How We Score Expert Wallets: The 6-Factor Model Behind Our Signals
Our signals are only as good as the experts we track. Learn the 6-factor scoring model we use to identify, rank, and weight the 2,300+ wallets that power Whale Predictions.
Why Wallet Scoring Matters
On Polymarket alone, tens of thousands of wallets place trades every day. Most of them are noise. Some are bots executing market-making strategies. Others are retail traders following social media hype. The signal-to-noise ratio is low unless you know which wallets to pay attention to.
Our wallet scoring model solves this problem. It identifies the wallets that have demonstrated genuine forecasting skill and weights their activity more heavily in our consensus signals. The result is a curated intelligence feed built on the actions of proven experts rather than the crowd.
The 6-Factor Scoring Model
Factor 1: Historical Win Rate
The foundation of our scoring model is historical accuracy. We track every resolved position for every wallet and calculate a rolling win rate across different time horizons. A wallet that has correctly predicted 70% of outcomes over 200+ resolved trades is far more valuable than one that went 3-for-4 on a handful of bets.
We weight recent performance more heavily than older results, recognizing that market conditions and a trader's edge can change over time.
Factor 2: Volume-Weighted Performance
Win rate alone can be misleading. A trader who bets $10 on high-probability outcomes and $1,000 on coin flips has a high win rate but poor risk-adjusted returns. We measure performance in dollar terms, calculating the actual profit and loss across all resolved positions.
Wallets that consistently generate positive PnL on large positions score significantly higher than those with inflated win rates on tiny bets.
Factor 3: Consistency Over Time
One hot streak does not make an expert. We measure the consistency of a wallet's performance across weeks and months. Wallets that show steady positive returns over extended periods score higher than those with volatile streaks of wins and losses.
This factor helps filter out wallets that got lucky on a few high-profile events from those with durable forecasting skill.
Factor 4: Category Specialization
Some traders excel in politics but struggle with crypto. Others have deep domain knowledge in sports but no edge in economics. Our model tracks performance by category and assigns higher scores to wallets trading within their area of demonstrated expertise.
When generating consensus signals, we weight a wallet's contribution more heavily in categories where it has shown consistent outperformance.
Factor 5: Timing and Entry Quality
Getting the direction right is only half the equation. The other half is getting in at a good price. We evaluate the quality of each wallet's entry points by comparing their average purchase price to the final resolution price. Wallets that consistently enter positions at favorable prices demonstrate superior information timing.
This factor captures an important dimension of trading skill that pure win rate misses.
Factor 6: Risk Management
The best traders manage risk actively. They size positions appropriately, avoid catastrophic concentrated bets, and sometimes exit positions before resolution when the odds shift against them. We evaluate risk management by looking at position sizing relative to portfolio size, diversification across markets, and the frequency of stop-loss-like behavior.
From Scores to Signals
Each wallet receives a composite score on a 0-100 scale, updated daily. Wallets scoring above 75 are classified as Tier A experts. Those between 55 and 75 are Tier B. Below 55, wallets are excluded from our signal generation process.
When multiple high-scoring wallets converge on the same position in the same market, a consensus signal is generated. The strength of the signal depends on the number of experts, their aggregate score, and the total capital they have committed.
Continuous Improvement
Our scoring model is not static. We regularly backtest new factors, adjust weights based on out-of-sample performance, and incorporate feedback from our accuracy tracking system. The goal is a model that identifies genuine forecasting skill while filtering out luck, manipulation, and market-making activity.
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