Returns game-by-game four factor statistics.
Usage
bart_game_factors(
year = current_season(),
team = NULL,
conf = NULL,
opp_conf = NULL,
type = NULL,
location = NULL,
result = NULL
)
Arguments
- year
Defaults to current season (YYYY).
- team
Filters to team.
- conf
Filters to conference.
- opp_conf
Filters to opponent's conference.
- type
Filters for game type ('nc', 'conf', 'conf_t', 'post')
- location
Filters for game location ('H', 'A', 'N')
- result
Filters for game result.
Details
For a brief explanation of each factor and its computation, please visit KenPom's blog. `avg_marg` and `opp_avg_marg` is the the average lead or deficit during a game.
Examples
bart_game_factors(year=2022)
#> ── Game Factors ──────────────────────────────────────────────── toRvik 1.1.0 ──
#> ℹ Data updated: 2022-09-09 13:47:27 EDT
#> # A tibble: 10,950 × 28
#> date type team conf opp opp_c…¹ loc result avg_marg opp_a…² adj_o
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 2022-04-… post Kans… B12 Nort… ACC N W, 72… -2.09 0.911 106
#> 2 2022-04-… post Nort… ACC Kans… B12 N L, 72… 2.09 0.958 108.
#> 3 2022-04-… post Duke ACC Nort… ACC N L, 81… 0.0621 0.911 123.
#> 4 2022-04-… post Nort… ACC Duke ACC N W, 81… -0.0621 0.944 123.
#> 5 2022-04-… post Kans… B12 Vill… BE N W, 81… 10.9 0.935 155.
#> 6 2022-04-… post Vill… BE Kans… B12 N L, 81… -10.9 0.958 125.
#> 7 2022-04-… post Coas… SB Fres… MWC H L, 85… -13.2 0.739 115
#> 8 2022-04-… post Fres… MWC Coas… SB A W, 85… 13.2 0.589 136.
#> 9 2022-03-… post Texa… SEC Xavi… BE N L, 73… -0.0246 0.821 114.
#> 10 2022-03-… post Xavi… BE Texa… SEC N W, 73… 0.0246 0.860 119.
#> # … with 10,940 more rows, 17 more variables: adj_d <dbl>, off_ppp <dbl>,
#> # off_efg <dbl>, off_to <dbl>, off_or <dbl>, off_ftr <dbl>, def_ppp <dbl>,
#> # def_efg <dbl>, def_to <dbl>, def_or <dbl>, def_ftr <dbl>, game_score <dbl>,
#> # season <int>, tempo <dbl>, game_id <chr>, coach <chr>, opp_coach <chr>, and
#> # abbreviated variable names ¹opp_conf, ²opp_avg_marg