In this paper, we present a model for predicting the game day outcomes of National Football League games. 3 of the most popular sources for game day predictions are analyzed for comparison. Player data and outcomes from previous games are used, but we also incorporate several weather factors into our models. Over 1,700 games were incorporated and 3 separate models are created using simple regression, principal component analysis, and a recursive model. We also discuss the ethicality of using data science techniques by individuals with the knowledge in order to gain an advantage over a population lacking this specialized training.
Klein, Josh; Frowein, Anna; and Irwin, Chris
"Predicting Game Day Outcomes in National Football League Games,"
SMU Data Science Review: Vol. 1
, Article 6.
Available at: https://scholar.smu.edu/datasciencereview/vol1/iss2/6
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License