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 | 1 | 28% | 0% | 28.3% |
| 0.30-0.40 | 4 | 34% | 0% | 34.2% |
| 0.40-0.50 | 4 | 43% | 0% | 43.1% |
| 0.50-0.60 | 1 | 54% | 0% | 53.8% |
| 0.60-0.70 | 1 | 70% | 0% | 69.8% |
Skill measures calibration quality (0-100). Variance measures return volatility (0-100, higher = more volatile).
On-chain verification: wallet age 91 days, 2000 txs, provenance grade C. Bot score: 0/100, wash trading score: 0/100.
2000 total trades across 219 markets.
11 bets on resolved markets available for calibration scoring.
Calibration error: 41.9% — needs improvement.
Skill: 10/100 (calibration quality). Variance: 5/100 (higher = more volatile returns).
Brier Skill Score: 24.5% vs naive baseline (>0% = better than always predicting base rate).
Brier decomposition: REL=0.1881 RES=0.0000 UNC=0.0000.
Log loss: 0.5675 (skill: -7077.2% 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/50 ± 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.