Split conference-wide four factor data on a number of variables.
Usage
cbd_torvik_conf_factors(
year,
venue = "all",
game_type = "all",
quad = "all",
top = 0,
start = NULL,
end = NULL
)Arguments
- year
Chosen year
- venue
Game venue ('all', 'home', 'away', 'neutral', 'road'). Defaults to 'all'.
- game_type
Game type ('all', 'nc', 'conf', 'reg', 'post', 'ncaa'). Defaults to 'all'.
- quad
Quad. rank of game ('1', '2', '3', '4', 'all') and quads are cumulative (games <= quad selection). Defaults to 'all', which is all games (all games Q4 or "better"/"lower").
- top
Games against Top X opponents. Defaults to 0, which is "all" games.
- start
Game start date (YYYYMMDD format).
- end
Game end date (YYYYMMDD format).
Examples
try(cbd_torvik_conf_factors(2023, start = '20230101'))
#> # A tibble: 33 × 30
#> conf games wins losses adj_t adj_o adj_d barthag efg def_efg ftr
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 B12 217 112 105 68.1 114. 95.3 0.882 50.3 50 33.5
#> 2 B10 299 149 150 66 112. 97.8 0.833 50.6 50.6 28.6
#> 3 BE 220 113 107 67.6 112. 97.5 0.832 50.9 50.3 29.1
#> 4 SEC 299 148 151 67.1 111. 98.4 0.802 48.6 48.9 34.2
#> 5 P12 236 118 118 66.9 109. 97.3 0.795 49.2 48.8 29.1
#> 6 MWC 212 107 105 66.5 110. 101. 0.744 51.4 51.4 30.4
#> 7 ACC 294 147 147 67.3 111. 101. 0.734 51.5 51.4 28.5
#> 8 Amer 213 107 106 68.6 109. 100. 0.720 50.5 50.4 32.7
#> 9 WCC 168 85 83 68.4 112. 104. 0.702 53.9 53.8 33
#> 10 CUSA 231 123 108 66.7 108. 102. 0.652 51.2 50.8 30.6
#> # ℹ 23 more rows
#> # ℹ 19 more variables: def_ftr <dbl>, oreb_rate <dbl>, dreb_rate <dbl>,
#> # tov_rate <dbl>, def_tov_rate <dbl>, two_pt_pct <dbl>, three_pt_pct <dbl>,
#> # ft_pct <dbl>, def_two_pt_pct <dbl>, def_three_pt_pct <dbl>,
#> # def_ft_pct <dbl>, three_fg_rate <dbl>, def_three_fg_rate <dbl>,
#> # block_rate <dbl>, block_rate_allowed <dbl>, assist_rate <dbl>,
#> # def_assist_rate <dbl>, wab <dbl>, year <dbl>