In the vast arena of college basketball data, finding comprehensive, easily accessible, and up-to-date statistics can be a daunting task. cbbdata emerges as your reliable partner in this quest, offering an unparalleled depth of college basketball insights, just a line of code away.
Unmatched Access, Uncomplicated Process: cbbdata is your key to unlocking a treasure trove of college basketball statistics. Say goodbye to the hurdles of data scraping. Our package is designed for efficiency and simplicity, providing a straightforward pathway to the data you need.
Powered by Precision and Speed: At the heart of cbbdata lies a robust architecture, powered by Flask and Python. Leveraging SQL queries and direct file transfers, we ensure you receive the most accurate and recent data. Our commitment to speed and reliability means you get the data you need, when you need it, without the wait.
Diverse Data at Your Fingertips: Whether you’re looking for detailed player stats, team analytics, game outcomes, or advanced metrics, cbbdata has it all. Our comprehensive database is constantly evolving, bringing you the latest and most detailed insights into the college basketball world.
Getting Started is Easy: Begin your journey with cbbdata by signing up for a free API key. With this key, a world of college basketball data awaits you. Experience the ease and power of cbbdata and elevate your analysis to the next level.
Installation
You can install the development version of cbbdata from GitHub with:
# install.packages("devtools")
devtools::install_github("andreweatherman/cbbdata")Registering for an API key
An API key is free and easy to obtain by using the official cbbdata R package. To register, simply pass a username and password to the cbd_create_account endpoint. Your API key will be emailed to you – make sure to check your spam folder – but you need not manually store your API key anywhere. The preferred way to access your account is with your username and password.
Note: If you want to access KenPom data, your CBBData account email must match your KenPom account email
# to register
cbbdata::cbd_create_account(username = 'xxx', email = 'xxx', password = 'xxx', confirm_password = 'xxx')Obtaining Your Key
After registering, there are two ways to obtain your key using the cbbdata R package.
By-Session Log-In:
To obtain your key for use in your current R session, you can pass your username and password to the cbd_login function. This will retrieve your key and store it as a session variable. If you restart or leave your R session, you will need to log-in again.
# per-session log-in
cbbdata::cbd_login(username = 'xxx', password = 'xxx')Persistent Log-In (Preferred):
The recommended way to interact with the CBBData API is to store your username (CBD_USER) and password (CBD_PW) inside the .Renviron file. If you are unsure on how to do this, the cbd_login function will walk you through the process. Please note that this will require restarting your R session.
# persistent log-in
cbbdata::cbd_login()If your API key is not stored as a session variable, cbbdata functions will grab your credentials from the .Renviron file and automatically log you in. With this method, you will not have to log-in again.
Available Data
cbbdata provides broad access to leading college basketball resources. cbbdata is continuously growing and the available data includes:
Barttorvik:
Developer Andrew Weatherman wrote the popular toRvik R package. cbbdata replaces toRvik and brings with it a rich collection of Barttorvik data.
Metric Ratings:
- Year-end ratings (
cbd_torvik_ratings) - Day-by-day ratings (
cbd_torvik_ratings_archive) - Team four factor splits (
cbd_torvik_team_factors)
E.g., if you want to see what the no-bias T-Rank top 10 looks like:
cbbdata::cbd_torvik_team_factors(year = 2024, no_bias = TRUE) %>%
dplyr::slice(1:10) %>%
dplyr::select(team, barthag, adj_o, adj_d)
#> API Key set!
#> # A tibble: 10 × 4
#> team barthag adj_o adj_d
#> <chr> <dbl> <dbl> <dbl>
#> 1 Houston 0.979 117. 83.9
#> 2 Purdue 0.974 126. 92.3
#> 3 Auburn 0.955 120. 91.7
#> 4 Connecticut 0.955 123. 94.4
#> 5 Arizona 0.949 120. 92.9
#> 6 Tennessee 0.944 118. 91.9
#> 7 Alabama 0.941 126. 98.9
#> 8 Iowa St. 0.940 115. 90.2
#> 9 BYU 0.934 120. 95.3
#> 10 Marquette 0.930 117. 93.1Player Data:
- Individual game logs (
cbd_torvik_player_game) - Season averages (
cbd_torvik_player_season) - Season splits (
cbd_torvik_player_split)
E.g., if you want to see which ACC player averages the most points at home:
Team + Conference Data:
- Team stats splits (
cbd_torvik_team_split) - Team histories (
cbd_torvik_team_history) - Conference four factor splits (
cbd_torvik_conf_factors)
E.g., if you want to see which conferences shoot the best at home against top 100 teams:
cbbdata::cbd_torvik_conf_factors(2024, venue = 'home', top = 100) %>%
dplyr::filter(games >= 5) %>%
dplyr::slice_max(efg, n = 5) %>%
dplyr::select(conf, games, efg)
#> # A tibble: 5 × 3
#> conf games efg
#> <chr> <dbl> <dbl>
#> 1 P12 39 55.3
#> 2 MWC 36 52.4
#> 3 B10 64 52
#> 4 SEC 62 51.1
#> 5 B12 69 51.1Game Data:
- Individual game box (
cbd_torvik_game_box) - Individual game four factors (
cbd_torvik_game_factors) - Individual game stats (box + factors) (
cbd_torvik_game_stats) - Season schedule (
cbd_torvik_season_schedule)
E.g., if you want to track how Duke’s offense has performed this season:
cbbdata::cbd_torvik_game_factors(year = 2024, team = 'Duke') %>%
ggplot2::ggplot(aes(date, adj_o)) +
ggplot2::geom_line() +
ggplot2::geom_point(aes(color = result), size = 3) +
ggplot2::scale_color_manual(values = c('darkred', 'darkgreen'), guide = NULL) +
ggplot2::theme_minimal()
Predictions
- Individual game predictions (
cbd_torvik_game_prediction) - Team season predictions (
cbd_torvik_season_prediction) - Team season simulations (
cbd_torvik_season_simulation)
E.g., if you want to run 10,000 simulations of Duke’s season with their performance as of today:
# no date -> defaults to today
cbbdata::cbd_torvik_season_simulation('Duke', 2024) %>%
ggplot2::ggplot(aes(wins, probability)) +
ggplot2::geom_col() +
ggplot2::scale_y_continuous(labels = scales::label_percent())
Tournament Results + Resumes
- Daily NET rankings and quadrant records (
cbd_torvik_current_resume) - Tournament performance (
cbd_torvik_ncaa_results) - Tournament “committee sheets” (
cbd_torvik_ncaa_sheets) - Resume database (
cbd_torvik_resume_database) - Similar team tournament resumes (
cbd_torvik_similar_resumes)
E.g., if you want to pull the five teams with the most Q1 NET wins:
cbbdata::cbd_torvik_current_resume() %>%
dplyr::mutate(q1_wins = readr::parse_number(quad1)) %>%
dplyr::slice_max(q1_wins, n = 5) %>%
dplyr::select(team, conf, q1_wins, net)
#> # A tibble: 11 × 4
#> team conf q1_wins net
#> <chr> <chr> <dbl> <int>
#> 1 Purdue B10 8 2
#> 2 Connecticut BE 8 3
#> 3 Houston B12 6 1
#> 4 Wisconsin B10 6 15
#> 5 Arizona P12 5 4
#> 6 Kansas B12 5 12
#> 7 North Carolina ACC 5 10
#> 8 Marquette BE 5 11
#> 9 Baylor B12 5 14
#> 10 Duke ACC 5 20
#> 11 Boise St. MWC 5 40KenPom
To access KenPom data, you must have an active KenPom subscription and your CBBData account email must match your KenPom account email; this exists to curb account sharing. To activate your account, pass your KenPom account password through the cbd_kenpom_authorization function. The CBBData API will then confirm your account is active and log your expiration date. You will only be asked to re-authorize on that date.
# persistent log-in
cbbdata::cbd_kenpom_authorization(password = 'xxx')