IMPORTANT: The package will no longer be maintained. I am in the process of developing a new API in cbbdata and accompanying package cbbplotR. Will bring faster speeds and a more defined structure. Expected roll-out to coincide with the 2023-24 college basketball season. toRvik will remain operational in its current state until the new API and package are released.
toRvik is an R
package for working with and scraping men’s college basketball.
There are a lot of college basketball data out there, but most are
difficult to pull and clean or they are behind a paywall. With toRvik,
you have immediate access to some of the most detailed and extensive
college basketball statistics publicly available – all returned in tidy
format with just a single line of code! Best of all, no subscription is
required to access the data.
Most of toRvik’s functions are powered by a dedicated Fast API
framework – delivering data at rapid speeds with dependable up-times.
As of version 1.0.3, the package includes nearly 30 functions for
pulling player and team data, game results, advanced metric splits,
play-by-play shooting, and more. Leveraging the same data and models as
Barttorvik, the package now offers game
and tournament predictor functions, allowing you to simulate games
between any pair of teams on any date at any venue back to the 2014-15
season. toRvik also offers extensive transfer histories for over 5,000
players back to the 2011-12 season and detailed player recruiting
rankings for over 6,000 players back to 2007-08.
Install the released version of toRvik from CRAN:
install.packages("toRvik")Or install the development version from GitHub with:
if (!requireNamespace('devtools', quietly = TRUE)){
install.packages('devtools')
}
devtools::install_github("andreweatherman/toRvik") - Detailed game-by-game + season-long statistics by player and split
- Extensive transfer + recruiting histories
- Custom game and tournament predictions
- Shooting splits + shares by team
- Game box scores for all D-1 games
- Team + conference four factors by split
- Game-by-game four factors
- NCAA committee-style team sheets
- D-1 head coaching changes
All toRvik functions fall into one of six categories:
- Rating, pulling and slicing T-Rank + four factor data
- Player, pulling player data and histories
- Team, pulling team statistics and histories
- Game, pulling game-by-game data and schedules
- Tournament, pulling raw and adjusted tournament performance
- Miscellaneous
Calling bart_ratings will return the current T-Rank ranks and ratings.
head(bart_ratings())## ── Team Ratings: 2022 ────────────────────────────────────────── toRvik 1.1.0 ──
## ℹ Data updated: 2022-09-09 08:20:52 EDT
## # A tibble: 6 × 19
## team conf barthag barth…¹ adj_o adj_o…² adj_d adj_d…³ adj_t adj_t…⁴ wab
## <chr> <chr> <dbl> <int> <dbl> <int> <dbl> <int> <dbl> <int> <dbl>
## 1 Gonzaga WCC 0.966 1 120. 4 89.9 9 72.6 5 6.71
## 2 Houston Amer 0.959 2 117. 10 88.5 6 63.7 336 6.15
## 3 Kansas B12 0.958 3 120. 5 91.3 13 69.1 71 10.4
## 4 Texas T… B12 0.951 4 111. 41 85.4 1 66.3 223 6.57
## 5 Baylor B12 0.949 5 118. 8 91.3 14 67.6 149 8.91
## 6 Duke ACC 0.944 6 123. 1 96.0 53 67.4 161 7.19
## # … with 8 more variables: nc_elite_sos <int>, nc_fut_sos <dbl>,
## # nc_cur_sos <dbl>, ov_elite_sos <int>, ov_fut_sos <dbl>, ov_cur_sos <dbl>,
## # seed <dbl>, year <int>, and abbreviated variable names ¹barthag_rk,
## # ²adj_o_rk, ³adj_d_rk, ⁴adj_t_rk
Calling bart_factors will return four factor stats on a number of
splits. To filter by home games, set venue to ‘home.’
head(bart_factors(location='H'))## ── Team Factors ──────────────────────────────────────────────── toRvik 1.1.0 ──
## ℹ Data updated: 2022-09-09 08:20:52 EDT
## # A tibble: 6 × 22
## team conf rating rank adj_o adj_o…¹ adj_d adj_d…² tempo off_ppp off_efg
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Houston Amer 32.7 1 116. 15 83.0 1 66.1 117. 54.2
## 2 Gonzaga WCC 29.7 2 120. 5 89.9 18 72.7 123. 60.1
## 3 Baylor B12 28.8 3 116. 9 87.6 9 69.3 116. 55.0
## 4 Villanova BE 28.8 4 123. 2 94.0 50 63.2 122. 57.6
## 5 Purdue B10 28.4 5 124. 1 96.0 81 67.9 125. 58.2
## 6 Auburn SEC 27.6 6 115. 17 87.5 8 72.9 113. 53.1
## # … with 11 more variables: off_to <dbl>, off_or <dbl>, off_ftr <dbl>,
## # def_ppp <dbl>, def_efg <dbl>, def_to <dbl>, def_or <dbl>, def_ftr <dbl>,
## # wins <int>, losses <int>, games <int>, and abbreviated variable names
## # ¹adj_o_rank, ²adj_d_rank
Calling bart_team_box will return team box totals and per-game
averages by game type. To find how Duke performed during the month of
March:
bart_team_box(team='Duke', split='month') |>
dplyr::filter(month=='March')## ── Team Stats ────────────────────────────────────────────────── toRvik 1.1.0 ──
## ℹ Data updated: 2022-09-09 08:20:53 EDT
## # A tibble: 1 × 39
## team month min pos fgm fga fg_pct tpm tpa fg3_pct ftm fta
## <chr> <chr> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <int> <int>
## 1 Duke March 1800 599 270 518 0.521 63 176 0.358 118 149
## # … with 27 more variables: ft_pct <dbl>, oreb <int>, dreb <int>, reb <int>,
## # ast <int>, stl <int>, blk <int>, to <int>, pf <int>, pts <int>,
## # second_chance_pts <dbl>, second_chance_fgm <dbl>, second_chance_fga <dbl>,
## # second_change_fg_pct <dbl>, pts_in_paint <dbl>, pts_in_paint_fgm <dbl>,
## # pts_in_paint_fga <dbl>, pts_in_paint_fg_pct <dbl>, fast_brk_pts <dbl>,
## # fast_brk_fgm <dbl>, fast_brk_fga <dbl>, fast_brk_fg_pct <dbl>,
## # bench_pts <dbl>, pts_tov <dbl>, games <int>, wins <int>, losses <int>
Calling bart_player_season will return detailed season-long player
stats. To pull per-game averages for Duke players:
head(bart_player_season(team='Duke', stat='box'))## ── Player Season Stats ───────────────────────────────────────── toRvik 1.1.0 ──
## ℹ Data updated: 2022-09-09 08:20:53 EDT
## # A tibble: 6 × 21
## player pos exp hgt team conf g mpg ppg fg_pct oreb dreb
## <chr> <chr> <chr> <chr> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Paolo Banc… Wing… Fr 6-10 Duke ACC 39 33.0 17.2 0.513 1.77 6.05
## 2 Wendell Mo… Comb… Jr 6-5 Duke ACC 39 33.9 13.4 0.513 1.21 4.05
## 3 Trevor Kee… Comb… Fr 6-4 Duke ACC 36 30.2 11.5 0.422 0.833 2.61
## 4 Mark Willi… C So 7-0 Duke ACC 39 23.6 11.2 0.782 2.59 4.87
## 5 AJ Griffin Wing… Fr 6-6 Duke ACC 39 24.3 10.4 0.503 0.769 3.15
## 6 Jeremy Roa… Comb… So 6-1 Duke ACC 39 29 8.62 0.412 0.359 2.05
## # … with 9 more variables: rpg <dbl>, apg <dbl>, tov <dbl>, ast_to <dbl>,
## # spg <dbl>, bpg <dbl>, num <dbl>, year <int>, id <int>
Calling bart_player_game will return detailed game-by-game player
stats. To pull advance splits by game for Duke players:
head(bart_player_game(team='Duke', stat='advanced'))## ── Player Game Stats ─────────────────────────────────────────── toRvik 1.1.0 ──
## ℹ Data updated: 2022-09-09 08:20:54 EDT
## # A tibble: 6 × 25
## date year player exp team conf opp result min pts usg ortg
## <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2021-11-09 2022 Theo … Sr Duke ACC Kent… W 22 5 14.3 98.7
## 2 2021-11-12 2022 Theo … Sr Duke ACC Army W 15 0 11.9 18.1
## 3 2021-11-13 2022 Theo … Sr Duke ACC Camp… W 10 0 1.9 236
## 4 2021-11-16 2022 Theo … Sr Duke ACC Gard… W 15 4 19.1 100.
## 5 2021-11-19 2022 Theo … Sr Duke ACC Lafa… W 17 4 12.2 122.
## 6 2021-11-22 2022 Theo … Sr Duke ACC The … W 16 8 11.6 207.
## # … with 13 more variables: or_pct <dbl>, dr_pct <dbl>, ast_pct <dbl>,
## # to_pct <dbl>, stl_pct <dbl>, blk_pct <dbl>, bpm <dbl>, obpm <dbl>,
## # dbpm <dbl>, net <dbl>, poss <dbl>, id <dbl>, game_id <chr>
Calling transfer_portal will return transfer histories with matching
player IDs to join with other statistics. To find all players who
transferred to Duke:
head(transfer_portal(to='Duke'))## ── Transfer Portal ───────────────────────────────────────────── toRvik 1.1.0 ──
## ℹ Data updated: 2022-09-09 08:20:54 EDT
## # A tibble: 6 × 11
## id player from to exp year imm_e…¹ source from_d1 to_d1 sit
## <int> <chr> <chr> <chr> <chr> <int> <chr> <chr> <lgl> <lgl> <lgl>
## 1 65776 Kale Catchin… Harv… Duke Sr 2023 Yes Josep… TRUE FALSE NA
## 2 66209 Ryan Young Nort… Duke Jr 2023 Yes Jeff … TRUE FALSE NA
## 3 65826 Max Johns Prin… Duke Sr 2023 Yes <NA> TRUE FALSE NA
## 4 50593 Theo John Marq… Duke Sr 2022 Yes <NA> TRUE TRUE FALSE
## 5 51179 Bates Jones Davi… Duke Sr 2022 Yes <NA> TRUE TRUE FALSE
## 6 45926 Patrick Tape Colu… Duke Sr 2021 Yes Evan … TRUE TRUE TRUE
## # … with abbreviated variable name ¹imm_elig
Calling player_recruiting_rankings will return extensive recruit
histories with matching player IDs. To find all 5-star players who
played high school basketball in North Carolina:
head(player_recruiting_rankings(stars=5, state='NC'))## ── Recruiting Rankings ───────────────────────────────────────── toRvik 1.1.0 ──
## ℹ Data updated: 2022-09-09 08:20:55 EDT
## # A tibble: 6 × 31
## position player height weight team conf high_…¹ town state tfs_c…² tfs_c…³
## <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <int>
## 1 SF Patric… 6-6 215 Flor… ACC West C… Char… Nort… 99.1 5
## 2 PG Devon … 6-2 185 Kans… B12 Provid… Char… Nort… 99.3 5
## 3 SF Jaylen… 6-8 215 Wake… ACC Wesley… High… Nort… 99.3 5
## 4 SG Coby W… 6-5 185 Nort… ACC Greenf… Wils… Nort… 99.1 5
## 5 PF Harry … 6-10 240 Duke ACC Oak Hi… Wins… Nort… 100. 5
## 6 PG Dennis… 6-3 190 Nort… ACC Trinit… Faye… Nort… 99.7 5
## # … with 20 more variables: tfs_comp_national <dbl>, tfs_comp_position <dbl>,
## # tfs_comp_state <dbl>, tfs_rating <dbl>, tfs_star <int>, espn_rating <dbl>,
## # espn_grade <dbl>, espn_rank <dbl>, rivals_rating <dbl>, rivals_rank <dbl>,
## # avg_rank <dbl>, num_offers <int>, announce_date <chr>, tfs_cb <chr>,
## # tfs_cb_odds <dbl>, tfs_cb_alt <chr>, tfs_cb_alt_odds <dbl>, tfs_pid <int>,
## # year <int>, id <int>, and abbreviated variable names ¹high_school,
## # ²tfs_comp_rating, ³tfs_comp_star
Calling bart_game_predictions will returns expected points,
possessions, and win percentage for a given game on a given date. To
simulate North Carolina at Duke in mid-January:
bart_game_prediction('Duke', 'North Carolina', '20220113', location = 'H')## ── Duke vs. North Carolina Prediction ────────────────────────── toRvik 1.1.0 ──
## ℹ Data updated: 2022-09-09 08:20:55 EDT
## # A tibble: 2 × 8
## team date location tempo ppp pts win_per did_win
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <lgl>
## 1 Duke Jan. 13, 2022 Home 73.4 1.15 84.2 73.1 TRUE
## 2 North Carolina Jan. 13, 2022 Away 73.4 1.02 75.1 26.9 FALSE
Calling bart_tournament_prediction will simulate a single-elimination
tournament between a group of teams on a given date. To simulate the
2022 Final Four 25 times:
bart_tournament_prediction(teams = c('Duke', 'North Carolina', 'Kansas', 'Villanova'), '20220402', sims = 25, seed = 10)## ── Tournament Prediction: 25 Sims ────────────────────────────── toRvik 1.1.0 ──
## ℹ Data updated: 2022-09-09 08:20:56 EDT
## # A tibble: 4 × 4
## team wins finals champ
## <chr> <int> <int> <int>
## 1 Duke 31 21 10
## 2 Kansas 17 10 7
## 3 Villanova 21 15 6
## 4 North Carolina 6 4 2
For more information on the package and its functions, please see the
toRvik reference.
