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 smart money’s arbitrage strategies being regarded as the gold standard. Many have begun to imitate and experiment, as if a new wave of gold rush has started.

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 by the top ten profit-making whales on Polymarket in December, seeking the true nature of their profits.

After analysis, PANews found that although many of these “smart money” operations employed hedging arbitrage strategies, this hedging differs significantly from the simple hedging described on social media. The actual strategies are much more complex, involving not just straightforward “yes” or “no” combinations, but full utilization of rules like “over/under,” “win/loss” in sports events to form hedges. Another key discovery is that behind the extremely high win rates shown in historical holdings is a large number of “zombie orders” that remain unsettled, which artificially inflate the win rate. The real win rate is much lower than the historical data suggests.

Next, PANews will reveal the true operations of these “smart money” whales through actual cases.

1. SeriouslySirius: 73% Win Rate Masked by “Zombie Orders” and Complex Quantitative Hedging

SeriouslySirius is the address ranked first in December, with a profit of about $3.29 million in that month and a total historical profit of $2.94 million. Looking only at his completed orders, his win rate is as high as 73.7%. However, the reality is that he still holds 2,369 open orders, with 4,690 settled orders. Among these, 1,791 open orders have already failed completely, but the user has not closed them individually. On one hand, this saves significant effort and transaction fees. On the other hand, since he usually closes profitable orders, the historical settled data shows an extremely high win rate. When considering these “zombie” open orders, his true win rate drops to 53.3%, only slightly above random coin flips.

In his actual trading, about 40% of orders are hedges against the same event in multiple directions. However, this hedging is not just simple “YES” + “NO.” For example, in an NBA game between the 76ers and Mavericks, he bought 11 different directions, including Under, Over, 76ers, Mavericks, etc., ultimately earning $1,611. During this process, he also employed arbitrage strategies with low probability, such as betting on the 76ers to win with a 56.8% chance and the Mavericks with a 39.37% chance, with a total cost of about 0.962, ensuring profit regardless of the outcome. Ultimately, he made a profit of $17,000 in this game.

However, this strategy does not always yield profits. For example, in a Celtics vs Kings game, he participated in 9 directions but ended up losing $2,900.

Additionally, there are many cases where the capital allocation is severely unbalanced—for instance, placing bets on both sides but with over ten times difference in invested funds. Such results are likely caused by insufficient market liquidity, indicating that while arbitrage strategies look promising, liquidity can be the biggest obstacle in actual operations. Opportunities may appear, but they do not necessarily allow for balanced hedging across both sides.

Moreover, since these are automated trades, buy and sell actions often turn into significant losses.

Nevertheless, SeriouslySirius has managed to achieve large profits through this strategy mainly because of proper position management, with a profit-loss ratio of about 2.52. This is the main reason he can profit despite a relatively low actual win rate.

Furthermore, this strategy does not always produce profits. Before December, this address’s profit and loss situation was not optimistic, often hovering around break-even, with a maximum loss reaching $1.8 million. Now, with a more mature strategy, it remains uncertain whether such profitability can be sustained.

2. DrPufferfish: Turning Low-Probability Bets into High-Probability Wins, the Art of “Risk-Reward” Management

DrPufferfish is the second most profitable address in December, with a profit of about $2.06 million. His historical win rate is even more impressive at 83.5%. However, considering the large number of “zombie orders,” his effective win rate drops to 50.9%. The strategy and trading style of this address differ markedly from SeriouslySirius. Although about 25% of his orders are hedges, these are not against opposite directions but rather diversified bets. For example, in a US league baseball tournament, he bought into 27 teams with low probabilities, with the combined probability exceeding 54%. Through such strategies, he turned low-probability events into high-probability outcomes.

The main reason for his huge gains is his ability to control the risk-reward ratio. Take Liverpool, for example—this Premier League team is his favorite. He predicted its results 123 times, ultimately earning about $1.6 million. Among profitable predictions, the average profit was about $37,200, while the average loss on unsuccessful predictions was about $11,000. Most of these losing bets were sold early to limit losses.

This operational approach allowed his overall profit-loss ratio to reach 8.62, indicating high profit potential. But overall, his strategy is not just simple arbitrage hedging; it involves professional prediction analysis and strict position management to achieve large gains. Also, most of his hedging trades are in a loss state, with a total profit and loss of -$2.09 million, suggesting that these hedges are mainly used as insurance.

3. gmanas: High-Frequency Automated Assembly Line

Ranked third, gmanas has a similar style to DrPufferfish, with a total profit of about $1.97 million in December. His true win rate is 51.8%, close to DrPufferfish. He executes 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 Prediction Probabilities as “K-line” for Swing Trading

Ranked fourth, simonbanza is a professional prediction hunter. Unlike the previous addresses, he does not use hedging orders at all. His profit in December is about $1.04 million, with only $130,000 in unrealized losses from “zombie orders.” His capital and trading volume are modest, but his win rate is the highest at about 57.6%. The average profit per settled order is about $32,000, and the average loss is about $36,500. Although his profit-loss ratio is not high, his high win rate has resulted in good overall gains.

He also has very few “zombie orders,” only six, because he usually does not wait for event completion to settle but instead exploits probability fluctuations for profit. Simply put, he takes profits when available and does not hold out for the final outcome.

This represents a unique prediction market investment approach. In his logic, probability changes resemble stock market fluctuations. The exact reasoning behind his high win rate remains unknown, as it is his exclusive secret.

5. Whale gmpm: Asymmetric Hedging Strategy Using “Large Positions” to Secure Certainty

Ranked fifth, gmpm’s December profit ranking is fifth, but his total historical profit exceeds others at $2.93 million. His true win rate is about 56.16%, also relatively high. His approach is similar to the fourth address but with a unique core strategy.

For example, he often places bets on both sides of the same event, but his strategy seems to involve investing more heavily on the side with higher probability and less on the lower probability side, rather than seeking arbitrage between the two. This allows him to have larger positions when the outcome is likely to be favorable, while limiting losses when low-probability events occur, achieving a form of hedging.

In practice, this is a more advanced hedging strategy that does not rely solely on the “yes” + “no” < 1 mathematical arbitrage but combines comprehensive event judgment and hedging to reduce losses.

6. Model Worker swisstony: “Ant Moving” High-Frequency Arbitrage

Ranked sixth, swisstony is a high-frequency arbitrage address, with the highest trading frequency among these addresses, executing 5,527 trades. Despite earning over $860,000, the average profit per trade is only $156. His style resembles the “ant moving” approach. Like other arbitrage addresses, he often bets on all sides of a single event. For example, in a Jazz vs Clippers game, he bought 23 different betting options. Due to the small investment amounts, capital is relatively evenly distributed, which can help with hedging.

However, this strategy heavily depends on precise execution, such as ensuring “yes” + “no” < 1. Interestingly, his hedging orders often buy both sides with totals exceeding 1, which inevitably leads to losses. Nonetheless, with a reasonable profit-loss ratio and win rate, his overall expected value remains positive.

7. Outlier 0xafEe: “Pop Culture Prophet” with Unconventional Approach

The seventh address, 0xafEe, is a low-frequency, high-win-rate trader. His trading frequency is very low, averaging 0.4 trades per day, with a true win rate of 69.5%.

In his completed orders, he earned about $929,000 with very few “zombie” orders, only about $8,800 in unrealized losses. He never uses hedging orders, focusing solely on predictions. His predictions mainly target Google search trends and pop culture topics, such as “Will Pope Leo XIV become the most searched person on Google this year?” or “Will Gemini 3.0 be released before October 31?” He appears to have a unique analytical method for these topics, resulting in an extremely high success rate. Among top whales, he is the only one outside sports, taking a contrarian approach.

8. Manual Hedging Player 0x006cc: From Simple to Complex Hedging Strategy Upgrade

Ranked eighth, 0x006cc is similar to the other complex hedging addresses, with a total profit of about $1.27 million and a true win rate of about 54%. Compared to other automated addresses, his trading frequency is low, averaging 0.7 trades per day. Early on, he likely used a simple manual hedging approach.

Since December, this simple hedging has evolved into a more complex strategy. As more people understand hedging, his approach is gradually upgrading.

9. Negative Example RN1: When “Hedging” Turns into a “Loss Formula”

Ranked ninth, RN1 is among the top ten profit addresses in December but is currently overall in loss. His realized profit is about $1.76 million, but unrealized losses reach $2.68 million, totaling a loss of $920,000. As a negative example, there are many lessons to learn from RN1.

First, his true win rate is only 42%, the lowest among these addresses, and his profit-loss ratio is only 1.62. Combining these data points, his profit expectation is negative, meaning this strategy is unlikely to be profitable overall.

A closer look reveals that RN1 also employs arbitrage strategies, but many of his hedging trades, while satisfying the “yes” + “no” < 1 condition, tend to bet more on the low-probability side and less on the high-probability side, leading to unbalanced positions. When high-probability events occur, this imbalance results in actual losses.

10. Gambler Cavs2: Unilateral Heavy Bets on Ice Hockey, Luck Over Strategy

The tenth address, Cavs2, is also a prediction gambler favoring unilateral heavy bets. His main expertise is NHL hockey. Overall, his profit is about $630,000, with a true win rate of approximately 50.43% and a relatively low hedge ratio of 6.6%. The data is average, and luck plays a significant role, as he hit some high-yield single-game results. His strategy offers limited practical guidance.

The 5 Harsh Truths After Disenchanting “Smart Money”

After an in-depth analysis of these “smart money” trades, PANews summarizes the reality behind the “wealth stories” of prediction markets.

1. “Arbitrage hedging strategies” are far from just meeting probability conditions. Under fierce market competition and liquidity constraints, they can often turn into counterproductive loss formulas. Blind imitation is not advisable.

2. “Copy trading” also seems ineffective in prediction markets, mainly for several reasons: first, the rankings or win rates seen are based on historical settled profit data, which can be “distorted.” Behind such data, many “smart money” addresses are not truly smart; real win rates above 70% are rare, most are close to coin flips. Additionally, the current trading depth in prediction markets is limited, and arbitrage opportunities may only accommodate small amounts of capital, making it easy for copy traders to be squeezed out.

3. Managing risk-reward ratios and position sizes is more important than just pursuing win rate. Among the addresses with excellent strategies, a common trait is their skill in managing profit and loss ratios. Addresses like gmpm and DrPufferfish even adjust their positions dynamically based on probability trends to reduce losses and improve risk-reward.

4. The real secret lies beyond “mathematical formulas.” Currently, many social media explanations of “arbitrage formulas” seem reasonable at first glance, but in practice, these “smart money” strategies rely on skills beyond formulas—either strong judgment of certain events or unique analysis models of pop culture. These unseen decision algorithms are key to their success. For users without such “decision algorithms,” prediction markets remain a cold “Dark Forest.”

5. The profit scale in prediction markets is still small. For the top “smart money” addresses in December, the largest total profit is only around $3 million. Compared to the profit potential of the crypto derivatives market, it appears to have a clear upper limit. For those dreaming of overnight riches, this market is still too small. Such a niche, specialized, and small-scale market is unlikely to attract institutions for now, which may also be a key reason 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 are not hidden in those inflated win rate rankings but in the algorithms that a few top players bet with real money after filtering out noise.