2% of Users Contribute 90% of Trading Volume: The True Portrait of Polymarket

Original Author: sealaunch intelligence

Original Compilation: Chopper, Foresight News

Most reports on Polymarket only scratch the surface with data: trading volume milestones, user growth, number of trades, open interest, but they never delve into who is actually trading behind these numbers. This article categorizes all active wallets based on two dimensions—transaction frequency and trading volume—to outline the true user profile structure of Polymarket.

The vast majority of Polymarket's trading volume is contributed by a small group of algorithmic and high-frequency traders; the massive number of low-frequency retail users have almost no overlap with these professional traders. Recognizing the differences between these two groups directly determines platform fee design, product priority planning, and market category strategic layout.

Note: All data in this article is sourced from the Dune Analytics dashboard, covering the full wallet-level behavior over the past three months; user profiles are defined by cross-referencing transaction frequency tiers (T1–T7) and transaction amount tiers (V1–V7), with amounts denominated in USD.

User Transaction Frequency and Volume Distribution

Transaction frequency exhibits a typical log-normal distribution decay characteristic. The largest user group traded between 2 and 10 times during the entire study period, accounting for 32% of all users. Adding the user group that traded between 11 and 50 times accounts for nearly two-thirds of the total user base. These individuals typically participate in trading and place small bets during elections, sporting events, or major macroeconomic events.

Transaction Frequency Distribution Chart

The trading volume distribution is completely different. While transaction frequency drops sharply from the left, the volume histogram appears bell-shaped on a logarithmic scale, peaking at approximately $600 to $3,000 per user. This means the typical active user trades around four-figure amounts, but the right tail starting from $25,000 consists of fewer users who dominate the vast majority of the platform's trading volume.

Trading Volume Distribution Chart

These two histograms together reveal a structural split: one part consists of low-frequency participants; the other part consists of high-volume participants, whose footprint is almost invisible on the user chart, but their impact on the volume chart is dominant.

The User Proportion & Volume Concentration Matrix is more intuitive: user concentration is in the low-frequency, small-amount range, while the volume dimension is completely reversed.

How to Build the User Profile System

Relying solely on frequency or volume to segment users ignores the logical connection between the two. Making 500 trades totaling $50 is a completely different type of participant compared to making 500 trades totaling $5 million. We classify each wallet by integrating these two dimensions.

We first assign each wallet to a transaction frequency tier: from T1 (single transaction) to T7 (over 10,000 transactions). Then, we assign it to a transaction volume tier: from V1 (total volume below $100) to V7 (over $2 million). The intersection of these two dimensions produces seven user profiles, each representing a distinct type of participant.

• P1 Single Transaction Dormant User: Only 1 transaction, total amount less than $100, a one-time trial of the platform.
• P2 Low-Activity Retail User: 2–10 transactions, total volume below $1,000, purely event-driven casual participants.
• P3 Moderate Participant: 11–200 transactions, volume $1,000–$10,000, repeatedly enters the market but lacks systematic trading logic.
• P4 Highly Active Retail User: 201–1,000 transactions, volume $10k–$100k, actively and consistently participates but not at an institutional level.
• P5 Low-Frequency High-Net-Worth Whale: Fewer than 50 transactions, single large trades over $100k, selectively targets opportunities with concentrated positions.
• P6 High-Frequency Professional Core: Over 200 transactions, volume exceeding $100k, the group of algorithmic strategists and institutional traders.
• P7 High-Frequency Small-Capital Player: Over 200 transactions, total amount less than $10k, participants with high activity but limited capital.

2% of Users Account for Nearly 90% of Trading Volume

The P2 low-activity retail user group is as large as 849,000, accounting for 69% of total users; the P6 high-frequency, high-investment user group is only 27,000, accounting for about 2%.

However, during the statistical period, the P6 group generated a total trading volume of up to $39 billion. This is the most extreme manifestation of the Pareto Principle: not the conventional 80/20, but 2% of users supporting nearly 90% of the trading volume.

User Profile Summary Table: Seven user types derived from cross-referencing transaction frequency and transaction size tiers.

Number of users, median number of trades, and median trading volume for each user group: the three sets of data show distinctly different user distribution characteristics.

The user growth chart and the trading volume growth chart describe almost entirely different user groups. Platforms targeting user growth and those targeting trading volume growth make completely different product decisions.

Category Preferences of Different User Profiles

Sports and cryptocurrency are the two largest trading categories on Polymarket, accounting for 42% and 31% of total trading volume respectively, with vastly different underlying user structures.

Trading volume share by different user profiles and trading categories.

The proportion of high-frequency, high-capital (P6) traders in the cryptocurrency market is significantly higher than the overall user base, a pattern consistent with algorithmic trading. These participants are not casual bettors but employ systematic strategies for cryptocurrency trading. High trading volume coupled with high trading frequency suggests that trade execution is automated, not based on subjective judgment.

Share of number of trades by different user profiles and categories.

Although sports betting is also dominated by high-frequency, high-capital (P6) trading volume, the proportion of moderate (P3) and high (P4) engagement participants is higher than in the cryptocurrency category. Sports betting involves both institutional algorithmic capital and a large number of experienced manual research players who place firm orders based on subjective judgment, not machine-driven high-frequency iteration.

User share by different user profiles and categories: The user distribution is completely opposite to that of trading volume and number of trades.

The political category has the highest user share at 19%, but the number of users is relatively evenly distributed across various user groups. Low-engagement users (P2) have the highest proportion within the political category. Compared to other categories, these users are typically event-driven, one-time retail participants who register accounts to participate in election betting.

The economics and finance category attracts a disproportionate number of low-frequency, high-capital (P5) participants, meaning participants trade infrequently but with large single-ticket sizes, deploying significant capital into macroeconomic outcomes with relatively few trades.

The categories on the platform directly determine the user groups attracted and influence liquidity depth, user retention, and fee tolerance.

A new cryptocurrency market will attract algorithmic traders and high-frequency traders; a new political market will attract event-driven participants who may never return after the event. More niche market formats like binary options or structured outcome markets might further attract the high-frequency, high-capital (P6) user group, which already dominates the cryptocurrency market. If the goal is trading volume, build for the P6 user group. If the goal is user growth and brand influence, build for the P2 user group. These two objectives require completely different category choices.

Implications for the Fee Model

The layered user profile directly determines the fee design for prediction markets.

A fixed per-trade fee model would excessively suppress the P6 high-frequency, high-capital and P7 high-frequency, small-capital groups; yet it is precisely these groups that support the liquidity foundation essential for the platform's survival.

This is the value of category-differentiated fee rates. Polymarket's current fee system is the implementation of this logic:

• Crypto category has the highest effective fee rate: 1.80%
• Sports category: 0.75%
• Politics & Finance category: 1.00%
• Geopolitics category: Zero fees throughout

This standard is not arbitrarily set but precisely matches the user structure and trading habits of each category. The crypto track is filled with P6 algorithmic professional capital, which can tolerate high fees without disrupting liquidity. The political track is dominated by low-barrier retail users, requiring low friction costs to maintain retention. Fee design detached from user profiles is essentially blind trial and error.

Core Conclusions

• The P6 high-frequency, high-capital group accounts for only 2% of users but creates 88% of the platform's trading volume.
• Fee policies that harm P6 interests would severely damage the platform's foundation.
• 69% of users are low-frequency, small-amount retail users, purely driven by hot events.
• Cryptocurrency trading is highly concentrated among algorithmic high-frequency capital, while the sports track has a more diverse participant structure.
• The average active user makes only 12 trades over 90 days, with a median total investment of $224.
• Expanding into new categories must anchor on the target user profile, not merely chase topic热度.

Conclusion

If trading volume is concentrated in a small, high-frequency core, why does Polymarket position itself as a retail product? Professional algorithmic capital supports the vast majority of the flow, yet product experience, marketing strategies, and category布局 consistently cater to ordinary retail users.

Part of the answer may lie in structural factors. The proliferation of agent frameworks, Telegram bots, and no-code tools allows retail users to easily engage in automated trading. If retail users are now starting algorithmic trading, the next natural evolution is AI agents operating autonomously at large scale and high frequency.

This is precisely why Polymarket might incubate the first killer application at the intersection of cryptocurrency and artificial intelligence. In a liquid, event-driven market with binary outcomes, autonomous agents can operate precisely, absorbing world events, social sentiment, and real-time reasoning information to identify mispriced outcomes and execute trades without human intervention. When this application achieves a breakthrough, it will no longer be just a cryptocurrency product. It will be the moment agent trading goes mainstream.