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 |
|---|---|---|---|---|
| 0.20-0.30 | 2 | 28% | 0% | 27.8% |
| 0.30-0.40 | 1 | 30% | 0% | 30.2% |
| 0.40-0.50 | 3 | 44% | 0% | 44.4% |
| 0.50-0.60 | 4 | 53% | 0% | 53.5% |
| 0.60-0.70 | 1 | 60% | 0% | 60.0% |
Skill measures calibration quality (0-100). Variance measures return volatility (0-100, higher = more volatile).
On-chain verification: wallet age 0 days, 373 txs, provenance grade D. Bot score: 0/100, wash trading score: 0/100.
249 total trades across 53 markets.
11 bets on resolved markets available for calibration scoring.
Calibration error: 44.8% — needs improvement.
Skill: 6/100 (calibration quality). Variance: 16/100 (higher = more volatile returns).
Brier Skill Score: 14.6% vs naive baseline (>0% = better than always predicting base rate).
Brier decomposition: REL=0.2127 RES=0.0000 UNC=0.0000.
Log loss: 0.6146 (skill: -7673.2% vs naive). Lower log loss = better calibration on rare events.
Timeliness: avg entry 0.5 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/35 ± 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.