When this trader buys at $0.70, they imply 70% probability. Perfect calibration = the event happens 70% of the time.
| Bucket | Bets | Expected | Actual | Error |
|---|---|---|---|---|
| 0.10-0.20 | 6 | 16% | 0% | 16.2% |
| 0.20-0.30 | 16 | 25% | 0% | 24.6% |
| 0.30-0.40 | 29 | 34% | 0% | 34.0% |
| 0.40-0.50 | 19 | 45% | 0% | 44.5% |
| 0.50-0.60 | 37 | 54% | 0% | 54.4% |
| 0.60-0.70 | 24 | 64% | 0% | 64.4% |
| 0.70-0.80 | 10 | 74% | 0% | 73.7% |
| 0.80-0.90 | 5 | 82% | 0% | 82.3% |
| 0.90-1.00 | 4 | 93% | 0% | 93.1% |
Skill measures calibration quality (0-100). Variance measures return volatility (0-100, higher = more volatile).
On-chain verification: wallet age 0 days, 1259 txs, provenance grade D. Bot score: 0/100, wash trading score: 0/100.
Polymarket on-chain coverage: $0 in / $0 out across 0 withdrawal tx since never.
1110 total trades across 151 markets.
150 bets on resolved markets available for calibration scoring.
Calibration error: 49.4% — needs improvement.
Skill: 1/100 (calibration quality). Variance: 21/100 (higher = more volatile returns).
Brier Skill Score: -10.8% vs naive baseline (>0% = better than always predicting base rate).
Brier decomposition: REL=0.2763 RES=0.0000 UNC=0.0000.
Log loss: 0.7740 (skill: -9688.1% vs naive). Lower log loss = better calibration on rare events.
No demonstrated forecasting skill. This trader performs at or below random chance based on available data.
Confidence: F/34 [CI95: F→D, 32-36] (150 resolved bets). Moderate confidence — score may shift as more markets resolve.
Methodology: Brier Score Decomposition (Murphy 1973), Log Loss, On-Chain USDC Verification. Same approach used by IARPA to identify superforecasters.