The goal of {fantasypl} is to retrieve data from the Fantasy Premier
League API endpoints and provide it in a convenient tabular format
(tibble).
You can install the development version of {fantasypl} from
GitHub with:
# install.packages("pak")
pak::pak("danieloc1989/fantasypl")There is no intention of releasing this package on CRAN.
library(fantasypl)The package provides functions to retrieve various types of FPL data, including player information, team details, gameweek information, and more.
You can obtain general player information for all 714 registered players in the Premier League:
# General details of all players
fpl_player_info()
#> # A tibble: 714 × 9
#> player_season_id player_name first_name second_name team squad_number
#> <int> <chr> <chr> <chr> <chr> <lgl>
#> 1 1 Balogun Folarin Balogun ARS NA
#> 2 2 Cédric Cédric Alves Soares ARS NA
#> 3 3 M.Elneny Mohamed Elneny ARS NA
#> 4 4 Fábio Vieira Fábio Ferreira Vieira ARS NA
#> 5 5 Gabriel Gabriel dos Santos Magal… ARS NA
#> 6 6 Havertz Kai Havertz ARS NA
#> 7 8 G.Jesus Gabriel Fernando de Jesus ARS NA
#> 8 9 Jorginho Jorge Luiz Frello Filho ARS NA
#> 9 10 Kiwior Jakub Kiwior ARS NA
#> 10 11 Marquinhos Marcus Oliveira Alencar ARS NA
#> # ℹ 704 more rows
#> # ℹ 3 more variables: player_position <chr>, player_fpl_code <int>,
#> # special <lgl>This function returns basic information such as names, teams, positions, and player IDs.
For more all player season statistics, you can use:
# Season statistics of all players
fpl_player_stats()
#> # A tibble: 714 × 53
#> player_season_id player_name selected_pct selected_rank selected_rank_type
#> <int> <chr> <dbl> <int> <int>
#> 1 1 Balogun 0.004 242 41
#> 2 2 Cédric 0.003 258 101
#> 3 3 M.Elneny 0.001 448 146
#> 4 4 Fábio Vieira 0.001 379 119
#> 5 5 Gabriel 0.143 26 7
#> 6 6 Havertz 0.021 117 35
#> 7 8 G.Jesus 0.025 109 20
#> 8 9 Jorginho 0.002 325 97
#> 9 10 Kiwior 0.001 423 165
#> 10 11 Marquinhos 0.001 469 160
#> # ℹ 704 more rows
#> # ℹ 48 more variables: current_price <dbl>, now_cost_rank <int>,
#> # now_cost_rank_type <int>, cost_change_start <dbl>,
#> # cost_change_start_fall <dbl>, cost_change_event <dbl>,
#> # cost_change_event_fall <dbl>, total_transfers_in <int>,
#> # gw_transfers_in <int>, transfers_out <int>, transfers_out_event <int>,
#> # minutes <int>, starts <int>, starts_per_90 <dbl>, total_points <int>, …If more detailed level of information is required
# Find player's ID
haaland <- fpl_find_player_id("haaland")
#> ✔ Haaland (Erling Haaland) for MCI matched your search.
# Retrieve Haaland season statistics
fpl_player_fixture_stats(haaland)
#> Haaland's per game season statistics
#> # A tibble: 4 × 31
#> gameweek game_id opponent total_points starts minutes goals_scored assists
#> <int> <int> <chr> <int> <int> <int> <int> <int>
#> 1 1 1 BUR 13 1 79 2 0
#> 2 2 16 NEW 2 1 90 0 0
#> 3 3 30 SHU 4 1 90 1 0
#> 4 4 39 FUL 20 1 90 3 1
#> # ℹ 23 more variables: clean_sheets <int>, goals_conceded <int>,
#> # own_goals <int>, penalties_saved <int>, penalties_missed <int>,
#> # yellow_cards <int>, red_cards <int>, saves <int>, bonus <int>, bps <int>,
#> # influence <chr>, creativity <chr>, threat <chr>, ict_index <chr>,
#> # xg_scored <chr>, xa <chr>, xg_involvement <chr>, xg_conceded <chr>,
#> # price <dbl>, selected <int>, transfers_in <int>, transfers_out <int>,
#> # transfers_balance <int>To get information about all Premier League teams, you can use:
# PL team info
fpl_team_info()
#> # A tibble: 20 × 18
#> team_fpl_code team_name team_abb season_id form played win draw loss
#> <int> <chr> <chr> <int> <lgl> <int> <int> <int> <int>
#> 1 3 Arsenal ARS 1 NA 0 0 0 0
#> 2 7 Aston Villa AVL 2 NA 0 0 0 0
#> 3 91 Bournemouth BOU 3 NA 0 0 0 0
#> 4 94 Brentford BRE 4 NA 0 0 0 0
#> 5 36 Brighton BHA 5 NA 0 0 0 0
#> 6 90 Burnley BUR 6 NA 0 0 0 0
#> 7 8 Chelsea CHE 7 NA 0 0 0 0
#> 8 31 Crystal Pala… CRY 8 NA 0 0 0 0
#> 9 11 Everton EVE 9 NA 0 0 0 0
#> 10 54 Fulham FUL 10 NA 0 0 0 0
#> 11 14 Liverpool LIV 11 NA 0 0 0 0
#> 12 102 Luton LUT 12 NA 0 0 0 0
#> 13 43 Man City MCI 13 NA 0 0 0 0
#> 14 1 Man Utd MUN 14 NA 0 0 0 0
#> 15 4 Newcastle NEW 15 NA 0 0 0 0
#> 16 17 Nott'm Forest NFO 16 NA 0 0 0 0
#> 17 49 Sheffield Utd SHU 17 NA 0 0 0 0
#> 18 6 Spurs TOT 18 NA 0 0 0 0
#> 19 21 West Ham WHU 19 NA 0 0 0 0
#> 20 39 Wolves WOL 20 NA 0 0 0 0
#> # ℹ 9 more variables: points <int>, league_position <int>, strength <int>,
#> # strength_overall_home <int>, strength_defence_home <int>,
#> # strength_attack_home <int>, strength_overall_away <int>,
#> # strength_defence_away <int>, strength_attack_away <int>Team information has not been updated as of yet on the system. A warning will be introduced to this function if this persists.
To obtain information about gameweeks and fixtures, you can use the following functions:
# Information on gameweeks
fpl_gameweek_info()
#> # A tibble: 38 × 17
#> gameweek gw_deadline gw_finished data_checked gw_current gw_previous
#> <chr> <dttm> <lgl> <lgl> <lgl> <lgl>
#> 1 Gameweek… 2023-08-11 17:30:00 TRUE TRUE FALSE FALSE
#> 2 Gameweek… 2023-08-18 17:15:00 TRUE TRUE FALSE FALSE
#> 3 Gameweek… 2023-08-25 17:30:00 TRUE TRUE FALSE TRUE
#> 4 Gameweek… 2023-09-01 17:30:00 TRUE TRUE TRUE FALSE
#> 5 Gameweek… 2023-09-16 10:00:00 FALSE FALSE FALSE FALSE
#> 6 Gameweek… 2023-09-23 12:30:00 FALSE FALSE FALSE FALSE
#> 7 Gameweek… 2023-09-30 10:00:00 FALSE FALSE FALSE FALSE
#> 8 Gameweek… 2023-10-07 10:00:00 FALSE FALSE FALSE FALSE
#> 9 Gameweek… 2023-10-21 10:00:00 FALSE FALSE FALSE FALSE
#> 10 Gameweek… 2023-10-27 17:30:00 FALSE FALSE FALSE FALSE
#> # ℹ 28 more rows
#> # ℹ 11 more variables: gw_next <lgl>, gw_highest_score <int>,
#> # gw_average_score <int>, transfers_made <int>, chip_plays <list>,
#> # player_most_selected <chr>, player_most_captained <chr>,
#> # player_most_vice_captained <chr>, player_most_transferred_in <chr>,
#> # top_player <chr>, top_player_points <int>
# Each PL fixture
fpl_fixtures_info()
#> # A tibble: 380 × 12
#> gw_id game_id team_h team_h_score team_a team_a_score stats
#> <int> <int> <chr> <int> <chr> <int> <list>
#> 1 1 1 BUR 0 MCI 3 <df [10 × 3]>
#> 2 1 2 ARS 2 NFO 1 <df [10 × 3]>
#> 3 1 3 BOU 1 WHU 1 <df [10 × 3]>
#> 4 1 4 BHA 4 LUT 1 <df [10 × 3]>
#> 5 1 5 EVE 0 FUL 1 <df [10 × 3]>
#> 6 1 6 NEW 5 AVL 1 <df [10 × 3]>
#> 7 1 7 SHU 0 CRY 1 <df [10 × 3]>
#> 8 1 8 BRE 2 TOT 2 <df [10 × 3]>
#> 9 1 9 CHE 1 LIV 1 <df [10 × 3]>
#> 10 1 10 MUN 1 WOL 0 <df [10 × 3]>
#> # ℹ 370 more rows
#> # ℹ 5 more variables: kickoff_time <dttm>, kickoff_time_provisional <lgl>,
#> # started <lgl>, finished <lgl>, fpl_game_code <int>You can retrieve the Dream Team for the current gameweek like this:
# The Dream Team for the current gameweek
fpl_dreamteam()
#> ℹ The dream team for Gameweek 4 is:
#> # A tibble: 11 × 4
#> player points team position
#> <chr> <int> <chr> <chr>
#> 1 A.Becker 8 LIV GKP
#> 2 Alexander-Arnold 12 LIV DEF
#> 3 Zouma 11 WHU DEF
#> 4 Boly 9 NFO DEF
#> 5 Son 20 TOT MID
#> 6 Gilmour 11 BHA MID
#> 7 Rice 10 ARS MID
#> 8 Solomon 10 TOT MID
#> 9 Haaland 20 MCI FWD
#> 10 Ferguson 17 BHA FWD
#> 11 J.Alvarez 14 MCI FWDThe API may be unavailable for a brief period after a gameweek deadline as the system updates manager selections.
If you find issues or have suggestions for improvements, please open an issue on the GitHub repository. I welcome contributions and collaboration.
This package is released under the MIT License.
Happy fantasy gaming with {fantasypl}!