Essential tools, data sources, and learning materials for basketball analytics. Everything you need to get started and continue growing.
Get your environment ready in minutes:
# Install required packages
install.packages(c(
"tidyverse",
"hoopR",
"ggplot2"
))
# Test installation
library(hoopR)
nba_schedule(2024)# Install required packages
pip install pandas nba_api matplotlib seaborn
# Test installation
from nba_api.stats.endpoints import playercareerstats
career = playercareerstats.PlayerCareerStats(player_id='201939')
print(career.get_data_frames()[0].head())Official NBA and team job listings.
Aggregated sports industry job listings.
LinkedIn job search for sports analytics.
Guide to breaking into sports analytics careers.
Serious NBA discussion subreddit with analytics focus.
Original basketball analytics community forum.
Follow analysts on Twitter/X for latest insights.
Open source NBA analytics projects on GitHub.
Official NBA statistics portal with box scores, tracking data, and advanced metrics.
Comprehensive historical database with advanced stats back to 1946.
Unofficial Python library for accessing NBA.com statistics API.
R package for NBA and college basketball data access.
Premium analytics with luck-adjusted stats and lineup data.
Free play-by-play analytics and shot quality data.
Public tracking metrics from Second Spectrum cameras.
Professional play-type breakdown and video analysis (paid).
Dean Oliver's foundational basketball analytics book - Four Factors and more.
YouTube channel with excellent visual analytics explanations.
Analytics-focused NBA writing and analysis.
Former Bucks analyst writing on The Athletic.
Deep analytical writing on player evaluation and history.
Analytics blog with R tutorials and NBA analysis.
Public NBA datasets for practice and projects.
Premier sports analytics conference with research papers.
Academic journal for sports statistics research.
Preprint research papers on NBA analytics.
Blog posts on methodology and new metrics.
Essential data manipulation library for basketball analytics.
Collection of R packages for data science and visualization.
Machine learning library for Python - classification, regression, clustering.
Interactive visualization library for Python and R.
Comprehensive plotting library for Python.
Grammar of graphics visualization for R.
Gradient boosting for predictive modeling.
Build interactive dashboards in Python.