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 | 7% | 0% | 7.5% |
| 0.20-0.30 | 8 | 24% | 0% | 24.0% |
| 0.30-0.40 | 14 | 33% | 0% | 33.1% |
| 0.40-0.50 | 18 | 45% | 0% | 44.7% |
| 0.50-0.60 | 24 | 54% | 0% | 53.7% |
| 0.60-0.70 | 4 | 65% | 0% | 65.1% |
| 0.70-0.80 | 6 | 77% | 0% | 76.7% |
| 0.80-0.90 | 2 | 82% | 0% | 82.0% |
| 0.90-1.00 | 2 | 97% | 0% | 96.9% |
Skill measures calibration quality (0-100). Variance measures return volatility (0-100, higher = more volatile).
On-chain verification: wallet age 32 days, 1655 txs, provenance grade C. Bot score: 0/100, wash trading score: 0/100.
1019 total trades across 738 markets.
82 bets on resolved markets available for calibration scoring.
Calibration error: 47.0% — needs improvement.
Skill: 4/100 (calibration quality). Variance: 14/100 (higher = more volatile returns).
Brier Skill Score: -2.6% vs naive baseline (>0% = better than always predicting base rate).
Brier decomposition: REL=0.2559 RES=0.0000 UNC=0.0000.
Log loss: 0.7404 (skill: -9264.0% vs naive). Lower log loss = better calibration on rare events.
This trader shows some skill signal, but not enough to clearly distinguish from luck. More data needed.
Confidence: C/55 ± 8 (medium confidence, 82 resolved bets). Moderate confidence — score may shift as more markets resolve.
Methodology: Brier Score Decomposition (Murphy 1973), Log Loss, On-Chain USDC Verification. Same approach used by IARPA to identify superforecasters.