I created a logistic regression model with scikit-learn which predicts the outcome of an NFL football game. It predicts the result based on features such as the team's record, opponent's record, pass yards, rush yards, etc.
I created the model and calculated the coefficients of each predictor and found the team's record and the opponent's record have huge influences on whether the team wins or not. In fact, whoever has the better record, the model will predict them to win no matter what their other features say.
Here is a graph showing the coefficients of each of my predictors, ordered by importance:
I know that record is important in determining the result of a football game, but there are plenty of factors that also should have an influence.
The first idea I thought of was to decrease the weight of the team's records so that other predictors would come into play. However, I don't think that this is the right thing to do. Is there a better model for this problem or should I get more data?