Chicago Bears vs Carolina Panthers — Win Probability

Model's Chicago projection proved far too conservative in 59-37 rout of Carolina

The model entered Week 1's Chicago-Carolina matchup calling it nearly even, despite rating the Bears as slight favorites. It projected a Chicago win by 2.9 points with a final score of 25.2-22.3, assigning the Bears a 59% win probability. The market agreed on the spread—2.9 points in Chicago's favor—but the game itself bore no resemblance to that cautious forecast.

Chicago won 59-37, a 22-point margin that exceeded the projection by nearly eight times over. The Bears scored 33.8 more points than the model expected; Carolina scored 14.7 more. This was not a case of the model picking the right winner but miscalculating depth. The gap between projected and actual outcome was substantial enough to register as a significant miss.

The model's 59% win probability for Chicago proved justified in outcome—the Bears did win—but the confidence level attached to that projection drastically understated what would unfold. A 22-point victory leaves almost no room for alternative scenarios; this was not a close decision that required the 59% assessment. The actual game was a blowout in which Chicago's offense exploded beyond any reasonable pregame simulation.

For an analytics model whose entire function is to distribute probability across outcomes, this gap matters. The model was right about direction but wrong about magnitude by a wide margin. Whether this reflects an outlier performance by Chicago, a worse-than-expected Carolina showing, or a genuine model calibration issue cannot be determined from the game result alone. What is clear: the pregame projection asked for a low-scoring, tightly contested affair. What actually happened was a 96-point combined output that never looked close after the opening minutes.