Wrangling Major League Baseball Pitchf/x Data with Python

Dive into the world of SaberMetrics with this hands-on Python course! Explore the fascinating intersection of baseball and data science, inspired by the principles behind Moneyball and modern player development.

Learn how professional baseball leverages advanced analytics – from PitchF/x and TrackMan to StatCast and GameDay – to gain a competitive edge. We'll analyze detailed pitch data, including player movement on every pitch, made publicly available by MLB.

This course provides a practical introduction to SaberMetrics, the empirical study of baseball statistics. You'll gain experience using Python to:

• Wrangle MLB game data: We'll use data from Clayton Kershaw's historic 2014 no-hitter as a case study.
• Data Analysis with Python: Learn to manipulate and clean baseball data using built-in Python libraries.
• Data Visualization: Create insightful graphs of pitches using Matplotlib and PyPlot.
• Jupyter Notebooks: Master best practices for using Jupyter Notebooks for interactive data analysis.
• Python Coding Fundamentals: Reinforce your Python skills with practical coding exercises.
• XML Parsing: Extract data from XML sources commonly used in baseball analytics.

Whether you're a coder, aspiring SaberMetrician, or simply a passionate baseball fan curious about the data driving the game, this course is for you. Join us as we "scrub" pitch data from one of MLB's greatest pitching performances and uncover the insights that are revolutionizing the sport. Founded by Chaz Henry, a software engineer and 12-year little league coach, this course offers a unique blend of technical skills and baseball knowledge.