Vignette on using Statistical Modeling to predict the winning team of an NFL game; created as a class project for PSTAT197A in Fall 2024
Anshi Arora, Joshua Charfauros, Christina Cui, Sean Reagan
The objective of this vignette is to use a multitude of variables to predict binary win/loss outcomes of a game.
To determine which variables have strong correlations with game win, and thereby likely will serve as strong predictors, we will be conducting some exploratory data analysis. Then, we will train a random forest model on the data. After making the model, we can evaluate its accuracy on the test set and account for any issues that arise. We will also calculate variable importance scores to determine which predictors serve the largest roles in determining the prediction. This model is further developed by adding training controls.
The following files are in the root directory:
README (.md/.html)this overview documentVignette (.qmd/.html)final compiled reportVignette_cachesupplementary folder aids in rendering of vignette.qmdVignette_filessupplementary folder aids in rendering of vignette.qmdScriptsDraftssubdirectory with drafts from each contributerVignette.Rfinal compiled script
RDS filesincludes RDS files created in Exploratory Data Analysis, aid in rendering of graphs in vignette.qmdFiguresgraphs and figures stored as png'sDataraw preprocessed csv
https://www.nflfastr.com/index.html https://nflreadr.nflverse.com/ https://www.nflfastr.com/articles/beginners_guide.html#real-life-example-lets-make-a-win-total-model www.nflfastr.com/articles/stats_variables.html