The Ultimate Guide to
MLB Analytics
Master baseball analytics with R and Python. From traditional sabermetrics to cutting-edge Statcast data, learn the exact skills used by MLB front offices, scouts, and professional analysts.
library(baseballr)
library(tidyverse)
# Get 2024 Statcast data for elite hitters
statcast <- statcast_search(
start_date = "2024-04-01",
end_date = "2024-09-30"
)
# Find barrel leaders
barrel_leaders <- statcast %>%
filter(!is.na(launch_speed)) %>%
group_by(player_name) %>%
summarize(
avg_ev = mean(launch_speed),
max_ev = max(launch_speed),
barrels = sum(barrel == 1),
xwoba = mean(estimated_woba_using_speedangle, na.rm = TRUE)
) %>%
arrange(desc(barrels))Built with data from industry leaders
Everything You Need to Master Baseball Analytics
A comprehensive curriculum designed for aspiring analysts, data scientists, and passionate fans
Dual Language Coverage
Every concept explained with complete code examples in both R and Python. Switch between languages with a single click, or master both to maximize your career options.
- Side-by-side R & Python code
- Identical outputs & analysis
- Best practices for each language
Statcast Era Analytics
Go beyond traditional stats with modern tracking data. Learn to analyze exit velocity, launch angle, spin rates, sprint speed, and expected statistics like xwOBA and xERA.
- Real Statcast data examples
- Expected statistics deep dives
- Pitch movement analysis
Career-Ready Skills
Learn the exact analytical techniques used by MLB front offices. Build portfolio projects that demonstrate your abilities to potential employers in baseball operations.
- Industry-standard methods
- Portfolio project ideas
- Career guidance chapter
Machine Learning
Apply modern ML techniques to baseball problems. Build pitch classifiers, player projection systems, and your own custom metrics using scikit-learn and tidymodels.
Interactive Visualizations
Create stunning visualizations with ggplot2, matplotlib, and Plotly. Build interactive dashboards with Shiny and Streamlit to present your analyses professionally.
Historical Analysis
Access 150+ years of baseball history through the Lahman Database and Retrosheet. Learn to compare players across eras with proper statistical adjustments.
Your Learning Journey
Progress from fundamentals to advanced techniques in a logical sequence
Foundations
Learn R/Python basics, data wrangling with tidyverse/pandas, and how to access MLB data sources.
Chapters 1-4Core Analytics
Master traditional sabermetrics (wOBA, WAR, FIP) and modern Statcast metrics for hitting and pitching.
Chapters 5-8Advanced Methods
Build predictive models, create custom metrics, and explore machine learning applications in baseball.
Chapters 9-12Applied Topics
Specialize in areas like historical analysis, team building, fantasy sports, catcher analytics, and more.
Chapters 13-24All Chapters
From fundamentals to cutting-edge techniques - everything you need in one place
R or Python? Why Not Both?
Every example is available in both languages. Learn one or master both.
R
The Statistical Computing LanguageR has been the dominant language in baseball analytics for over a decade. Its statistical heritage, powerful visualization capabilities, and dedicated baseball packages make it the traditional choice for serious analysts.
baseballr- Comprehensive MLB data access including Statcast, FanGraphs, and Baseball ReferenceLahman- Complete historical database from 1871 to presenttidyverse- Modern data manipulation with dplyr, tidyr, and purrrggplot2- Publication-quality visualizations with grammar of graphicsShiny- Build interactive web applications and dashboards
Python
The Versatile Programming LanguagePython has rapidly grown in sports analytics due to its versatility, strong machine learning ecosystem, and general-purpose capabilities. Many modern MLB front offices now use Python as their primary language.
pybaseball- Access Statcast, FanGraphs, Baseball Reference, and morepandas- Industry-standard data manipulation and analysismatplotlib&seaborn- Comprehensive visualization librariesscikit-learn- Machine learning algorithms and model evaluationStreamlit- Rapid dashboard and app development
Perfect for Every Baseball Enthusiast
Whether you're pursuing a career in baseball or just want to understand the game better
Aspiring Front Office Analysts
Learn the exact skills MLB teams look for. Build a portfolio of projects demonstrating player evaluation, projection systems, and strategic analysis.
Fantasy Baseball Players
Gain a competitive edge with data-driven player evaluation. Identify breakout candidates, understand regression, and optimize your draft strategy.
Students & Researchers
Perfect for sports analytics courses, independent study, or research projects. Combines statistical theory with practical application using real data.
Journalists & Content Creators
Tell compelling data-driven stories. Learn to find insights, create visualizations, and communicate complex analytics to general audiences.
Coaches & Scouts
Integrate modern analytics into player development and evaluation. Understand what the numbers mean and how to apply them on the field.
Passionate Fans
Deepen your understanding and enjoyment of the game. See baseball through the lens of data and appreciate the strategy behind every decision.
Ready to Start Your Analytics Journey?
Join thousands of baseball fans learning to analyze the game they love.
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