Finishing up the NFL season on a high note

This week was a solid week for the model, with a win accuracy of 81.25% and a spread accuracy of 62.5%. The model did not know how to interpret the Chiefs deciding to bench their important offensive starters, so the model took a hit there. I think that it is ready to use in the playoffs. Later on this week, I will provide hypotheticals that I think will happen in the playoffs based on the model output. Also, sorry for not posting last week. I was very busy applying to jobs and grad schools. 

Next year, the model will ride all 17 weeks instead of just 8 as to get more accurate predictions and results. I also was a little too busy to transfer the model to Python and adapt it to look at NBA games, so that will be a future project. 

Statistics for the model for the 8 weeks I was able to use it: 

• Even though I had a really really really rough start, we ended with 53.8% accuracy. This includes the first week using the adaptive spread, which was 35% accurate. The last 5 weeks sported an average accuracy closer to 60%. 
• The model had over 50% spread accuracy for 27 of the 32 NFL teams for the season.
• The model had over 50% spread accuracy for 7 of the 8 divisions for this season.

Obviously, these numbers leave lots of room for improvement, but this was a great introductory personal project to learn how to model. As always, you can check out the visualization for the model results here.

Thank you to whoever has read through these posts, and I'll provide the predictions for the NFL playoffs soon! 

Cheers
​Brendan