MLB Pitch Analytics Platform
Repository. This is an open, in-progress build. Browse the code on GitHub. It runs on a deployed data pipeline, but there isn't a public demo URL yet.
Overview
This application analyzes MLB Statcast pitch-level data to uncover the pitch characteristics and sequencing patterns that influence whiff rates and hitter outcomes. By combining an automated data pipeline with interactive analytical tools, the platform helps translate millions of pitch events into insights that can support player evaluation and scouting decisions.
Project Objective
This project transforms raw Statcast data into interactive analytical tools that help evaluate pitch effectiveness, identify hitter tendencies, and explore how velocity, movement, sequencing, count, and location influence swing-and-miss outcomes.
Key Questions
- Which pitch sequences generate the highest whiff rates?
- How does pitch location influence swing-and-miss outcomes?
- Which pitch types are most effective in different counts?
- How do pitcher and hitter handedness affect pitch performance?
- How do velocity and pitch movement influence whiff rates?
- Which pitch characteristics lead to the greatest swing-and-miss success?
Built With
Languages
- Python
- SQL
Frontend
- React
- Vite
Database
- DuckDB
Data
- MLB Statcast
Development
- Git
- GitHub
Development Status
Completed
- Automated Statcast Data Pipeline
- Data Cleaning
- SQL Database Development
- Interactive React Application Framework
- Pitch-Level Data Analysis
In Progress
- Advanced Analytical Tools
- Interactive Visualizations
- Additional Player Comparison Features
- Expanded Pitch-Level Analysis