Final Report Peer Review

[warrenmblood]

This group analyzed NFL game to data to attempt to create a model that predicts NFL game point spreads more accurately than Vegas lines. They created models for both a season average only (SAO) dataset and a season and moving (SAM) dataset. Using predictive models including linear regression, ridge regression, lasso regression, and EBM, they were able to produce predictions that compared favorably to Vegas lines in many scenarios.

I really appreciated how well-written this group’s report was and how the logic of all their decisions was clear and easy to follow. I also liked the use of the EBM model we learned about from our guest lecturer, which strengthened your analysis with the use of a very effective non-linear model. Finally, I thought the evaluation of your model by comparing to Vegas lines from the actual NFL games your model is predicting was excellent and did a great job showing the potential usefulness of your model in practice. For an area to explore further for these models, I think the effect of incorporating player injury would be very interesting to see, as you mentioned in your introduction that injuries can play a major role in swinging a game towards another team. This was a fascinating project to learn about and I thought you did an excellent job in getting useful predictions.