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 | 14% | 100% | 85.8% |
| 0.20-0.30 | 2 | 30% | 0% | 30.0% |
| 0.30-0.40 | 4 | 39% | 100% | 61.1% |
| 0.60-0.70 | 2 | 66% | 100% | 34.0% |
| 0.80-0.90 | 2 | 86% | 100% | 14.0% |
| 0.90-1.00 | 6 | 93% | 100% | 6.6% |
Skill measures calibration quality (0-100). Variance measures return volatility (0-100, higher = more volatile).
On-chain verification: wallet age 47 days, 2960 txs, provenance grade B. Bot score: 30/100, wash trading score: 20/100.
2000 total trades across 1406 markets.
22 bets on resolved markets available for calibration scoring.
Calibration error: 43.4% — needs improvement.
Skill: 21/100 (calibration quality). Variance: 41/100 (higher = more volatile returns).
Brier Skill Score: -251.7% vs naive baseline (>0% = better than always predicting base rate).
Brier decomposition: REL=0.2905 RES=0.0826 UNC=0.0826.
Log loss: 0.8110 (skill: -166.2% 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/30 ± 15 (low confidence, 22 resolved bets). Low confidence — take this score with a grain of salt until more markets resolve.
Methodology: Brier Score Decomposition (Murphy 1973), Log Loss, On-Chain USDC Verification. Same approach used by IARPA to identify superforecasters.