Model Accuracy
We believe in showing our work. Here's exactly how accurate our win probability model has been across every game we've analyzed.
Overall Performance
Calibration by Confidence
When the model says 70%, does the favored team actually win ~70% of the time? Points above the dashed line outperform; below underperform.
| Model Confidence | Games | Correct | Actual Accuracy | Calibration |
|---|---|---|---|---|
| 50-55% | 481 | 250 | 52.0% | Well calibrated |
| 55-60% | 307 | 167 | 54.4% | Well calibrated |
| 60-65% | 127 | 72 | 56.7% | Underperforming |
| 65-70% | 118 | 79 | 66.9% | Well calibrated |
| 70-75% | 116 | 80 | 69.0% | Well calibrated |
| 75%+ | 474 | 408 | 86.1% | Well calibrated |
Week-over-Week Trend
How the model has performed over recent weeks. The dotted line marks 50% (coin flip).
How We Measure Accuracy
A prediction is correct when the team our model gives > 50% win probability actually wins the game. Ties are excluded from accuracy calculations.
Calibration measures whether our confidence levels are meaningful. If we say a team has a 70% chance of winning, they should win roughly 70% of those games -- not 90%, not 50%.
For context, a coin flip gives 50% accuracy. Our model targets outright winners: predicting which team wins the game.