Accuracy History
Model performance breakdown by confidence level
2025-26 Season
High Confidence (75%+)
75.0%
527 of 703 correct
Medium Confidence (60-74%)
58.1%
182 of 313 correct
Low Confidence (<60%)
48.4%
89 of 184 correct
Notable Upsets
High-confidence predictions that were wrong| Date | Matchup | Final Score | Predicted Winner | Model Confidence |
|---|---|---|---|---|
| Apr 6 | DET @ ORL | 107 - 123 | DET | 86.2% |
| Apr 5 | LAL @ DAL | 128 - 134 | LAL | 86.5% |
| Apr 4 | SAS @ DEN | 134 - 136 | SAS | 81.7% |
| Apr 3 | NOP @ SAC | 113 - 117 | NOP | 83.7% |
| Apr 1 | SAC @ TOR | 123 - 115 | TOR | 95.0% |
| Apr 1 | IND @ CHI | 145 - 126 | CHI | 83.8% |
| Mar 29 | MIA @ IND | 118 - 135 | MIA | 92.1% |
| Mar 28 | PHI @ CHA | 118 - 114 | CHA | 90.1% |
| Mar 27 | DAL @ POR | 100 - 93 | POR | 93.0% |
| Mar 19 | LAC @ NOP | 99 - 105 | LAC | 80.7% |
How accuracy is measured: A prediction is correct when the predicted winner matches the actual game winner. Confidence levels are based on the model's win probability: High (75%+), Medium (60-74%), and Low (<60%).
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