Get Standings
Source:R/0_generics.R, R/espn_standings.R, R/flea_standings.R, and 2 more
ff_standings.RdThis 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.
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 franch…¹ leagu…² h2h_w…³ h2h_l…⁴ h2h_t…⁵ h2h_w…⁶ point…⁷ point…⁸
#> <int> <chr> <int> <int> <int> <int> <dbl> <dbl> <dbl>
#> 1 1 "The Ea… 7 3 9 0 0.25 1313. 1587.
#> 2 2 "Coom … 3 7 5 0 0.583 1561. 1608.
#> 3 3 "PAKI S… 6 4 8 0 0.333 1484. 1573.
#> 4 4 "I'm Al… 5 7 5 0 0.583 1448. 1495.
#> 5 5 "The Ju… 1 9 3 0 0.75 1620. 1472.
#> 6 6 "OBJ's … 4 8 4 0 0.667 1609. 1420.
#> 7 7 "Tony E… 9 5 7 0 0.417 1677. 1592.
#> 8 8 "Big Co… 8 6 6 0 0.5 1480. 1385.
#> 9 9 "RAFI C… 2 7 5 0 0.583 1562. 1521.
#> 10 10 "Austin… 10 4 8 0 0.333 1336. 1434.
#> # … with 3 more variables: allplay_wins <dbl>, allplay_losses <dbl>,
#> # allplay_winpct <dbl>, and abbreviated variable names ¹franchise_name,
#> # ²league_rank, ³h2h_wins, ⁴h2h_losses, ⁵h2h_ties, ⁶h2h_winpct, ⁷points_for,
#> # ⁸points_against
# }
# \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 franch…¹ h2h_w…² h2h_wlt allpl…³ point…⁴ point…⁵ avg_p…⁶ avg_p…⁷
#> <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0009 Team Li… 0.769 10-3-0 0.725 2063. 1862. 159. 143.
#> 2 0010 Team Yo… 0.692 9-4-0 0.65 1956. 1706. 150. 131.
#> 3 0014 Team Lu… 0.769 10-3-0 0.72 2086. 1882. 160. 145.
#> 4 0003 Team Do… 0.462 6-7-0 0.615 1937. 1957. 149 151.
#> 5 0006 Team Ki… 0.615 8-5-0 0.68 2138. 1864. 164. 143.
#> 6 0011 Team Di… 0.615 8-5-0 0.56 1927. 1786. 148. 137.
#> 7 0008 Team Bo… 0.538 7-6-0 0.462 1820. 1861. 140 143.
#> 8 0007 Team Ki… 0.538 7-6-0 0.545 1829. 1800. 141. 138.
#> 9 0002 Team Si… 0.462 6-7-0 0.328 1623. 1719. 125. 132.
#> 10 0004 Team Ic… 0.385 5-8-0 0.495 1852. 1823. 142. 140.
#> 11 0005 Team Dr… 0.385 5-8-0 0.368 1698. 1874. 131. 144.
#> 12 0013 Team Ne… 0.231 3-10-0 0.26 1553. 1828. 119. 141.
#> 13 0012 Team Me… 0.308 4-9-0 0.396 1708. 1865. 131. 144.
#> 14 0001 Team Pi… 0.231 3-10-0 0.151 1459. 1821. 112. 140.
#> # … with 1 more variable: faab_balance <dbl>, and abbreviated variable names
#> # ¹franchise_name, ²h2h_winpct, ³allplay_winpct, ⁴points_for,
#> # ⁵points_against, ⁶avg_points_for, ⁷avg_points_against
# }
# \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 franch…¹ h2h_w…² h2h_l…³ h2h_t…⁴ h2h_w…⁵ point…⁶ point…⁷ poten…⁸
#> <int> <chr> <int> <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 1 Fake Ne… 8 5 0 0.615 1271. 1278. 1632.
#> 2 2 KingGabe 1 12 0 0.0769 887. 1245. 1019.
#> 3 3 solarpo… 8 5 0 0.615 1413. 1327. 1767.
#> 4 4 The FAN… 8 5 0 0.615 1441. 1237. 1823.
#> 5 5 Barbari… 6 7 0 0.462 1281. 1121. 1585.
#> 6 6 sox05syd 8 5 0 0.615 1259. 1159. 1643.
#> 7 7 Flipade… 10 3 0 0.769 1274. 1124. 1554.
#> 8 8 Hocka F… 7 6 0 0.538 1186. 1281. 1558.
#> 9 9 ZPMille… 4 9 0 0.308 1243. 1346. 1463.
#> 10 10 JMLarkin 1 12 0 0.0769 871. 1289. 1106.
#> 11 11 Permian… 8 5 0 0.615 1415. 1215. 1763.
#> 12 12 jaydk 9 4 0 0.692 1396. 1314. 1763.
#> # … with 3 more variables: allplay_wins <dbl>, allplay_losses <dbl>,
#> # allplay_winpct <dbl>, and abbreviated variable names ¹franchise_name,
#> # ²h2h_wins, ³h2h_losses, ⁴h2h_ties, ⁵h2h_winpct, ⁶points_for,
#> # ⁷points_against, ⁸potential_points
# }