Original author: Leo (X: @runes_leo)
What did the strategy look like for someone who made $10 million on Polymarket?
Using the Data API and on-chain data, we reverse-engineered the top 20 rankings in both sports and crypto. We analyzed 40 addresses, examining over 100,000 transactions one by one.
Don't rely on dashboard screenshots. Reconstruct every buy, sell, and redemption as strategic actions. Method: Use the Polymarket Data API to fetch transaction records for each address, verify profits and losses via the LB API, and reconstruct actual cash flows using on-chain REDEEM/MERGE data. Each address has between 2,000 and 15,000 transactions.
After breaking it down, I found that regardless of sports or crypto, profit-making addresses fall into three categories. The differences between these three categories aren't just about varying parameters—they're playing entirely different games.
Type 1: Directional—Hold Until You Reach Your Target If You're Right
The most profitable strategy in sports betting—so simple I didn’t believe it at first.
Of the 18 active addresses, 14 only buy and never sell. They hold until settlement—winning positions are redeemed, losing positions go to zero, and no trading is conducted.
Even when buying only and not selling, the ways to profit are completely different.
Swisstony: $494 million in trading volume, 1% return rate, net profit of $4.96 million. Fully automated, placed 353 trades in 30 minutes across five major leagues. Profit per match is small, but the volume is enormous.
Majorexploiter: Return rate of 39%, with a single bet as high as $990,000. Over 600 trades were almost entirely concentrated on two Arsenal matches. Willing to place big bets—winning means millions.
One placed many bets, the other placed a single bet—both made several million. Their methods were opposite, but they shared one common trait: they had informational advantages on the events they bet on.
The top-ranked asset is losing momentum.
kch123, ranked #1 in sports betting, with total profits of $10.35 million.
However, as of the analysis in mid-March, the past 30 days resulted in a loss of 479,000. The win rate over the past 7 days was only 31% (15 wins, 33 losses). All 14,303 trades were buys, with zero sells. The average daily trading volume was 493 trades, and 74% of trades had intervals of less than 10 seconds.
A machine that made ten million is losing speed. You won’t know this just by looking at the leaderboard—you need to analyze on-chain data to see it.
I fooled myself with my own label.
Fengdubiying, sports rank 13, profit of $3.13 million.
I labeled him as "sell-dominated" during batch analysis, suggesting he engages in swing trading.
Breakdown of data: 93.6% of repayments came from redemptions, while sales accounted for only 6%. The actual strategy involves concentrated betting on LoL esports matches. The largest single-market bet was $1.58 million (T1 vs. KT Rolster), with a win rate of 74.4% and a profit-to-loss ratio of 7.5:1.
Selling is his stop-loss tool, not his main strategy. Looking only at the buy/sell ratio on the dashboard, you would completely misinterpret what this person is doing.
Second type: Structural, not profiting from predictions
The crypto leaderboard is an entirely different breed. In sports, you bet on the outcome; in crypto, you're the house.
Dive Deep into Crypto Top 5: Three market-making bots trading crypto binary options, one price-threshold market maker using MERGE for inventory management, and one specialized in arbitrage around public milestone events (43.3% return rate).
Retail traders are betting on price movements, while major players are acting as the house.
How do market makers make money?
0x8dxd, BTC 5/15-minute price movement market maker.
94% of trades are symmetric orders, simultaneously buying both up and down. Operating 24/7, with a median trade size of less than $6. The sum of the buy prices for up and down is less than $1, with the difference between them representing profit. At least three independent addresses are running the same strategy.
Another market-making address was even more extreme: it nearly monopolized liquidity provision in the Economics category—982 buy orders, 0 sell orders, with a six-figure PnL. Profits came from maker rebates combined with the liquidity premium.
Good code doesn't equal profit.
Seeing this, you might think market making is guaranteed profit? There’s an open-source Polymarket market-making bot on GitHub—its code is well-engineered, featuring real-time WebSocket data, a three-part risk management system (stop-loss + volatility freeze + cooldown period), and automatic position consolidation. The author admits: it’s not profitable.
The reason is that the pricing logic is penny jumping—inserting a price one cent ahead of the current best quote. In simple terms, it’s copy trading without any independent pricing capability.
No matter how well-written the code is, whether market-making is profitable depends on whether your pricing model can be more accurate than the market.
Another noteworthy data point: Analysis of on-chain transaction timestamps reveals that over 70% of arbitrage profits in Polymarket’s crypto price markets are captured by bots with latency under 100 milliseconds. Less than 8% of all wallets in the market are profitable. Bots with second-level latency are essentially providing liquidity to high-frequency traders.
Third type: Cognitive—place fewer bets, but each one is based on careful judgment.
The third type of address is completely different from the first two. Transactions occur infrequently—perhaps only two or three times per month—but each one is backed by thorough research.
Here are a few examples. One address, modeling data from official meteorological agency sources for a weather market, only enters trades when the win rate exceeds 0.77—resulting in just two or three trades per month, each generating profits in the tens of thousands of dollars. Another address has 89% of its trades as "NO" bets, with holding periods lasting months; although the win rate is low, the average payoff multiplier exceeds 9x, relying on a few large winning bets to offset all minor losses.
Here’s an even more extreme one: In the FDV (fully diluted) market, do nothing but buy NO at 50–55 cents and collect $1 upon settlement. Win rate: 100%. This isn’t luck—it’s that others haven’t noticed this pricing discrepancy.
But cognitive trading isn't about "trading just because you've researched deeply." I analyzed a case where someone built a probability matrix for BTC price deviations using 1.37 million rows of historical data—backtesting showed perfect results, but it collapsed immediately during rolling validation. Markets become efficient quickly; patterns that worked last month have already been arbitraged away this month.
The true edge of cognitive insight is having a deeper understanding of a particular asset class than the market pricing reflects, not having a more complex model.
Comparison of Three Ways of Living
Comparison Table of Three Ways of Living
What am I doing myself?
After talking about others, let me talk about myself.
I’m simultaneously running several lines: crypto market making (structured), sports probability pricing (directional), and weather data modeling (cognitive). None of them are large—none come close to the scale of kch123, with 493 trades per day, or swisstony’s trading volume of $494 million.
After analyzing these 40 addresses, the one thing I thought about most: Understanding which game you're really playing is more important than optimizing any parameter.
Trading with direction but no informational edge is just guessing, no matter how well you execute. Trading with structure but failing to keep up with timing makes you the one getting harvested. This isn’t motivational fluff—it’s what I told myself after analyzing the data.
Currently, each line is running small-scale validations to confirm edge existence before scaling up. Don’t rush to expand—first, get one or two categories fully operational.
Data sources: Polymarket Data API + LB API + Polygon on-chain data | Analysis period: January–March 2026
Want to try Polymarket? First, decide which market you want to participate in.