Polymarket Million-Dollar Winners Recap: 40 Addresses, 100,000 Transactions, Only Three Ways to Make Money

Title: “Breaking Down Polymarket’s Top 40 Addresses: Only Three Ways to Make Money”

Author: Leo

Source:

Repost: Mars Finance

What do strategies look like for those who made $10 million on Polymarket?

Using Data API + on-chain data, reverse-engineered the top 20 rankings in both sports and crypto tracks.

40 addresses, over 100,000 trades, analyzed one by one.

Not just screenshots of dashboards. Every buy, sell, and redemption was reconstructed into strategic actions.

Method: Pull transaction records per address via Polymarket Data API, verify profit and loss with LB API, and restore real cash flow using on-chain REDEEM/MERGE data. Each address has between 2,000 and 15,000 trades.

After analysis, I found that regardless of sports or crypto, profitable addresses fall into three categories. The differences between these categories are not just parameter variations—they’re playing entirely different games.

First Type: Directional, Hold Until the End

The most profitable sports strategy is so simple I couldn’t believe it at first.

Out of 18 effective addresses, 14 only buy and hold until settlement. Win, redeem; lose, reset—no swing trading.

Even among those only buying, the profit methods differ completely.

swisstony: $494 million in trading volume, 1% return, net profit of $4.96 million. Fully automated, 353 trades in 30 minutes, covering five major leagues. Each game earns just a little, but volume is huge.

majorexploiter: 39% return, max single trade $990,000. Nearly all of over 600 trades are on two Arsenal matches. Willing to bet big—winning means millions.

One focuses on volume, the other on big bets, but both profit in the millions. Their methods are opposite, but share a common advantage: they have informational edges on the events they bet on.

Top of the leaderboard is slowing down

kch123, sports leaderboard top, has accumulated $10.35 million in profit.

But as of mid-March, in the last 30 days, they lost $47,900. Over the past week, win rate is only 31% (15 wins, 33 losses). All 14,303 trades are buys; zero sells. Average of 493 trades per day, with 74% of trades happening within 10 seconds.

The machine that made over $10 million is losing steam. You wouldn’t see this just from the leaderboard—you need to analyze on-chain data.

My own labels misled me

fengdubiying, ranked 13th in sports, with $3.13 million profit.

When I analyzed him in bulk, I labeled him as “sell-dominant,” thinking he was swing trading.

But data shows: 93.6% of his cash flow comes from redemptions, only 6% from sales. His real strategy is concentrated betting on LoL esports. Max single market bet: $1.58 million (T1 vs KT Rolster), 74.4% win rate, profit/loss ratio of 7.5 to 1.

Selling is just his stop-loss tool, not his main strategy. Looking only at dashboard buy/sell ratios can lead to complete misjudgment of what he’s doing.

Second Type: Structural, Profiting Without Prediction

Crypto rankings are a completely different beast. While sports betting is about direction, crypto is about market making.

Deep dive into the top 5 crypto addresses: three are market-making bots trading binary options on price movements, one uses MERGE to manage inventory with a price threshold, and another arbitrages milestone events in public offerings (return rate 43.3%).

Retail traders bet on price directions, while top players act as market makers.

How do market makers profit?

0x8dxd: BTC market maker for 5/15-minute price movements.

94% of trades are symmetrical orders—both buy and sell bets. Operates all day, with a median trade size under $6. Price difference between buy and sell is less than $1, and the spread is the profit. At least three independent addresses run similar models.

Another market-making address is even more extreme: nearly monopolizing liquidity in the Economics category. 982 buy orders, zero sell orders, with six-figure PnL. Profits come from maker rebates plus liquidity premiums.

Good code doesn’t guarantee profit

You might think market making is a sure thing. There’s an open-source Polymarket market-making bot on GitHub, with engineering-quality code, real-time WebSocket data, risk controls (stop-loss, volatility freeze, cooldown), and automatic position merging. The author admits: it’s not profitable.

Why? Because its pricing logic is penny jumping—placing bids just one cent ahead of the best available price. Essentially, it’s copy trading, lacking independent pricing ability.

Even the best code is useless if your pricing model can’t beat the market.

Another important data point: analysis of on-chain transaction timestamps shows over 70% of arbitrage profits in Polymarket crypto markets are captured by bots with latency under 100 milliseconds. Less than 8% of wallets are profitable overall. When latency is in seconds, they’re essentially providing liquidity for high-frequency traders.

Third Type: Cognitive, Small Bets with Judgement Behind Each

The third category of addresses is completely different from the first two. They trade infrequently—maybe only two or three times a month—but each trade is backed by research.

Examples:

One weather-related address models using public meteorological data, entering only when the win probability exceeds 0.77. They might only make two or three trades a month, each earning tens of thousands of dollars.

Another address: 89% of trades are buying NO, with holding periods measured in months. Win rate isn’t high, but the payoff multiple averages over 9x, covering small losses with a few big bets.

An even more extreme case: in FDV (full outcome) markets, they only do one thing—buy NO at 50-55 cents, and settle at $1. Win rate 100%. Not luck—others haven’t noticed this pricing bias.

But cognitive strategies don’t just profit from “deep research.” I analyzed a case where someone used 1.37 million lines of historical data to create a probability matrix of BTC price deviations. Backtesting looked perfect, but when rolled out in live trading, it immediately failed. Market efficiency improves rapidly—patterns that worked last month are arbitraged away this month.

The real edge in cognitive strategies is having a deeper understanding of a particular category than the market’s pricing, not just having a more complex model.

Comparison of the Three Approaches

What am I doing?

Now, a word about myself.

I’m running several strategies simultaneously: crypto market making (structural), sports probability pricing (directional), weather data modeling (cognitive). Each is small-scale—nothing like kch123’s average of 493 trades per day or swisstony’s $494 million in volume.

After analyzing these 40 addresses, the most important thing I realized is: understanding which game I’m playing is more crucial than optimizing any parameter.

If you pursue a directional strategy without an informational edge, even perfect execution is just guessing. If you rely on a structural approach but your latency can’t keep up, you’re just being harvested. This isn’t motivational talk—it’s what I told myself after reviewing the data.

Now, each strategy is being tested on a small scale. Once I confirm an edge exists, I’ll scale up. No rush to expand—first, I want to get one or two categories running smoothly.

Data sources: Polymarket Data API + LB API + Polygon on-chain data | Analysis period: January-March 2026

Thinking of trying Polymarket? First, figure out which game you want to play.