NFL 4th Down Decision Machine Learning Model

Developed by Connor Cozad and Maddie Carrino

This repo contains the data and Jupyter notebooks used to create a machine learning model that can decide whether an NFL team should punt, kick a field goal, or "go for it" on 4th down. This served as the final project in CSCI 334: Data Mining for Dr. Navid Hashemi, Department of Computer Science, College of Charleston.

Project Overview

Data Sources: Every NFL play run between the 2003 and 2021 seasons was scraped using BeautifulSoup4 in Python. Scraped data was saved into the CSV files found in /data. Code is provied in data_scraping.ipynb.

Data Preprocessing and Features: Data is transformed in data_processing_and_ml_models.ipynb, and variables are prepared for use in the machine learning model. Variables such as the number of yards to first down, number of yards to the endzone, minutes remaining in the game, the score of the game, and home team advantage are used to determine the play that should be run: punt, kick a field goal, or "go for it".

ML Models: Seven different machine learning algorithms were considered, shown in data_processing_and_ml_models.ipynb. We found the highest accuracy when using XGBoost, an algorithm based on a gradient boosted ensemble of decision trees.

87% Accuracy: Our final XGBoost model chooses the best play to run on 4th down 87% of the time.

Install and Run the Project

The bulk of this project can be viewed in data_processing_and_ml_models.ipynb, but you can use the following instructions to install and run this project yourself.

These steps will allow you to run data_processing_and_ml_models.ipynb, which processes, trains, and tests a machine learning model on the data found in the data directory.

1. Download the data directory and data_processing_and_ml_models.ipynb into the same location on your computer.
2. Create a Python environment that has pandas, numpy, seaborn, matplotlib, sklearn, xgboost, and jupyter installed.
3. Use jupyter in this Python environment to open data_processing_and_ml_models.ipynb.
4. Run all cells in the notebook, and watch as the data is processed and machine learning models are trained and tested.

You may have noticed that these instructions don't include data_scraping.ipynb. This Juypter notebook was used retrieve and store the data found in the data directory in this repo. Feel free to use this notebook if you're interested in scraping data yourself. Otherwise, it is easier to use the CSV files we've provided in /data.