Applying Self-Referential Grades to NFL Quarterback Play
A practical method to isolate individual performance from noisy play-by-play data.
About Me
Hello! My name is Macsen and I am a senior at Kennesaw State University studying Computer Science and Data Science and Analytics. This site serves as a host for my personal analytics projects.
About a year ago, I took up a passion for sports analytics, with NFL data analysis being a particular interest of mine. Applying the statistical fundamentals I have acquired from my previous courses with my love of the game of football, I've designed a myriad of tools that bring crucial insights out from the murky haze of raw data.
Tools I Use
Below are some of the tools I am proficient in and use to create these projects.
Data Science Articles & Projects
A collection of my deep dives into sports analytics, play-by-play modeling, and data visualization.
Evaluating QB EPA Progression Through Time-decay Weighted Averages
A model inspired by the NBA's DARKO DPM statistic that balances recent performance with wholistic evaluation.
Visualizing Rushing Offense Through Run Gap and Location Data
Bringing new perspective to rushing success rate with isomorphic data modeling
Showcasing NFL Receiver Efficiency Within a Team Context
A tool for creating interactive charts to add context to the strengths and weaknesses of an NFL passing offense.
Upcoming and In Progress
I am currently working on expanding my analytics portfolio with the following projects:
- Analyzing the NYC Taxi System Using Big Data Concepts: A big-picture analysis of New York City's taxi industry in the wake of the COVID pandemic and the rise of Rideshare apps.
- Measuring Opioid Addiction Treatment Plans by Success Rate: An analysis of the 2023 TEDS recovery dataset and the correlation between treatment methods and outcomes based on patient circumstances.
- Electricity Load Forecasting with LSTMs: A machine learning project predicting energy grid usage on the East Coast using Long Short Term Memory (LSTM) algorithms.
- Predicting Sack Variance Regression for NFL Edge Rushers: A predictive model that estimates regression for NFL EDGEs from their ability to generate pressure and schedules faced.
Get in Touch
I am always open to discussing data projects, NFL analytics, or potential opportunities. Feel free to reach out via any of the platforms below!