Dissecting 112,000 Polymarket Addresses: The Top 1% Who Truly Profit Are All Doing These Five Things

Original Title:I Analyzed 112,000 Polymarket Wallets. Here's What Separates the Top 1% from Everyone Else, Author: darkzodchi (@zodchiii)

Compiled by | Odaily (@OdailyChina); Translator | Asher (@Asher_ 0210)

After systematically sorting and analyzing over 112,000 Polymarket wallets and six months of on-chain data, a rather intuitive yet surprising result emerged. Approximately 87.3% of users ultimately lost money trading on the platform.

This analysis covered multiple key dimensions, including every on-chain transaction record, trading volume, win rate, profit/loss, market types participated in, entry timing, and position size. The entire data processing took three weeks, and the final conclusion contradicted many people's intuition.

Many believe that top players in prediction markets often possess some obvious advantage, such as having insider information or using obscure, complex computational models. However, the data shows this is not the case. The top 1% of players consistently and persistently do a few things right and repeat them. The other 99% often do the exact opposite and then wonder why their capital keeps dwindling.

Polymarket's Leaderboard Is Actually Highly Misleading

If you open Polymarket's leaderboard and sort by profit (PnL) right now, you'd notice some anomalies. For example, the top-ranked wallet has only 22 positions in total; the fourth-ranked wallet has only 8 trades; and the eighth-ranked wallet has even made just 1 bet, yet still ranks in the historical top ten.

These addresses can hardly be called genuine traders. In many cases, they are just whales making a single bet of over $5 million on one event and happening to be right; or they could be individuals with informational advantages, or both. But in any case, data from just a few trades offers almost no learnable trading patterns. This outcome is more like a massive "coin flip" rather than a replicable strategy.

Therefore, the first step of the analysis was to filter out this noise and retain only samples with true statistical significance. The filtering criteria included the following aspects:

• At least 100 settled positions to ensure a statistically meaningful sample size;
• Active trading period of no less than 4 months, excluding accounts that won purely by luck once;
• Participation in at least 2 different markets to avoid betting on a single event;
• Total trading volume exceeding $10,000 to ensure participants have genuinely invested capital.

Under these conditions, the initial pool of 112,000 wallets was filtered down to approximately 8,400 wallet addresses with sufficient data value. These 8,400 addresses constitute the truly meaningful dataset for research, not the "hero accounts" on the leaderboard that made a few trades and earned millions. The common characteristic of these addresses is sustained trading and stable data, making it easier to observe genuine behavioral patterns.

Interestingly, after the filtering, the most consistently performing traders look completely different from their leaderboard image. They are not prominent, and most people have never even heard of their names. Their profit scale typically ranges between $50,000 and $500,000, not tens of millions.

But what's truly worth attention is not how much they earned, but the trading process and methodology behind it. Because what can truly be replicated is never the outcome, but the process.

Three Common Misconceptions to Debunk

Misconception 1: Top Traders Have Win Rates Between 80% and 90%

This is not true. Based on the filtered data sample, not the whale accounts on the leaderboard that made a fortune from one bet, the win rates of truly long-term profitable wallets mostly range only between 55% and 67%. This means even top traders are wrong in a significant portion of their trades. For instance, one address has completed over 900 settled positions, accumulating $2.6 million in profit, yet its win rate is only 63%. In other words, he was wrong in over one-third of his bets but still earned substantial returns in the prediction market.

Obsession with win rate is often the easiest trap for novice accounts to fall into. Many beginners like to buy contracts priced at $0.90 because it seems "very safe." The probability of YES is already 90%, seemingly almost certain, so they buy at $0.90. If the event occurs, they only profit $0.10. But if they are wrong just once, they lose $0.90 directly, resulting in a risk-reward ratio of 9 to 1. Repeating this cycle enough times quickly depletes account funds. In the dataset, this pattern has repeatedly appeared across hundreds of addresses.

Misconception 2: The Strongest Traders Participate in All Markets

The reality is precisely the opposite. The best-performing wallets typically participate in at most three categories of markets, with most focusing on just one or two areas. Some addresses only make predictions related to cryptocurrency events; some only participate in weather-related markets; there's even an address that almost exclusively trades questions like "Will Bitcoin reach a certain price by Friday?"

In prediction markets, excessive diversification often leads to a decline in judgment quality. General participants tend to perform averagely, while highly focused participants are more likely to achieve consistent profits.

Misconception 3: Speed Is Everything

This is only true in very few cases. For example, certain crypto markets that settle in 15 minutes indeed require quick reactions. But in the vast majority of markets, top traders do not win by speed. Their more common practice is to gradually build positions over days or even weeks. They are not in a hurry to compete on click speed but patiently wait for prices to show significant deviations. When the price deviates sufficiently, even if the market takes two weeks to correct, the overall mathematical expectation remains in their favor.

Five Trading Patterns Worth Learning

Pattern 1: Trading Against Extreme Sentiment

This is the most obvious and stable profitable signal in the entire dataset. Among the filtered 8,400 wallets, this behavior is almost the primary indicator of whether an account is profitable in the long term.

When a contract is pushed to 88% by market sentiment, many top wallets start selling YES; conversely, when the price drops to around 12%, they begin buying gradually. Of course, this is not blindly going against the trend, nor are they contrarian for the sake of it. They only enter the market on a large scale when they judge that market sentiment is clearly overreacting.

The effectiveness of this strategy is related to a classic phenomenon known as the "favorite-longshot bias." This phenomenon was first discovered in horse race betting research in the 1940s and appears in almost all markets involving human betting. Simply put, people tend to overestimate outcomes that "seem almost certain to happen" and underestimate low-probability events.

Further analysis revealed that the average entry price of the top 50 most profitable wallets typically deviates by 6% to 11% from the market consensus probability. They do not participate in bets at 50/50 odds but patiently wait for odds to become clearly favorable before entering. This trading style might seem boring, but in long-term data, it is stable and highly profitable.

Pattern 2: Position Sizing Closely Resembles the Kelly Criterion

Comparing the position sizes of the top 200 wallets by profit with the "implied edge" they faced at the time reveals a very clear correlation. In other words, they do not bet arbitrarily; their bet size almost proportionally changes with the size of the advantage they believe they have. When they perceive a large edge, their position size increases significantly; with a smaller edge, they only place smaller bets; if there is no clear edge, they simply do not trade.

It's hard to determine whether these traders have actually read the Kelly Criterion or simply developed this intuition through long-term losses and实战 experience. But mathematically, their behavior closely resembles the Kelly Criterion.

The Kelly Criterion is typically written as: f* = (p × b − q) / b, where: p represents the probability the trader believes the event will actually occur; q = 1 − p; b represents the odds payout ratio (potential profit ÷ risk cost).

For a simple example, suppose a trader judges an event has a 60% probability of occurring, and the market price is $0.45. The payout ratio is: b = (1 / 0.45) − 1 ≈ 1.22. Substituting into the formula: f* = (0.60 × 1.22 − 0.40) / 1.22 ≈ 0.272. This means the full Kelly strategy suggests betting 27% of the capital on this trade.

However, this approach carries extremely high risk in actual trading, with very high volatility, potentially dragging the account into significant drawdowns in a short time. Based on the data, truly profitable wallets typically use a more conservative version, approximately one-quarter Kelly. In other words, if the full Kelly suggests a 27% bet, they usually only bet around 7%.

In their most confident trading opportunities, the position size might increase to 12% to 15%; for moderately confident opportunities, they typically allocate only 2% to 5%; and for markets with no clear edge, they often choose not to participate. In contrast, losing accounts usually fall into two extremes. Either they bet 80% of their capital on a single trade, relying entirely on luck; or they spread $10 across forty or fifty markets, thinking they are "diversifying risk." But in reality, this is more like constantly paying fees to keep the account looking busy.

Pattern 3: Extreme Focus and Specialized Trading

Dividing the 112,000 wallets by the market categories they participated in reveals very clear differences. These categories include crypto markets, political events, sports, weather, geopolitics, entertainment, and science. The analysis shows:

• Wallets participating in only 1 to 2 categories had an average PnL of approximately +$4,200;
• Wallets participating in 3 to 4 categories had an average PnL of approximately -$380;
• Wallets participating in 5 or more categories had an average PnL of approximately -$2,100.

This relationship shows an almost clear linear trend. The more market categories participated in, the higher the probability of loss.

Different categories of prediction markets rely on completely different information systems. Crypto markets are often influenced by exchange fund flows, whale addresses, funding rates, etc.; political markets rely on polling data, grassroots information, congressional schedules, etc.; while weather markets depend more on NOAA weather models, atmospheric data, and satellite observations.

Two cases are particularly representative. Case One: Wallet A only trades Bitcoin 15-minute settlement prediction markets, never participating in other types, such as "Will BTC be above a certain price in the next 15 minutes." This address completed 502 predictions with a win rate of 98%, accumulating about $54,000 in profit. Its edge is actually very simple: continuously monitoring Binance order book depth and trading quickly when Polymarket's price lags by 10 to 30 seconds. In other words, it repeatedly exploited an information gap of just over ten seconds hundreds of times.

Case Two: Wallet B only participates in weather-related markets. The trading strategy is also straightforward: reading NOAA's publicly released daily temperature forecast data and comparing it with Polymarket's market pricing. If the market price shows a significant deviation from these supercomputer predictions optimized over decades, it enters the trade directly. In the New York temperature prediction market alone, this address achieved an accuracy rate of 94%.

It's important to emphasize that these people are not geniuses. The real key is that they found a niche they understand better than the average Polymarket participant and then repeatedly leveraged this advantage. They didn't frequently change strategies or experience FOMO due to market hype. They simply executed the same logic around the same advantage, over and over again.

Pattern 4: Trading Price Movements, Not Event Outcomes

Most Polymarket users trade in a very simple way: buying a contract and holding it until event settlement, resulting in either profit or loss—a typical binary outcome. But top wallets operate completely differently. Often, they buy at $0.40 and sell to exit when news or market sentiment pushes the price to $0.65. They don't care whether the event ultimately happens; as long as the price has reflected new information, they complete the trade and exit.

In the dataset, some of the best-performing addresses don't even have any settled positions. They never hold contracts until final settlement but continuously engage in swing trading based on price mismatches. Statistics show that the average holding period for top wallets is typically only 18 to 72 hours, while wallets in the bottom 50% by profitability often hold until settlement, sometimes for weeks or months.

This doesn't mean holding until settlement is always wrong. Sometimes, when conviction is very strong, long-term holding is indeed a better strategy. But overall, the data shows top wallets use their capital more actively and flexibly than most imagine. They are not passive bettors but genuine traders.

Pattern Five: Always Avoiding Breaking News

Our intuition often tells us that the sharpest capital should enter the market immediately when sudden events occur, such as military conflicts, election results, or corporate executive resignations. But the data shows that top wallets often actively avoid the period immediately after news breaks. They typically wait for emotional capital to flood the market first, causing short-term price volatility, and only start trading after market sentiment stabilizes.

A very clear pattern emerges from the entire dataset: The best trading opportunities often appear before the market notices an event or after market sentiment has overreacted. When everyone is discussing the same thing, it's often precisely the worst time to enter. At that point, market prices are usually highly efficient, and the obtainable edge is minimal.

Five Operational Suggestions

Choose a Niche and Stay Focused Long-Term

Whether it's crypto, politics, weather, or sports, choose the field you know best. Then, trade only in this category for at least the next three months. No exceptions, and don't participate in other hot events on a whim. Even "just placing a casual bet on the election" can easily disrupt your original judgment system.

Record Every Prediction

Before each trade, write down several key data points: your judged true probability, current market price, expected edge, and planned position size. Review after accumulating over 50 trades. For example, if some predictions were marked as 70% probability, check if the actual hit rate is truly close to 70%. If there's a significant deviation, it indicates bias in probability judgment, which must be calibrated before increasing position sizes.

Manage Positions Close to One-Quarter Kelly

First, calculate the theoretical position size given by the Kelly Criterion, then divide it by 4 for the actual position. This number will often seem very small, but that's precisely the key to risk control. The result of over-leveraging is almost always one thing—account blow-up.

Only Trade When the Edge Is Sufficiently Clear

If the expected edge is below 8% to 10%, just skip it. Even if an opportunity looks tempting, learn to wait. The best-performing wallets in the data typically make only 2 to 3 trades per week per market category. Trading quality is far more important than trading quantity.

Persist in Recording and Reviewing

Establish a comprehensive trading log to record every trade, its outcome, and any issues that arose. Wallets with continuously improving long-term performance almost systematically review their mistakes; those that stagnate or keep losing often repeat the same errors but attribute the results to bad luck.