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 | 4% | 0% | 4.5% |
| 0.30-0.40 | 4 | 36% | 0% | 36.5% |
| 0.40-0.50 | 2 | 47% | 0% | 47.0% |
| 0.50-0.60 | 6 | 53% | 0% | 53.3% |
| 0.60-0.70 | 2 | 61% | 0% | 61.0% |
| 0.90-1.00 | 2 | 99% | 0% | 99.0% |
Skill measures calibration quality (0-100). Variance measures return volatility (0-100, higher = more volatile).
On-chain verification: wallet age 24 days, 242 txs, provenance grade D. Bot score: 0/100, wash trading score: 0/100.
157 total trades across 71 markets.
20 bets on resolved markets available for calibration scoring.
Calibration error: 44.9% — needs improvement.
Skill: 6/100 (calibration quality). Variance: 7/100 (higher = more volatile returns).
Brier Skill Score: -7.9% vs naive baseline (>0% = better than always predicting base rate).
Brier decomposition: REL=0.2696 RES=0.0000 UNC=0.0000.
Log loss: 0.9422 (skill: -11816.1% 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/45 ± 15 (low confidence, 20 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.