Baseball analytics and machine learning for understanding the game of baseball.

We created BaseballML.com to develop new ways to analyze the game.

This site began as our Capstone project as master’s degree candidates in the Masters of Applied Data Science program at the University of Michigan School of Information.  We host several baseball analytics tools including:

• Player Explorer Dashboard for exploring information about individual players. This dashboard includes:
  • Player Statistics including selection of player and user selection of filters.
  • Player Similarity for interactive comparison of players to understand their potential similarity or differences.
  • Similarity Exploration to interactively select statistics and explore players performance.
  • Hall of Fame (HOF) Worthiness to explore how HOF worthiness changes for players at different points in their career.
• Strike Zone Explorer Dashboard for understanding strike zones in actual game conditions. This dashboard includes:
  • Zone Batting Average Profiles for hitters and pitchers to visualize player success based on pitch location.
  • Umpire Strike Zone Visualizer for seeing strike zones based on actual umpire calls.
  • “What if I pitch here?” for pitching outcome prediction to analyze the likelihood of throwing a strike under different conditions.
• Team Similarity Tool to compare teams based on player similarity and other statistics.
• Data Download Tool so you can leverage our data collection effort and download data to perform your own analyses.

We’ve created a series of blogs for our site launch discussing our tools and the data science behind them. You can start with our first Baseball Machine Learning Blog explains the data science behind our work.

We hope you enjoy exploring!

– Anthony Giove, Avinash Reddy, and Ryan Maley