Polymarket Data Revealed: Why Are You Willing to Replace Others, While True Winners Never Do This

When you repeatedly validate others’ trading ideas in prediction markets and are willing to surrender your judgment rights, those who truly make money have long since uncovered the dark logic of the market.

Hubble AI analyzes 90,000 active addresses and 2 million settled transactions on the Polymarket platform, using data to shatter the illusions most followers have about “high win rates,” “high-frequency trading,” and “betting on certainty.” This research isn’t about teaching you how to willingly substitute others’ choices but rather reveals—by reverse logic—why most followers are doomed to fail and how a few can profit steadily from prediction markets.

Cognitive Trap 1: The “Mediocre Kill” of Mid-Frequency Trading—High Win Rate Means Inevitable Loss

The seemingly safest trading strategies often hide the cruelest truths.

The research team divided traders on Polymarket into three tiers based on activity:

• Low-frequency traders: about 0.35 trades per day | win rate ~40%
• Mid-frequency traders: about 3.67 trades per day | win rate ~43%
• High/ultra-high frequency traders: over 14 trades per day | win rate ~21-26%

On the surface, mid-frequency traders appear to be the market elites—they have the highest win rate (43%) and the lowest proportion of losing accounts (only 50.3%). This creates an illusion for countless followers: by maintaining 3-4 moderate trades daily, you can achieve steady profits.

But when actual profit and loss data surface, the truth hits hard:

• Median profit/loss: 0.001 (almost zero)
• Average profit/loss: +915 (pulled up by a few top addresses)

What does this mean? The vast majority of mid-frequency traders, despite seemingly researching, betting, and winning more often than losing, see their account net worth stagnate. Their win rate hasn’t translated into real wealth.

In contrast, high and ultra-high frequency traders, though median losses (-0.30 to -1.76), have their average profit/loss driven up to +922 or even +2717 by a tiny number of top institutions. This indicates that high-frequency trading is a “machine’s battlefield”—a playground exclusive to market makers and arbitrage bots that ordinary traders cannot replicate.

Why does mid-frequency fall into the “Mediocre Kill”?

First, these traders lack systemic advantages. A 43% win rate combined with near-zero median returns essentially indicates performance akin to a “random walk”—participating based on intuition or fragmented information, avoiding large drawdowns like high-frequency traders but failing to build a real moat.

Second, survivor bias masks tail risks. Within the mid-frequency group, there’s extreme polarization: a few “big players” with exceptional judgment lift the average, while over 50% are just spinning their wheels.

Third, this is the most crowded “Red Ocean.” Ordinary users can’t learn systematic high-frequency strategies (too high technical barriers, low win rates, psychological stress), yet they are unwilling to accept the extremely low activity of low-frequency trading. As a result, massive capital accumulates in mid-frequency, creating the fiercest internal competition.

Insight: Most mid-frequency addresses are just performing “Brownian motion,” with no real followership value. True alpha hides in the tail on the right—those 1% who, at the same frequency, outperform the “gravity of zero.”

Cognitive Trap 2: The Temptation of Certainty—Why Buying “Sure Wins” Leads to the Worst Losses

Intuitively, buying a “sure-win” at 0.95 on Polymarket seems low-risk. But from a financial mathematics perspective, it’s a terrible trade.

The team tested three typical strategies:

• High-certainty strategy: positions concentrated at >0.9 (events “almost nailed”)
• High-odds strategy: positions concentrated at <0.2 (betting on rare, unpopular events)
• Mixed strategy: balanced positions, not fixated on extreme odds

The results are sobering: the average return of the mixed strategy is 13 times higher than that of the high-certainty strategy.

Why does betting on “certainty” fail?

The reason is simple: asymmetric downside risk. Entering at 0.95 means risking 1.0 of your capital to gain 0.05. When a black swan event occurs (e.g., Biden suddenly withdraws, or a last-minute reversal in a game), the zero-loss event requires 19 consecutive correct trades to recover. Over a long cycle, the probability of a black swan exceeds 5%.

Moreover, once prices exceed 0.9, market consensus has already formed. Entering at this point is essentially taking over the position of the informed—no informational advantage remains.

Why are high-odds “lottery” bets equally bleak?

Buying small-probability events (<0.2) often leads retail traders to overestimate their ability to catch “rare” opportunities. In efficient prediction markets, prices already reflect most implicit information. Long-term buying of market-priced “lotteries” will inevitably erode capital over time.

Although individual gains are multiples, the very low win rate causes long-term capital drawdowns, making compounding impossible.

Insight: Reject “one-track” traders. When screening followers, avoid those with extreme position prices. True alpha traders are characterized by strategic flexibility—they bet at 0.3 when divergence exists and take profits at 0.8.

Counterintuitive Breakthrough: The Secret Weapon of Polymarket Winners—Price Range 0.2-0.4

If the first two traps tell you what not to do, this discovery points to the real goldmine.

The research finds that genuine alpha does not exist at the extremes (very high or very low certainty), but concentrates in the 0.2-0.4 price range.

Data comparison is straightforward:

• 0.9 range: average returns negative, win rate only 19.5%

• 0.2-0.4 range: highest win rate at 49.7%, best returns
• <0.2 range: although theoretically offering the highest odds, actual performance is far inferior to 0.2-0.4

Why is 0.2-0.4 the most profitable?

This range essentially involves “cognitive arbitrage.” Buying at 0.2-0.4 implies the market perceives the event probability at only 20%-40%. Traders who consistently profit here can identify underestimated events that the public undervalues. Compared to following consensus, successful validation yields explosive gains of 2.5 to 5 times.

Additionally, 0.2-0.4 offers a perfect “asymmetric reward structure.” Downside risk is capped (your capital), while upside potential is elastic. Skilled traders maximize returns by leveraging both high win rates and high odds in this zone.

This differs from >0.8 “small profit on wins, zero on losses,” and <0.2 “pure noise.” The 0.2-0.4 zone is a “convex” region—market mispricing often occurs here.

Insight: Focus on “divergence hunters.” When screening followers, prioritize those whose average buy-in prices stay in the 0.2-0.4 range long-term. Such data indicates they are seeking value mispricings—neither blindly chasing high-risk lotteries nor picking up change in consensus zones.

Professional Premium: Why Deep Specialization in Few Tracks Yields 4x Returns

Most followers make a fatal mistake: blindly tracking “jack-of-all-trades” traders. But data shows this is the least efficient strategy.

The team introduces a key metric—Focus Ratio (total trades / number of markets participated in).

Based on this, addresses are categorized as:

• Diversified strategies: participate in many markets, fewer trades per market | average return $306 | win rate 41.3%
• Focused strategies: concentrate on few markets, trade repeatedly | average return $1,225 | win rate 33.8%

The surprising but conclusive result: focused strategies yield 4 times the returns of diversified ones, despite having lower win rates.

This shatters the common “win rate above all” myth. Higher returns often come with lower win rates because focused experts tend to act during high-odds/high-divergence moments.

• Diversified logic: many small wins (high win rate), one big loss (black swan), resulting in mediocre overall profit.
• Focused logic: tolerate multiple small errors (low win rate) for a few precise, high-conviction bets that produce explosive gains (high risk-reward ratio).

This mirrors venture capital logic. Buffett said: “Diversification is protection for the ignorant.” In zero-sum prediction markets, diversification often dilutes attention. If you are confident in your edge, the best approach isn’t broad but to concentrate firepower on the most promising few opportunities.

Why does deep focus generate excess returns?

First, asymmetric information creates a moat. Diversified traders attempt to cross political, sports, and crypto fields, leading to shallow knowledge in each. Focused traders, by deep diving into specific domains (e.g., NBA player stats or swing states), develop vertical information advantages, enabling them to spot subtle mispricings.

Second, focused strategies allow higher trial-and-error costs. Deep research helps them identify high-odds opportunities precisely, even with lower win rates, as the payoff compensates.

Insight: Seek “vertical track experts.” In follower selection, high Focus Ratio is more important than high win rate. Prioritize accounts active in specific niches—e.g., a trader specializing solely in “US Election” with steady profit curves is more valuable than one trading both “NBA” and “Bitcoin.” Professional specialization directly correlates with alpha purity.

Follower Self-Help Guide: How to Identify True Alpha and Avoid Substitutes

When you are willing to substitute others’ judgments, you lose the chance to profit in prediction markets. True winners never do this.

Based on the above four findings, an effective follower screening framework should be:

Blacklist to avoid:

1. Unskilled mid-frequency addresses: those with 3-5 trades daily, win rates 40-45%, but near-zero returns. No matter how diligent, it’s futile.

2. Extreme odds bettors: addresses that consistently buy >0.9 or <0.2. They are either consensus followers or gamblers.

3. “Jack-of-all-trades” with low Focus Ratio: participating in many markets with shallow engagement dilutes advantage.

4. Market makers and arbitrage bots: characterized by extremely high trade counts (average >20/day) but tiny profits. Following them only incurs slippage losses.

Whitelist features to look for:

1. Operate within 0.2-0.4 range: long-term average buy-in prices in this zone indicate they seek market mispricings.

2. High domain focus: high Focus Ratio, concentrated in specific sectors (e.g., political events, sports, specific tokens). Professionalism = informational advantage.

3. Low win rate + high risk-reward: don’t be fooled by win rates over 50%. Focus on the ratio of single-trade gains to losses.

4. Stable profit curves: avoid accounts that spike temporarily—likely luck. Seek long-term steady growth.

5. Consistent strategy: accounts maintaining specific trading patterns (e.g., particular price ranges, sectors, time windows) suggest systematic approaches.

From Insight to Action: Hubble’s Intelligent Follow-Order Tool

Data analysis’s value ultimately must translate into actionable tools.

Hubble AI is packaging these exclusive insights into an automated filtering and risk control system, addressing the three toughest issues in follow trading:

1. Automatic elimination of market maker noise

Current public leaderboards are cluttered with wash-trading market makers and arbitrage bots. Following them yields no profit and may cause losses due to slippage. Through proprietary order book analysis and trading pattern recognition, the system automatically filters out systematic market makers, focusing only on active traders who profit from genuine viewpoints.

2. Vertical matching based on “Focus Ratio”

Generic “profit leaderboards” are limited. Using Focus Ratio and historical behavior, the system assigns high-precision “ability labels” (e.g., US elections, NBA, crypto whales). When you focus on a specific sector, it matches you with vertical experts possessing informational advantages.

3. Dynamic style drift monitoring

The greatest hidden risk in follow trading is strategy breakdown. When a long-term stable address suddenly deviates from its historical pattern (shifting from low-frequency focused to high-frequency broad, or exposing excessive single-position risk), the system issues an immediate alert, helping users avoid drawdowns.

Conclusion

Prediction markets are a brutal zero-sum game. Data from 90,000 addresses proves a simple yet profound truth: long-term winners succeed because they are extremely disciplined—focusing on specific domains, seeking mispricings, and rejecting unnecessary diversification.

They never willingly substitute others but instead use deep insights to replace大众直觉.

This is the essence of “smart money.”