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 | 2 | 3% | 50% | 47.5% |
| 0.10-0.20 | 5 | 14% | 0% | 14.3% |
| 0.20-0.30 | 2 | 25% | 0% | 25.2% |
| 0.30-0.40 | 3 | 35% | 0% | 35.1% |
| 0.40-0.50 | 2 | 45% | 0% | 44.8% |
| 0.50-0.60 | 4 | 54% | 0% | 54.0% |
| 0.60-0.70 | 1 | 64% | 0% | 64.3% |
| 0.70-0.80 | 1 | 72% | 0% | 72.1% |
| 0.90-1.00 | 3 | 97% | 100% | 3.3% |
Skill measures calibration quality (0-100). Variance measures return volatility (0-100, higher = more volatile).
On-chain verification: wallet age 5 days, 2000 txs, provenance grade C. Bot score: 0/100, wash trading score: 0/100.
2000 total trades across 1999 markets.
23 bets on resolved markets available for calibration scoring.
Calibration error: 33.7% — needs improvement.
Skill: 39/100 (calibration quality). Variance: 10/100 (higher = more volatile returns).
Brier Skill Score: -23.5% vs naive baseline (>0% = better than always predicting base rate).
Brier decomposition: REL=0.1545 RES=0.1219 UNC=0.1437.
Log loss: 0.5790 (skill: -25.3% 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/28 ± 15 (low confidence, 23 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.