This repository translates baseball analytics concepts from "Analyzing Baseball Data with R" to Python using pandas, matplotlib, seaborn, statsmodels, pygam, pybaseball, and scikit-learn.
Chapters:
- Starting from Chapter 4 (Chapters 1-3 cover R installation and basics)
- Chapters 4-10 and 13 complete
- Chapters 11-12 cover SQL and database setup (R code specific, not included)
Data:
- Extended through 2024 season (book uses data through 2022) so some metrics may differ slightly
pip install -r requirements.txt- 04 - The Relation Between Runs and Wins - Linear regression, Pythagorean expectation
- 05 - Value of Plays Using Run Expectancy - Run expectancy matrices, base-out states
- 06 - Balls and Strikes Effects - Count effects on batting outcomes/pitch sequencing analysis
- 07 - Catcher Framing - Measuring catcher pitch framing ability and strike zone boundaries
- 08 - Career Trajectories - Modeling player performance over age
- 09 - Simulation - Markov chain half-inning simulation, Bradley-Terry season modeling
- 10 - Exploring Streaky Performances - DiMaggio's 56-game streak, at-bat clumpiness testing, Statcast launch velocity streakiness
- 13 - Home Run Hitting - GAM launch variable modeling, spray angle, ballpark effects, batter vs. pitcher random effects
- Book:Analyzing Baseball Data with R by Max Marchi, Jim Albert, and Benjamin S. Baumer
- Book Website:Baseball With R (3rd Edition)
- BlogExploring Baseball Data with R
- Original R Code:Github
This project is independent and all Python code is original but examples directly follow the book. Please support the authors and their work.