2% user contribution, 90% trading volume: The real picture of Polymarket
Original author: sealaunch intelligence
Original translation: Chopper, Foresight News
Most reports about Polymarket only scratch the surface: trading volume milestones, user growth, transaction counts, and open positions, but never delve into who is actually trading behind these numbers. This article categorizes all active wallets from two dimensions: trading frequency and trading volume, outlining the true user profile structure of Polymarket.
The vast majority of trading volume on Polymarket is contributed by a small group of algorithmic traders and high-frequency trading groups; there is almost no overlap between the massive low-frequency retail investors and these professional traders. Understanding the differences between these two groups directly determines the platform's fee structure, product prioritization, and market category strategy.
Note: All data in this article comes from the Dune data dashboard, covering nearly three months of wallet-level behavior; user profiles are cross-defined based on trading frequency levels (T1--T7) and trading amount levels (V1--V7), with the amount statistics in US dollars.
User Trading Frequency and Volume Distribution
Trading frequency shows a typical log-normal distribution decay characteristic. The largest user group has between 2 to 10 transactions during the entire study period, accounting for 32% of all users. Together with the user group that has between 11 to 50 transactions, they account for nearly two-thirds of the total user base. These individuals typically participate in trading during elections, sports events, or significant macroeconomic events, betting small amounts of money.
Trading frequency distribution chart
The trading volume distribution, however, is entirely different. Although the transaction frequency sharply declines from the left side, the trading volume histogram presents a bell shape on a logarithmic scale, peaking at around $600 to $3000 per user. This means that the typical active user's trading amount is in the four-digit range, but the number of users on the right tail starting from $25,000 is small, yet they account for 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 footprints are almost invisible in the user chart, but their impact on the trading volume chart is dominant.
User proportion & volume concentration matrix is more intuitive: user dimension is concentrated in the low-frequency small amount range, while the volume dimension is completely reversed
How to Build a User Profile System
Relying solely on frequency or volume to categorize users ignores the correlation logic between the two. Completing 500 transactions with a total amount of $50 is entirely different from completing 500 transactions with a total amount of $5 million. We classify each wallet based on these two dimensions.
We first assign each wallet to different trading frequency levels: from T1 (single transaction) to T7 (over 10,000 transactions). Then, we assign them to different trading volume levels: from V1 (total trading amount below $100) to V7 (over $2 million). The intersection of these two dimensions generates seven user profiles, each representing a distinctly different type of participant.
- P1 Single Silent User: Only 1 transaction, total amount less than $100, a one-time trial experience of the platform
- P2 Low-Active Retail Investor: 2--10 transactions, total amount below $1000, casually participating driven by hot events
- P3 Moderate Participant: 11--200 transactions, amount $1000--$10,000, repeatedly entering but without systematic trading logic
- P4 High-Depth Retail Investor: 201--1000 transactions, amount $10,000--$100,000, actively and steadily participating but not at an institutional level
- P5 Low-Frequency High-Net-Worth Investor: Less than 50 transactions, single large amount over $100,000, selectively seizing opportunities with concentrated positions
- P6 High-Frequency Professional Main Force: Over 200 transactions, amount over $100,000, algorithmic strategies and institutional trader groups
- P7 High-Frequency Small Player: Over 200 transactions, total amount less than $10,000, participants with high activity but limited capital
2% of Users Account for Nearly 90% of Trading Volume
The scale of P2 low-active retail investors reaches 849,000, accounting for 69% of all users; the P6 high-frequency high-investment users number only 27,000, accounting for about 2%.
However, during the statistical period, the P6 group generated a total trading amount of up to $39 billion. This is the most extreme manifestation of the Pareto principle: not the conventional 80/20, but rather 2% of users supporting nearly 90% of the trading volume.
User profile summary table: Seven major user types derived from the intersection of trading frequency and trading scale
The number of users in each group, median transaction counts, and median transaction amounts: the three sets of data present distinctly different user distribution characteristics
The user growth chart and trading volume growth chart describe almost completely different user groups. Platforms targeting user growth and those targeting trading volume growth have entirely different product decisions.
Category Preferences of Different User Profiles
Sports and cryptocurrencies are the two largest trading categories on Polymarket, accounting for 42% and 31% of total trading volume, respectively, with significant differences in the underlying user structures.
Trading volume share of 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, which aligns with algorithmic trading. These participants are not casual bettors but use systematic strategies for cryptocurrency trading. The high trading volume and frequency indicate that trade execution is automated rather than based on subjective judgment.
Transaction count share of different user profiles and categories
While sports betting is also dominated by high-frequency, high-capital (P6) trading volume, the proportion of moderate (P3) and high (P4) participation users is higher than in the cryptocurrency category. Sports betting includes both institutional algorithmic funds and a large number of experienced manual judgment players who make firm orders based on subjective judgment rather than machine-driven high-frequency iterations.
User proportion of different user profiles and categories: user distribution is completely opposite to trading volume and transaction count
Political users account for the highest proportion at 19%, but the user numbers are distributed relatively evenly among various user groups. Low-participation users (P2) have the highest proportion among political users, and compared to other categories, these users are typically one-time retail investors driven by events, registering accounts to participate in election betting.
The economic and financial sectors attract a disproportionately high number of low-frequency high-capital (P5) participants, indicating that these participants trade infrequently but with large single transaction amounts, investing substantial capital into macroeconomic outcomes while trading relatively less often.
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 ends. More specialized market forms like binary options or structured outcome markets may further attract high-frequency high-capital (P6) user groups, who have already dominated the cryptocurrency market. If the goal is trading volume, then build towards the P6 user group. If the goal is user growth and brand influence, then build towards the P2 user group. These two goals require entirely different category choices.
Insights for Fee Model
User stratification profiles directly determine the fee design of prediction markets.
A fixed per-transaction fee model will overly suppress the P6 high-frequency high-capital and P7 high-frequency small groups; yet it is precisely these groups that support the liquidity foundation on which the platform relies.
The value of differentiated fee rates by category lies here; Polymarket's current fee rate system is precisely based on this logic:
- Cryptocurrency sector effective fee rate: 1.80%
- Sports sector: 0.75%
- Political & Financial sector: 1.00%
- Geopolitical sector: zero fees throughout
This standard is not arbitrarily set but is precisely matched to the structural and trading habits of each category's user group. The cryptocurrency sector is filled with P6 algorithmic professional funds that can withstand high fees without disrupting liquidity; the political sector primarily consists of low-barrier retail investors who must lower friction costs to maintain retention. Designing fee rates without considering user profiles is essentially blind trial and error.
Core Conclusions
- The P6 high-frequency high-capital group accounts for only 2% of users, creating 88% of the platform's trading volume;
- Fee policies that harm P6 interests will severely damage the platform's foundation;
- 69% of users are low-frequency small retail investors, purely driven by hot events;
- Cryptocurrency trading is highly concentrated among algorithmic high-frequency funds, while the sports sector has a more diverse participant structure;
- Ordinary users complete an average of only 12 transactions within 90 days, with a median total investment of $224;
- Expanding new categories requires anchoring target user profiles, rather than simply chasing topic popularity.
Conclusion
If trading volume is concentrated in a small high-frequency core area, why does Polymarket position itself as a retail product? Professional algorithmic funds support the vast majority of the flow, yet product experience, marketing strategies, and category layouts always cater to ordinary retail investors.
Part of the answer may lie in structural factors. The proliferation of agent frameworks, Telegram bots, and no-code tools allows retail investors to easily engage in automated trading. If retail investors have begun algorithmic trading, the next natural evolution would be AI agents autonomously operating on a large scale with high frequency.
This is also why Polymarket may give rise to the first killer application at the intersection of cryptocurrency and artificial intelligence. In a market characterized by strong liquidity, event-driven dynamics, and binary outcomes, autonomous agents can operate precisely, absorbing global events, social sentiments, and real-time reasoning information, identifying mispriced trading outcomes, and executing trades without human intervention. When this application achieves breakthrough progress, it will no longer be just a cryptocurrency product. This will be the moment when agent trading enters the mass market.
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