Independent research
NFL Statistical Research System
A Python-based football analytics project focused on data processing, model evaluation, integrity checks, and repeatable experiments.
The problem
Statistical experiments are only useful when the inputs, evaluation process, and limitations can be examined.
My contribution
I built an independent research workflow around processing football data, evaluating models, and checking the integrity of experiments before interpreting results.
Project status & scope
Independent research project. Predictive performance and profitability have not been established. This portfolio highlights engineering and research discipline.
Data and integrity
The Python project includes data validation and processing code, historical-data tests, temporal-feature tests, and a benchmark-integrity script. These checks help make assumptions and data problems visible.
Evaluation workflow
Modeling and evaluation code is paired with tests and repeatable experiment workflows. The emphasis is on inspecting evidence and maintaining reproducibility.
Engineering practice
The project uses pytest, type checking, and linting alongside its analytics dependencies. The presence of this tooling is not a claim that a model has demonstrated reliable predictive performance or profitability.