In-Depth Analysis of Polymarket's Top 10 Whales' 27,000 Transactions: The Illusion of "Smart Money" Win Rate and the True Rules of Survival

Author: Frank, PANews

Recently, the popularity of prediction markets has continued to rise, especially with the arbitrage strategies of smart money being regarded as the gold standard. Many have begun to imitate and experiment, as if a new gold rush is underway.

But behind the hype, how effective are these seemingly clever and reasonable strategies? How exactly are they executed? PANews conducted an in-depth analysis of 27,000 trades made by the top ten profit-generating whales on Polymarket in December, aiming to uncover the true nature of their profits.

Our analysis revealed that while many of these “smart money” operations employ hedging arbitrage strategies, these hedges differ significantly from the simple hedging described on social media. The actual strategies are far more complex, involving not just straightforward “yes” or “no” combinations, but full utilization of rules like “over/under,” “win/loss,” and other sports betting principles to create composite hedges. Another key finding is that the extremely high win rates seen in historical holdings are often inflated by a large number of “zombie orders” that remain unsettled, meaning the real win rate is much lower than the historical data suggests.

Next, PANews will reveal the real operations behind these “smart money” strategies through actual case studies.

1. SeriouslySirius: A 73% win rate masked by “zombie orders,” with a complex quantitative hedge network

SeriouslySirius was the top address in December, with a profit of approximately $3.29 million and a total historical profit of $2.94 million. Based solely on completed orders, his win rate appears to be 73.7%. However, the reality is that he still holds 2,369 open orders, with 4,690 settled. Among these, 1,791 open orders have already failed completely, but the user has not closed them individually. This saves effort and transaction fees. Since most of his closed orders are profitable, the historical win rate appears very high. When considering the large number of “zombie orders” still open, his true win rate drops to 53.3%, only slightly above random chance.

In his actual trading, about 40% of orders are hedged by betting on multiple outcomes for the same event. However, this hedging isn’t just “yes” + “no.” For example, in an NBA game between the 76ers and Mavericks, he bought into 11 different outcomes—Under, Over, 76ers, Mavericks, etc.—and ultimately made a profit of $1,611. He also employed arbitrage strategies with low probability, such as betting on the 76ers to win with a 56.8% implied probability and the Mavericks with 39.37%, with a combined cost of about 0.962, ensuring a guaranteed profit regardless of the outcome. In this game, he made a profit of $17,000.

However, this strategy isn’t always profitable. For instance, in a game between the Celtics and Kings, he participated in nine different outcomes but ended up losing $2,900.

Additionally, there are cases where the capital allocation is heavily skewed—placing bets on two outcomes with over ten times the investment difference. Such results are likely due to market liquidity issues, highlighting that while arbitrage strategies look promising, liquidity can be a major obstacle in practice. Opportunities may appear, but achieving balanced hedges across both sides isn’t guaranteed. Moreover, automated execution often results in buy/sell actions that can turn into significant losses.

Nevertheless, SeriouslySirius managed to generate large profits primarily because of proper position management, with a profit-loss ratio of about 2.52. This explains how, despite a relatively low true win rate, he can still be profitable.

It’s worth noting that this strategy isn’t always successful. Before December, this address’s profit and loss were not promising, often hovering around break-even, with a maximum loss reaching $1.8 million. Now, with a more mature strategy, it’s uncertain whether such profitability can be sustained.

2. DrPufferfish: Turning low probability into high probability, an artful “risk-reward” management

DrPufferfish was the second most profitable address in December, with a profit of about $2.06 million and an even more impressive historical win rate of 83.5%. However, considering the large number of “zombie orders,” his actual win rate is around 50.9%. His strategy differs markedly from SeriouslySirius. While about 25% of his orders are hedges, these aren’t opposite bets but diversified bets. For example, in a Major League Baseball final, he bought into 27 teams with low probabilities, whose combined probabilities exceeded 54%. This approach effectively turns low-probability events into high-probability outcomes.

His ability to generate huge profits also stems from controlling the risk-reward ratio. For example, he is a fan of Liverpool FC, having predicted their results 123 times, earning about $1.6 million. On winning predictions, the average profit is around $37,200; on losing predictions, the average loss is about $11,000. Most of these losing bets are sold early to limit losses.

This operational approach yields an overall profit-loss ratio of 8.62, indicating high profitability potential. Overall, his strategy isn’t just simple arbitrage but involves professional prediction, analysis, and strict position management. Also, most of his hedging trades are in a loss position, totaling a net loss of $2.09 million, suggesting that these hedges are mainly used as insurance.

3. gmanas: High-frequency automated pipeline operations

The third-ranked address, gmanas, has a similar style to DrPufferfish, with a total profit of about $1.97 million in December. His true win rate is close to 51.8%. He has completed over 2,400 predictions, indicating an automated trading system. His betting style is similar to the previous address, so details are omitted here.

4. Hunter simonbanza: Using probability predictions as “K-line” swing trading

Fourth place goes to simonbanza, a professional prediction hunter. Unlike the previous addresses, he doesn’t use hedging orders. His profit is about $1.04 million, with only $130,000 in unrealized losses. Although his capital and trading volume are modest, his win rate is the highest at around 57.6%. His average profit per settled order is about $32,000, with an average loss of $36,500. While his profit-loss ratio isn’t high, his high win rate yields good returns.

He also has very few zombie orders—only six—because he typically doesn’t wait for event outcomes to settle, instead betting on probability fluctuations to profit. Simply put, he takes profits when they appear and doesn’t hold out for the final result.

This is a unique prediction market approach, where probability changes resemble financial market swings. The specific logic behind his high win rate remains his secret.

5. Whale gmpm: Asymmetric hedging with “large positions” to secure certainty

Fifth place, gmpm, ranks fifth in profit/loss for December but has a total profit exceeding the previous addresses, reaching $2.93 million. His true win rate is about 56.16%, also relatively high. His approach is similar to gmanas but with a unique core strategy.

For example, he often places bets on both sides of the same event, but instead of arbitraging between the two, he invests more heavily on the outcome with higher probability, and less on the lower probability side. This creates a hedge where larger positions are taken when the chance of winning is high, and smaller when the risk is lower, reducing potential losses in unlikely events.

In practice, this is a more advanced hedging strategy that combines probabilistic judgment with risk reduction, rather than relying solely on “yes” + “no” < 1 arbitrage.

6. Workaholic swisstony: “Ants moving” high-frequency arbitrage

Sixth is swisstony, a high-frequency arbitrage address with the highest trading volume—5,527 trades. Despite earning over $860,000, the average profit per trade is only about $156. His style resembles “ants moving,” often buying all outcomes of a match. For example, in a Jazz vs Clippers game, he bought 23 different outcomes. Due to small investment amounts, his capital is relatively evenly distributed, which can help with hedging.

However, this strategy heavily depends on precise execution. For instance, his “yes” + “no” bets often sum to more than 1, which shouldn’t happen in proper hedging. This sometimes causes his orders to lose money regardless of outcome. Nonetheless, with a reasonable profit-loss ratio and win rate, his expected profit remains positive.

7. Outlier 0xafEe: A “pop culture prophet” with unconventional tactics

Seventh is 0xafEe, a low-frequency, high-win-rate trader. His trading frequency is about 0.4 trades per day, with a win rate of 69.5%. His orders have yielded about $929,000, with minimal unrealized losses of around $8,800. He never uses hedging, focusing solely on predictions. His predictions often relate to Google search trends or pop culture topics, such as “Will Pope Leo XIV be the most searched person on Google this year?” or “Will Gemini 3.0 be released before October 31?” His unique analysis methods give him a very high success rate, making him an outlier among whales—outside the sports category.

8. Manual hedging player 0x006cc: From simple to complex hedging strategies

Eighth is 0x006cc, similar to the previous complex hedgers, with a net profit of about $1.27 million and a true win rate of 54%. Compared to automated addresses, his trading frequency is low—about 0.7 trades per day. Early on, he likely used simple manual hedging strategies.

Since December, he has upgraded to more complex hedging, reflecting market understanding and strategy evolution.

9. Cautionary example RN1: When “hedging” becomes a “loss formula”

Ninth is RN1, a top ten profit address in December but currently overall loss-making. His realized profit is about $1.76 million, but unrealized losses reach $2.68 million, for a net loss of $920,000. As a cautionary example, there are lessons to learn.

First, his true win rate is only 42%, the lowest among these addresses, with a profit-loss ratio of 1.62. These figures suggest his expected value is negative, making his overall strategy unprofitable.

A closer look shows he employs obvious arbitrage strategies, but many of his hedges involve betting more on the less likely outcome, and less on the more probable one, leading to unbalanced positions and actual losses when unlikely events occur.

10. Gambler Cavs2: Unilateral heavy bets on ice hockey, luck over strategy

Tenth is Cavs2, a prediction gambler who prefers single-sided heavy bets, mainly on NHL hockey. His total profit is about $630,000, with a win rate of 50.43% and a low hedge ratio of 6.6%. The results are average, with significant luck involved—he hit some high-yield single-game outcomes. His strategy offers limited practical guidance.

The 5 Harsh Truths About “Smart Money” Demystified

After analyzing these “smart money” trades, PANews summarizes the harsh realities behind the “wealth stories” of prediction markets:

1. “Arbitrage hedging strategies” are far from simple probability conditions; under fierce market competition and liquidity constraints, they can backfire as loss formulas. Blind imitation is not advisable.

2. “Copy trading” in prediction markets also seems ineffective, mainly because the ranking or win rate data are based on historical settled profits, which can be misleading. Many “smart money” traders aren’t truly “smart,” with actual win rates over 70% being rare; most are close to coin flips. Additionally, current market depth is limited, so arbitrage opportunities can only accommodate small capital, and copy traders may be squeezed out.

3. Managing profit-loss ratios and position sizes is more important than just chasing win rates. Successful addresses tend to excel at risk-reward management, with some like gmpm and DrPufferfish actively closing positions to reduce losses and improve ratios.

4. The real secret lies beyond “mathematical formulas.” Many social media “arbitrage formulas” seem reasonable at first glance but rely on other unseen decision-making algorithms—either strong judgment on specific events or unique cultural analysis models. These hidden algorithms are key to their success. For users lacking such “decision algorithms,” prediction markets remain a cold, dark forest.

5. The profit scale in prediction markets is still small. The top addresses in December earned around $3 million at most. Compared to the overall crypto derivatives market, the profit potential appears limited. For those dreaming of overnight riches, the market size isn’t large enough. This niche, highly specialized market may also lack appeal for institutions, possibly explaining why prediction markets haven’t grown significantly.

In the seemingly golden prediction market of Polymarket, most so-called “god-level whales” are just surviving gamblers or diligent “brick movers.” True wealth secrets aren’t hidden in inflated win rate rankings but in the algorithms used by a few top players, based on real money bets after filtering out noise.