Introduction to eurolig

Datasets

Several datasets are included in the package. You can see all the available datasets by calling:

data(package = "eurolig")

Sample datasets of play-by-play and shot location data are stored in samplepbp and sampleshots, respectively.

A particularly useful dataset in the package is gameresults. It contains all game results in the Euroleague from the 2001/2002 season to the 2018/2019 season. As you will see in the next section, this dataset can be useful to find the games you want to get data from.

Finally, if we need to find the name or identifying code of teams, the teaminfo dataset can be helpful.

extract functions

Functions that retrieve data from Euroleague’s website API start with the verb extract. You will need to be online for these functions to work.

The main functions to get data are:

• extractPbp() for play-by-play data.

• extractShots() for shot location data.

These functions can only retrieve data from a single game. That means that if you want to get data for several games you will need to iterate the function over the games of interest. Note, however, that Euroleague’s robot.txt asks for a 15 (!) seconds delay between requests. Take this into consideration when requesting data for a lot of games.

Games are uniquely identified by a combination of season and game code. In order to indicate the extract functions what game we want to get data from, we need to pass the corresponding game code and season as arguments.

Let’s find the highest scoring games from the 2018/2019 season in the gameresults dataset:

games <- gameresults %>% 
  filter(season == 2018) %>% 
  mutate(total_points = points_home + points_away) %>% 
  arrange(desc(total_points))

head(games)

## # A tibble: 6 x 14
##   season phase round_name team_home points_home team_away points_away
##    <int> <chr> <chr>      <chr>           <int> <chr>           <int>
## 1   2018 RS    Round 21   Herbalif…         104 AX Arman…         106
## 2   2018 RS    Round 16   AX Arman…         111 Buducnos…          94
## 3   2018 RS    Round 1    Real Mad…         109 Darussaf…          93
## 4   2018 RS    Round 4    Herbalif…          91 CSKA Mos…         106
## 5   2018 RS    Round 8    CSKA Mos…          99 Zalgiris…          97
## 6   2018 RS    Round 25   CSKA Mos…         101 AX Arman…          95
## # … with 7 more variables: game_code <int>, date <chr>, round_code <int>,
## #   game_url <chr>, team_code_home <chr>, team_code_away <chr>,
## #   total_points <int>

We can see that last season’s highest scoring game was the Herbalife Gran Canaria vs. AX Armani Exchange Olimpia Milan with 210 total points scored between the two teams. The identifying game code and season for this game are

games$season[1]

## [1] 2018

games$game_code[1]

## [1] 168

We can get play-by-play and shot location data for this game by passing these values as arguments to extractShots() and extractPbp():

game_shots <- extractShots(game_code = 168, season = 2018)
game_pbp <- extractPbp(168, 2018)

If you want to find games for the current season (not included in the gameresults dataset), you have two options: either look up the game code in the game’s url or use extractResults().

Plus-Minus

Plus-minus (+/-) measures the difference in team points scored and team points allowed while a player or a set of players of the same team are on the court. getPlusMinus() parses a play-by-play data frame with one or more games and returns the indicated player/s plus-minus statistic in each game.

Although widely used nowadays, it is important to note that raw plus-minus is very unstable and totally context dependent. It is, however, the building block for other more advanced stats such as RAPM or RPM.

Let’s check what Sergio Rodriguez (aka El Chacho) plus-minus was in the two Final Four games that he played in the 2018/2019 season:

chacho_pm <- getPlusMinus(pbp = samplepbp, players = "RODRIGUEZ, SERGIO")
# Select only a few columns so that data frame fits in the document
chacho_pm %>% 
  select(game_code, team_code_opp, poss, poss_opp, plus_minus)

## # A tibble: 2 x 5
##   game_code team_code_opp  poss poss_opp plus_minus
##       <int> <chr>         <dbl>    <dbl>      <dbl>
## 1       258 MAD              44       42          8
## 2       260 IST              24       25        -10

Note that you can find the plus-minus statistic for combinations of players by entering a character vector with the player names in the players argument.

On/Off Statistics

On/Off statistics for a player or a set of players measure team statistics when the player or players where on the court and when they were on the bench.

You can use getOnOffStats() to find on/off statistics. For instance, I can find out how Real Madrid did when Rudy and Ayón were together on the court versus when both were on the bench in the two games played in the 2018/2019 Final Four.

getOnOffStats(pbp = samplepbp, players = c("FERNANDEZ, RUDY", "AYON, GUSTAVO"))

## # A tibble: 8 x 28
##   season game_code players on    type  team_code home   fg2a  fg2m fg2_pct
##    <int>     <int> <chr>   <lgl> <chr> <chr>     <lgl> <int> <int>   <dbl>
## 1   2018       258 FERNAN… TRUE  defe… CSK       TRUE      5     1   0.2  
## 2   2018       258 FERNAN… TRUE  offe… MAD       FALSE     3     2   0.667
## 3   2018       259 FERNAN… TRUE  offe… MAD       FALSE    20    14   0.7  
## 4   2018       259 FERNAN… TRUE  defe… ULK       TRUE     15     7   0.467
## 5   2018       258 FERNAN… FALSE defe… CSK       TRUE     33    16   0.485
## 6   2018       258 FERNAN… FALSE offe… MAD       FALSE    43    22   0.512
## 7   2018       259 FERNAN… FALSE offe… MAD       FALSE    15     9   0.6  
## 8   2018       259 FERNAN… FALSE defe… ULK       TRUE     17     9   0.529
## # … with 18 more variables: fg3a <int>, fg3m <int>, fg3_pct <dbl>,
## #   fga <int>, fgm <int>, fg_pct <dbl>, fta <int>, ftm <int>,
## #   ft_pct <dbl>, orb <int>, drb <int>, tov <int>, ast <int>, stl <int>,
## #   cpf <int>, blk <int>, pts <dbl>, poss <dbl>

Note that getOnOffStats() returns 4 rows per game corresponding to:

• Team statistics when players were on the court together.

• Opposing team statistics when players were on the court together.

• Team statistics when players were on the bench together.

• Opposing team statistics when players were on the bench together.

Assists patterns

Play-by-play data allows us to find out who assists who on assisted baskets. The function getAssists() returns a data frame with the passer and the shooter (plus more contextual information) for all assisted baskets in the input play-by-play data.

I am going to use getAssists() and some data wrangling to find out the most common assists in CSKA Moscow during the two games of the 2018/2019 Final Four:

assists_csk <- getAssists(pbp = samplepbp, team = "CSK")
assists_csk %>% 
  count(passer, shooter) %>% 
  arrange(desc(n))

## # A tibble: 25 x 3
##    passer            shooter              n
##    <chr>             <chr>            <int>
##  1 CLYBURN, WILL     HIGGINS, CORY        2
##  2 DE COLO, NANDO    HACKETT, DANIEL      2
##  3 HACKETT, DANIEL   HUNTER, OTHELLO      2
##  4 RODRIGUEZ, SERGIO HINES, KYLE          2
##  5 CLYBURN, WILL     DE COLO, NANDO       1
##  6 DE COLO, NANDO    CLYBURN, WILL        1
##  7 DE COLO, NANDO    HUNTER, OTHELLO      1
##  8 DE COLO, NANDO    KURBANOV, NIKITA     1
##  9 DE COLO, NANDO    PETERS, ALEC         1
## 10 HACKETT, DANIEL   CLYBURN, WILL        1
## # … with 15 more rows