Abstract
This paper describes sports quantitative analysis research which investigates the use of statistics and modern machine learning methods applied to the problem of predicting the point spreads for United States (US) National Football League (NFL) football games. Insights and results are presented for several modern machine learning techniques for both exploratory analysis and predictive analysis. The case study presented here and the results thereof may be quite useful for those involved in the huge global sports betting arena both the gaming industry and the bettors therein. NFL game statistics also provides a rich source of relevant real-world data for the deployment of several modern data science methodologies and is thus a great teaching tool for the university classroom. Since sports gambling has now made its way onto college campuses with a growing number of schools signing million dollar deals with sports books and casinos, the topic of this article is of even more current relevance.
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