This function returns a tidy dataframe with one row for every team for every weekly matchup

ff_schedule(conn, ...)

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

# S3 method for flea_conn
ff_schedule(conn, week = 1:17, ...)

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

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

Arguments

conn

a conn object created by ff_connect()

...

for other platforms

week

a numeric or numeric vector specifying which weeks to pull

Value

A tidy dataframe with one row per game per franchise per week

Methods (by class)

  • espn_conn: ESPN: returns schedule data, one row for every franchise for every week. Completed games have result data.

  • flea_conn: Flea: returns schedule data, one row for every franchise for every week. Completed games have result data.

  • mfl_conn: MFL: returns schedule data, one row for every franchise for every week. Completed games have result data.

  • sleeper_conn: Sleeper: returns all schedule data

Examples

# \donttest{ try({ # try only shown here because sometimes CRAN checks are weird espn_conn <- espn_connect(season = 2020, league_id = 899513) ff_schedule(espn_conn) }) # end try
#> # A tibble: 140 × 6 #> week franchise_id franchise_score result opponent_id opponent_score #> <int> <int> <dbl> <chr> <int> <dbl> #> 1 1 1 102. L 4 130. #> 2 1 2 156. W 3 135. #> 3 1 3 135. L 2 156. #> 4 1 4 130. W 1 102. #> 5 1 5 133. W 7 131. #> 6 1 6 119. L 9 124. #> 7 1 7 131. L 5 133. #> 8 1 8 120. L 10 122. #> 9 1 9 124. W 6 119. #> 10 1 10 122. W 8 120. #> # … with 130 more rows
# } # \donttest{ try({ # try only shown here because sometimes CRAN checks are weird conn <- fleaflicker_connect(season = 2019, league_id = 206154) ff_schedule(conn, week = 2:4) }) # end try
#> # A tibble: 48 × 14 #> week franchise_id franchise_name franchise_score result opponent_id #> <int> <int> <chr> <dbl> <chr> <int> #> 1 2 1373991 Top City Terrors 226. WIN 1373393 #> 2 2 1373480 Goldenrod City Nightmares 161. LOSE 1373475 #> 3 2 1371776 Winter Hill Black Shamrocks 178. WIN 1373501 #> 4 2 1373993 Boomtown Sly Foxes 167. WIN 1373970 #> 5 2 1373883 Manitoba Marmots 201. WIN 1373973 #> 6 2 1373535 Winterthur Angry Ducks 147. LOSE 1373988 #> 7 2 1374252 Central City Crusaders 160. LOSE 1374255 #> 8 2 1374271 Clutch City Ballers 174. WIN 1374315 #> 9 2 1373393 Philadelphia Fire 138. LOSE 1373991 #> 10 2 1373475 Winterfell Dire Wolves 300. WIN 1373480 #> # … with 38 more rows, and 8 more variables: opponent_name <chr>, #> # opponent_score <dbl>, game_id <chr>, isFinalScore <lgl>, #> # isDivisional <lgl>, isPlayoffs <lgl>, isThirdPlaceGame <lgl>, #> # isChampionshipGame <lgl>
# } # \donttest{ try({ # try only shown here because sometimes CRAN checks are weird ssb_conn <- ff_connect(platform = "mfl", league_id = 54040, season = 2020) ff_schedule(ssb_conn) }) # end try
#> # A tibble: 234 × 6 #> week franchise_id franchise_score result opponent_id opponent_score #> <dbl> <chr> <dbl> <chr> <chr> <dbl> #> 1 1 0001 123. W 0002 103. #> 2 1 0002 103. L 0001 123. #> 3 1 0003 128. L 0004 174. #> 4 1 0004 174. W 0003 128. #> 5 1 0005 144. W 0011 130. #> 6 1 0006 173. W 0013 125. #> 7 1 0007 145. W 0010 127. #> 8 1 0008 185. W 0009 176. #> 9 1 0009 176. L 0008 185. #> 10 1 0010 127. L 0007 145. #> # … with 224 more rows
# } # \donttest{ try({ # try only shown here because sometimes CRAN checks are weird jml_conn <- ff_connect(platform = "sleeper", league_id = "522458773317046272", season = 2020) ff_schedule(jml_conn) }) # end try
#> # A tibble: 184 × 6 #> week franchise_id franchise_score opponent_id opponent_score result #> <int> <int> <dbl> <int> <dbl> <chr> #> 1 1 1 97.8 12 160. L #> 2 1 2 65.9 8 70.2 L #> 3 1 3 103. 10 71 W #> 4 1 4 133. 7 106. W #> 5 1 5 82.4 6 99.3 L #> 6 1 6 99.3 5 82.4 W #> 7 1 7 106. 4 133. L #> 8 1 8 70.2 2 65.9 W #> 9 1 9 78.3 11 147 L #> 10 1 10 71 3 103. L #> # … with 174 more rows
# }