Small analysis on 3 markets where we see a huge gap between where smart money is positioned vs where the crowd is positioned.

Usually, we see that kind of huge gaps for long term markets highly influenced by news where smart traders can take a position at a low price, then wait for a peak due to crowd reaction to news so they can exit and take the profit.

That's precisely what we see here and it's usually on that kind of markets that we see the biggest concentration of high-confidence traders, with a good distribution (not concentrated on a single smart trader)

Last time I posted a profile with >94% winrate, I got roasted because the trader was farming near-resolution markets. And tbh it was a mistake on my side to present this trader.

So I come back with another profile I identified, which has an outstanding 94% winrate with an average entry price of 0.545¢.

What is interesting is that this trader focuses on holding to resolution, curious behavior considering his average entry price.

He's entirely focusing on football markets.

Curious if you guys have any hints on what kind of strategies he might be applying with the available data ?

I'm working on smart money analysis product and we have a specific score we calculate for each traders to define whether we believe if the trader is great or not.

We had the formula but didn't have enough datapoints to make the first backtest on them yet. Happy to announce that it works haha.

At >50/100 score, >60% of the identified smart traders have made a positive PnL at +14D.

Special credits for the 37 traders at >80/100 score, where 70% of them made a positive PnL at +14D with a +$1.2k median PnL.

Feels good to finally be able to assess the efficiency of a homemade formula

I’ve been curious about comparing stats from the average traders sample from Poly vs smart traders.

Getting the average trader was hard since most API pull from Polymarket were giving me active traders and not random traders, so I pulled wallets directly from Polymarket exchange contract events instead of starting from the API or leaderboard.

From 861,344 unique wallets that traded on-chain between Jan–Jun 2025, I sampled 50,367 active traders and compared them to 46,754 wallets classified as consistently profitable / “smart money.”

Here’s the median trader in each group:

Metric	Random active traders	Smart money
Win rate	33%	60% (+27.1pp)
Total volume	$875	$17,246 (19.7x)
Avg position size	$31	$150 (4.8x)
Markets traded	12	36 (3.0x)
Volume per market	$70	$382 (5.4x)
Closed Positions count	13	28 (2.2x)

The win-rate gap is the obvious one: 33% vs 60%.

But the thing I found more interesting is the shape of the behavior.

Smart money doesn’t just win more. It also trades more markets, sizes much larger, and still puts way more volume into each market.

I would have guessed the best wallets were mostly specialists: fewer markets, deeper conviction, narrower scope.

But at least in this sample, they look broader and more concentrated at the same time.

More markets traded, bigger average position, more volume per market, higher hit rate.

That’s a very different game from the median active trader.

Methodology

I queried Polymarket’s CTFExchange and NegRiskCTFExchange contracts for OrderFilled events between January and June 2025.

That gave me a 861k wallet universe.

Then I randomly sampled wallets until I had 50,367 with at least one visible trade through the data API.

For each wallet, I pulled:

• volume
• position count
• markets traded
• average position size
• closed-position win rate

Win rate here means:

% of closed positions where realizedPnl > 0

Open positions are excluded. Wallets with fewer than 2 closed positions are excluded from the win-rate distribution because otherwise the chart becomes mostly noise.

The smart money group comes from a homemade API that scores wallets based on long-term realized performance and consistency.

My read

The naive takeaway is “follow smart money.”

And tbh I don’t think that’s right.

Wallet tracking is messy. Big wallets can be wrong. One trader can split across multiple wallets. Bots and market makers are in the data. One actual decision can show up as many fills.

So raw activity is a bad signal.

The better question is probably:

When a wallet with a real history of being right sizes up, is that meaningful?

That seems much closer to the actual edge.

Not “wallet X bought this.”

More like:

• has this wallet been right repeatedly?
• across how many markets?
• is this position large relative to its normal size?
• is this one whale, or a cluster of independent good traders?
• is this actual conviction or just noisy fill activity?

A few caveats

Win rate is not ROI. A 33% hit rate can still make money if the wins are much bigger than the losses.

The smart money set is selected by design, so yes, there is survivorship bias.

Closed positions only means open losses are not counted.

And wallets are not humans. Some are bots, market makers, or the same person split across addresses.

But still, the gap is hard to ignore, the median active trader in this sample looks nothing like the wallets that consistently make money !

Playing around with my smart money API. Asked it to make an analysis of some of the best smart traders profiles and fell on this profile that has such crazy stats. He's almost only on sport markets and has a pretty recent wallet (started trading 11th of April).

He doesn't have such an big edge, but the confidence is quite high due to his frequency of trades and wins

I have a smart money API that tells me where smart money is positioned, but tbh I always wondered if this data was even useful.

So here is the question I asked myself:

Once we detect smart money positioning at a time T, how does the price actually moves at T + 14d ?

📈Data:

To isolate one data, I’ve focused on bearish signals: markets where smart money thinks the probability is overpriced.

• Took historical bearish signals snapshots from our API (317 snapshots across 70 distinct markets)
• Compared the price of the market between the snapshot and the price at +14d
• Plotted confidence on the y-axis (confidence is a score we give on how confident we are on the smart traders positions), 14-day price drift on the x-axis

👉 The key results:

Confidence	Markets lowered	Price lowered at +14d	Price stayed flat at +14d	Price increased at +14d
>= 0.60	8/9 (89%)	33/45 (73%)	3/45 (7%)	9/45 (20%)
< 0.60	17/62 (27%)	62/272 (23%)	149/272 (55%)	61/272 (22%)

🔍 Interpretation:

The key point is that “smart money thinks the market is overpriced” is not enough.

You still need to ask: who is behind the flow, how broad is it, how concentrated is it, is the market probability sane, and does the signal have enough confidence to matter ?

In this sample, that filter matters:

  - Below 0.60 confidence: 17/62 markets moved lower after 14d

  - Above 0.60 confidence: 8/9 markets moved lower after 14d

So the bearish label gives direction and a confidence score tells you whether that direction is worth taking seriously.

Btw you should not even trust me, 9 high confidence markets is still a low sample to take good conclusions. What I want to do is raise awareness about smart money, conduct robust analysis or you'll get fooled by shitty promises on X or reddit.

👉 If you want more details about the confidence score, check our docs https://docs.radion.app/concepts/confidence-scoring#confidence-scoring

Working on a smart wallet data API and I just fell in love with playing around, doing data-visualisation on it. Thought it would be interesting to share !

Took 5,000 smart traders, mapped them across two dimensions: estimated edge (per-trade skill) and confidence score (statistical reliability based on activity).

The result: these two metrics move in opposite directions (correlation r = −0.55)

The higher the confidence score is, the lower the edge seems to be. Seems logical as outstanding edge often come from some lucky trades.

What I read from the quadrant: 

🟡 WHALES (top-left: high confidence, low edge)
High confidence because they trade a lot. But median win rate: 69%, the lowest of an archetype. These wallets account for 61% of total volume. Obvious but clearly showcases again that volume ≠ skill. 

🟣 SWEET SPOT (top-right: high confidence AND high edge)
Only 15% of traders (n = 746) clear both thresholds. This is the actionable zone. Snipers live here: 21 traders, 90% win rate, high edge.

⚫️ NOISE (bottom-left: low confidence, low edge)
35% of traders. Fewer than 10 effective positions. Their win rates look strong precisely because small samples produce extreme numbers. This signal is not really exploitable tbh, maybe I should remove them from our data

🔴 RAW UNPROVEN EDGE (bottom-right: high edge, low confidence)
High edge on paper, but median volume under $500. These wallets had 3–9 good trades. More luck than skill !