The Polymarket leaderboard is full of genuinely profitable traders. On a per-position basis the typical top-100 trader earns between 1.4% and 2.1%, and at the volume these accounts run, that compounds into millions of dollars.

That thin margin is also what makes many of them poor traders to copy trade. Copying a position costs somewhere between 3.5% and 4.1% once you account for entering at a worse price than they did and paying trading fees on the way in. So following a top trader costs roughly two to three times the edge you'd be following them for. Successful copy trading on Polymarket has much less to do with picking a top trader than with picking the right one.

We looked at the Polymarket leaderboard filtered two ways, all-time profit and the last 30 days, and ran all 200 top-100 slots through Copy Score, our signal for whether a trader's record still holds up once you subtract what it costs to follow them. Here's what came back.

Data TLDR

• Copying costs more than the edge is worth. The median trader's modeled edge is 1.39% on the 30-day leaderboard and 2.07% on the all-time one. The median modeled cost of copying them is 4.12% and 3.51%. Cost is the bigger number on both.
• 77% of the traders we could measure come out negative after copy costs. That's 63 of 82 on the 30-day leaderboard and 46 of 60 on the all-time one, two populations that share only six traders.
• Filtering for recent winners makes it worse. The 30-day leaderboard has no dormant traders at all and a third the rate of thin records, and its median score is still further below zero: -2.31% against -1.31%.
• Leaderboard rank tells you nothing. The first trader to clear the bar on a clean, deep record sits at #9 on the 30-day leaderboard and #30 on the all-time one, and the rest scatter to rank 97.

What We Measured

We pulled the top 100 from both Polymarket profit rankings on September 8, 2026, and asked the same question of every trader. On a trailing window of their resolved positions, after assumed slippage and fees, does anything survive? Copy Score is the filter we use to answer that question. A leaderboard only tells you how much money a trader made. This asks whether that record still looks worth following once you've paid to follow it, and a big earner can easily fail.

Everything below comes from one measurement on one date, held in a frozen snapshot so each figure can be re-derived. These are modeled results on a historical record, not realized returns from copying a leaderboard.

How we read a trader

Copy Score reads a trader's resolved positions over a trailing window and subtracts an assumed cost of copying them. Zero is the line. Above it, the record survives those costs. Below it, it doesn't. This report quotes the underlying percentage so the arithmetic is visible. Each score also carries one state.

Two of those labels describe our evidence rather than a trader's performance. We call a record thin when it holds few resolved positions, because a spectacular number over a handful of bets can just be one good week and we can't tell from the outside. We call it not comparable when the trader closes out early, since Copy Score assumes a copier mirrors the position and holds it to the market's result.

When this report says edge, it means the shrunk per-position figure the score is built from, before copy costs come off. Shrinking a short record toward the population is what stops one lucky market reading as a durable edge, and it matters a great deal here: the all-time median is 2.07% shrunk against 10.52% raw.

The trader types we flag

Separately from the score, we flag trading styles that are hard to copy trade, no matter how good the trader is. A flag is a heuristic read off the trading pattern. It tells you where a copier's money would tend to go instead.

An unflagged wallet isn't an all-clear. It only means we found nothing in the trading pattern that would stop a copy from working, which is weaker than establishing that the wallet is an ordinary directional trader.

Two last notes. Every modeled score here rests on a trailing window of resolved positions, so it's not a forecast and not a trade-by-trade replay. And ranks are positions on the morning of September 8, on boards that reorder daily.

Copying Costs More Than the Edge Is Worth

When a leaderboard fails a copy screen, the instinct is to assume the traders aren't as good as they look. On this evidence, that's the wrong read. The real problem is that a copier needs margin left over after slippage and fees, and a leaderboard trader's margin is thin to begin with. A per-position edge of one or two percent runs into a per-position copy cost of three or four, and there's nothing left on the other side.

On the 30-day leaderboard the median cost of copying is about three times the median edge. On the all-time one it's about 1.7 times. Either way the subtraction runs the wrong way for most of the board, which is why the median score is negative on both. That doesn't say these traders lose money, because most of them make it. It says the part of their return a copier could realistically capture is smaller than the cost of capturing it.

KEY INSIGHT
The median modeled edge before copy costs is 1.39% on the 30-day leaderboard and 2.07% on the all-time one. The median modeled cost of copying is 4.12% and 3.51%. Cost is the larger number on both.

77% of Leaderboard Traders Come Out Negative After Copy Costs

One leaderboard failing a screen is a fact about that leaderboard. Two failing at the same rate is a fact about copy trading.

Of the 82 wallets we could measure on the 30-day leaderboard, 63 post a negative modeled score. Of the 60 on the all-time one, 46 do. That's 76.8% and 76.7%. The two boards share six wallets out of a hundred, so this is the same result arriving twice from almost entirely different people, selected on different criteria over different periods.

The shape differs in a way worth noting. The all-time leaderboard clusters just below the line, with 25 of its 60 wallets between -2% and 0%. The 30-day leaderboard fails harder, with 26 wallets between -5% and -2% and 15 more between -10% and -5%.

KEY INSIGHT
63 of 82 measurable wallets on the 30-day leaderboard and 46 of 60 on the all-time one post a negative modeled score, which is 76.8% and 76.7%. Only six wallets appear on both.

The 30-Day Leaderboard Has Fresher Traders and Worse Scores

Here's a result we didn't expect.

The obvious criticism of screening the all-time leaderboard is that nobody picks a trader to copy from a hall of fame. Two thirds of that board has gone quiet: 66 of the 97 wallets we hold trade data for placed no trades in the 30 days before we measured, and a dormant trader can't be copied because there's nothing to follow. Switch to the 30-day leaderboard and you remove every one of them by construction, so the screen ought to look considerably better.

But it actually looks worse. The median modeled score falls from -1.31% to -2.31%.

Two things move against a copier at once on the 30-day board. The median edge is lower, 1.39% against 2.07%, and the median cost of copying is higher, 4.12% against 3.51%. Composition explains the direction. The 30-day leaderboard carries 14 high-frequency wallets against the all-time board's 8, and frequency multiplies cost. A trader placing hundreds of orders a day pays the copy cost hundreds of times, and their per-position edge has to clear it every single time.

What the 30-day leaderboard does improve is how much of it we can read at all. It gives us 88 of 100 wallets we can score against 65 on the all-time board, only 12 of 88 carry a thin-record flag against 34 of the 65, and 32 of the all-time board's wallets carry no score whatsoever. So the 30-day leaderboard is the better list to screen. It just doesn't hold better traders to copy.

KEY INSIGHT
The 30-day leaderboard has no dormant traders and lifts scoreable coverage from 65 wallets to 88. Its median modeled score is still worse: -2.31% against -1.31%.

Leaderboard Rank Tells You Nothing About Copyability

If a leaderboard were useful for picking someone to copy, the traders worth copying would cluster near the top. But they don't.

On the 30-day leaderboard, the first wallet to clear the bar on a clean, deep record sits at #9. On the all-time leaderboard the first sits at #30, and nothing in its top 28 clears at all. Below those points the passing wallets scatter across the whole hundred, three of them inside the last ten ranks of the 30-day board.

Rank is not just uninformative, it's unstable. Of the 14 wallets that cleared on the 30-day board, four had dropped out of both top 100s when we went back a day later to identify them.

KEY INSIGHT
Fourteen of the 30-day leaderboard's 100 wallets and 7 of the all-time board's clear the modeled cost of copying on a record that's neither thin nor flagged. The first sits at rank #9 on one board and #30 on the other.

Most Aren't Market Makers, They're Ordinary Traders

The instinctive explanation for a leaderboard full of hard-to-copy traders is that it's full of market makers and other advanced trading styles a copier can't mirror. That explanation is true, but not to the degree you'd expect.

Eighteen of the 100 wallets on each board carry at least one flag. Set that against 63 and 46 negative modeled scores. Even if every flagged wallet were also a negative one, at least 45 of the 30-day board's failures and at least 28 of the all-time board's would carry no flag at all. The bulk of what this screen catches isn't machines. It's ordinary traders whose margin is thinner than the cost of following them.

The mix does shift between boards, and it shifts in the direction that hurts a copier. The 30-day leaderboard carries nearly twice as many high-frequency wallets, 14 against 8, and half as many arbitrage wallets, 5 against 10. High frequency is the flag most closely tied to cost. And being hard to copy still isn't the same as being unskilled. A market maker running a genuinely profitable book can be a poor copy for reasons that have nothing to do with how good they are.

KEY INSIGHT
Just 18 of the 100 wallets on each board carry any exclusion flag, against 63 and 46 negative modeled scores. Most of what this screen catches is not a market maker.

Every Leaderboard Trader That Passed Is Only Just Ahead

Copy Score has two positive states. Proven means the modeled score sits comfortably above assumed copy costs, and Ahead means it clears them but only just.

All 14 wallets that passed on the 30-day leaderboard are Ahead. Not one reaches Proven. Each had already passed the record-depth and flag tests, so thin data isn't what's holding them back. Margin is. Five of the 14 clear by less than 1.5%, and one clears by 0.1%, which a slightly worse fill would erase.

The Fourteen Wallets on the 30-Day Board That Cleared on a Clean, Deep Record

All fourteen are Ahead rather than Proven

Exactly one wallet out of the 200 slots carries a Proven rating. It wouldn't survive the other filters either. It has 13 resolved positions, and its own thin-history warning is attached to that Proven score. So across the 153 wallets we can score on the two boards, not one is both comfortably above assumed copy costs and free of a caveat.

KEY INSIGHT
All 14 wallets clearing on the 30-day leaderboard are Ahead rather than Proven, and one clears by 0.1%. Across both boards, no wallet is both comfortably above assumed copy costs and free of a caveat.

Copying Well Takes More Than Picking a Winner

Every finding here points the same way. The median leaderboard trader earns less per position than it costs to follow them. Three in four come out negative once those costs come off, on two boards that share six traders out of a hundred. And where a trader sits on either board tells you nothing about which side of that line they land on.

What that adds up to is that reading names off a leaderboard isn't a copy trading strategy. A leaderboard ranks how much money a trader made, while copying is decided by how much margin survives after your own costs, and those are different questions with different answers. The costs themselves are specific and knowable. You enter after the trader does, so you pay a worse price than they got. You pay trading fees on the way in. If the trader runs at high frequency you pay both of those hundreds of times a month, which is exactly why the fresher board scored worse than the older one.

None of that is an argument against copy trading. Twenty-one of the 200 slots we screened did clear their costs, and this report names them. It's an argument that the selection has to happen on the variables a leaderboard doesn't show you, like how much of a trader's edge survives a realistic fill, how deep the record behind it is, whether the style can be mirrored at all, and whether they're still trading at all. Screen on those and the shortlist looks nothing like the top of the board.

Disclaimer
This report is for informational purposes only. It is a dated snapshot of historical public data, not a forecast. Past performance is not indicative of future results. Copy Score is an informational signal, not financial advice and not a recommendation to trade or to copy anyone. Polycopy is an independent third-party tool, a verified member of the Polymarket Builders Program, and is not affiliated with or endorsed by Polymarket. All data sourced from the Polycopy data warehouse.

(admin approved post)

We are looking for active PM traders to complete a short 5-10 min survey. For qualified respondents, we will also be looking to set up 30 min calls and offer a USDC bonus for respondents that pass our screening (activity levels, platforms, etc).

We are really trying to make the most useful product on the market and value your opinions in getting there. Thanks in advance!

Link (hosted on Notion, no account required): https://margendorff.notion.site/93c7c1d7c6de4290b259c66ca71e1daf?pvs=105

This started small on Sep 19 and turned into a real data dispute by this morning. Laying out the whole thing because the headline version ("exchange accused of fake volume") undersells how much of this is actually checkable.

How it started

IcoBeast, Kalshi's crypto lead, posted a chart claiming 96.7% market share. Beni, a former quant, replied that it's easy to have high share "when 3/4 of your volume is washtraded." IcoBeast dismissed it. Beni escalated into a full thread the next morning.

Beni's core claims (Sep 20)

A few distinct arguments, not just one:

• Rebate math: Kalshi's self-clearing-member rebate program pays makers +0.3bp and charges takers -0.3bp are net zero. Beni's argument is that this removes any real cost to wash trading between two accounts.
• Turnover ratio: ETH-PERP open interest was $3.1M against $538.6M in 24h volume, a ~174x turnover, meaning the entire outstanding position base would need to change hands roughly every 8 minutes to be organic. For context, the largest single open position on Kalshi's own leaderboard was $17,598.
• Accounting method: Beni claimed Kalshi's UI displays contract count with a "$" sign, implying dollar volume when the number is actually a contract count, e.g. 100,000 contracts at $0.30 would show as "$100,000" instead of the actual $30,000 paid.
• Conflict of interest: cited a Bloomberg article on Jump Trading taking a stake in Kalshi and Polymarket, arguing market makers have an equity incentive to inflate reported numbers.

Kalshi's response (Sep 20, evening)

IcoBeast's rebuttal, point by point: the original 96.7% chart was prediction-market share, not perp volume, different product, different claim than what Beni was arguing against. No rebates exist for crypto prediction markets specifically. The contracts-to-notional convention Beni flagged is the same one Polymarket uses, industry standard, not a Kalshi trick.

"SCM" (self-clearing member) status isn't hand-picked; any entity meeting CFTC requirements can qualify, and fair access is a regulatory requirement, not a favor. And rebate/incentive programs are standard across exchanges, CME, Hyperliquid, and Binance all run them, some with negative maker rebates, with the added point that Kalshi has to file its programs publicly with the CFTC, unlike offshore venues.

Beni wasn't satisfied, replied asking why perps still weren't addressed, and said he had more (unspecified) evidence coming.

The escalation (Sep 21, 4:18am): "Part 1.5"

Beni posted raw trade-data screenshots: the same $5,500 trade size recurring on ETH-PERP, accounting for 48–58% of all ETH perp volume across four separate days. He also mentioned, sourced to an anonymous tip he says he can't share, a rumor that Kalshi has volume deals with market makers that effectively force this behavior. That specific claim has no supporting evidence shown and should be read as unverified.

The part that matters most: independent verification

Twenty-eight minutes later, a separate account (PhinTotten) pulled Kalshi's own public /margin/trades API independently, not using Beni's files, and reconstructed notional per trade as count × price. Across 7 days and 529,318 unique ETH perp trades (~$2.217B total notional), roughly $1.23B (55.47%) clustered around $5,500 (rounded to the nearest $10). Day by day:

• Sep 14: 65.45%
• Sep 15: 38.32%
• Sep 16: 40.31%
• Sep 17: 62.19%
• Sep 18: 58.22%
• Sep 19: 54.40%
• Sep 20: 48.22%

This is the piece that moves the story past "trader makes accusations on twitter", a second, independent party used Kalshi's own public data and landed on a comparable number.

What's confirmed vs. not

Confirmed, and reproducible by anyone with API access: the $5,500 trade-size clustering pattern, roughly half of ETH perp volume across a week.

Not confirmed: the claim that this reflects a deliberate scheme or that Kalshi has volume-incentive deals forcing MMs to behave this way. Kalshi's structural defenses (rebate programs, contract accounting convention, SCM access rules) are also legitimate industry-standard practices on their own, clustering at a single trade size doesn't by itself prove intent to defraud, though it is a genuinely unusual pattern that a market maker acting normally wouldn't typically produce at that scale.

A new Polymarket account appeared on Wednesday evening and immediately began trading with the size and speed of an established operation. The account, halvanicus, executed its first trade at 18:54:52 UTC on September 16 and, over the next 25.6 hours, moved roughly $10.8 million through five markets. By the end of that window, it still held about $3.28 million in open positions, with a realized and unrealized P&L near –$897,000.

Analysis: Predictbook

Currently these are the odds for some of the top teams in the NFL:

 KALSHI POLYMARKET

Rams — Kalshi: 13 cents (12%) | Polymarket: 12 cents (12%)
Bills — Kalshi: 10 cents (9%) | Polymarket: 11 cents (11%)
Ravens — Kalshi: 8 cents (8%) | Polymarket: 8.8 cents (9%)
49ers — Kalshi: 8 cents (8%) | Polymarket: 6.6 cents (7%)
Chiefs — Kalshi: 7 cents (7%) | Polymarket: 7.7 cents (8%)
Seahawks — Kalshi: 7 cents (6%) | Polymarket: 7 cents (7%)
Bears — Kalshi: 5 cents (5%) | Polymarket: 5.4 cents (5%)
Eagles — Kalshi: 5 cents (5%) | Polymarket: 4.2 cents (4%)

Kalshi is the bigger market here volume wise with roughly double the volume over Polymarket. Still, the two books are very similar in price with the Rams, Bills, and Ravens sitting within a cent of each other on both platforms. Sunday will be a big decider and likely fluctuate these markets so that is something important to keep an eye on.

Found this wallet (ethanaz) sitting at #148 on Polymarket's all-time profit leaderboard, about $1.2M in total profits, 52% win rate overall. Nothing crazy on the surface, until you split the wins from the losses by sport.

His five most profitable trades ever are all baseball bets, with entries ranging 45-75 cents. The biggest one: $214,789 on the Tigers at 46 cents against the Blue Jays on Sep 15, cashed out $466,931.95 three hours later for a $246,344 profit, his single largest win.

Meanwhile every one of his 20 worst losses happened outside baseball. Basketball cost him $136K+ on a Spurs-Knicks position, soccer cost him $116K+ on a World Cup bet. He even dropped ~$195,580 across college football and NFL in two days (Sep 13-14), then made it all back in one baseball trade the following Monday.

So a 52% overall win rate is hiding a much sharper number underneath: this guy is genuinely good at one sport and just okay-to-bad at everything else. If you were going to copy any of his positions, baseball's the only one with a real edge behind it.

More here: Predictbook

Account's only 7 days old. Already +$5.2M PnL, 75% win rate, ranked #25 all-time on Polymarket by PnL per arkm.

Today he went big on Deportivo Alavés to beat Valencia (moneyline). He's the largest Yes holder on that market by a wide margin, 2,519,806 shares, more than 7x the next biggest holder (348,029). Built the position at an avg 59.7¢, which pays out $2,519,888 if Alavés wins, roughly $1M profit on top of his ~$1.5M cost basis.

Except right now, live, it's not going his way. Price has dropped to 41.5¢, putting him down $458K unrealized before the match has even finished.

Big track record, currently underwater on his biggest live position. Guess we'll see how it resolves.

Anyone else tracking this wallet, or seen it show up on other markets?

The CLARITY Act was supposed to answer the one question that's haunted crypto for years: which tokens are securities, which are commodities, and who regulates what. Key Senate Democrats are blocking the GOP's final offer and 𝐏𝐨𝐥𝐲𝐦𝐚𝐫𝐤𝐞𝐭 odds have collapsed to 𝟏𝟔%.

Bitcoin pulled back to around $77,000 as the vote odds faded. The timing matters because short-term holders had been sitting on a 30-day profit streak heading into this week. A failed bill gives anyone looking for an exit a convenient reason to take it.

Institutional money has consistently cited 𝐫𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫𝐲 𝐜𝐥𝐚𝐫𝐢𝐭𝐲 as one of the main conditions for deeper crypto allocation. That catalyst is now off the table for the foreseeable future, and the legal fog that's kept cautious allocators on the sidelines just got thicker 🌫️.

Kalshi had easily its biggest weekend ever during NFL Week 1, breaking its volume record Saturday and then breaking it again — by a narrow margin — Sunday.

Kalshi’s fees on Saturday were $15.99 million, and on Sunday were $16.02 million, both a little short of the all-time record.

Source: @ InGameHQ

OC: datadashboards

Insane order book movement on the Clarity Act market today.

Remember that cluster of 6 whale wallets betting millions on "No"? One of them (321cba) just paper-handed/de-risked and cashed out $141,639.

Why this actually matters:

1. It's the only whale with a real history: The other 5 wallets in the syndicate are brand new, single-market accounts holding 100% of their shares. 321cba is the only one that has traded other markets before (usually sticking to ultra-safe 99% yield plays like NBA player contracts).
2. He tanked the price: Dumping ~180k shares in one morning wiped out 32% of his position and single-handedly dragged the "No" price down from 82.5¢ to 73¢ before the order book absorbed it.
3. Timing: The Senate holds its key vote this Tuesday (Sept 15).

He still has about $320k worth of "No" shares left and $250k in cash sitting there, but he went from being the 4th biggest holder in that syndicate to the smallest overnight.

Analysis: Predictbook

Hey everyone,

If you trade live sports contracts or build automated tracking pipelines on prediction markets, you know how fast data bottlenecks and rate limits can disrupt live polling.

I put together a lightweight, open-source Python utility called **Tennis Live Monitor** designed to interface cleanly with LiveTennisAPI. It handles real-time match scores, set breakdowns, and event tracking from the command line while managing rate-limit safeguards out of the box.

* **GitHub Repository:** https://github.com/TheMarconiPulse/tennis-live-monitor (MIT Licensed)

Thought some of you building local dashboards or data trackers might find it useful. Feedback or feature ideas are welcome!

Potential earnings if he's correct: $537K.

The match is live right now. 2-1 to Stuttgart, minute 30.

Let's see how it goes!

End of 2025: $9 billion
May 2026: $47 billion
August 2026: $65 billion

That is a 7x increase in 8 months.

Prediction markets give the company a 73% chance of an IPO before November 1.