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itscalledsoccer is a wrapper around the same API that
powers the American
Soccer Analysis app. It enables R users to programmatically retrieve
advanced analytics for their favorite players and teams, with coverage
of the following competitions:
# Install release version from CRAN
install.packages("itscalledsoccer")
# Install development version from GitHub
devtools::install_github("American-Soccer-Analysis/itscalledsoccer-r")Initialize the main class with the new method.
asa_client <- AmericanSoccerAnalysis$new()If you’re in an environment where a proxy server is required, or if
you need to need to alter any other CURL options, you can
pass any number of httr
configs when initializing the class. Use these at your own
discretion.
asa_client <- AmericanSoccerAnalysis$new(
httr::config(ssl_verifypeer = 0L),
httr::use_proxy("64.251.21.73", 8080)
)Any of the get_* methods can be used to retrieve the
same data made available in the American Soccer Analysis
app. Partial matches or abbreviations are accepted for any player or
team names. For most methods, arguments must be named.
Additionally, dataframes of complete players, teams, games, and more are
also available for joining additional information. A variety of examples
are below, and full documentation can be found here.
# Initialize the main class
asa_client <- AmericanSoccerAnalysis$new()# Access dataframes of games, players, teams, and more from the ASA client object created above
all_games <- asa_client$games
all_teams <- asa_client$teams
all_players <- asa_client$players
# see league options
asa_client$LEAGUES# Get all players named "Dax"
dax_players <- asa_client$get_players(names = "Dax")# Get cumulative player shot information (i.e., xgoals) from the NWSL over three seasons
# see other parameters in package documentation
asa_xgoals <- asa_client$get_player_xgoals(
leagues = "nwsl",
season_name = c(2021:2023),
split_by_seasons = FALSE)
head(shots_df)# Get season-by-season shot information (i.e., xgoals) for all players named "Dax" in MLS
asa_xgoals <- asa_client$get_player_xgoals(
leagues = "mls",
player_names = "Dax",
split_by_seasons = TRUE
)
# Join player names
library(dplyr)
asa_xgoals <- asa_xgoals %>%
left_join(all_players,
by = "player_id")# Get player passing information (i.e., xpass) from MLS in 2023
asa_xpass <- asa_client$get_player_xpass(
leagues = "mls",
season_name = 2023)
# Get cumulative/career xPass data for all USL League One teams
asa_xpass <- asa_client$get_team_xpass(
leagues = "usl1"
)# Get team g+ information (i.e., goals added) from MLS
library(tidyr)
# tall version
asa_goals_added <- asa_client$get_team_goals_added(
leagues = "mls",
season_name = 2023) %>%
tidyr::unnest(data)
# wide version
asa_goals_added <- asa_client$get_team_goals_added(
leagues = "mls",
season_name = 2023) %>%
tidyr::unnest(data) %>%
tidyr::pivot_wider( id_cols = team_id,
names_from = action_type,
values_from = c(num_actions_for:goals_added_against))
# Get game-by-game goals added (g+) data for all goalkeepers named "Matt Turner"
asa_goals_added <- asa_client$get_goalkeeper_goals_added(
leagues = c("mls", "uslc"),
player_names = "Matt Turner",
split_by_game = TRUE
)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.