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eurolig

lifecycle

The eurolig package provides a set of tools to obtain and analyze basketball generated data from the Euroleague.

Overview

  • API wrapper around the official website of the Euroleague.

  • Functions to obtain stats, play-by-play data and shot location data in a tidy format within R.

  • Tools to analyze and visualize the data.

Installation

# install.packages("devtools")
devtools::install_github("solmos/eurolig")

Example

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)