Week 10 proved to be a decently reliable model.
The model could not predict the upset of the Giants over the Eagles. The model gave the Eagles a 63.2% chance of winning, the game. This was a clear upset, and not many people expected this to occur. The model uses common statistics, and doesn't necessarily account for a team's ability to upset unless they are close with regards to the winning percentage. A potential change for next week's model could account for the general team momentum. Next week, I would like to account for a team's ability to play against the spread they were dealt. To do this, I need to obtain each team's spreads from a consistent book and do a few calculations. I feel like ATS % could be useful with regards to my spread % but not to my winning percentage. I would also like to account for win streak for each team, as a team on a hot streak is undoubtedly better than a team coming off of a healthy mixtures of losses and wins.
This model would have improved to a little over 60% cover accuracy if the Cardinals did not kneel for the extra point after their game-winning touchdown.
As for the O/U accuracy, I need to rework a new algorithm to make this more consistent. Before, I used the sum of the predicted scores as the over/under, but that resulted in a worse outcome than just randomly predicting the O/U. so I need to brainstorm idea on that
If you'd like access to the model, just email me, I'd be happy to share :)