A Jupyter Notebook project to scrape detailed football data from FBref, covering various leagues. The repository includes both the data collection process and a final cleaned dataset for immediate use in analysis. Ideal for football data science projects, including xG, passing metrics, and tactical insights.

A Python-based web scraping toolkit that extracts and processes NFL kicking statistics from Pro-Football-Reference. This project automates the collection of comprehensive game data, with a particular focus on field goal attempts and environmental conditions.

Visual history showing the time since something last happened - starting with football clubs winning X trophy.

ML-Premier-League-Wins-Predictor is my first machine learning project that predicts the number of wins for each team in the Premier League using linear regression. Explore the key factors that contribute to becoming a champion in one of the world's most competitive football leagues. Jupyter Notebook and code included.

This data science project attempts to predict whether Tennessee State University will win or lose a football game using data from 2003 to 2019.