Hi, I’m Andrew!
Hello there! I’m Andrew Weatherman, an avid open-source R developer
and a die-hard college basketball enthusiast. My journey with R began
during COVID, and I am entirely self-taught. In 2022, I ventured into
the realm of “real coding” and developed the toRvik package, a tool
for accessing college basketball data. It was my first major project
and, despite its success, presented a fair share of challenges in
maintenance.
In May 2023, I embarked on a new project: cbbdata. This
Flask-based API is designed to deliver comprehensive, up-to-date college
basketball statistics faster and more efficiently than its predecessor,
toRvik. cbbdata has been a significant
undertaking, and I’m excited to finally share it with the community.
A bit more about me for those joining from Twitter: I graduated from Duke University in 2023. During my time there, I was a student manager for the men’s basketball program, coinciding with Coach K’s final run and Jon Scheyer’s first season.
Currently, I’m pursuing a degree in Mathematics at the University of North Carolina at Charlotte, with an expected graduation date of May 2024. Post-graduation, my plans are to either continue my education with a master’s in data science or jump straight into the workforce.
What is cbbdata?
cbbdata is an R package designed for anyone passionate
about college basketball statistics. It simplifies the process of
accessing and analyzing a wealth of college basketball data, making it
more efficient and user-friendly than ever before.
Key Features of cbbdata
cbbdata ships with 26 functions, and there is a lot to
love about this package. Here are some brief highlights:
Comprehensive Game-by-Game Logs: Access detailed box scores and advanced metrics for players and teams dating back to 2008 (
cbd_torvik_player_game()andcbd_torvik_game_box()/cbd_torvik_factors()). You can further find per-game player and team splits on month, location, result, and game type factors (cbd_torvik_player()/cbd_torvik_team_split()).Daily Updated NET Rankings: Find the latest NET rankings every morning (
cbd_torvik_current_resume()).Game Predictions with Barttorvik: Simulate any matchup, real or otherwise, using our game prediction feature, powered by Barttorvik (
cbd_torvik_game_prediction()). It’s perfect for pre-game analyses and hypothetical scenario discussions.Plot team logos with
gt: Okay, sure, this one isn’t data-focused, but it’s perhaps my favorite function. Rebuild yourteamcolumn with a logo and team name and render in agttable. Gone are the days of awkwardly having separate columns for your logo and team name (cbd_gt_logos()). Just like here!
Why cbbdata?
cbbdata is designed with the end-user in mind. It’s
built from the ground up to be more powerful, yet as intuitive as
toRvik. The package aims to address some of the limitations
of toRvik while introducing new features and capabilities.
Importantly, say goodbye to loops! With cbbdata, you can
access full data files by passing no arguments to functions. Otherwise,
you can filter by year, team, conference, etc.
A Note on Documentation
I am aware that the documentation is still a work in progress. As a
developer passionate about this project, I wanted to release
cbbdata to the community as soon as possible. Continuous
improvements and updates to the documentation will be made.
This release marks just the beginning. I am committed to regular
updates and improvements to cbbdata, based on community
feedback and evolving needs.
Get Started with cbbdata
cbbdata is now available for use! Start by signing up
for a free API key, and you’ll be on your way to exploring the depths of
college basketball statistics.
Registering
cbbdata::cbd_create_account(username, email, password, confirm_password)Your Feedback Matters
As with toRvik, your feedback is invaluable in shaping
cbbdata. I encourage you to share your experiences,
suggestions, and questions. You can find me on Twitter.