Getting reliable, up-to-date college basketball data shouldn’t be a
hassle. With cbbdata, it isn’t. This package makes it straightforward
to access a wide range of stats with minimal effort.
Easy Access: cbbdata simplifies the process of fetching college
basketball statistics. No more scraping or searching through multiple
sources: just clean, easy access to the data you need.
Fast and Accurate: Built with Flask and Python, cbbdata uses SQL
queries and direct file transfers to ensure you get accurate and current
data quickly. Our database is updated every 15 minutes during the
season.
Comprehensive Data: Whether you need player stats, team analytics,
game results, or advanced metrics, cbbdata has you covered with nearly
30 endpoints.
You can install the development version of cbbdata from GitHub with:
# install.packages("devtools")
devtools::install_github("andreweatherman/cbbdata")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')After registering, there are two ways to obtain your key using the
cbbdata R package.
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')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.
cbbdata provides broad access to leading college basketball resources.
cbbdata is continuously growing and the available data includes:
Developer Andrew Weatherman wrote
the popular toRvik R package. cbbdata replaces toRvik and brings
with it a rich collection of Barttorvik data.
- 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)
#> # A tibble: 10 × 4
#> team barthag adj_o adj_d
#> <chr> <dbl> <dbl> <dbl>
#> 1 Houston 0.978 119. 85.7
#> 2 Connecticut 0.977 127. 92.0
#> 3 Purdue 0.967 126. 93.8
#> 4 Auburn 0.953 120. 92.6
#> 5 Arizona 0.952 121. 93.0
#> 6 Iowa St. 0.950 114. 88.0
#> 7 Tennessee 0.945 116. 90.5
#> 8 North Carolina 0.937 119. 93.9
#> 9 Duke 0.935 121. 95.7
#> 10 Marquette 0.928 118. 94.4- 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:
cbbdata::cbd_torvik_player_split(year = 2024, conf = 'ACC', split = 'location') %>%
dplyr::filter(games >= 10) %>%
dplyr::slice_max(pts, n = 1) %>%
dplyr::select(team, player, pts, games)
#> team player pts games
#> 1: North Carolina RJ Davis 22.75 12- 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: 6 × 3
#> conf games efg
#> <chr> <dbl> <dbl>
#> 1 P12 78 52.8
#> 2 B10 125 51.6
#> 3 B12 121 51.3
#> 4 MWC 70 51.2
#> 5 BE 108 51.1
#> 6 ACC 112 51.1- 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()
- 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())
- Daily NET rankings and quadrant records (
cbd_torvik_current_resume) - Historical NET rankings and per-day metrics (
cbd_net_archive) - 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: 5 × 4
#> team conf q1_wins net
#> <chr> <chr> <dbl> <int>
#> 1 Connecticut BE 19 12
#> 2 Purdue B10 18 16
#> 3 Houston B12 17 1
#> 4 Tennessee SEC 14 5
#> 5 Texas A&M SEC 13 21To 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')- Year-end ratings (
cbd_kenpom_ratings) - Day-by-day ratings (
cbd_kenpom_ratings_archive)