The truth about Polymarket whales from 27,000 transactions: The five major pitfalls behind smart money manipulation

Smart money’s market operations may seem precise in their predictions, but the reality is far more complex than it appears on the surface. PANews conducted an in-depth analysis of the top ten whales with the highest profits on Polymarket at the end of 2024. After examining 27,000 transaction records, it was found that these so-called high-win-rate traders hide a large amount of false data, liquidity dilemmas, and the art of position management behind their success.

The Truth About False Win Rates: How Zombie Orders Mask Earnings

When reviewing these whales’ operations, the most shocking discovery is the degree of distortion in their win rate data.

Ranked first, SeriouslySirius claims a win rate of 73.7%, but a deeper analysis of their open positions reveals the truth. This address currently holds 2,369 open orders, of which 1,791 have completely failed but have never been closed. These “zombie orders” exist partly to save on trading fees and operational costs, but more importantly—they make the historical win rate look very impressive. Because traders typically only close profitable orders and leave losing orders open indefinitely, the settled order data automatically filters out unsuccessful trades.

Once these unrealized losses are considered, SeriouslySirius’s true win rate drops to 53.3%—a number only slightly above random coin flips. A similar situation applies to the second-ranked DrPufferfish, whose public win rate is 83.5%, but the actual win rate is only 50.9%. This phenomenon is common among top players and reveals a fundamental rule in market prediction rankings: The win rate data you see is heavily distorted by survivorship bias.

The Dilemma of Automated Hedging Strategies: Liquidity Traps and Complexity

Many whales’ operations involve “hedge arbitrage,” but the simple hedge formulas circulated on social media (buying “YES” and “NO” to keep total costs below 1) differ greatly from actual practice.

For example, in SeriouslySirius’s operation on the NBA 76ers vs. Mavericks market, they didn’t simply buy both sides; instead, they participated in 11 different directions simultaneously: including under/over, 76ers win, Mavericks win, and more. The purpose of this complex hedging is to find mathematical arbitrage opportunities—for instance, with the implied probability of the 76ers winning at 56.8% and Mavericks at 39.37%, the combined cost is about 0.962, theoretically guaranteeing profit regardless of outcome. Ultimately, this operation yielded a profit of $17,000.

However, the problem lies in liquidity becoming the biggest enemy of hedging. When executing these hedges, traders often cannot buy equal amounts on both sides simultaneously, leading to severe imbalance in capital allocation—it’s not uncommon for the two directions of the same event to have more than a tenfold difference in invested funds. This imbalance directly stems from market depth issues; when automated programs execute trades, they often end up incurring significant losses.

High-frequency trader swisstony experienced this dilemma firsthand. He executed 5,527 trades, with an average profit of only $156 per trade. When trying to replicate the “YES” + “NO” < 1 arbitrage formula, his hedge orders frequently bought more than 1 unit in total, inevitably leading to losses. This indicates that precise execution of hedging requires not only mathematical formulas but also extremely strong real-time liquidity awareness.

The Art of Position Management: Beyond Simple Arbitrage Secrets

Top-tier whales are not just simple arbitrageurs—they are masters of sophisticated position management.

DrPufferfish’s style is quite different. He also hedges but not by buying opposing sides; instead, he disperses bets across multiple low-probability events. For example, in predicting the champion of Major League Baseball, he bought 27 teams with low probabilities, whose combined probabilities exceed 54%. This transforms a low-probability event into a high-probability one.

More critically, he controls the risk-reward ratio. Take Liverpool FC as an example: he made 123 predictions on this Premier League team, with an average profit of $37,200 per successful prediction, and an average loss of only $11,000 on unsuccessful ones. He pre-sells losing orders to limit losses, achieving an overall risk-reward ratio of 8.62. This disciplined approach enabled him to realize a profit of $2.06 million by the end of 2024.

Ranked fifth, gmpm employs an even more sophisticated “asymmetric hedging” strategy: when placing dual-sided bets, he invests more in the higher-probability side and less in the lower-probability side. This allows him to gain larger profits when high-probability events occur and control losses in low-probability events—an advanced strategy combining event judgment with risk hedging.

Low-Frequency High-Win-Rate vs. High-Frequency Small Profits: Contrasting Different Strategies

The operational styles of prediction market whales vary greatly, but each has its own survival logic.

An outlier, 0xafEe, uses a completely different approach: low frequency, high win rate, and no hedging at all. He makes only about 0.4 trades per day but achieves a true win rate of 69.5%. He focuses on predictions related to Google search indices and pop culture, seemingly mastering unique analytical models in these areas. With approximately $929,000 in profit and very few losing orders, he proves that accurate subjective judgment can be more powerful than automated arbitrage in certain fields.

In contrast, simonbanza employs a “wave trading” style—he does not hedge at all but treats probability fluctuations as “K-line” charts to trade on. He doesn’t wait for the final outcome but looks for opportunities within probability swings. As long as there is profit, he exits immediately, avoiding holding onto results. This approach results in very few zombie orders (only 6), with a true win rate of 57.6%. Although the average profit per trade isn’t high, his high win rate still earned him $1.04 million.

The cautionary example, RN1, demonstrates another side of operational failure. Despite realizing $1.76 million in profit, he has an unrealized loss of $2.68 million, with an overall loss of $920,000. His true win rate is only 42%, with a profit-to-loss ratio of 1.62—this combination guarantees losses. His problem lies in probabilistic mismatches during hedging: he often invests too much in low-probability directions and too little in high-probability ones, leading to substantial losses when high-probability events occur. This case proves that even meeting the mathematical arbitrage condition “YES” + “NO” < 1 does not guarantee success.

The Secrets of Smart Money: Seeing the Reality Behind False Data in Prediction Markets

After stripping away the illusions of false win rates and liquidity, what is truly behind the operations of smart money?

First, position management outweighs win rate. Most successful whales (like DrPufferfish, gmpm) do not chase extremely high win rates; instead, they focus on controlling the risk-reward ratio. They adjust their positions in real-time based on probability changes, exiting profitably when possible and cutting losses early when risks threaten. This disciplined approach is more critical than the accuracy of probability predictions.

Second, decision algorithms beyond arbitrage formulas are the core competitive advantage. Successful whales either have strong judgment in specific events (e.g., DrPufferfish’s deep research on Liverpool), master analysis models in niche fields (e.g., 0xafEe’s insights into pop culture), or possess high-frequency automated trading advantages. Simply copying the “YES” + “NO” < 1 formula cannot succeed.

Third, liquidity and capital scale directly limit profit ceilings. Looking at these top ten whales, even the highest profit in late 2024—SeriouslySirius’s monthly profit—is only about $3.29 million, with a total historical profit under $3 million. Compared to the size of the crypto derivatives market, prediction markets are still niche playgrounds. This small scale means that even with operational secrets, achieving billion-dollar gains remains difficult.

The Five Major Operational Traps and Breakthrough Strategies in Prediction Markets

Summarizing the deep analysis of these whales’ operations, five systemic traps exist in prediction markets:

Trap 1: Liquidity backlash against hedging arbitrage. Seemingly perfect mathematical formulas often fail in practice because simultaneous equal-position entries on both sides are impossible; market depth issues cause imbalance, and automated execution often results in losses.

Trap 2: Survivorship bias in win rate data. Since losing orders tend to remain open indefinitely while profitable ones are closed, the leaderboard’s win rate data is heavily inflated. Following these “high win rate” addresses actually replicates false signals.

Trap 3: Mechanical errors in capital allocation. Hedging requires precise position balancing, but high-frequency automated trading often suffers from liquidity fluctuations, leading to severe imbalance and eventual losses.

Trap 4: Blind pursuit of high win rates ignoring risk-reward ratios. The key to profitability is not just win rate but the ratio of profit to loss per trade. Even with a 50% win rate, a high enough risk-reward ratio can generate profits; conversely, a high win rate with poor risk management can still lose money.

Trap 5: Market size growth bottleneck. The current liquidity and participant scale in prediction markets are relatively small, limiting even skilled operators from achieving enormous gains. Institutional capital interest remains limited.

In the seemingly opportunity-rich Polymarket prediction market, so-called “smart money” is mostly surviving gamblers or diligent arbitrageurs. The real operational secrets are not hidden in inflated win rate rankings but in the algorithms of a few top players who achieve steady profits through strict position management, unique judgment, and risk control. For ordinary participants, understanding these traps is far more crucial than blindly following “smart money.”