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 |
|---|---|---|---|---|
| 0.00-0.10 | 4 | 5% | 0% | 5.2% |
| 0.10-0.20 | 2 | 20% | 0% | 20.0% |
| 0.20-0.30 | 10 | 27% | 0% | 26.6% |
| 0.30-0.40 | 6 | 37% | 0% | 37.1% |
| 0.40-0.50 | 30 | 46% | 0% | 45.5% |
| 0.50-0.60 | 42 | 54% | 0% | 53.5% |
| 0.60-0.70 | 14 | 65% | 14% | 50.4% |
| 0.70-0.80 | 2 | 77% | 0% | 77.0% |
| 0.90-1.00 | 2 | 97% | 0% | 97.0% |
Skill measures calibration quality (0-100). Variance measures return volatility (0-100, higher = more volatile).
On-chain verification: wallet age 8 days, 2223 txs, provenance grade C. Bot score: 100/100, wash trading score: 0/100.
2000 total trades across 104 markets.
112 bets on resolved markets available for calibration scoring.
Calibration error: 46.6% — needs improvement.
Skill: 5/100 (calibration quality). Variance: 54/100 (higher = more volatile returns).
Brier Skill Score: -1350.8% vs naive baseline (>0% = better than always predicting base rate).
Brier decomposition: REL=0.2367 RES=0.0022 UNC=0.0175.
Log loss: 0.7220 (skill: -706.0% vs naive). Lower log loss = better calibration on rare events.
Below average. The data shows poor calibration, thin evidence, or both. When this trader expresses high confidence, events don't happen at the rate they imply.
Confidence: D/43 ± 8 (medium confidence, 112 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.