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 | 5% | 0% | 5.4% |
| 0.10-0.20 | 6 | 16% | 0% | 15.9% |
| 0.20-0.30 | 4 | 24% | 0% | 23.9% |
| 0.30-0.40 | 3 | 37% | 0% | 36.9% |
| 0.40-0.50 | 2 | 42% | 0% | 42.5% |
Skill measures calibration quality (0-100). Variance measures return volatility (0-100, higher = more volatile).
On-chain verification: wallet age 51 days, 2000 txs, provenance grade C. Bot score: 0/100, wash trading score: 0/100.
2000 total trades across 1867 markets.
17 bets on resolved markets available for calibration scoring.
Calibration error: 23.4% — needs improvement.
Skill: 32/100 (calibration quality). Variance: 8/100 (higher = more volatile returns).
Brier Skill Score: 72.6% vs naive baseline (>0% = better than always predicting base rate).
Brier decomposition: REL=0.0680 RES=0.0000 UNC=0.0000.
Log loss: 0.2787 (skill: -3424.8% vs naive). Lower log loss = better calibration on rare events.
Timeliness: avg entry 2.0 days before resolution, 0% early mover.
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/42 ± 25 (very_low confidence, 17 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.