90,000 Polymarket addresses exposed: What does your trading method imply?

In the zero-sum prediction market of Polymarket, there are quite a few “god-level” accounts with single trades earning $100,000 in profit, but who are the truly worth following winners? Hubble AI analyzes 90,000 active addresses and 2 million settled transactions since the platform’s launch, revealing an on-chain truth that shocks most traders—the “smart” trading methods you think you use are very likely just doing “Brownian motion”. What do these findings imply? They mean traditional rankings, win rate worship, and broad-sweep strategies are all ineffective.

Key Findings at a Glance (TL;DR)

This study, based on comprehensive on-chain data, uses four “counterintuitive” discoveries to redefine the profit logic in prediction markets:

• Mediocre trap of medium-frequency trading: Retail traders with the highest activity (average 3.67 trades/day) have the highest win rate (43%), but median PnL hovers near zero. This means most diligent medium-frequency traders’ accounts are still stagnant—they are “participating repeatedly” rather than “profiting from the market.”

• The endgame of “certainty” bets: Betting on high-probability events (price > 0.9) faces a poor risk-reward ratio—small gains if won, wiped out by a black swan event. Data shows these traders’ average returns are negative, indicating they are long-term losers.

• The golden zone of 0.2-0.4: True alpha does not come from extremes but is concentrated in the middle range where market disagreement is greatest. Traders in this zone avoid lottery traps and low-odds mediocrity, gaining an “asymmetric advantage.”

• Fourfold premium of concentrated strategies: Vertical experts focusing on a few tracks achieve average returns four times higher than diversified traders, despite having lower win rates. This proves that “less is more”—focus is the real moat.

The “Diligence Paradox” of Medium-Frequency Traders

The hidden truth behind surface glory

When stratified by trading frequency, the data reveals a perplexing phenomenon:

Medium-frequency traders (average 3.67 trades/day) have a win rate of 43%, the highest among all groups. Accounts with losses are only 50.3%, far lower than the 77.1% in high-frequency groups. These indicators suggest that “trading 3-4 times a day moderately” seems to be the golden rule for steady gains.

But once median PnL is introduced as a key metric, the truth emerges: the median PnL for medium-frequency traders is 0.001, almost zero.

What does this mean? It indicates that for the vast majority of medium-frequency traders, their daily research, bets, and seemingly winning more than losing ultimately result in no net account growth. Meanwhile, a tiny fraction of top addresses in the medium-frequency group (extreme right tail) generate an average profit of +915 with the same trading frequency. This internal polarization reflects a brutal reality: medium-frequency trading has become the most crowded “red ocean”.

Why has medium-frequency become a gathering place for mediocrity?

Lack of systematic advantage in a “coin flip” game

~43% win rate and near-zero median returns tell us that medium-frequency traders’ overall performance is close to a random walk. They participate based on intuition or fragmented information, avoiding the nightmare of large drawdowns caused by strategy failure like high-frequency bots, but also failing to establish any real moat. They are “participating” repeatedly rather than “profiting” from the market.

High-frequency machines are unlearnable, low-frequency trades are too sparse

Ordinary users cannot replicate systematic high-frequency strategies (average >14 trades/day)—the technical barrier is too high, win rates are only 21-26%, and psychological pressure is immense. But they are also reluctant to engage in low-frequency trades (average 0.35 trades/day). As a result, large amounts of capital and effort pile into the mid-frequency zone, which becomes the most competitive, mediocre, and crowded battlefield.

Practical insight: how to stand out from the “denominator” of medium-frequency trading?

The key lies in differentiation rather than frequency imitation. Data shows:

• Avoid pitfalls: Most medium-frequency addresses have no follow-on value
• Mining alpha: True alpha hides in the right tail of the medium-frequency group—those 1% who consistently beat the “gravity of zero”

This is exactly where intelligent copy-trading tools add value: using algorithms to directly identify those addresses generating excess returns from the vast sea of mediocre medium-frequency addresses.

“Certainty” vs. “Lottery”: The failure of two extreme strategies

The “small profit trap” of high-probability events

Intuition suggests buying a 0.95 “sure win” has very low risk. But from a financial mathematics perspective, this is an extremely asymmetric trade.

Investors risk 1.0 of their capital to gain 0.05. If a black swan event occurs (e.g., Biden suddenly withdraws, or a last-minute game reversal), the loss from a single event resets the account to zero. To recover, one must make 19 consecutive correct trades. Over a long cycle, the probability of such black swans exceeds 5%. Moreover, once the price exceeds 0.9, market consensus has already formed, and entering at this point is essentially taking over the position of the informed—no informational advantage remains.

Data shows: traders in this category have an average return of negative, with only a 19.5% win rate. This means most accounts buying >0.9 ultimately perish due to black swan events.

The “pure noise” dilemma of tiny-probability events

Buying <0.2 “lotteries” also performs poorly because retail traders tend to overestimate their ability to catch rare events. In efficient prediction markets, prices already incorporate most implicit information. Long-term purchase of extremely low-probability events correctly priced by the market will inevitably erode capital. Although individual gains are high multiples, the very low win rate causes long-term capital drawdowns, making compound growth impossible.

Key insight from the data

What do these findings imply? Whether you only buy “lotteries” or only buy “certainty,” you are long-term losers. A hybrid strategy (balanced position distribution) yields an average return 13 times higher than a purely high-certainty approach. But even in the best-performing hybrid group, profits are highly concentrated among top players, indicating most people do not beat transaction costs.

The golden zone of 0.2-0.4: where true alpha hides

Why is this range the most profitable?

Stratifying addresses by implied probability (average buy-in cost), the data reveals a non-linear distribution of returns: true alpha does not exist at the extremes but is concentrated in the 0.2-0.4 price range.

Cognitive arbitrage exploiting pricing divergence

Traders consistently profitable in the 0.2-0.4 range are essentially engaging in “cognitive arbitrage.” Buying at 0.2-0.4 implies the market consensus assigns only a 20%-40% chance of the event occurring. The real winners are those who identify underestimated events—e.g., market overly pessimistic about a candidate’s chances. Compared to following consensus blindly, once this cognitive edge is validated, they can achieve explosive gains of 2.5x to 5x.

A perfect “asymmetric payoff” structure

This range features “convexity”—downside risk is capped (maximum loss of 100%), while upside potential is elastic. Top traders maximize returns by leveraging high win rates (49.7%) and high odds. In contrast, >0.8 range yields only small profits, and <0.2 range is pure noise.

This means that 0.2-0.4 is not only the highest-yielding zone but also the most sustainable profit region.

The “4x profit premium” of focused strategies

Concentrated vs. diversified: an astonishing discovery

Calculating the Focus Ratio (total trades / number of markets participated in), the data shows two distinct trading styles:

• Diversified traders: average profit $306, win rate 41.3%, 68,016 addresses
• Concentrated traders: average profit $1,225, win rate 33.8%, 22,458 addresses

The concentrated approach yields four times higher returns. But an even more misleading phenomenon appears: concentrated strategies have significantly lower win rates (33.8% vs. 41.3%).

What does this imply? It suggests that the profit logic of top prediction market players is completely opposite to most retail perceptions—winning more times does not necessarily mean higher final profit.

Why is “less is more”?

Vertical information advantage moat

Prediction markets are fundamentally information games. Diversified traders attempt to cover politics, sports, crypto, and more, staying at a “shallow cognition” level across markets, making them easy targets for being the “denominator.” Concentrated traders, by deepening expertise in a single domain (e.g., only NBA player data or only US swing states polls), develop vertical information advantages. This depth allows them to detect tiny mispricings.

Breaking the “win rate superstition”

High returns often come with relatively low win rates. Concentrated experts tend to act during high-odds/high-disagreement moments (buy at odds 0.3), rather than chasing >0.9 “sure bets.” As a result, diversified traders win many small bets (high win rate), but suffer occasional big losses (black swans), leading to mediocre overall returns; while concentrated traders endure multiple small errors for a few precise big wins, resulting in explosive gains—akin to venture capital logic.

How to identify the truly “smart money”?

Redefining selection criteria

These findings mean that traditional follow-on filters are now invalid. You should not only look at win rate but focus on:

Bad signals (must avoid):

• “Single-minded” traders with extreme position prices (all high or all low probability)
• “Jacks of all trades”—frequent switching across politics, sports, crypto
• Addresses with moderate frequency but no advantage
• Accounts that recently changed their historical behavior

Good signals (priority):

• “Disagreement hunters” with long-term average buy-in prices in 0.2-0.4
• Vertical experts focusing on specific tags (e.g., “US Election” or “NBA”)
• Accounts with low win rate but significant single-trade gains
• Addresses with stable historical behavior

Three dimensions for precise matching

1. Focus: Higher Focus Ratio is better—more important than win rate.
2. Pricing strategy: Prioritize traders active in the 0.2-0.4 range.
3. Vertical depth: Seek experts specialized in specific fields rather than generalists.

For example, an address trading only “US Election” with steady profit curves is far more valuable than one trading both “NBA” and “Bitcoin.” Specialization directly determines alpha purity.

From data insights to intelligent tools

Hubble AI’s practical response

The core value of this report is transforming exclusive data insights into actionable tools. Achieving long-term profitability on Polymarket with manual screening of 90,000 addresses is unrealistic. Hubble is building an automated intelligent copy-trading system to solve three toughest problems:

1. Intelligent elimination of market makers’ noise

Public leaderboards are cluttered with market makers (MM) and arbitrage bots. Following them yields no profit and may cause losses due to slippage. Using proprietary order book analysis and trading feature recognition algorithms, the system automatically filters out systematic market makers, locking in only those active traders who profit from their views.

2. Vertical matching based on focus

Generic “profit leaderboards” are of limited value. The system tags addresses with “ability labels” (e.g., “US Election,” “NBA,” “Crypto whales”) based on Focus Ratio and historical behavior. It then precisely matches you with vertical experts possessing informational advantages in your chosen track.

3. Dynamic style drift monitoring

The biggest hidden risk in copy trading is sudden strategy failure. The system establishes real-time risk control models that flag accounts deviating from their historical behavior (e.g., shifting from low-frequency focused to high-frequency broad participation) and issue early warnings.

Conclusion

In the zero-sum world of Polymarket, data from 90,000 addresses proves a simple truth: long-term winners win because they are extremely disciplined—focusing on specific domains, seeking pricing deviations, and accepting low win rates for high payoff multiples.

All core metrics mentioned in this report (Focus Ratio, pricing zone analysis, market maker filtering) are integrated into Hubble’s data backend. Our goal is simple: to use institutional-grade data perspective to replace blind retail intuition.

If you agree with this logical framework, Hubble’s Polymarket AI copy-trading tool is currently in limited beta testing. Like/share this content, comment “Waitlist,” and we will send a private invitation for early access. We hope this data-driven filtering system can help you truly outperform the market.

(Note: This study is based on settled transaction data from Polymarket since its launch, with all conclusions derived from Hubble’s exclusive on-chain PnL analysis.)