Overview
This Python project started as a way to study and practice for my data analysis class at Houston City College and focuses on the analysis of pitcher and batter trends using historical MLB datasets. It leverages pandas and numpy for data manipulation, while matplotlib and seaborn generate visual insights into pitch selection frequency, dominant pitch types, and annual patterns. The system currently supports CLI interaction and player-specific breakdowns.
To make this tool accessible on the web, I am working on developing an API using Python libraries and frameworks that will serve as a backend that processes user input, like pitcher or batter names, and return statistical insights such as dominant pitch type, pitch usage trends, and pitch count history.