Master basketball analytics with R and Python. From traditional box scores to cutting-edge player tracking data, learn the exact skills used by NBA front offices, scouts, and professional analysts.
library(hoopR)
library(tidyverse)
# Get 2024 player tracking data
player_stats <- nba_leaguedashplayerstats(
season = "2023-24",
measure_type = "Advanced"
)
# Find most efficient volume scorers
elite_scorers <- player_stats %>%
filter(MIN > 25, FGA > 10) %>%
mutate(
true_shooting = PTS / (2 * (FGA + 0.44 * FTA)),
points_added = (true_shooting - 0.572) * TSA
) %>%
arrange(desc(points_added)) %>%
head(20)Built with data from industry leaders
Everything You Need to Master Basketball Analytics
Every concept explained with complete code examples in both R and Python. Switch between languages with a single click.
Go beyond box scores with modern tracking data. Learn to analyze player movement, shot quality, and defensive impact.
Learn the exact analytical techniques used by NBA front offices. Build portfolio projects that demonstrate your abilities.
Apply modern ML techniques to basketball problems. Build player projections, clustering models, and win probability systems.
Create stunning shot charts, court plots, and dashboards with ggplot2, matplotlib, Shiny, and Streamlit.
Compare players across eras with proper pace and era adjustments. Analyze all-time rankings and Hall of Fame probability.
Progress from fundamentals to advanced techniques in a logical sequence
Learn R/Python basics, data wrangling with tidyverse/pandas, and how to access NBA data sources.
Chapters 1-4Master traditional stats (PER, TS%), plus-minus metrics (BPM, RPM, RAPTOR), and modern tracking data.
Chapters 5-10Build predictive models, create custom metrics, and explore machine learning applications in basketball.
Chapters 11-16Specialize in draft analytics, salary cap, player development, fantasy sports, betting models, and more.
Chapters 17+From fundamentals to cutting-edge techniques - everything you need in one place
Every example is available in both languages. Learn one or master both.
R has deep roots in sports analytics with powerful statistical packages and visualization tools.
hoopR - Play-by-play and player datanbastatR - Comprehensive NBA data accesstidyverse - Modern data manipulationggplot2 - Publication-quality visualizationsShiny - Interactive web applicationsBest for: Statistical analysis, visualization, academic research
Python dominates modern NBA analytics departments with its ML ecosystem and production capabilities.
nba_api - Official NBA stats API wrapperpandas - Industry-standard data manipulationbasketball_reference_scraper - Historical datamatplotlib & seaborn - Visualizationsscikit-learn - Machine learningBest for: Machine learning, automation, production systems
Whether you're pursuing a career in basketball or just want to understand the game better
Learn the exact skills NBA teams look for. Build a portfolio of projects demonstrating player evaluation and projection systems.
Gain a competitive edge with data-driven player evaluation. Identify breakout candidates and optimize your draft strategy.
Perfect for sports analytics courses, independent study, or research projects. Combines statistical theory with practical application.
Tell compelling data-driven stories. Learn to find insights, create visualizations, and communicate analytics to general audiences.
Integrate modern analytics into player development and evaluation. Understand what the numbers mean and how to apply them.
Deepen your understanding and enjoyment of the game. See basketball through the lens of data and appreciate every decision.
Join thousands of basketball fans learning to analyze the game they love.
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