for crypto-native users. Platforms that are too complex or require complicated wallet setups can scare users away.

Pay attention to emerging prediction markets that focus on a smooth onboarding experience, as a clean interface, useful charts, and clear odds display will attract more users, which in turn improves market quality.

On the other hand, cumbersome processes (for example, the need to manually acquire and stake specific tokens to place a bet, or the long wait times required to process transaction finality) may lead traders to feel that the market is not worth the effort to participate.

Therefore, always consider the ease of use of a platform.

• Can you easily deposit funds?
• Does it support mobile devices?
• If there are issues, is there customer support or community help available?

Reputation and Community Trust:

Since it involves real money, trust is crucial. Trust can come from transparency (open-source code, audited contracts, or reputable supporters), or from a record of fair operations.

Therefore, check if the platform has had any scandals or incidents of failure to pay. Community-operated and decentralized markets like Polymarket seem to be trustless, while other markets like Kalshi build trust through full regulation and compliance. As we see in 2024, Kalshi becomes the first CFTC-regulated exchange in the U.S. to offer legal event contracts and even won the lawsuit to provide election betting.

This regulatory approval mark provides credibility and indicates to users that they can trust the platform to operate within legal boundaries.

At the same time, platforms operating in the gray area are a warning sign. Either you are decentralized with audited code, or you are fully regulated.

User Incentives and Behavior:

Another human factor is the reason for user participation. Are they amateur gamblers, profit-seeking traders, or domain experts hedging risks? I believe that a market with a strong community of predictors may generate better insights.

The culture of a platform, whether it resembles a gambling atmosphere or a serious prediction tool, will impact its suitability for your purposes. When deciding if a prediction market is worth using, evaluate the community:

• Is it active and serious?
• Do they have opposing views?

The presence of "active participants with diverse information" is one of the key factors for predicting the success of the market.

I believe that a constructive community will support meaningful markets that can properly determine outcomes, while a poorly managed community may indulge in poorly defined markets.

In summary, 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 valuable experiences.

If a market has almost no users or community, then regardless of the technology behind it, you might want to steer clear. 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, it is essential to return to three core considerations:

• Market Design
• Economic Feasibility
• User factors

A community platform with a sound mechanism, sufficient liquidity, and a vibrant, trustworthy community is more likely to provide value in terms of profitable trading opportunities and accurate predictions.