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") All toRvik functions fall into one of six
categories:
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_rkCalling 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_rankCalling 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_eligCalling 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_starCalling 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 FALSECalling 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 2For more information on the package and its functions, please see the
toRvik
reference.
