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 | 3 | 5% | 0% | 4.7% |
| 0.10-0.20 | 2 | 14% | 0% | 14.4% |
| 0.20-0.30 | 3 | 24% | 0% | 24.2% |
| 0.30-0.40 | 2 | 37% | 0% | 36.6% |
| 0.40-0.50 | 5 | 44% | 0% | 43.8% |
| 0.50-0.60 | 5 | 55% | 0% | 55.3% |
| 0.60-0.70 | 2 | 62% | 0% | 62.2% |
| 0.90-1.00 | 4 | 95% | 0% | 94.6% |
Skill measures calibration quality (0-100). Variance measures return volatility (0-100, higher = more volatile).
On-chain verification: wallet age 436 days, 495 txs, provenance grade B. Bot score: 0/100, wash trading score: 0/100.
365 total trades across 183 markets.
26 bets on resolved markets available for calibration scoring.
Calibration error: 45.6% — needs improvement.
Skill: 5/100 (calibration quality). Variance: 32/100 (higher = more volatile returns).
Brier Skill Score: -13.1% vs naive baseline (>0% = better than always predicting base rate).
Brier decomposition: REL=0.2820 RES=0.0000 UNC=0.0000.
Log loss: 0.9060 (skill: -11357.4% vs naive). Lower log loss = better calibration on rare events.
Timeliness: avg entry 21.5 days before resolution, 71% early mover.
This trader shows some skill signal, but not enough to clearly distinguish from luck. More data needed.
Confidence: C/58 ± 15 (low confidence, 26 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.