Era-Adjusted Baseball Analytics

Full House Modeling · Public-Facing Data Tools

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  • Python
  • R
  • React
  • Firebase
  • Docker
  • GitHub Actions
An automated data pipeline connects baseball statistics, statistical models, and an interactive application.

Overview

How can we compare baseball players who competed in different eras? The Full House Modeling project at Eck Sports Lab, led by Professor Daniel J. Eck at the University of Illinois Urbana-Champaign, develops statistical tools for this question.

The lab’s approach considers both performance relative to contemporaries and the quality of the available talent pool. Its era-adjusted measures include ebWAR (era-adjusted Baseball-Reference wins above replacement) and efWAR (era-adjusted FanGraphs wins above replacement). Explore the lab’s research.

As a Software Engineering Research Assistant (August 2024–May 2025), I contributed public-facing software and data tools that help users explore these statistics. My work connected the lab’s statistical methods with data ingestion, backend services, and an interactive application.

My engineering contributions

Automated ingestion. Built a Python pipeline using Selenium and BeautifulSoup to scrape and process baseball statistics, with GitHub Actions scheduling daily updates.

Statistical data services. Developed Python backend services integrating R modules for statistical transformations. Deployed Docker containers backed by Firebase Realtime Database and improved database schema and indexing.

Interactive web application. Built and deployed a responsive React application with Material UI, desktop and mobile layouts, 250 ms-debounced search, and interactive statistical filters.

Research communication. Helped make era-adjusted player statistics accessible through public-facing tools and content for the Full House Modeling project. The underlying methodology and era-adjusted metrics are part of the lab’s collaborative research.

Engineering outcomes

Measure Result from my research-assistant work
Baseball statistics processed Over 2 TB
Query latency 45% reduction
Initial application load time 40% reduction
Application audience 1,000+ active users
Data refresh Scheduled daily updates

These engineering results describe my contributions during the research-assistant period. The linked research application continues to evolve as part of the lab’s ongoing work.

Explore the project

The project website provides access to era-adjusted baseball statistics. The Eck Sports Lab website explains the research, related publications, and the people behind it.