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 | 1 | 5% | 100% | 95.2% |
| 0.10-0.20 | 1 | 13% | 0% | 12.7% |
| 0.40-0.50 | 3 | 46% | 0% | 46.3% |
| 0.50-0.60 | 1 | 52% | 0% | 52.0% |
| 0.60-0.70 | 1 | 61% | 0% | 61.0% |
| 0.70-0.80 | 1 | 76% | 0% | 75.9% |
| 0.80-0.90 | 1 | 82% | 0% | 81.7% |
| 0.90-1.00 | 2 | 93% | 0% | 93.0% |
Skill measures calibration quality (0-100). Variance measures return volatility (0-100, higher = more volatile).
On-chain verification: wallet age 0 days, 2016 txs, provenance grade C. Bot score: 80/100, wash trading score: 0/100.
613 total trades across 82 markets.
11 bets on resolved markets available for calibration scoring.
Calibration error: 64.0% — needs improvement.
Skill: 13/100 (calibration quality). Variance: 25/100 (higher = more volatile returns).
Brier Skill Score: -470.6% vs naive baseline (>0% = better than always predicting base rate).
Brier decomposition: REL=0.4712 RES=0.0826 UNC=0.0826.
Log loss: 1.3968 (skill: -358.5% 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 ± 25 (very_low confidence, 11 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.