Analysis of Basketball Data

Description

Collection of tools to work with European basketball data. Functions available are related to friendly web scraping, data management and visualization. Data were obtained from <https://www.euroleaguebasketball.net/euroleague/>, <https://www.euroleaguebasketball.net/eurocup/> and <https://www.acb.com/>, following the instructions of their respectives robots.txt files, when available. Box score data are available for the three leagues. Play-by-play data are also available for the Spanish league. Methods for analysis include a population pyramid, 2D plots, circular plots of players' percentiles, plots of players' monthly/yearly stats, team heatmaps, team shooting plots, team four factors plots, cross-tables with the results of regular season games, maps of nationalities, combinations of lineups, possessions-related variables, timeouts, performance by periods, personal fouls and offensive rebounds. Please see Vinue (2020) <doi:10.1089/big.2018.0124> and Vinue (2024) <doi:10.1089/big.2023.0177>.

Details

acb_games_1718: ACB games 2017-2018.
acb_games_2223_coach: ACB coaches in the 2022-2023 season.
acb_games_2223_info: ACB games 2022-2023, days and codes.
acb_players_1718: ACB players 2017-2018.
acb_shields: Shields of the ACB teams.
acb_vbc_cz_pbp_2223: ACB play-by-play data, 2022-2023, Valencia Basket-Casademont Zaragoza.
acb_vbc_cz_sl_2223: ACB starting lineups, 2022-2023, Valencia Basket-Casademont Zaragoza.
capit_two_words: Capitalize two-word strings.
do_add_adv_stats: Advanced statistics.
do_clutch_time: Get games with clutch time.
do_EPS: Efficient Points Scored (EPS).
do_four_factors_df: Four factors data frame.
do_ft_fouls: Compute free throw fouls.
do_join_games_bio: Join games and players' info.
do_lineup: Compute ACB lineups.
do_map_nats: Data frame for the nationalities map.
do_OE: Offensive Efficiency (OE).
do_offensive_fouls: Compute offensive fouls.
do_possession: Compute when possessions start.
do_prepare_data: Prepare ACB play-by-play data.
do_prepare_data_or: Prepare data for the offensive rebounds computation.
do_prepare_data_to: Prepare data for the timeouts computation.
do_process_acb_pbp: Processing of the ACB website play-by-play data.
do_reb_off_success: Check if scoring after offensive rebounds.
do_scraping_games: Player game finder data.
do_scraping_rosters: Players profile data.
do_stats: Accumulated or average statistics.
do_stats_per_period: Compute stats per period.
do_stats_teams: Accumulated and average statistics for teams.
do_sub_lineup: Compute ACB sub-lineups.
do_time_out_success: Check if timeouts resulted in scoring.
eurocup_games_1718: Eurocup games 2017-2018.
eurocup_players_1718: Eurocup players 2017-2018.
euroleague_games_1718: Euroleague games 2017-2018.
euroleague_players_1718: Euroleague players 2017-2018.
get_barplot_monthly_stats: Barplots with monthly stats.
get_bubble_plot: Basketball bubble plot.
get_four_factors_plot: Four factors plot.
get_games_rosters: Get all games and rosters.
get_heatmap_bb: Basketball heatmap.
get_map_nats: Nationalities map.
get_pop_pyramid: ACB population pyramid.
get_shooting_plot: Shooting plot.
get_similar_players: Similar players to archetypoids.
get_similar_teams: Similar teams to archetypoids.
get_stats_seasons: Season-by-season stats.
get_table_results: League cross table.
join_players_bio_age_acb: Join ACB games and players' info.
join_players_bio_age_euro: Join Euroleague and Eurocup games and players' info.
scraping_games_acb: ACB player game finder data.
scraping_games_acb_old: Old ACB player game finder data.
scraping_games_euro: Euroleague and Eurocup player game finder data.
scraping_rosters_acb: ACB players' profile.
scraping_rosters_euro: Euroleague and Eurocup players' profile.

Author(s)

Guillermo Vinue <Guillermo.Vinue@uv.es>, <guillermovinue@gmail.com>

References

Vinue, G., (2020). A Web Application for Interactive Visualization of European Basketball Data, Big Data 8(1), 70-86. https://doi.org/10.1089/big.2018.0124, https://www.uv.es/vivigui/AppEuroACB.html

Vinue, G., (2024). A Basketball Big Data Platform for Box Score and Play-by-Play Data, Big Data (), -. https://doi.org/10.1089/big.2018.0124, https://www.uv.es/vivigui/basketball_platform.html

ACB games 2017-2018

Description

Games of the first seventeen days of the ACB 2017-2018 season.

Usage

acb_games_1718

Format

Data frame with 3939 rows and 38 columns.

Source

https://www.acb.com/

ACB coaches in the 2022-2023 season.

Description

Coach for each team in all the games of the ACB 2022-2023 season.

Usage

acb_games_2223_coach

Format

Data frame with 612 rows and 4 columns.

Note

The game_code column allows us to detect the source website, for example, https://jv.acb.com/es/103389/jugadas.

Source

https://www.acb.com/

ACB games 2022-2023, days and codes.

Description

Game codes, games and days from the ACB 2022-2023 season.

Usage

acb_games_2223_info

Format

Data frame with 306 rows and 3 columns.

Note

The game_code column allows us to detect the source website, for example, https://jv.acb.com/es/103389/jugadas.

Source

https://www.acb.com/

ACB players 2017-2018

Description

Players corresponding to the games of the first seventeen days of the ACB 2017-2018 season.

Usage

acb_players_1718

Format

Data frame with 255 rows and 7 columns.

Source

https://www.acb.com/

Shields of the ACB teams

Description

Links to the official shields of the ACB teams.

Usage

acb_shields

Format

Data frame with 20 rows and 2 columns.

Source

https://www.acb.com/

ACB play-by-play data, 2022-2023, Valencia Basket-Casademont Zaragoza

Description

Play-by-play data from the game Valencia Basket-Casademont Zaragoza from the ACB 2022-2023 season.

Usage

acb_vbc_cz_pbp_2223

Format

Data frame with 466 rows and 9 columns.

Note

Actions are given in Spanish. A bilingual basketball vocabulary (Spanish/English) is provided in https://www.uv.es/vivigui/docs/basketball_dictionary.xlsx. The game_code column allows us to detect the source website, namely, https://jv.acb.com/es/103389/jugadas.

Source

https://www.acb.com/

ACB starting lineups, 2022-2023, Valencia Basket-Casademont Zaragoza

Description

Starting lineups in each period from the game Valencia Basket-Casademont Zaragoza from the ACB 2022-2023 season.

Usage

acb_vbc_cz_sl_2223

Format

Data frame with 40 rows and 9 columns.

Note

The action column refers to starting lineup (Quinteto inicial, in Spanish). The initial score in each period does not really matter for the creation of this data set. The game_code column allows us to detect the source website, for example, https://jv.acb.com/es/103389/jugadas.

Source

https://www.acb.com/

Capitalize two-word strings

Description

Ancillary function to capitalize the first letter of both words in a two-word string. This can be used for example to capitalize the teams names for the plots title.

Usage

capit_two_words(two_word_string)

Arguments

Value

Vector with the two words capitalized.

Author(s)

Guillermo Vinue

Examples

capit_two_words("valencia basket")

Efficient Points Scored (EPS)

Description

A limitation of do_OE is that it doesn't rely on the quantity of the player's offense production, that's to say, whether the player provides a lot of offense or not. In addition, it does not give credit for free-throws. An extension of do_OE has been defined: the Efficient Points Scored (EPS), which is the result of the product of OE and points scored. Points scored counts free-throws, two-point and three-point field goals. A factor F is also added to put the adjusted total points on a points scored scale. With the factor F, the sum of the EPS scores for all players in a given season is equal to the sum of the league total points scored in that season.

Usage

do_EPS(df)

Arguments

Value

EPS values.

Author(s)

Guillermo Vinue

References

Shea, S., Baker, C., (2013). Basketball Analytics: Objective and Efficient Strategies for Understanding How Teams Win. Lake St. Louis, MO: Advanced Metrics, LLC.

See Also

do_OE, do_add_adv_stats

Examples

df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)
df1 <- do_add_adv_stats(df)
do_EPS(df1)[1]

Offensive Efficiency (OE)

Description

Offensive Efficiency (OE) is a measure to evaluate the quality of offense produced. OE counts the total number of successful offensive possessions the player was involved in, regarding the player's total number of potential ends of possession.

This measure is used in the definition of do_EPS.

Usage

do_OE(df)

Arguments

Value

OE values.

Note

When either both the numerator and denominator of the OE expression are 0 or just the denominator is 0, the function returns a 0.

Author(s)

Guillermo Vinue

References

Shea, S., Baker, C., (2013). Basketball Analytics: Objective and Efficient Strategies for Understanding How Teams Win. Lake St. Louis, MO: Advanced Metrics, LLC.

See Also

do_EPS, do_add_adv_stats

Examples

df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)
df1 <- do_add_adv_stats(df)
# Players with OE = 0:
# df1[55, c("Player.x", "FG", "AST", "FGA", "ORB", "TOV")]
# Player.x     FG  AST  FGA  ORB  TOV
# Triguero, J.  0    0    0    0    0
# OE can be greater than 1, for example:
# df1[17, c("Player.x", "FG", "AST", "FGA", "ORB", "TOV")]
# Player.x        FG  AST  FGA  ORB  TOV
# Diagne, Moussa   3    0    3    1    0
do_OE(df1[1,])

Advanced statistics

Description

This function adds to the whole data frame the advanced statistics for every player in every game.

Usage

do_add_adv_stats(df)

Arguments

Details

The advanced statistics computed are as follows:

• GameSc: Game Score.

• PIE: Player Impact Estimate.

• EFGPerc: Effective Field Goal Percentage.

• ThreeRate: Three points attempted regarding the total field goals attempted.

• FRate: Free Throws made regarding the total field goals attempted.

• STL_TOV: Steal to Turnover Ratio.

• AST_TOV: Assist to Turnover Ratio.

• PPS: Points Per Shot.

• OE: Offensive Efficiency.

• EPS: Efficient Points Scored.

The detailed definition of some of these stats can be found at https://www.basketball-reference.com/about/glossary.html and https://www.nba.com/stats/help/glossary/.

Value

Data frame.

Author(s)

Guillermo Vinue

See Also

do_OE, do_EPS

Examples

df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)
df1 <- do_add_adv_stats(df)
          

Get games with clutch time

Description

Obtain the games that have clutch time. The clutch time is the game situation when the scoring margin is within 5 points with five or fewer minutes remaining in a game.

Usage

do_clutch_time(data)

Arguments

Value

Data frame of the game that has clutch time.

Author(s)

Guillermo Vinue

Examples

df0 <- do_clutch_time(acb_vbc_cz_pbp_2223)
#df0 # If no rows, that means that the game did not have clutch time.

Four factors data frame

Description

This function computes team's offense and defense four factors. The four factors are Effective Field Goal Percentage (EFGP), Turnover Percentage (TOVP), Offensive Rebound Percentage (ORBP) and Free Throws Rate (FTRate). They are well defined at http://www.rawbw.com/~deano/articles/20040601_roboscout.htm and https://www.basketball-reference.com/about/factors.html.

As a summary, EFGP is a measure of shooting efficiency; TOVP is the percentage of possessions where the team missed the ball, see https://www.nba.com/thunder/news/stats101.html to read about the 0.44 coefficient; ORBP measures how many rebounds were offensive from the total of available rebounds; Finally, FTRate is a measure of both how often a team gets to the line and how often they make them.

Usage

do_four_factors_df(df_games, teams)

Arguments

Details

Instead of defining the Offensive and Defensive Rebound Percentage as mentioned in the previous links, I have computed just the Offensive Rebound Percentage for the team and for its rivals. This makes easier to have four facets, one per factor, in the ggplot.

In order to establish the team rankings, we have to consider these facts: In defense (accumulated statistics of the opponent teams to the team of interest), the best team in each factor is the one that allows the smallest EFGP, the biggest TOVP, the smallest ORBP and the smallest FTRate, respectively.

In offense (accumulated statistics of the team of interest), the best team in each factor is the one that has the biggest EFGP, the smallest TOVP, the biggest ORBP and the biggest FTRate, respectively.

Value

A list with two data frames, df_rank and df_no_rank. Both have the same columns:

• Team: Team name.

• Type: Either Defense or Offense.

• EFGP, ORBP, TOVP and FTRate.

The df_rank data frame contains the team ranking label for each statistic between parentheses. Therefore, df_no_rank is used to create the ggplot with the numerical values and df_rank is used to add the ranking labels.

Author(s)

Guillermo Vinue

See Also

get_four_factors_plot

Examples

df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)
df1 <- do_add_adv_stats(df)
# When only one team is selected the rankings between parentheses
# do not reflect the real rankings regarding all the league teams.
# The rankings are computed with respect to the number of teams 
# passed as an argument.
df_four_factors <- do_four_factors_df(df1, "Valencia")

Compute free throw fouls

Description

Compute how many 1-,2- and 3-free throw fouls has committed or received every player.

Usage

do_ft_fouls(data, type)

Arguments

Value

Data frame with the following columns:

team: Name of the team. player: Name of the player. n_ft_fouls_x: Number of free throw fouls committed or received. n_ft_x: Number of free throws given or got. n_ft_char: Type of free throw. Options can be 1TL, 2TL and 3TL. n: Number of free throws of each type.

Author(s)

Guillermo Vinue

Examples

df01 <- do_ft_fouls(acb_vbc_cz_pbp_2223, "comm")
#df01  
 
df02 <- do_ft_fouls(acb_vbc_cz_pbp_2223, "rec")
#df02

Join games and players' info

Description

This function calls the needed ancillary functions to join the games played by all the players in the desired competition (currently ACB, Euroleague and Eurocup) with their personal details.

Usage

do_join_games_bio(competition, df_games, df_rosters)

Arguments

Value

Data frame.

Author(s)

Guillermo Vinue

See Also

join_players_bio_age_acb, join_players_bio_age_euro

Examples

df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)
          

Compute ACB lineups

Description

Compute all the lineups that a given team shows during a game.

Usage

do_lineup(data, day_num, game_code, team_sel, verbose)

Arguments

Value

Data frame. Each row is a different lineup. This is the meaning of the columns that might not be explanatory by themselves:

team_in: Time point when that lineup starts playing together. team_out: Time point when that lineup stops playing together (because there is a substitution). num_players: Number of players forming the lineup (must be 5 in this case). time_seconds: Total of seconds that the lineup played. diff_points: Game score in the time that the lineup played. plus_minus: Plus/minus achieved by the lineup. This is the difference between the game score of the previous lineup and of the current one. plus_minus_poss: Plus/minus per possession.

Note

A possession lasts 24 seconds in the ACB league.

Author(s)

Guillermo Vinue

Examples

library(dplyr)
df0 <- acb_vbc_cz_pbp_2223

day_num <- unique(acb_vbc_cz_pbp_2223$day)
game_code <- unique(acb_vbc_cz_pbp_2223$game_code)

acb_games_2223_sl <- acb_vbc_cz_sl_2223 %>%
  filter(period == "1C")

df1 <- do_prepare_data(df0, day_num, 
                       acb_games_2223_sl, acb_games_2223_info,
                       game_code)
                
df2 <- do_lineup(df1, day_num, game_code, "Valencia Basket", FALSE)    
#df2

Data frame for the nationalities map

Description

This function prepares the data frame with the nationalities to be mapped with get_map_nats. It is used inside it.

Usage

do_map_nats(df_stats)

Arguments

Value

List with the following elements:

• df_all: Data frame with each country, its latitudes and longitudes and whether it must be coloured or not (depending on if there are players from that country).

• countr_num: Vector with the countries from where there are players and the number of them.

• leng: Number of countries in the world.

Author(s)

Guillermo Vinue

See Also

get_map_nats

Compute offensive fouls

Description

Compute how many offensive fouls has committed or received every player.

Usage

do_offensive_fouls(data, type)

Arguments

Value

Data frame with the following columns:

team: Name of the team. player: Name of the player. n_offensive_fouls_x: Number of offensive fouls.

Author(s)

Guillermo Vinue

Examples

df01 <- do_offensive_fouls(acb_vbc_cz_pbp_2223, "comm")
#df01

df02 <- do_offensive_fouls(acb_vbc_cz_pbp_2223, "rec")
#df02

Compute when possessions start

Description

Compute when the possession starts for each team during each period of a game.

Usage

do_possession(data, period_sel)

Arguments

Value

Data frame. This is the meaning of the columns that might not be explanatory by themselves:

time_start: Time point when the action starts. time_end: Time point when the action ends. poss_time: Duration of the possession. possession: Indicates when the possession starts. This is encoded with the Spanish word inicio (start, in English). points: Number of points scored from a given action.

Note

1. A possession lasts 24 seconds in the ACB league.

2. Actions are given in Spanish. A bilingual basketball vocabulary (Spanish/English) is provided in https://www.uv.es/vivigui/docs/basketball_dictionary.xlsx.

3. The game_code column allows us to detect the source website, for example, https://jv.acb.com/es/103389/jugadas.

Author(s)

Guillermo Vinue

Examples

library(dplyr)
df0 <- acb_vbc_cz_pbp_2223

day_num <- unique(acb_vbc_cz_pbp_2223$day)
game_code <- unique(acb_vbc_cz_pbp_2223$game_code)

acb_games_2223_sl <- acb_vbc_cz_sl_2223 %>%
  dplyr::filter(period == "1C")

df1 <- do_prepare_data(df0, day_num, 
                       acb_games_2223_sl, acb_games_2223_info,
                       game_code)
                       
df2 <- do_possession(df1, "1C")                         
#df2

Prepare ACB play-by-play data

Description

Prepare the ACB play-by-play data to be analyzed in further steps. It involves correcting some inconsistencies and filtering some unnecessary information.

Usage

do_prepare_data(data, day_num, data_gsl, data_ginfo, game_code_excel)

Arguments

Value

Data frame. Each row represents the action happened in the game. It has associated a player, a time point and the game score. The team column refers to the team to which the player belongs.

Note

1. Actions are given in Spanish. A bilingual basketball vocabulary (Spanish/English) is provided in https://www.uv.es/vivigui/docs/basketball_dictionary.xlsx.

2. The game_code column allows us to detect the source website, for example, https://jv.acb.com/es/103389/jugadas.

Author(s)

Guillermo Vinue

Examples

library(dplyr)
df0 <- acb_vbc_cz_pbp_2223

day_num <- unique(acb_vbc_cz_pbp_2223$day)
game_code <- unique(acb_vbc_cz_pbp_2223$game_code)

acb_games_2223_sl <- acb_vbc_cz_sl_2223 %>%
  filter(period == "1C")

df1 <- do_prepare_data(df0, day_num, 
                       acb_games_2223_sl, acb_games_2223_info,
                       game_code)
#df1                        

Prepare data for the offensive rebounds computation

Description

The computation of the scoring after offensive rebounds requires a specifical data preparation. This function does this data processing.

Usage

do_prepare_data_or(data, rm_overtime, data_ginfo)

Arguments

Value

Data frame. Each row represents the action happened in the game. The points column is added to transform the action that finished in scoring into numbers.

Note

1. Actions are given in Spanish. A bilingual basketball vocabulary (Spanish/English) is provided in https://www.uv.es/vivigui/docs/basketball_dictionary.xlsx.

2. The game_code column allows us to detect the source website, for example, https://jv.acb.com/es/103389/jugadas.

Author(s)

Guillermo Vinue

See Also

do_reb_off_success

Examples

df0 <- acb_vbc_cz_pbp_2223

df1 <- do_prepare_data_or(df0, TRUE, acb_games_2223_info)
#df1                     

Prepare data for the timeouts computation

Description

The computation of the successful timeouts requires a specific data preparation. This function does this data processing.

Usage

do_prepare_data_to(data, rm_overtime, data_ginfo, data_gcoach)

Arguments

Value

Data frame. Each row represents the action happened in the game. The team column refers in this case both to the team to which the player belongs and the coach of that team. In addition, a points column is added to transform the action that finished in scoring into numbers .

Note

1. Actions are given in Spanish. A bilingual basketball vocabulary (Spanish/English) is provided in https://www.uv.es/vivigui/docs/basketball_dictionary.xlsx.

2. The game_code column allows us to detect the source website, for example, https://jv.acb.com/es/103389/jugadas.

Author(s)

Guillermo Vinue

See Also

do_time_out_success

Examples

df0 <- acb_vbc_cz_pbp_2223

df1 <- do_prepare_data_to(df0, TRUE, acb_games_2223_info, acb_games_2223_coach)
#df1                        

Processing of the ACB website play-by-play data

Description

This function disentangles the play-by-play data coming from the ACB website and creates a common data structure in R.

Usage

do_process_acb_pbp(game_elem, day, game_code, period, acb_shields, verbose)

Arguments

Value

Data frame with eight columns:

• period: Period of the game.

• time_point: Time point when the basketball action happens.

• player: Player who performs the action.

• action: Basketball action.

• local_score: Local score at that time point.

• visitor_score: Visitor score at that time point.

• day: Day of the game.

• game_code: Game code.

Note

1. Actions are given in Spanish. A bilingual basketball vocabulary (Spanish/English) is provided in https://www.uv.es/vivigui/docs/basketball_dictionary.xlsx.

2. The game_code column allows us to detect the source website, for example, https://jv.acb.com/es/103389/jugadas.

Author(s)

Guillermo Vinue

Examples

## Not run: 
# Load packages required:
library(RSelenium)

# Provide the day and game code:
day <- "24"
game_code <- "103170"

# Open an Internet server:
rD <- rsDriver(browser = "firefox", chromever = NULL)

# Follow this procedure on the server:
# 1. Copy and paste the game link https://jv.acb.com/es/103170/jugadas
# 2. Click on each period, starting with 1C.
# 3. Scroll down to the first row of data.
# 4. Go back to R and run the following code:
 
# Set the remote driver:
remDr <- rD$client

# Get the play-by-play data:
game_elem <- remDr$getPageSource()[[1]]

# Close the client and the server:
remDr$close()
rD$server$stop()

period <- "1C"
data_game <- do_process_acb_pbp(game_elem, day, game_code, 
                                period, acb_shields, FALSE)

## End(Not run)

Check if scoring after offensive rebounds

Description

For each team and player, locate the position of offensive rebounds and check if they resulted in scoring points.

Usage

do_reb_off_success(data, day_num, game_code, team_sel, verbose)

Arguments

Value

List with two data frames, one for the results for the team (stats_team) and the other for the players (stats_player). The team data frame shows the number of offensive rebounds, the number of those that finished in scoring (and the percentage associated) and the total of points scored. The player data frame shows the player who grabbed the offensive rebound, the player who scored and how many points.

Author(s)

Guillermo Vinue

See Also

do_prepare_data_or

Examples

df0 <- acb_vbc_cz_pbp_2223

day_num <- unique(acb_vbc_cz_pbp_2223$day)
game_code <- unique(acb_vbc_cz_pbp_2223$game_code)

df1 <- do_prepare_data_or(df0, TRUE, acb_games_2223_info)

df2 <- do_reb_off_success(df1, day_num, game_code, "Valencia Basket", FALSE)
#df2                        

Player game finder data

Description

This function calls the needed ancillary functions to scrape the player game finder data for the desired competition (currently, ACB, Euroleague and Eurocup).

Usage

do_scraping_games(competition, type_league, nums, year, verbose, accents, r_user)

Arguments

Value

A data frame with the player game finder data for the competition selected.

Author(s)

Guillermo Vinue

See Also

scraping_games_acb, scraping_games_euro

Examples

## Not run: 
# Not needed to scrape every time the package is checked, built and installed.
df1 <- do_scraping_games(competition = "ACB", type_league = "ACB", nums = 62001,
                         year = "2017-2018", verbose = TRUE, accents = FALSE, 
                         r_user = "guillermo.vinue@uv.es")
                         
df1_eur <- do_scraping_games(competition = "Euroleague", nums = 1,
                         year = "2017", verbose = TRUE,
                         r_user = "guillermo.vinue@uv.es")                          

## End(Not run)                          

Players profile data

Description

This function calls the needed ancillary functions to scrape the players' profile data for the desired competition (currently, ACB, Euroleague and Eurocup).

Usage

do_scraping_rosters(competition, pcode, verbose, accents, year, r_user)

Arguments

Value

A data frame with the players' information.

Author(s)

Guillermo Vinue

See Also

scraping_games_acb, scraping_rosters_euro

Examples

## Not run: 
# Not needed to scrape every time the package is checked, built and installed.
df_bio <- do_scraping_rosters(competition = "ACB", pcode = "56C", 
                              verbose = TRUE, accents = FALSE, 
                              r_user = "guillermo.vinue@uv.es")
                              
df_bio_eur <- do_scraping_rosters(competition = "Euroleague", pcode = "007969", 
                              year = "2017", verbose = TRUE, 
                              r_user = "guillermo.vinue@uv.es")                               

## End(Not run)                               

Accumulated or average statistics

Description

This function computes either the total or the average statistics.

Usage

do_stats(df_games, type_stats = "Total", season, competition, type_season)

Arguments

Value

Data frame.

Author(s)

Guillermo Vinue

Examples

compet <- "ACB"
df <- do_join_games_bio(compet, acb_games_1718, acb_players_1718)
df1 <- do_add_adv_stats(df)
df2 <- do_stats(df1, "Total", "2017-2018", compet, "Regular Season")
        

Compute stats per period

Description

Compute time played and points scored for a player of interest in any period of the game.

Usage

do_stats_per_period(data, day_num, game_code, team_sel, period_sel, player_sel)

Arguments

Value

Data frame with one row and mainly time played (seconds and minutes) and points scored by the player of interest in the period of interest.

Note

The game_code column allows us to detect the source website, for example, https://jv.acb.com/es/103389/jugadas.

Author(s)

Guillermo Vinue

Examples

library(dplyr)
df0 <- acb_vbc_cz_pbp_2223

day_num <- unique(acb_vbc_cz_pbp_2223$day)
game_code <- unique(acb_vbc_cz_pbp_2223$game_code)

# Remove overtimes:
rm_overtime <- TRUE
if (rm_overtime) {
 df0 <- df0 %>%
   filter(!grepl("PR", period)) %>%
   mutate(period = as.character(period))
}
 
team_sel <- "Valencia Basket" # "Casademont Zaragoza"
period_sel <- "1C"            # "4C"
player_sel <- "Webb"          # "Mara"
 
df1 <- df0 %>%
  filter(team == team_sel) %>%
  filter(!action %in% c("D - Descalificante - No TL", "Altercado no TL")) 
   
df2 <- df1 %>%
  filter(period == period_sel)
   
df0_inli_team <- acb_vbc_cz_sl_2223 %>% 
   filter(team == team_sel, period == period_sel)
 
df3 <- do_prepare_data(df2, day_num, 
                       df0_inli_team, acb_games_2223_info,
                       game_code)
                        
df4 <- do_stats_per_period(df3, day_num, game_code, team_sel, period_sel, player_sel)
#df4

Accumulated and average statistics for teams

Description

This function computes the total and average statistics for every team.

Usage

do_stats_teams(df_games, season, competition, type_season)

Arguments

Value

A list with two elements:

• df_team_total: Data frame with the total statistics for every team.

• df_team_mean: Data frame with the average statistics for every team.

Author(s)

Guillermo Vinue

Examples

compet <- "ACB"
df <- do_join_games_bio(compet, acb_games_1718, acb_players_1718)
df$Compet <- compet
df_teams <- do_stats_teams(df, "2017-2018", "ACB", "Regular Season")
# Total statistics:
#df_teams$df_team_total
# Average statistics:
#df_teams$df_team_mean

Compute ACB sub-lineups

Description

Compute all the sub-lineups that a given team shows during a game. They can be made up of four, three or two players.

Usage

do_sub_lineup(data, elem_choose)

Arguments

Value

Data frame. Each row is a different sub-lineup. This is the meaning of the columns that might not be explanatory by themselves:

team_in: Time point when that sub-lineup starts playing together. team_out: Time point when that sub-lineup stops playing together (because there is a substitution). time_seconds: Total of seconds that the sub-lineup played. plus_minus: Plus/minus achieved by the sub-lineup. This is the difference between the game score of the previous lineup and of the current one. plus_minus_poss: Plus/minus per possession.

Note

A possession lasts 24 seconds in the ACB league.

Author(s)

Guillermo Vinue

Examples

library(dplyr)
df0 <- acb_vbc_cz_pbp_2223

day_num <- unique(acb_vbc_cz_pbp_2223$day)
game_code <- unique(acb_vbc_cz_pbp_2223$game_code)

acb_games_2223_sl <- acb_vbc_cz_sl_2223 %>%
  filter(period == "1C")

df1 <- do_prepare_data(df0, day_num, 
                       acb_games_2223_sl, acb_games_2223_info,
                       game_code)
                
df2 <- do_lineup(df1, day_num, game_code, "Valencia Basket", FALSE)    

df3 <- do_sub_lineup(df2, 4)
#df3

Check if timeouts resulted in scoring

Description

For each team, locate the position of timeouts and check if they resulted in scoring points.

Usage

do_time_out_success(data, day_num, game_code, team_sel, verbose)

Arguments

Value

Data frame. This is the meaning of the columns:

day: Day number. game_code: Game code. team: Name of the corresponding team and coach. times_out_requested: Number of timeouts requested in the game. times_out_successful: Number of timeouts that resulted in scoring. times_out_successful_perc: Percentage of successful timeouts. points_scored: Total of points achieved after the timeouts.

Author(s)

Guillermo Vinue

See Also

do_prepare_data_to

Examples

df0 <- acb_vbc_cz_pbp_2223

day_num <- unique(acb_vbc_cz_pbp_2223$day)
game_code <- unique(acb_vbc_cz_pbp_2223$game_code)

df1 <- do_prepare_data_to(df0, TRUE, acb_games_2223_info, acb_games_2223_coach)

# sort(unique(df1$team))
# "Casademont Zaragoza_Porfirio Fisac" "Valencia Basket_Alex Mumbru"

df2 <- do_time_out_success(df1, day_num, game_code, 
                           "Casademont Zaragoza_Porfirio Fisac", FALSE)
#df2

Eurocup games 2017-2018

Description

Games of the ten days of regular season and the first three days of top 16 of the Eurocup 2017-2018 season.

Usage

eurocup_games_1718

Format

Data frame with 3604 rows and 38 columns.

Source

https://www.euroleaguebasketball.net/eurocup/

Eurocup players 2017-2018

Description

Players corresponding to the games of the ten days of regular season and the first three days of top 16 of the Eurocup 2017-2018 season.

Usage

eurocup_players_1718

Format

Data frame with 351 rows and 7 columns.

Source

https://www.euroleaguebasketball.net/eurocup/

Euroleague games 2017-2018

Description

Games of the first nineteen days of the Euroleague 2017-2018 season.

Usage

euroleague_games_1718

Format

Data frame with 3932 rows and 38 columns.

Source

https://www.euroleaguebasketball.net/euroleague/

Euroleague players 2017-2018

Description

Players corresponding to the games of the first nineteen days of the Euroleague 2017-2018 season.

Usage

euroleague_players_1718

Format

Data frame with 245 rows and 7 columns.

Source

https://www.euroleaguebasketball.net/euroleague/

Barplots with monthly stats

Description

In all the available basketball websites, the stats are presented for the whole number of games played. This function represents a barplot with the players' stats for each month, which is very useful to analyse the players' evolution.

Usage

get_barplot_monthly_stats(df_stats, title, size_text = 2.5)

Arguments

Value

Graphical device.

Author(s)

Guillermo Vinue

See Also

capit_two_words

Examples

## Not run: 
library(dplyr)
compet <- "ACB"
df <- do_join_games_bio(compet, acb_games_1718, acb_players_1718)
df1 <- do_add_adv_stats(df)

months <- c(df %>% distinct(Month))$Month
months_order <- c("septiembre", "octubre", "noviembre", "diciembre", "enero")
months_plot <- match(months_order, months)
months_plot1 <- months_plot[!is.na(months_plot)]
months_plot2 <- months[months_plot1]

df3_m <- df1 %>%
filter(Team == "Real_Madrid", 
      Player.x == "Doncic, Luka") %>%
 group_by(Month) %>%
 do(do_stats(., "Average", "2017-2018", "ACB", "Regular Season")) %>%
 ungroup() %>%
 mutate(Month = factor(Month, levels = months_plot2)) %>%
 arrange(Month)

stats <- c("GP", "MP", "PTS", "FGA", "FGPerc", "ThreePA", 
           "ThreePPerc", "FTA", "FTPerc",
           "TRB", "ORB", "AST", "TOV", "STL")
           
df3_m1 <- df3_m %>%
  select(1:5, stats, 46:50) %>%
  mutate(Month = plyr::mapvalues(Month, 
                                 from = c("octubre", "noviembre", "diciembre", "enero"),
                                 to = c("October", "November", "December", "January")))
  
get_barplot_monthly_stats(df3_m1, paste("ACB", "2017-2018", "Average", sep = " ; "), 2.5)

# For all teams and players:
teams <- as.character(sort(unique(df1$Team)))
df3_m <- df1 %>%
filter(Team == teams[13]) %>%
 group_by(Month) %>%
 do(do_stats(., "Average", "2017-2018", "ACB", "Regular Season")) %>%
 ungroup() %>%
 mutate(Month = factor(Month, levels = months_plot2)) %>%
 arrange(Month)

df3_m1 <- df3_m %>%
  select(1:5, stats, 46:50) %>%
  mutate(Month = plyr::mapvalues(Month, 
                                 from = c("octubre", "noviembre", "diciembre", "enero"),
                                 to = c("October", "November", "December", "January")))

for (i in unique(df3_m1$Name)) {
  print(i)
  print(get_barplot_monthly_stats(df3_m1 %>% filter(Name == i), 
                                  paste("ACB", "2017-2018", "Average", sep = " ; "), 
                                  2.5))
}

## End(Not run)

Basketball bubble plot

Description

This plot is a representation of the percentiles of all statistics for a particular player. The figure shows four cells. The first box contains the percentiles between 0 and 24. The second, between 25 and 49. The third, between 50 and 74 and the fourth, between 75 and 100. The percentiles are computed with the function percentilsArchetypoid. Boxes of the same percentile category are in the same color in the interests of easy understanding.

This type of visualization allows the user to analyze each player in a very simple way, since a general idea of those aspects of the game in which the player excels can be obtained.

Usage

get_bubble_plot(df_stats, player, descr_stats, size_text, size_text_x, size_legend)

Arguments

Details

In the example shown below, it can be seen that Alberto Abalde has a percentile of x in free throws percentage. This means that the x percent of league players has a fewer percentage than him, while there is a (100-x) percent who has a bigger percentage.

Value

Graphical device.

Author(s)

This function has been created using the code from this website: https://www.r-bloggers.com/2017/01/visualizing-the-best/.

See Also

percentilsArchetypoid

Examples

## Not run: 
compet <- "ACB"
df <- do_join_games_bio(compet, acb_games_1718, acb_players_1718)
df1 <- do_add_adv_stats(df)
df2 <- do_stats(df1, "Total", "2017-2018", compet, "Regular Season")
# When choosing a subset of stats, follow the order in which they appear
# in the data frame.
stats <- c("GP", "MP", "PTS", "FGA", "FGPerc", "ThreePA", "ThreePPerc", 
           "FTA", "FTPerc", "TRB", "ORB", "AST", "STL", "TOV")
df2_1 <- df2[, c(1:5, which(colnames(df2) %in% stats), 46:49)]
descr_stats <- c("Games played", "Minutes played", "Points", 
                "Field goals attempted", "Field goals percentage", 
                "3-point field goals attempted", "3-point percentage", 
                "FTA: Free throws attempted", "Free throws percentage", 
                "Total rebounds", "Offensive rebounds", 
                "Assists", "Steals", "Turnovers")
get_bubble_plot(df2_1, "Abalde, Alberto", descr_stats, 6, 10, 12)

## End(Not run)

Four factors plot

Description

Once computed the team's factors and its rankings with do_four_factors_df, this function represents them.

Usage

get_four_factors_plot(df_rank, df_no_rank, team, language)

Arguments

Value

Graphical device.

Author(s)

Guillermo Vinue

See Also

do_four_factors_df

Examples

## Not run: 
df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)
df1 <- do_add_adv_stats(df)
team <- "Valencia"
df_four_factors <- do_four_factors_df(df1, team)
# If only one team is represented the ranking between parentheses is just one.
get_four_factors_plot(df_four_factors$df_rank, 
                      df_four_factors$df_no_rank, team, "en")

## End(Not run)

Get all games and rosters

Description

This function is to get all the games and rosters of the competition selected.

Usage

get_games_rosters(competition, type_league, nums, verbose = TRUE, 
                         accents = FALSE, r_user, df0, df_bio0)

Arguments

Value

Data frame.

Author(s)

Guillermo Vinue

Examples

## Not run: 
library(readr)
# 1. The first time, all the historical data until the last games played can be 
# directly scraped. 

# ACB seasons available and corresponding games numbers:
acb_nums <- list(30001:30257, 31001:31262, 32001:32264, 33001:33492, 34001:34487,
                 35001:35494, 36001:36498, 37001:37401, 38001:38347, 39001:39417,
                 40001:40415, 41001:41351, 42001:42350, 43001:43339, 44001:44341,
                 45001:45339, 46001:46339, 47001:47339, 48001:48341, 49001:49341,
                 50001:50339, 51001:51340, 52001:52327, 53001:53294, 54001:54331,
                 55001:55331, 56001:56333, 57001:57333, 58001:58332, 59001:59331,
                 60001:60332, 61001:61298, 
                 62001:62135)
names(acb_nums) <- paste(as.character(1985:2017), as.character(1986:2018), sep = "-")

df0 <- data.frame()
df_bio0 <- data.frame(CombinID = NA, Player = NA, Position = NA, 
                      Height = NA, Date_birth = NA, 
                      Nationality = NA, Licence = NA, Website_player = NA)
                      
# All the games and players:
get_data <- get_games_rosters(competition = "ACB", type_league = "ACB", 
                              nums = acb_nums, verbose = TRUE, accents = FALSE, 
                              r_user = "guillermo.vinue@uv.es", 
                              df0 = df0, df_bio0 = df_bio0)
acb_games <- get_data$df0
acb_players <- get_data$df_bio0
write_csv(acb_games, path = "acb_games.csv")
write_csv(acb_players, path = "acb_players.csv")        

# 2. Then, in order to scrape new games as they are played, the df0 and df_bio0 objects are
# the historical games and rosters:
acb_nums <- list(62136:62153)
names(acb_nums) <- "2017-2018"
df0 <- read_csv("acb_games.csv", guess_max = 1e5)
df_bio0 <- read_csv("acb_players.csv", guess_max = 1e3)
get_data <- get_games_rosters(competition = "ACB", type_league = "ACB", 
                              nums = acb_nums, verbose = TRUE, accents = FALSE, 
                              r_user = "guillermo.vinue@uv.es", 
                              df0 = df0, df_bio0 = df_bio0)

# -----                               

# ACB Copa del Rey seasons available and corresponding games numbers (rosters were 
already downloaded with the ACB league):
acb_crey_nums <- list(50001:50004, 51001:51007, 52001:52007, 53033:53039, 
                      54033:54039, 55033:55040, 56033:56040, 57029:57036, 
                      58025:58032, 59038:59045, 60001:60008, 61001:61007, 
                      62001:62007, 63001:63007, 64001:64007, 65001:65007, 
                      66001:66007, 67001:67007, 68001:68007, 69001:69007, 
                      70001:70007, 71001:71007, 72001:72007, 73001:73007, 
                      74001:74007, 75001:75007, 76001:76007, 77001:77007, 
                      78001:78007, 79001:79007, 80001:80007, 81001:81007)
names(acb_crey_nums) <- paste(as.character(1985:2016), as.character(1986:2017), sep = "-")

df0 <- data.frame()
get_data <- get_games_rosters(competition = "ACB", type_league = "CREY", 
                              nums = acb_crey_nums, verbose = TRUE, accents = FALSE, 
                              r_user = "guillermo.vinue@uv.es", 
                              df0 = df0, df_bio0 = NULL)
acb_crey_games <- get_data$df0
write_csv(acb_crey_games, path = "acb_crey_games.csv")

# -----  

# ACB Supercopa seasons available and corresponding games numbers (rosters were 
already downloaded with the ACB league):
acb_scopa_nums <- list(1001, 2001, 3001, 4001, 5001:5004, 6001:6004, 
                       7001:7003, 9001:9003, 10001:10003, 11001:11003,
                       12001:12003, 13001:13003, 14001:14003, 15001:15003, 
                       16001:16003, 17001:17003, 18001:18003, 19001:19003)
# I haven't found the data for the supercopa in Bilbao 2007 ; 8001:8003
# http://www.acb.com/fichas/SCOPA8001.php
names(acb_scopa_nums) <- c(paste(as.character(1984:1987), as.character(1985:1988), sep = "-"),
                           paste(as.character(2004:2006), as.character(2005:2007), sep = "-"),
                           paste(as.character(2008:2018), as.character(2009:2019), sep = "-"))
                           
df0 <- data.frame()
get_data <- get_games_rosters(competition = "ACB", type_league = "SCOPA", 
                              nums = acb_scopa_nums, verbose = TRUE, accents = FALSE, 
                              r_user = "guillermo.vinue@uv.es", 
                              df0 = df0, df_bio0 = NULL)
acb_scopa_games <- get_data$df0
write_csv(acb_scopa_games, path = "acb_scopa_games.csv")

# -----  

# Euroleague seasons available and corresponding games numbers:
euroleague_nums <- list(1:128,
                        1:263, 1:250, 1:251, 1:253, 1:253, 1:188, 1:189, 
                        1:188, 1:188, 1:231, 1:231, 1:231, 1:229, 1:220, 
                        1:220, 1:275, 1:169)
names(euroleague_nums) <- 2017:2000

df0 <- data.frame()
df_bio0 <- data.frame(CombinID = NA, Player = NA, Position = NA, 
                     Height = NA, Date_birth = NA, 
                     Nationality = NA, Website_player = NA)
get_data <- get_games_rosters(competition = "Euroleague", nums = euroleague_nums, 
                              verbose = TRUE, r_user = "guillermo.vinue@uv.es", 
                              df0 = df0, df_bio0 = df_bio0)
euroleague_games <- get_data$df0
euroleague_players <- get_data$df_bio0
write_csv(euroleague_games, path = "euroleague_games.csv")
write_csv(euroleague_players, path = "euroleague_players.csv")                         

# ----- 

# Eurocup seasons available and corresponding games numbers:
eurocup_nums <- list(1:128,
                     2:186, 1:306, 1:306, 1:366, 1:157, 1:156, 1:156, 1:156, 
                     1:151, 1:326, 1:149, 1:149, 1:239, 1:209, 1:150)
names(eurocup_nums) <- 2017:2002

df0 <- data.frame()
df_bio0 <- data.frame(CombinID = NA, Player = NA, Position = NA, 
                     Height = NA, Date_birth = NA, 
                     Nationality = NA, Website_player = NA)
get_data <- get_games_rosters(competition = "Eurocup", nums = eurocup_nums, 
                              verbose = TRUE, r_user = "guillermo.vinue@uv.es", 
                              df0 = df0, df_bio0 = df_bio0)
eurocup_games <- get_data$df0
eurocup_players <- get_data$df_bio0
write_csv(eurocup_games, path = "eurocup_games.csv")
write_csv(eurocup_players, path = "eurocup_players.csv") 


## End(Not run)

Basketball heatmap

Description

The heatmap created with this function allows the user to easily represent the stats for each player. The more intense the color, the more the player highlights in the statistic considered. The plot can be ordered by any statistic. If all the statistics are represented, the offensive statistics are grouped in red, the defensive in green, the rest in purple and the advanced in pink. Otherwise, the default color is red.

Usage

get_heatmap_bb(df_stats, team, levels_stats = NULL, stat_ord, base_size = 9, title)

Arguments

Value

Graphical device.

Author(s)

This function has been created using the code from these websites: https://learnr.wordpress.com/2010/01/26/ggplot2-quick-heatmap-plotting/ and https://stackoverflow.com/questions/13016022/ggplot2-heatmaps-using-different-gradients-for-categories/13016912

Examples

## Not run: 
compet <- "ACB"
df <- do_join_games_bio(compet, acb_games_1718, acb_players_1718)
df1 <- do_add_adv_stats(df)
df2 <- do_stats(df1, "Total", "2017-2018", compet, "Regular Season")
teams <- as.character(rev(sort(unique(df2$Team))))
get_heatmap_bb(df2, teams[6], NULL, "MP", 9, paste(compet, "2017-2018", "Total", sep = " "))

## End(Not run)

Nationalities map

Description

A world map is represented. The countries from where there are players in the competition selected are in green color.

Usage

get_map_nats(df_stats)

Arguments

Value

Graphical device.

Author(s)

Guillermo Vinue

See Also

do_map_nats

Examples

## Not run: 
compet <- "ACB"
df <- do_join_games_bio(compet, acb_games_1718, acb_players_1718)
df1 <- do_add_adv_stats(df)
df2 <- do_stats(df1, "Total", "2017-2018", compet, "Regular Season")
get_map_nats(df2)

## End(Not run)
        

Population pyramid

Description

This is the code to get a population pyramid with the number of both Spanish and foreigner players along the seasons for the ACB league. This aids in discussion of nationality imbalance.

Usage

get_pop_pyramid(df, title, language)

Arguments

Value

Graphical device.

Author(s)

Guillermo Vinue

Examples

## Not run: 
 # Load the data_app_acb file with the ACB games 
 # from seasons 1985-1986 to 2017-2018:
 load(url("http://www.uv.es/vivigui/softw/data_app_acb.RData"))
 title <- " Number of Spanish and foreign players along the ACB seasons \n Data from www.acb.com"
 get_pop_pyramid(data_app_acb, title, "eng")

## End(Not run)

Shooting plot

Description

This plot represents the number of shots attempted and scored by every player of the same team, together with the scoring percentage. The players are sortered by percentage.

Usage

get_shooting_plot(df_stats, team, type_shot, min_att, title, language)

Arguments

Value

Graphical device.

Author(s)

Guillermo Vinue

Examples

## Not run: 
compet <- "ACB"
df <- do_join_games_bio(compet, acb_games_1718, acb_players_1718)
df1 <- do_add_adv_stats(df)
df2 <- do_stats(df1, "Total", "2017-2018", compet, "Regular Season")
get_shooting_plot(df2, "Valencia", 3, 1, 
                  paste("Valencia", compet, "2017-2018", sep = " "), "en")

## End(Not run)

Similar players to archetypoids

Description

Similar players to the archetypoids computed with archetypoids according to a similarity threshold.

Usage

get_similar_players(atype, threshold, alphas, cases, data, variables, compet, season)

Arguments

Value

Data frame with the features of the similar players.

Author(s)

Guillermo Vinue

See Also

archetypoids

Examples

(s0 <- Sys.time())
# Turn off temporarily some negligible warnings from the 
# archetypes package to avoid missunderstandings. The code works well.
library(Anthropometry)
df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)
df1 <- do_add_adv_stats(df)
df2 <- do_stats(df1, "Total", "2017-2018", "ACB", "Regular Season")
df3 <- df2[which(df2$Position == "Guard")[1:31], c("MP", "PTS", "Name")]
preproc <- preprocessing(df3[,1:2], stand = TRUE, percAccomm = 1)
set.seed(4321)
suppressWarnings(lass <- stepArchetypesRawData(preproc$data, 1:2, 
                numRep = 20, verbose = FALSE))
res <- archetypoids(2, preproc$data, huge = 200, step = FALSE, ArchObj = lass,
                    nearest = "cand_ns", sequ = TRUE)
# The S3 class of anthrCases from Anthropometry has been updated.
cases <- anthrCases(res) 
df3[cases,] # https://github.com/r-quantities/units/issues/225
alphas <- round(res$alphas, 4)
df3_aux <- df2[which(df2$Position == "Guard")[1:31], ]
get_similar_players(1, 0.99, alphas, cases, df3_aux, c("MP", "PTS"), 
                    unique(df3_aux$Compet), unique(df3_aux$Season))
s1 <- Sys.time() - s0
s1                    

Similar teams to archetypoids

Description

Similar teams to the archetypoids computed with archetypoids according to a similarity threshold.

Usage

get_similar_teams(atype, threshold, alphas, cases, data, variables)

Arguments

Value

Data frame with the features of the similar teams.

Author(s)

Guillermo Vinue

See Also

archetypoids

Examples

## Not run: 
(s0 <- Sys.time())
library(Anthropometry)
df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)
df$Compet <- "ACB"
df_teams <- do_stats_teams(df, "2017-2018", "ACB", "Regular Season")
df_team_total <- df_teams$df_team_total

df3 <- df_team_total[, c("PTS", "PTSrv", "Team")]
preproc <- preprocessing(df3[,1:2], stand = TRUE, percAccomm = 1)
set.seed(4321)
lass <- stepArchetypesRawData(preproc$data, 1:2, numRep = 20, verbose = FALSE)
res <- archetypoids(2, preproc$data, huge = 200, step = FALSE, ArchObj = lass,
                    nearest = "cand_ns", sequ = TRUE)
cases <- anthrCases(res)
df3[cases,]
alphas <- round(res$alphas, 4)

get_similar_teams(1, 0.95, alphas, cases, df_team_total, c("PTS", "PTSrv"))
s1 <- Sys.time() - s0
s1

## End(Not run)                     

Season-by-season stats

Description

This function represents the average values of a set of statistics for certain players in every season where the players played. It gives an idea of the season-by-season performance.

Usage

get_stats_seasons(df, competition, player, variabs, type_season, add_text, show_x_axis)

Arguments

Value

List with two elements:

• gg Graphical device.

• df_gg Data frame associated with the plot.

Author(s)

Guillermo Vinue

Examples

## Not run: 
competition <- "ACB"
df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)
df$Compet <- competition
player <- "Carroll, Jaycee"
variabs <- c("GP", "MP", "PTS", "EFGPerc", "TRB", "AST", "TOV", "PIR")
plot_yearly <- get_stats_seasons(df, competition, player, variabs, "All", TRUE, TRUE)
plot_yearly$gg
# There are only games from the regular season in this demo data frame.
plot_yearly1 <- get_stats_seasons(df, competition, player, variabs, "Regular Season", 
                                  TRUE, TRUE)
plot_yearly1$gg

## End(Not run)

League cross table

Description

The league results are represented with a cross table.

Usage

get_table_results(df, competition, season)

Arguments

Value

List with these two elements:

• plot_teams Graphical device with the cross table.

• wins_teams Vector with the team wins.

Author(s)

Guillermo Vinue

Examples

## Not run: 
df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)
df$Compet <- "ACB"

gg <- get_table_results(df, "ACB", "2017-2018")

gg$wins_teams
gg$plot_teams

## End(Not run)

Join ACB games and players' info

Description

This function joins the ACB games with the players' bio and computes the players' age at each game.

Usage

join_players_bio_age_acb(df_games, df_rosters)

Arguments

Value

Data frame.

Author(s)

Guillermo Vinue

See Also

do_join_games_bio

Examples

df <- join_players_bio_age_acb(acb_games_1718, acb_players_1718)
          

Join Euroleague and Eurocup games and players' info

Description

This function joins the Euroleague/Eurocup games with the players' bio and computes the players' age at each game.

Usage

join_players_bio_age_euro(df_games, df_rosters)

Arguments

Value

Data frame.

Author(s)

Guillermo Vinue

See Also

do_join_games_bio

Examples

df <- join_players_bio_age_euro(euroleague_games_1718, euroleague_players_1718)
          

ACB player game finder data

Description

This is the new function to obtain the ACB box score data.

Usage

scraping_games_acb(code, game_id, season = "2020-2021", 
                   type_season = "Regular Season",
                   user_email, user_agent_goo)

Arguments

Value

A data frame with the player game finder data (box score data).

Author(s)

Guillermo Vinue

See Also

scraping_games_acb_old

Examples

## Not run: 
# Not needed to scrape every time the package is checked, built and installed.
user_email <- "yours"       
user_agent_goo <- "yours" 
df1 <- scraping_games_acb("103350", 1, "2022_2023", "Regular Season",
                          user_email, user_agent_goo)

## End(Not run)                           

Old ACB player game finder data

Description

This function allowed us to get all the player game finder data for all the desired ACB seasons available from: https://www.acb.com. It was an old version that worked before the internal structure of the ACB website changed. The updated function is now scraping_games_acb.

Usage

scraping_games_acb_old(type_league, nums, year, verbose = TRUE, 
                       accents = FALSE, r_user = "guillermo.vinue@uv.es")

Arguments

Details

The official website of the Spanish basketball league ACB used to present the statistics of each game in a php website, such as: https://www.acb.com/fichas/LACB62090.php.

In some cases, https://www.acb.com/fichas/LACB60315.php didn't exist, so for these cases is where we can use the httr package.

Value

A data frame with the player game finder data.

Note

In addition to use the email address to stay identifiable, the function also contains two headers regarding the R platform and version used.

Furthermore, even though in the robots.txt file at https://www.acb.com/robots.txt, there is no information about scraping limitations and all robots are allowed to have complete access, the function also includes the command Sys.sleep(2) to pause between requests for 2 seconds. In this way, we don't bother the server with multiple requests and we do carry out a friendly scraping.

Author(s)

Guillermo Vinue

See Also

do_scraping_games

Examples

## Not run: 
# Not needed to scrape every time the package is checked, built and installed.
df1 <- scraping_games_acb_old(type_league = "ACB", nums = 62001:62002, year = "2017-2018",
                              verbose = TRUE, accents = FALSE, 
                              r_user = "guillermo.vinue@uv.es")

## End(Not run)                           

Euroleague and Eurocup player game finder data

Description

This function should allow us to get all the player game finder data for all the desired Euroleague and Eurocup seasons available from https://www.euroleaguebasketball.net/euroleague/game-center/ and https://www.euroleaguebasketball.net/eurocup/game-center/, respectively.

NOTE (2023): The Euroleague and Eurocup websites have changed their format, so this function will need to be updated.

Usage

scraping_games_euro(competition, nums, year, verbose = TRUE,
                    r_user = "guillermo.vinue@uv.es")

Arguments

Details

See the examples in get_games_rosters to see the game numbers to scrape in each season.

Value

A data frame with the player game finder data.

Note

In addition to use the email address to stay identifiable, the function also contains two headers regarding the R platform and version used.

Furthermore, in the robots.txt file located at https://www.euroleaguebasketball.net/robots.txt there is no Crawl-delay field. However, we assume crawlers to pause between requests for 15 seconds. This is done by adding to the function the command Sys.sleep(15).

Author(s)

Guillermo Vinue

See Also

do_scraping_games

Examples

## Not run: 
# Not needed to scrape every time the package is checked, built and installed.
# It takes 15 seconds.
df1 <- do_scraping_games(competition = "Euroleague", nums = 1:2, 
                         year = "2017", verbose = TRUE, r_user = 
                         "guillermo.vinue@uv.es")

## End(Not run)

ACB players' profile

Description

This function allows us to obtain the basic information of each player, including his birth date. Then, we will be able to compute the age that each player had in the date that he played each game. The website used to collect information is https://www.acb.com.

Usage

scraping_rosters_acb(pcode, verbose = TRUE, accents = FALSE, 
                     r_user = "guillermo.vinue@uv.es")

Arguments

Details

Some players have a particular licence, which does not necessarily match with their nationality, in order not to be considered as a foreign player, according to the current ACB rules.

Value

Data frame with eight columns:

• CombinID: Unique ID to identify the players.

• Player: Player's name.

• Position: Player's position on the court.

• Height: Player's height.

• Date_birth: Player's birth date.

• Nationality: Player's nationality.

• Licence: Player's licence.

• Website_player: Website.

Note

In addition to use the email address to stay identifiable, the function also contains two headers regarding the R platform and version used.

Furthermore, even though in the robots.txt file at https://www.acb.com/robots.txt, there is no information about scraping limitations and all robots are allowed to have complete access, the function also includes the command Sys.sleep(2) to pause between requests for 2 seconds. In this way, we don't bother the server with multiple requests and we do carry out a friendly scraping.

Author(s)

Guillermo Vinue

See Also

do_scraping_rosters

Examples

## Not run: 
# Not needed to scrape every time the package is checked, built and installed.
df_bio <- scraping_rosters_acb("56C", verbose = TRUE, accents = FALSE, 
                               r_user = "guillermo.vinue@uv.es")

## End(Not run)

Euroleague and Eurocup players' profile

Description

This function should allow us to obtain the basic information of each Euroleague/Eurocup player, including his birth date. Then, we will be able to compute the age that each player had in the date that he played each game. The websites used to collect information are https://www.euroleaguebasketball.net/euroleague/ and https://www.euroleaguebasketball.net/eurocup/.

Usage

scraping_rosters_euro(competition, pcode, year, verbose = TRUE, 
                      r_user = "guillermo.vinue@uv.es")

Arguments

Value

Data frame with seven columns:

• CombinID: Unique ID to identify the players.

• Player: Player's name.

• Position: Player's position on the court.

• Height: Player's height.

• Date_birth: Player's birth date.

• Nationality Player's nationality.

• Website_player: Website.

Note

In addition to use the email address to stay identifiable, the function also contains two headers regarding the R platform and version used.

https://www.euroleaguebasketball.net/robots.txt there is no Crawl-delay field. However, we assume crawlers to pause between requests for 15 seconds. This is done by adding to the function the command Sys.sleep(15).

Author(s)

Guillermo Vinue

See Also

do_scraping_rosters

Examples

## Not run: 
# Not needed to scrape every time the package is checked, built and installed.
# It takes 15 seconds.
df_bio <- scraping_rosters_euro("Euroleague", "005791", "2017", verbose = TRUE,
                                 r_user = "guillermo.vinue@uv.es")

## End(Not run)