Original Title: I Analyzed 112,000 Polymarket Wallets. Here's What Separates the Top 1% from Everyone Else

Original Author: darkzodchi, Polymarket Eye Founder

Original Translation: Asher, Odaily Planet Daily

After systematically sorting and analyzing on-chain data from over 112,000 Polymarket wallets over a period of 6 months, a seemingly intuitive yet surprising result emerged. Approximately 87.3% of users ended up with losses from their platform transactions.

This analysis covered various key dimensions, including each on-chain transaction record, transaction volume, win rate, profit and loss situation, participated market types, entry time, and position size. The entire data processing process lasted for 3 weeks, and the final conclusion was quite different from many people's intuition.

Many believe that the top players in prediction markets often have some clear advantage, such as having insider information or using obscure complex calculation models. However, from the data performance perspective, this is not the case. The top 1% of players consistently adhere to doing a few things right in the long term and keep repeating those actions. Meanwhile, the other 99% of users often do exactly the opposite and then wonder why their funds keep draining.

The Polymarket Leaderboard Can Be Highly Misleading

If one were to now open Polymarket's leaderboard and sort by Profit and Loss (PnL), some anomalies would actually be revealed. For example, the wallet ranked first only had a total of 22 positions; the wallet ranked fourth had only 8 trades; and the wallet ranked eighth had just 1 bet, yet still managed to enter the historical top ten.

These addresses can hardly be considered true traders. In many cases, it is merely a whale betting over $5 million in one go on a single event and happening to be right; or perhaps someone with an informational advantage participating, or even a combination of both. However, in either case, data from just a few trades is almost unable to provide any learnable trading patterns. This outcome is more akin to a "coin toss" with a massive capital size rather than a replicable strategy.

Therefore, the first step of the analysis is to filter out this noisy data and only keep samples that are truly statistically significant. The selection criteria include the following:

· At least 100 settled positions to ensure the sample size is statistically significant;

· Trading activity period of no less than 4 months to exclude accounts that rely on a one-time stroke of luck; participation in at least 2 different markets to avoid betting only on a single event;

· Total trading volume exceeding $10,000 to ensure participants have truly invested funds.

Under these conditions, the initially analyzed 112,000 wallets were filtered down to approximately 8,400 wallet addresses with sufficient data value. These 8,400 addresses constitute a truly meaningful dataset for research, rather than the "hero accounts" on the leaderboard that made only a few transactions but earned millions of dollars. The common characteristic of these addresses is continuous trading activity and stable data, making it easier to observe real behavioral patterns.

Interestingly, after the filtering process, the traders who demonstrate the most stable performance are completely different from those on the leaderboard. They are not conspicuous; most people have never even heard their names. Their profit scale usually ranges from $50,000 to $500,000, rather than millions of dollars.

What is truly worth noting is not how much money they have earned, but the trading process and methods behind them. Because what can truly be replicated is never the result but the process.

Three Common Misconceptions that Need to Be Dispelled

· Misconception One: The win rate of top traders is between 80% and 90%

That is not the case. Based on the filtered data sample, rather than the whale accounts on the leaderboard who made a fortune with one bet, the wallets that truly achieve long-term profitability mostly have a win rate between 55% and 67%. In other words, even top traders make incorrect judgments in a considerable portion of their trades. For example, an address has completed over 900 settled positions, accumulated $2.6 million in profits, but has a win rate of only 63%. This means that over one-third of their bets were wrong, yet they still made huge profits predicting the market.

An obsession with win rates is often the most common pitfall for novice accounts. Many beginners like to buy contracts at $0.90 because it looks "very safe." The probability of YES is already 90%, so the outcome seems almost certain. Therefore, they buy at $0.90, and if the event does occur, they only profit $0.10. However, if there is one incorrect judgment, they will directly lose $0.90, with a risk-reward ratio of 9 to 1. If this pattern repeats enough times, the account funds will quickly be depleted. In the dataset, such situations have repeatedly occurred in hundreds of addresses.

· Misconception 2: The Best Traders Will Trade Any Market

On the contrary, the top-performing wallets usually only participate in up to three market categories, 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; and there's even one address that trades almost exclusively on questions like "Will Bitcoin reach a certain price before Friday."

In prediction markets, excessive diversification often leads to a decrease in prediction quality. Broad participants tend to perform averagely, while highly focused participants are more likely to sustain profitability.

· Misconception 3: Speed Is Everything

This statement holds true only in very few circumstances. For example, in some 15-minute settlement crypto markets, quick reactions are indeed necessary. However, in the vast majority of markets, top traders do not rely on speed to win. Their more common approach is to gradually build their positions over days or even weeks. They are not in a rush to compete in click speed with others, but patiently wait for price discrepancies to emerge. When the price deviates significantly, even if it takes the market two weeks to correct, the overall mathematical expectation is still in their favor.

Five Trading Patterns Worth Learning

· Pattern One: Contrarian Trading in Extreme Emotions

Across the entire dataset, this is the most obvious and stable profit signal. In the filtered 8,400 wallets, this behavior is almost a primary indicator of whether the account is profitable in the long run.

When a contract is bid up to 88% by market sentiment, many top wallets start selling YES instead; and when the price drops to around 12%, they begin to buy gradually. Of course, this is not blind contrarianism, and they are not simply opposing the market for the sake of it. Only when they perceive a clear overreaction in market sentiment, they enter the market on a large scale.

The effectiveness of this strategy is related to a classic phenomenon known as "hot-cold empathy bias." This phenomenon was discovered as early as the 1940s in horse race betting studies and appears in almost all markets where humans participate in betting. Simply put, people tend to overestimate the likelihood of outcomes that seem almost certain and underestimate low-probability events.

Furthermore, additional statistics reveal that the top 50 most profitable wallets usually enter at prices that deviate 6% to 11% from the market consensus probability. They do not bet on a 50/50 scenario but patiently wait to enter when the odds are significantly in their favor. This trading approach may seem somewhat boring, but in long-term data, it is stable and highly profitable.

· Pattern 2: Position Sizing Approach Very Close to Kelly Criterion

Comparing the position size of the top 200 profit-ranking wallets with the "implied edge" they faced at the time clearly shows a significant correlation. In other words, they did not bet randomly; their position size almost varied in proportion to the edge they believed they had, meaning that when they perceived a large edge, their position would significantly increase; with a smaller edge, they would only take a smaller position; and if there was no clear edge, they simply would not trade.

It is hard to determine whether these traders have actually studied the Kelly Criterion or simply formed this intuition gradually through long-term losses and real-world experience. However, mathematically, their behavior is very close to the Kelly Criterion.

The Kelly Criterion is usually written as: f* = (p × b − q) / b, where: p represents the trader's perceived probability of an event actually happening; q = 1 − p; b represents the odds return ratio (potential payoff ÷ risk cost).

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

However, this practice carries very high risk in actual trading, with significant volatility that could quickly lead the account into a deep drawdown. From the data, wallets that truly profit usually employ a more conservative version, roughly around a quarter of the Kelly Criterion. In other words, if the full Kelly Criterion suggests a 27% bet, they typically only wager around 7%.

In the most certain trading opportunities, positions may increase to 12% to 15%; positions for medium-confidence opportunities usually range from 2% to 5%; and in markets without a clear edge, they often opt not to participate. In contrast, losing accounts typically fall into two extremes. They either bet 80% of the funds on a single trade, completely relying on luck, or they spread $10 across forty to fifty markets, thinking they are "diversifying risk." In reality, this is more like constantly paying fees, making the account appear busy but achieving little.

· Pattern Three: Hyper-Focused Specialized Trading

After dividing 112,000 wallets according to the market categories they participate in, a very clear difference can be seen. These categories include the crypto market, political events, sports events, weather, geopolitics, entertainment, and science, among others. The analysis revealed:

· Wallets participating in only 1 to 2 categories had an average PnL of around +$4200;

· Wallets participating in 3 to 4 categories had an average PnL of around -$380;

· Wallets participating in 5 or more categories had an average PnL of around -$2100.

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

Different prediction markets rely on completely different information systems. The crypto market is often influenced by factors such as exchange funding rates, whale addresses, and funding rates; political markets rely on polling data, grassroots messages, congressional schedules, and other information; while weather markets rely more on NOAA meteorological models, atmospheric data, and satellite observations.

Two cases are particularly representative. Case One: Wallet A only traded in prediction markets settled in 15 minutes for Bitcoin, never participating in other types of markets, such as "Will BTC be above a certain price within the next 15 minutes." This address completed a total of 502 predictions with a win rate of 98%, accumulating a profit of around $54,000. Its advantage was actually very simple: it continuously monitored the Binance order book depth and quickly traded when Polymarket's price lagged behind by 10 to 30 seconds. In other words, it was repeatedly exploited based on just a few seconds of informational edge.

Case Two: Wallet B only participated in weather-related markets. The trading strategy was also straightforward: it read NOAA's daily publicly released temperature forecast data and compared it with Polymarket's market pricing. If the market price significantly deviated from these decades-optimized supercomputer predictions, it would enter a trade directly. In the New York temperature prediction market alone, this address achieved an accuracy rate of 94%.

It is important to emphasize that these individuals are not geniuses. The real key is that they found a niche field they understood better than the average Polymarket participant and then repeatedly exploited this advantage. They did not frequently change strategies or experience FOMO due to market trends. They simply focused on the same advantage and executed the same logic time and time again.

· Pattern Four: Trading Price Fluctuation, Not Event Outcome

The majority of Polymarket users trade in a very simple way: they buy a contract and hold it until the event resolves, either profiting or losing, in a typical binary outcome. However, the approach of top wallets is entirely different. Often, they buy in at $0.40 and sell out directly at $0.65 when news or market sentiment drives the price up. They do not care whether the event actually occurs; as long as the price reflects new information, they execute the trade and exit.

In the dataset, some of the top-performing addresses do not even have any settled positions. They never hold the contract until final settlement but engage in continuous price discrepancy scalping. Statistical data shows that the average holding time of top wallets is usually only 18 to 72 hours, while wallets in the bottom 50% in terms of profitability often hold until settlement, sometimes for weeks or even months.

This does not mean that holding until settlement is always wrong. Sometimes, when the judgment is very certain, long-term holding is indeed a better strategy. However, from the overall data, it is seen that top wallets are more proactive and flexible in their use of funds than most people imagine. They are not passive bettors but true traders.

· Pattern Five: Always Avoid Sudden News

Our intuition would always lead us to believe that the most astute funds should enter the market first when a sudden event occurs, such as military conflicts, election results, or the resignation of a company executive. However, data shows that top wallets often actively avoid entering during the immediate aftermath of breaking news. They typically wait for emotional funds to enter the market first, causing a significant price swing in a short period. They then start trading once market sentiment gradually stabilizes.

From the entire dataset, a very clear pattern emerges: the best trading opportunities often arise before the market takes note of an event or after the market sentiment has overreacted. When everyone is discussing the same thing, it is often the worst time to enter. At this point, the market price is usually highly efficient, and any advantage one can gain is minimal.

Five Operational Recommendations

· Choose a Track and Focus Long-Term

Whether it's crypto, politics, weather, or sports, it's all fair game, but you must choose the field you are most familiar with. Trade only this type of market for at least the next three months. No exceptions, and do not participate in other hot events on a whim. Even just "casually betting on an election" can easily disrupt your original judgment system.

· Record Every Prediction

Before each trade, jot down key data points including your assessed true probability, current market price, expected edge, and planned position size. Review these notes after accumulating over 50 trades. For instance, if some predictions were labeled as 70% probability, check if the actual hit rate is indeed close to 70%. If there's a significant deviation, it indicates a bias in probability assessment that needs calibration before scaling up position size.

· Aim for Position Sizing Close to Quarter Kelly Criterion

Calculate the theoretical position size based on the Kelly Criterion, then use one-fourth of that as your actual position. This number may seem small, but it's crucial for risk control. Overleveraging typically leads to one outcome—margin call.

· Only Trade When the Edge is Sufficiently Clear

If the expected edge is below 8% to 10%, walk away. Even if the opportunity seems enticing, practice the art of patience. The wallets that perform the best in the data usually make only 2 to 3 trades per market category per week. Trade quality far outweighs quantity.

· Maintain Records and Review Consistently

Establish a comprehensive trading log, documenting each trade, its outcome, and any encountered issues. Wallets with continually improving long-term performance almost systematically review their mistakes; on the other hand, stagnant or continuously losing accounts often keep making the same mistakes and attribute the results to bad luck.

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