Deconstructing 112,000 Polymarket addresses: The top 1% who actually make money are doing these five things.

Author: darkzodchi

Compiled by: Asher, Odaily Planet Daily

After systematically reviewing and analyzing six months of on-chain data from over 112,000 Polymarket wallets, a rather intuitive yet surprising result emerged: approximately 87.3% of users ultimately lost money in their transactions on the platform .

This statistical analysis covered multiple key dimensions, including every on-chain transaction record, trading volume, win rate, profit and loss, types of markets participated in, entry time, and position size. The entire data processing process lasted three weeks, and the final conclusions differed from many people's intuition .

Many believe that top players in prediction markets often possess a significant advantage, such as insider information or the use of complex, little-known computational models. However, data suggests otherwise. The top 1% consistently and reliably do a few things right over the long term, repeatedly executing them. Meanwhile, the other 99% of users often do the exact opposite, subsequently wondering why their funds are continuously draining away.

Polymarket's rankings are actually quite misleading.

If you open the Polymarket leaderboard now and sort it by profit (PnL), you'll find some anomalies. For example, the top-ranked wallet only has 22 positions; the fourth-ranked wallet only has 8 trades; and the eighth-ranked wallet only has 1 bet, yet it still manages to rank in the top ten all-time.

These addresses are hardly worthy of being called genuine traders. In many cases, it's simply a few whales betting over $5 million on a single event and winning by chance; or it could be people with an informational advantage participating, or even both. But in either case, data from only a few trades offers almost no learnable trading patterns. This result is more like a massive "coin toss" rather than a replicable strategy.

Therefore, the first step in the analysis is to filter out this noisy data and retain only the samples that are truly statistically significant. The screening criteria include the following aspects:

• There are at least 100 settled positions to ensure that the sample size is statistically significant;
• Accounts must have been actively trading for at least four months, excluding those that win solely through luck.
• Participate in at least two different markets to avoid betting on a single event;
• Total transaction volume exceeded $10,000, ensuring that participants actually invested their funds.

Under these conditions, after filtering the initial 112,000 wallets, only about 8,400 wallet addresses remained with sufficient data value. These 8,400 addresses are the truly meaningful dataset for research, rather than the "hero accounts" on the leaderboard that made millions of dollars with only a few transactions. These addresses share the characteristics of continuous transactions and stable data, making it easier to observe real behavioral patterns from them.

Interestingly, once the screening was complete, the truly most consistently performing traders were completely different from those on the leaderboard. They were unremarkable, and most people had never even heard of them. Their profits typically ranged from $50,000 to $500,000, rather than the millions of dollars they often made.

But what's truly noteworthy isn't how much money they made, but rather the trading process and methods behind it. Because what can truly be replicated is never the result, but the process.

Three common misconceptions that need to be dispelled

Myth 1: Top traders have a win rate between 80% and 90%.

This is not the case. Based on a filtered data sample, rather than the whale accounts on the leaderboard that made huge profits from a single bet, the win rate of truly profitable wallets in the long run is mostly between 55% and 67%. In other words, even top traders make mistakes in a significant portion of their trades. For example, one address may have completed over 900 settled positions, accumulating a profit of $2.6 million, but its win rate is only 63%. In other words, more than a third of its bets were wrong, yet it still made a huge profit predicting the market.

An obsession with winning percentage is often the biggest trap for novice accounts. Many beginners like to buy contracts at $0.90 because it seems "safe." The probability of a "yes" outcome is already 90%, seemingly a certainty, so they buy at $0.90, only to earn $0.10 if the event actually occurs. But if they make a single wrong prediction, they lose $0.90 instantly, resulting in a risk-reward ratio of 9 to 1. If this pattern repeats itself many times, the account funds will quickly be depleted. In the dataset, this situation has been repeatedly observed on hundreds of addresses.

Myth 2: The best traders can trade in any market.

The reality is quite the opposite. The best-performing wallets typically participate in a maximum of three market categories, with most focusing on only one or two areas. Some addresses only make predictions about cryptocurrency-related events; some only participate in weather-related markets; and one address even trades almost exclusively questions like "Will Bitcoin reach a certain price before Friday?"

In prediction markets, excessive diversification often leads to a decline in the quality of judgments. Broad participants tend to perform poorly, while highly focused participants are more likely to consistently profit.

Myth 3: Speed ​​determines everything

This argument holds true only in rare cases. For example, some crypto markets with 15-minute settlements do require rapid reactions. However, in the vast majority of markets, top traders don't win by speed. Their more common approach is to gradually build positions over days or even weeks . They don't rush to outclick and compete with others, but patiently wait for a significant price deviation. 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 in the opposite direction during periods of extreme emotion

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

When a contract's price was driven up to 88% by market sentiment, many top wallets actually started selling YES; conversely, when the price dropped to around 12%, they began gradually buying in. Of course, this wasn't blindly contrarian trading, nor was it an attempt to oppose the market for its own sake. They only entered the market on a large scale when they judged that market sentiment was clearly overreacting.

The effectiveness of this strategy is related to a classic phenomenon known as the "hot/cold bias." This phenomenon was discovered as early as the 1940s in horse racing betting research and occurs in almost all markets where humans participate in betting. Simply put, people tend to overestimate outcomes that "almost certainly happen" while underestimating low-probability events.

Further analysis revealed that the top 50 most profitable wallets typically entered the market with an average entry price that deviated from the market consensus probability by 6% to 11%. They didn't bet on a 50/50 basis, but patiently waited for the odds to clearly favor them before entering . This trading style may seem somewhat boring, but it has proven stable and highly profitable in the long run.

Mode 2: Position management method is very similar to the Kelly Criterion.

Comparing the position sizes of the top 200 most profitable wallets with the "implied advantage" they faced at the time reveals a very clear correlation. In other words, they weren't betting randomly; their bet sizes varied almost proportionally to the size of the advantage they perceived they had. That is, when they believed their advantage was significant, they would significantly increase their positions; when the advantage was small, they would only place smaller positions; and if there was no clear advantage, they simply wouldn't trade.

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

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

For a simple example, suppose a trader believes 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 this into the formula, we get: f* = (0.60 × 1.22 − 0.40) / 1.22 ≈ 0.272. In other words, the complete Kelly strategy recommends allocating 27% of your capital to this trade.

However, this approach is extremely risky in actual trading, with very high volatility, and could potentially drag an account into a huge drawdown in a short period of time. Data shows that truly profitable wallets typically use a more conservative version, roughly one-quarter of the Kelly Criterion. In other words, if the full Kelly Criterion recommends a 27% bet, they usually only bet around 7%.

In the most promising trading opportunities, position sizes might increase to 12% to 15%; for opportunities with moderate confidence, positions are typically only allocated to 2% to 5%; and in markets where there is no clear advantage, they often choose not to participate at all. In contrast, losing accounts usually fall into two extremes. Either they bet 80% of their capital in a single trade, relying entirely on luck; or they spread $10 across forty or fifty markets, believing they are "diversifying risk." But in reality, this is more like constantly paying transaction fees, making the account appear busy.

Model 3: Highly Focused Professional Trading

After categorizing the 112,000 wallets according to the market categories they participate in, very significant differences emerge. These categories include crypto markets, political events, sporting events, weather, geopolitics, entertainment, and science, among others. The analysis concludes that:

• Wallets that only participate in 1 to 2 categories have an average PnL of approximately +4200 USD;
• For wallets participating in 3 to 4 categories, the average PnL is approximately -$380;
• Wallets participating in more than 5 categories have an average PnL of approximately -$2,100.

This relationship exhibits an almost linear trend. The more market categories one participates in, the higher the probability of loss.

Different types of prediction markets rely on completely different information systems. Crypto markets are often influenced by factors such as exchange fund flows, whale addresses, and funding rates; political markets rely on information such as poll data, grassroots news, and congressional schedules; while weather markets rely more on NOAA weather models, atmospheric data, and satellite observations.

Two cases are particularly representative. Case 1: Wallet A only trades in Bitcoin prediction markets with 15-minute settlements and never participates in other types of markets, such as "whether BTC will be higher than a certain price in the next 15 minutes." This address completed 502 predictions with a 98% win rate, accumulating a profit of approximately $54,000. Its advantage is actually very simple: it continuously monitors the depth of the Binance order book and quickly trades when the Polymarket price lags by 10 to 30 seconds. In other words, it reuses this information difference of just a few seconds hundreds of times.

Case 2: Wallet B only participates in weather-related markets. Its trading strategy is straightforward: it reads NOAA's daily publicly released temperature forecasts and compares them with Polymarket's market pricing. If the market price deviates significantly from these supercomputer predictions, which have been optimized over decades, it immediately enters a trade. In the New York temperature forecast market alone, this address boasts a 94% accuracy rate.

It's important to emphasize that these individuals aren't geniuses. The real key lies in their ability to identify a niche market they understand better than the average polymarket participant, and then consistently leverage that advantage. They didn't frequently change strategies, nor did they succumb to FOMO due to market trends. They simply focused on the same advantage, executing the same logic time and time again.

Pattern 4: Trading price fluctuations, not event outcomes.

Most Polymarket users trade very simply: they buy a contract and hold it until the event settles, resulting in either a profit or a loss—a typical binary outcome. However, top-tier wallets operate entirely differently. Often, they buy at $0.40 and sell immediately when news or market sentiment pushes the price to $0.65. They don't care whether the event actually occurs; as long as the price has reflected the new information, they complete the trade and exit.

In the dataset, some of the best-performing addresses didn't even have any settled positions. They never held contracts until final settlement, instead continuously engaging in price mismatch swing trading. Statistics show that top-performing wallets typically hold positions for only 18 to 72 hours on average, while wallets in the bottom 50% of profitability often hold positions until settlement, sometimes for weeks or even months.

This doesn't mean that holding until settlement is always a mistake. Sometimes, when the judgment is very certain, holding long-term is indeed a better strategy. But overall, the top wallets use their funds more proactively and flexibly than most people imagine. They are not passive bettors, but true traders .

Pattern 5: Always avoid breaking news

Intuitively, we tend to think that the most astute funds should enter the market immediately upon breaking news events, such as military conflicts, election results, or resignations of company executives. However, data shows that top wallets often proactively avoid the immediate aftermath of such news . They typically wait for sentimental funds to flood the market, causing significant price fluctuations in a short period, before commencing trading once market sentiment has stabilized.

Looking at the entire dataset, a very clear pattern emerges: the best trading opportunities often occur before the market takes notice of an event, or after market sentiment has overreacted. Conversely, when everyone is discussing the same thing, it is often the worst entry point. At this point, market prices are usually highly efficient, and the potential advantage is minimal.

Five operational suggestions

Choose a track and focus on it long-term.

Whether it's crypto, politics, weather, or sports, it's all fine, but you must choose the area you are most familiar with. For at least the next three months, trade only this type of market. No exceptions, and don't participate in other trending events on a whim. Even "casually betting on the election" can easily disrupt your original judgment system.

Record every prediction

Before each trade, write down several key data points, including your assessed probability, current market price, expected advantage, and planned position size. Review these data after accumulating more than 50 trades. For example, if a prediction is marked as having a 70% probability, is the actual hit rate truly close to 70%? If there's a significant deviation, it indicates a bias in the probability assessment, and this must be calibrated before increasing the position size.

Position management should be as close as possible to one-quarter of the Kelly Criterion.

First, calculate the theoretical position size given by the Kelly Criterion, then divide by 4 to get the actual position size. This number usually seems small, but it is crucial for controlling risk. Over-leveraging often results in only one outcome—account liquidation.

Trade only when the advantage is obvious enough.

If the expected advantage is less than 8% to 10%, abandon the trade immediately. Even if the opportunity seems tempting, learn to wait. The best-performing wallets in the data typically only make 2 to 3 trades per week per market category. Trade quality is far more important than trade quantity.

Keep records and review

Create a complete trading table to record every trade, its outcome, and any problems that arise. Wallets that consistently improve over the long term almost always systematically review their mistakes; while accounts that stagnate or even continuously lose money often simply repeat the same errors and attribute the results to bad luck.