Get Standings — ff_standings • ffscrapr

This function returns a tidy dataframe of season-long fantasy team stats, including H2H wins as well as points, potential points, and all-play.

Usage

ff_standings(conn, ...)

# S3 method for espn_conn
ff_standings(conn, ...)

# S3 method for flea_conn
ff_standings(conn, include_allplay = TRUE, include_potentialpoints = TRUE, ...)

# S3 method for mfl_conn
ff_standings(conn, ...)

# S3 method for sleeper_conn
ff_standings(conn, ...)

Arguments

conn: a conn object created by ff_connect()
...: arguments passed to other methods (currently none)
include_allplay: TRUE/FALSE - return all-play win pct calculation? defaults to TRUE
include_potentialpoints: TRUE/FALSE - return potential points calculation? defaults to TRUE.

Value

A tidy dataframe of standings data

Methods (by class)

ff_standings(espn_conn): ESPN: returns standings and points data.
ff_standings(flea_conn): Fleaflicker: returns H2H/points/all-play/best-ball data in a table.
ff_standings(mfl_conn): MFL: returns H2H/points/all-play/best-ball data in a table.
ff_standings(sleeper_conn): Sleeper: returns all standings and points data and manually calculates allplay results.

Examples

# \donttest{
try({ # try only shown here because sometimes CRAN checks are weird
  espn_conn <- espn_connect(season = 2020, league_id = 899513)
  ff_standings(espn_conn)
}) # end try
#> # A tibble: 10 × 12
#>    franchise_id franchise_name          league_rank h2h_wins h2h_losses h2h_ties
#>           <int> <chr>                         <int>    <int>      <int>    <int>
#>  1            1 "The Early GGod"                  7        3          9        0
#>  2            2 "Coom  Dumpster"                  3        7          5        0
#>  3            3 "PAKI STANS"                      6        4          8        0
#>  4            4 "I'm Also Sad "                   5        7          5        0
#>  5            5 "The Juggernaut"                  1        9          3        0
#>  6            6 "OBJ's Personal Porta …           4        8          4        0
#>  7            7 "Tony El Tigre"                   9        5          7        0
#>  8            8 "Big Coomers"                     8        6          6        0
#>  9            9 "RAFI CUNADO"                     2        7          5        0
#> 10           10 "Austin 🐐Drew Lock🐐"           10        4          8        0
#> # ℹ 6 more variables: h2h_winpct <dbl>, points_for <dbl>, points_against <dbl>,
#> #   allplay_wins <dbl>, allplay_losses <dbl>, allplay_winpct <dbl>
# }

# \donttest{
try({ # try only shown here because sometimes CRAN checks are weird
  conn <- fleaflicker_connect(season = 2020, league_id = 206154)
  x <- ff_standings(conn)
}) # end try
# }

# \donttest{
try({ # try only shown here because sometimes CRAN checks are weird
  ssb_conn <- ff_connect(platform = "mfl", league_id = 54040, season = 2020)
  ff_standings(ssb_conn)
}) # end try
#> Warning: There was 1 warning in `mutate()`.
#> ℹ In argument: `fname = .Primitive("as.double")(fname)`.
#> Caused by warning:
#> ! NAs introduced by coercion
#> # A tibble: 14 × 10
#>    franchise_id franchise_name     h2h_winpct h2h_wlt allplay_winpct points_for
#>    <chr>        <chr>                   <dbl> <chr>            <dbl>      <dbl>
#>  1 0009         Team Link               0.769 10-3-0           0.725      2063.
#>  2 0010         Team Yoshi              0.692 9-4-0            0.65       1956.
#>  3 0014         Team Luigi              0.769 10-3-0           0.72       2086.
#>  4 0003         Team Donkey Kong        0.462 6-7-0            0.615      1937.
#>  5 0006         Team King Dedede        0.615 8-5-0            0.68       2138.
#>  6 0011         Team Diddy Kong         0.615 8-5-0            0.56       1927.
#>  7 0008         Team Bowser             0.538 7-6-0            0.462      1820.
#>  8 0007         Team Kirby              0.538 7-6-0            0.545      1829.
#>  9 0002         Team Simon Belmont      0.462 6-7-0            0.328      1623.
#> 10 0004         Team Ice Climbers       0.385 5-8-0            0.495      1852.
#> 11 0005         Team Dr. Mario          0.385 5-8-0            0.368      1698.
#> 12 0013         Team Ness               0.231 3-10-0           0.26       1553.
#> 13 0012         Team Mewtwo             0.308 4-9-0            0.396      1708.
#> 14 0001         Team Pikachu            0.231 3-10-0           0.151      1459.
#> # ℹ 4 more variables: points_against <dbl>, avg_points_for <dbl>,
#> #   avg_points_against <dbl>, faab_balance <dbl>
# }

# \donttest{
try({ # try only shown here because sometimes CRAN checks are weird
  jml_conn <- ff_connect(platform = "sleeper", league_id = "522458773317046272", season = 2020)
  ff_standings(jml_conn)
}) # end try
#> # A tibble: 12 × 12
#>    franchise_id franchise_name    h2h_wins h2h_losses h2h_ties h2h_winpct
#>           <int> <chr>                <int>      <int>    <int>      <dbl>
#>  1            1 Fake News                8          5        0     0.615 
#>  2            2 KingGabe                 1         12        0     0.0769
#>  3            3 solarpool                8          5        0     0.615 
#>  4            4 The FANTom Menace        8          5        0     0.615 
#>  5            5 Barbarians               6          7        0     0.462 
#>  6            6 sox05syd                 8          5        0     0.615 
#>  7            7 Flipadelphia05          10          3        0     0.769 
#>  8            8 Hocka Flocka             7          6        0     0.538 
#>  9            9 ZPMiller97               4          9        0     0.308 
#> 10           10 JMLarkin                 1         12        0     0.0769
#> 11           11 Permian Panthers         8          5        0     0.615 
#> 12           12 jaydk                    9          4        0     0.692 
#> # ℹ 6 more variables: points_for <dbl>, points_against <dbl>,
#> #   potential_points <dbl>, allplay_wins <dbl>, allplay_losses <dbl>,
#> #   allplay_winpct <dbl>
# }