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.
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)
#> # 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
Player 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: 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
Game 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
) - 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 21
KenPom
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')