The eurolig package provides a set of tools to obtain and analyze basketball generated data from the Euroleague.
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API wrapper around the official website of the Euroleague.
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Functions to obtain stats, play-by-play data and shot location data in a tidy format within R.
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Tools to analyze and visualize the data.
# install.packages("devtools")
devtools::install_github("solmos/eurolig")Let’s say we want to analyze play-by-play data from the Championship
game of the 2017-2018 season between Real Madrid and Fenerbache Dogus
Istanbul. We can obtain the data by entering the game code and the
season to the function extractPbp(). The game code for the game can be
found in the game’s
URL.
library(eurolig)
pbp <- extractPbp(game_code = 260, season = 2017)
pbp
#> # A tibble: 583 x 29
#> season game_code play_number team_code player_name play_type
#> <int> <dbl> <int> <chr> <chr> <chr>
#> 1 2017 260 2 <NA> <NA> BP
#> 2 2017 260 3 MAD AYON, GUST… TPOFF
#> 3 2017 260 4 ULK GUDURIC, M… TPOFF
#> 4 2017 260 5 ULK DUVERIOGLU… 2FGM
#> 5 2017 260 6 ULK GUDURIC, M… AST
#> 6 2017 260 7 ULK VESELY, JAN CPF
#> 7 2017 260 8 MAD REYES, FEL… RPF
#> 8 2017 260 9 MAD AYON, GUST… 2FGA
#> 9 2017 260 10 MAD AYON, GUST… ORB
#> 10 2017 260 11 ULK GUDURIC, M… CPF
#> # … with 573 more rows, and 23 more variables: time_remaining <chr>,
#> # quarter <dbl>, points_home <dbl>, points_away <dbl>, play_info <chr>,
#> # seconds <dbl>, home_team <chr>, away_team <chr>, home <lgl>,
#> # team_name <chr>, last_ft <lgl>, and1 <lgl>, home_player1 <chr>,
#> # home_player2 <chr>, home_player3 <chr>, home_player4 <chr>,
#> # home_player5 <chr>, away_player1 <chr>, away_player2 <chr>,
#> # away_player3 <chr>, away_player4 <chr>, away_player5 <chr>,
#> # lineups <chr>From these data we can extract information about the assists in that game from, say, Real Madrid (MAD):
assists <- getAssists(pbp, team = "MAD")
assists
#> # A tibble: 16 x 13
#> season game_code team_code passer shooter shot_type points
#> <int> <dbl> <chr> <chr> <chr> <chr> <dbl>
#> 1 2017 260 MAD FERNA… LLULL,… 3FG 3
#> 2 2017 260 MAD LLULL… RANDOL… 3FG 3
#> 3 2017 260 MAD FERNA… TAYLOR… 2FG 2
#> 4 2017 260 MAD LLULL… TAVARE… 2FG 2
#> 5 2017 260 MAD DONCI… CARROL… 2FG 2
#> 6 2017 260 MAD TAYLO… THOMPK… 2FG 2
#> 7 2017 260 MAD DONCI… TAVARE… 2FG 2
#> 8 2017 260 MAD DONCI… CARROL… 3FG 3
#> 9 2017 260 MAD AYON,… CAUSEU… 3FG 3
#> 10 2017 260 MAD CAUSE… REYES,… 2FG 2
#> 11 2017 260 MAD DONCI… CAUSEU… 3FG 3
#> 12 2017 260 MAD CAUSE… REYES,… 2FG 2
#> 13 2017 260 MAD FERNA… DONCIC… 3FG 3
#> 14 2017 260 MAD TAVAR… LLULL,… <NA> 0
#> 15 2017 260 MAD TAVAR… CARROL… 3FG 3
#> 16 2017 260 MAD THOMP… TAVARE… 2FG 2
#> # … with 6 more variables: time_remaining <chr>, quarter <dbl>,
#> # seconds <int>, foul <lgl>, and1 <lgl>, ftm <dbl>Shot location data can also be retrieved and visualized:
shots <- extractShots(260, 2017)
plotShotchart(shots)
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