sportsdataverse python package
A set of functions to visualize National Football League analysis in 'ggplot2'
NFL Statsbot tweets NFL statistics for teams and players since 1999.
NFL | Play-By-Play Prediction Platform
Data Analytics Capstone Project following Professional Certification. Inspired by my love, passion, and vast interest in the game.
This project was a submission to the NFL's Big Data Bowl 2025 - Prediction competition.
R Shiny application and research paper testing whether modern NFL efficiency metrics (EPA, CPOE) predict quarterback and team success better than traditional stats. Multivariate regression on 2011-2020 nflfastR data across 74 QBs found EPA alone predicts Wins with an R-squared of 0.932.
A small Plotly practice repo for R, including one presentation (a 2022 San Francisco 49ers QB comparison built with nflfastR data) plus three standalone scatter and line chart scripts.
Bayesian Logistic Regression to Predict Pass/Rush NFL Plays
NFL Team Logos squared for easy table graphics and resizing.
A statistical regression course project modeling which play-by-play variables (yards gained, air yards, turnovers, field position) most strongly predict Expected Points Added, using nflfastR data from 2010-2019. Separate models were built for passing and rushing plays.
NFL play-by-play → BigQuery: idempotent nflverse ingestion + LLM-friendly docs + verification
A data visualization course project exploring NFL play-by-play data (nflfastR) using only ggplot2 in R, with no dedicated viz tool. For five analytical questions on team tendencies, QB efficiency, and penalty trends, multiple chart types are built and critiqued against each other to compare design choices.
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