Package 'BAwiR'

Description

Roster of the ACB team Valencia Basket for the 2025-2026 season and its players' age profiles.

Usage

acb_age_profile_data_2526

Format

Data frame with 15 rows and 7 columns.

Source

https://www.acb.com/

Description

Data set for illustrative purposes with the Unicaja five-men and one-man lineups in the game 104459 from the ACB 2025-2026 regular season.

Usage

acb_combs

Format

Data frame with 150 rows and 38 columns.

Source

https://www.acb.com/

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/

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://live.acb.com/es/partidos/103389/jugadas.

Source

https://www.acb.com/

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://live.acb.com/es/partidos/103389/jugadas.

Source

https://www.acb.com/

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/

Description

Player unique identifiers of a sample of 30 players from the ACB 2024-2025 season.

Usage

acb_players_2425

Format

Data frame with 30 rows and 5 columns.

Source

https://www.acb.com/

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/

Description

Spatial shooting data from a sample of 30 players from the ACB 2024-2025 season. See also acb_players_2425.

Usage

acb_shooting_data_2425

Format

Data frame with 4277 rows and 23 columns.

Source

https://www.acb.com/

Description

This data frame contains an example of the usage and context statistics for the Real Madrid player Alberto Abalde to illustrate the creation of the sticker plot.

Usage

acb_sticker_data_2526

Format

Data frame with 11 rows and 9 columns.

Source

https://www.acb.com/

Description

Sample of possessions played and ended for a game from the ACB 2025-2026 season and the particular number of actions used by the player to end the possession.

Usage

acb_usage_act_data_2526

Format

Data frame with 75 rows and 8 columns.

Source

https://www.acb.com/

Description

Sample of possessions played and ended for a game from the ACB 2025-2026 season. For each player we have the number of possessions he played and the ones he ended.

Usage

acb_usage_data_2526

Format

Data frame with 62 rows and 9 columns.

Source

https://www.acb.com/

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://live.acb.com/es/partidos/103389/jugadas.

Source

https://www.acb.com/

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://live.acb.com/es/partidos/103389/jugadas.

Source

https://www.acb.com/

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")

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)

Description

Creates a visualization of the players who shoot little and score a lot in several zones at the same time.

Usage

do_best_zones(data_best_archetypoid)

Arguments

Value

A plot.

Author(s)

Guillermo Vinue

See Also

archetypoids

Examples

## Not run: 
library(dplyr)
library(Anthropometry)

zones_court <- metrics_player_zone %>% 
  distinct(location) %>%
  pull()

numArch <- 10 
numRep <- 20
numArchoid <- 2 # Number of archetypoids.

data_arch <- data.frame()

# Run the algorithm for each zone one by one and save the archetypoid 
# with least shots and highest percentage.
i <- 1

zone <- metrics_player_zone %>% 
  filter(location == zones_court[i]) %>% 
  select(-pps_player)

zone_num <- zone %>% 
  select(total, perc_player) 

lass <- stepArchetypesRawData(data = zone_num, numArch = 1:numArch, 
                              numRep = numRep, verbose = FALSE)  

res_ns <- archetypoids(numArchoid, zone_num, huge = 200, step = FALSE, 
                       ArchObj = lass, nearest = "cand_ns",sequ = TRUE)
zone[res_ns$cases, ]                        

# Here [1, ] indicates the archetypoid of interest. Change it accordingly.
# Here 4 indicates the number of similar players to the archetypoid. Change it accordingly.
arch_targ <- zone[order(res_ns$alphas[1, ], decreasing = TRUE)[1:4], ]
data_arch <- rbind(data_arch, arch_targ)

i <- 2

zone <- metrics_player_zone %>% 
  filter(location == zones_court[i]) %>% 
  select(-pps_player)

zone_num <- zone %>% 
  select(total, perc_player) 

lass <- stepArchetypesRawData(data = zone_num, numArch = 1:numArch, 
                              numRep = numRep, verbose = FALSE)  

res_ns <- archetypoids(numArchoid, zone_num, huge = 200, step = FALSE, 
                       ArchObj = lass, nearest = "cand_ns",sequ = TRUE)

arch_targ <- zone[order(res_ns$alphas[2, ], decreasing = TRUE)[1:10], ]
data_arch <- rbind(data_arch, arch_targ)

do_best_zones(data_arch)

## End(Not run)

Description

Obtain the games that have clutch time. The clutch time is generally defined as the game situation when the scoring margin is within 5 points with five or fewer minutes remaining in a game. In order to allow more clutch situations, the user will be able to set the minutes left and the scoring difference.

Usage

do_clutch_time(data, min_left = "05:00", score_diff = 5)

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.

Description

Divide the basketball court into 10 zones.

Usage

do_divide_court_zones(data_shots)

Arguments

Value

The shooting data frame with a new column called "location" indicating the zone from which each shot was taken.

Author(s)

Guillermo Vinue

Examples

## Not run: 
do_divide_court_zones(acb_shooting_data_2425)

## End(Not run)

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]

Description

Filter the shooting data with the team or player of interest, and also by periods, minutes and game place. If neither team nor player is given, the data from the whole league is used.

Usage

do_filter_data(data_shots_zones, season_str, team, period, minute_vect, place, player)

Arguments

Value

A data frame with the filters applied.

Author(s)

Guillermo Vinue

See Also

do_divide_court_zones

Examples

## Not run: 
df0 <- do_divide_court_zones(acb_shooting_data_2425)

# Data for the whole league:
df1 <- do_filter_data(df0, "2024-2025", "", "", "", "", "")

# Data for a team:
df1 <- do_filter_data(df0, "2024-2025", "UCAM Murcia", "", "", "", "")

# Data for a player:
df1 <- do_filter_data(df0, "2024-2025", "", "", "", "", "D. Ennis")

# Other filters:
# By minutes:
df1 <- do_filter_data(df0, "2024-2025", "", "", c(8,10), "", "D. Ennis")

## End(Not run)

Description

This function computes team's offense and defense four factors.

The four factors are the effective field goal percentage (EFGP), the turnover percentage (TOVP), the offensive rebound percentage (ORBP) and the 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, and FTRate is a measure of how often a team gets to the line.

Usage

do_four_factors_df(df_games, teams, data_team_reb_tov)

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", NULL)

Description

This function computes offense and defense four factors for a given lineup. The four factors are the effective field goal percentage (EFGP), the turnover percentage (TOVP), the offensive rebound percentage (ORBP) and the 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; ORBP measures how many rebounds were offensive from the total of available rebounds, and FTRate is a measure of how often a team gets to the line.

Usage

do_four_factors_df_lineup(data_combs, team_name, type_lineup, type_period, type_opponent)

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.

Value

A data frame with the four factors in defense and in offense for a given lineup.

Author(s)

Guillermo Vinue

See Also

do_four_factors_df

Examples

## Not run: 
do_four_factors_df_lineup(acb_combs, "Unicaja", "quintet", NULL, NULL)

## End(Not run)

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

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)

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)

# Starting players:
acb_games_2223_sl <- acb_vbc_cz_sl_2223 %>%
  filter(period == "1C")

# Prepare data:
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)

Description

For every lineup, compute the number of possessions, points, assists, turnovers, field goals, rebounds and assisted field goals, both in defense and offense.

Usage

do_lineup_metrics(data_possess, team_sel, team_opp)

Arguments

Value

Data frame. Each row is a different lineup.

Author(s)

Guillermo Vinue

Examples

## Not run: 
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)

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

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

teams_game <- sort(unique(df1$team))
team_sel <- teams_game[1]

data <- df1
data <- data %>%
  mutate(row_num = row_number()) %>%
  mutate(time_point = ifelse(nchar(time_point) < 5, paste0("0", time_point), time_point))

# Filter by team:
data1 <- data %>%
  filter(team == team_sel)

# Set also the opponent team:
team_opp <- setdiff(unique(data$team), team_sel)

# Add the last row of games' data to have the real final 
# game score in case it is not available:
last_row_game <- data[nrow(data),]

last_row_game$time_point <- "00:00"
last_row_game$player <- NA
last_row_game$action <- NA
last_row_game$team <- team_sel

data1 <- bind_rows(data1, last_row_game)

# Get players out:
pl_out <- c(1, which(data1$action == "Sale de la pista"), nrow(data1))

i <- 1
data2 <- data1 %>%
  slice(pl_out[i]:pl_out[i + 1]) 

nr <- nrow(data2)

# Lineup:
lineup <- data2 %>%
  filter(action != "Sale de la pista") %>%
  # Avoid actions that are assigned to teams: 
  filter(player != team_sel) %>%
  distinct(player) %>%
  pull()

# Identify when the possessions start:
data2_rival <- data %>%
  filter(team == team_opp) %>%
  filter(between(row_num, data2$row_num[1], data2$row_num[nr]))

data3 <- rbind(data2, data2_rival) %>%
  arrange(row_num) %>%
  na.omit()

data4 <- do_possession(data3, NULL, "10:00") 

data4[data4$action == "Mate", "action"] <- "Tiro de 2 anotado"

# Obtain metrics for the lineup:
data5 <- do_lineup_metrics(data4, team_sel, team_opp)

## End(Not run)

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

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,])

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

Description

Compute when the possession starts for each team in a game.

Usage

do_possession(data, period_sel, time_point_start)

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://live.acb.com/es/partidos/103389/jugadas.

Author(s)

Guillermo Vinue

Examples

## Not run: 
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)

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

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

teams_game <- sort(unique(df1$team))
team_sel <- teams_game[1]

data <- df1
data <- data %>%
  mutate(row_num = row_number()) %>%
  mutate(time_point = ifelse(nchar(time_point) < 5, paste0("0", time_point), time_point))

# Filter by team:
data1 <- data %>%
  filter(team == team_sel)

# Set also the opponent team:
team_opp <- setdiff(unique(data$team), team_sel)

# Add the last row of games' data to have the real final 
# game score in case it is not available:
last_row_game <- data[nrow(data),]

last_row_game$time_point <- "00:00"
last_row_game$player <- NA
last_row_game$action <- NA
last_row_game$team <- team_sel

data1 <- bind_rows(data1, last_row_game)

# Get players out:
pl_out <- c(1, which(data1$action == "Sale de la pista"), nrow(data1))

i <- 1
data2 <- data1 %>%
  slice(pl_out[i]:pl_out[i + 1]) 

nr <- nrow(data2)

# Lineup:
lineup <- data2 %>%
  filter(action != "Sale de la pista") %>%
  # Avoid actions that are assigned to teams: 
  filter(player != team_sel) %>%
  distinct(player) %>%
  pull()

# Identify when the possessions start:
data2_rival <- data %>%
  filter(team == team_opp) %>%
  filter(between(row_num, data2$row_num[1], data2$row_num[nr]))

data3 <- rbind(data2, data2_rival) %>%
  arrange(row_num) %>%
  na.omit()

data4 <- do_possession(data3, NULL, "10:00") 

## End(Not run)

Description

Compute the possessions-related statistics for teams. These statistics are offensive rating, defensive rating, net rating, pace and number of possessions.

Usage

do_possession_stats(data_possess, season = "2025-2026")

Arguments

Details

See https://www.basketball-reference.com/about/glossary.html for formulas and explanations.

Both teams in the same game share the same pace. Pace reflects the tempo of the game itself, not just one team's style. Over many games, a team's average pace reflects how fast they usually play, but any individual game's pace is shared with their opponent.

Value

A data frame with the possessions statistics for each team.

Author(s)

Guillermo Vinue

See Also

do_possession

Examples

## Not run: 
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)

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

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

teams_game <- sort(unique(df1$team))

# Study the lineups:
data_res <- do_study_lineup(df1, day_num, game_code, teams_game[1], "2022-2023", FALSE) 
data_all_posse <- data_res$data_pos %>% select(-row_num)

# Statistics of possessions:
data_poss_st <- do_possession_stats(data_all_posse, "2022-2023")

## End(Not run)

Description

Compute the possessions-related statistics for lineups. These statistics are offensive rating, defensive rating, net rating, pace and number of possessions.

Usage

do_possession_stats_lineup(data_combs, team_name, type_lineup, type_period, type_opponent, 
                           cols_group = c("team", "lineup"))

Arguments

Details

See https://www.basketball-reference.com/about/glossary.html for formulas and explanations.

Value

A data frame with the possessions statistics for each lineup.

Author(s)

Guillermo Vinue

See Also

do_possession_stats

Examples

## Not run: 
do_possession_stats_lineup(acb_combs, "Unicaja", "quintet", NULL, NULL)

## End(Not run)

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://live.acb.com/es/partidos/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

Description

Prepare the data for the gradient shooting visualizations at a player level.

Usage

do_prepare_data_gradient(all_shots_pl, summary_shots_zone_pl, summary_shots_zone_league)

Arguments

Value

A list with the following three elements:

• all_shots_comp_data: Summary of the shooting data of the player and of the league.

• all_shots_comp_viz: Summary of the shooting data prepared for the visualization.

• player: Player's name.

Author(s)

Guillermo Vinue

See Also

do_divide_court_zones, do_shots_stats

Examples

## Not run: 
library(dplyr)

df0 <- do_divide_court_zones(acb_shooting_data_2425)

df1 <- do_filter_data(df0, "2024-2025", "", "", "", "", "")

# LEAGUE METRICS:
shots_stats <- do_shots_stats(df1, df0) 

summary_shots_zone_lg <- shots_stats$summary_shots_zone

summary_shots_zone_league <- summary_shots_zone_lg %>%
  mutate(pps_league = ifelse(location_color == "2pt", 
                             (2 * count) / total,  
                             (3 * count) / total)) %>%
  select(location, perc_league = perc, pps_league)

# PLAYER METRICS:
df1 <- do_filter_data(df0, "2024-2025", "", "", "", "", "D. Ennis")

shots_stats <- do_shots_stats(df1, df0) 

all_shots_pl <- shots_stats$all_shots

summary_shots_zone_pl <- shots_stats$summary_shots_zone

res_grad <- do_prepare_data_gradient(all_shots_pl, summary_shots_zone_pl, summary_shots_zone_league)

## End(Not run)

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://live.acb.com/es/partidos/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

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://live.acb.com/es/partidos/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

Description

Preprocess the data that will be needed for computing statistics per period.

Usage

do_preproc_period(data, team_sel, period_sel, data_sl)

Arguments

Author(s)

Guillermo Vinue

Examples

team_sel <- "Valencia Basket"
period_sel <- "1C"

pre_per <- do_preproc_period(acb_vbc_cz_pbp_2223, team_sel, period_sel, acb_vbc_cz_sl_2223)

df2 <- pre_per$df2
df0_inli_team <- pre_per$df0_inli_team

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://live.acb.com/es/partidos/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)

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

Description

Counts the number of times that a given R package was downloaded in a given year.

Usage

do_rpackage_stats(r_packages, year, verbose)

Arguments

Value

A data frame.

Author(s)

Guillermo Vinue

See Also

cranDownloads

Examples

## Not run: 
do_rpackage_stats(c("BAwiR", "BasketballAnalyzeR"), 2025, TRUE)

## End(Not run)

Description

Obtain the game codes of any regular season day from any ACB season. These game codes will be used to define the target url from which collecting the shooting data of every game.

NOTE: The ACB website has changed its format, so this function is not valid anymore. I keep it in the package in case it can provide users with some insights to carry out a friendly web scraping procedure.

Usage

do_scrape_days_acb(season, analyst_name, verbose, num_days, edition_id)

Arguments

Value

A data frame with two columns, one with the days and the other with the game codes.

Note

Before starting the web scraping, we must visit https://www.acb.com/robots.txt to check for permissions.

Author(s)

Guillermo Vinue

See Also

do_scrape_shots_acb

Examples

## Not run: 
data_days <- do_scrape_days_acb("2024", "analyst_name", TRUE, 2, 975)

## End(Not run)

Description

Obtain the shooting data from the ACB website and creates a common R data structure. Each shot is described with its (x, y) coordinates and other additional information, such as the outcome of the shot (made or missed) or the player who took that shot.

Usage

do_scrape_shots_acb(data_days, verbose, user_agent_def, x_apikey)

Arguments

Value

A data frame with the shooting data.

Note

The original codes of the playType column have the following meaning: 92: ft made; 93: 2pt made; 94: 3pt made; 96: ft missed. 97: 2pt missed; 98: 3pt missed; 100: dunk.

Author(s)

Guillermo Vinue

See Also

do_scrape_days_acb

Examples

## Not run: 
data_days <- do_scrape_days_acb("2024", "analyst_name", TRUE, 2, 975)

data_shots <- do_scrape_shots_acb(data_days[1:2, ], TRUE, "user_agent_def", "x_apikey")

## End(Not run)

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 = "[email protected]")
                         
df1_eur <- do_scraping_games(competition = "Euroleague", nums = 1,
                         year = "2017", verbose = TRUE,
                         r_user = "[email protected]")                          

## End(Not run)

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 = "[email protected]")
                              
df_bio_eur <- do_scraping_rosters(competition = "Euroleague", pcode = "007969", 
                              year = "2017", verbose = TRUE, 
                              r_user = "[email protected]")                               

## End(Not run)

Description

Compute both the total and by zone two-point and threes statistics.

Usage

do_shots_stats(data_filter, data_shots_zones)

Arguments

Value

A list with the following three elements:

• all_shots: Shooting data frame associated with the filters given to the function.

• summary_shots: Summary of the shots as a whole.

• summary_shots_zone: Summary of the shots by zone.

Author(s)

Guillermo Vinue

See Also

do_divide_court_zones, do_filter_data

Examples

## Not run: 
df0 <- do_divide_court_zones(acb_shooting_data_2425)

df1 <- do_filter_data(df0, "2024-2025", "", "", "", "", "")

shots_stats <- do_shots_stats(df1, df0) 

all_shots <- shots_stats$all_shots

summary_shots <- shots_stats$summary_shots

summary_shots_zone <- shots_stats$summary_shots_zone

## End(Not run)

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")

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://live.acb.com/es/partidos/103389/jugadas.

Author(s)

Guillermo Vinue

Examples

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

team_sel <- "Valencia Basket"
period_sel <- "1C"
player_sel <- "Webb" 
 
pre_per <- do_preproc_period(acb_vbc_cz_pbp_2223, team_sel, period_sel, acb_vbc_cz_sl_2223)
 
df2 <- pre_per$df2
df0_inli_team <- pre_per$df0_inli_team
 
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

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

Description

This is an improvement of do_lineup to obtain all the information related to the lineups that a given team shows during a game.

Usage

do_study_lineup(data, day_num, game_code_num, team_sel, season = "2025-2026", verbose)

Arguments

Value

A list with four data frames:

data_lin: Statistics obtained by every lineup. data_pos: Start of each possession. data_usg: Possessions ended by each player. data_usg_act: Actions that ended each possession per player.

Note

A possession lasts 24 seconds in the ACB league.

Author(s)

Guillermo Vinue

See Also

do_lineup, do_possession, do_lineup_metrics, do_usage

Examples

## Not run: 
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)

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

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

teams_game <- sort(unique(df1$team))

# Study the lineups:
data_res <- do_study_lineup(df1, day_num, game_code, teams_game[1], "2022-2023", FALSE) 

## End(Not run)

Description

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

Usage

do_sub_lineup(data, elem_choose, col_diff_points = FALSE)

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 the sub-lineup starts playing together. team_out: Time point when the sub-lineup stops playing together (because there is a substitution). time_seconds: Total of seconds that the sub-lineup played.

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

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

Description

This is an alternative to do_time_out_success from season 2025-2026 because to my best of knowledge the timeouts are not directly collected from web scraping and I have to check manually when they were called.

Usage

do_time_out_success_altern(data, day_num, game_code, team_sel, data_to_list, 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_time_out_success

Description

This function computes the players' usage to indicate how many possessions each player ended. A possession ends with a field-goal or free-throw attempt, or with a turnover.

Usage

do_usage(data_all_posse, team_name, lineup_curr, season = "2025-2026")

Arguments

Value

A list with two data frames:

data_all: Possessions ended by each player. data_all_act: Actions that ended each possession per player.

Author(s)

Guillermo Vinue

Examples

## Not run: 
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)

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

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

teams_game <- sort(unique(df1$team))
team_sel <- teams_game[1]

data <- df1
data <- data %>%
  mutate(row_num = row_number()) %>%
  mutate(time_point = ifelse(nchar(time_point) < 5, paste0("0", time_point), time_point))

# Filter by team:
data1 <- data %>%
  filter(team == team_sel)

# Set also the opponent team:
team_opp <- setdiff(unique(data$team), team_sel)

# Add the last row of games' data to have the real final 
# game score in case it is not available:
last_row_game <- data[nrow(data),]

last_row_game$time_point <- "00:00"
last_row_game$player <- NA
last_row_game$action <- NA
last_row_game$team <- team_sel

data1 <- bind_rows(data1, last_row_game)

# Get players out:
pl_out <- c(1, which(data1$action == "Sale de la pista"), nrow(data1))

i <- 1
data2 <- data1 %>%
  slice(pl_out[i]:pl_out[i + 1]) 

nr <- nrow(data2)

# Lineup:
lineup <- data2 %>%
  filter(action != "Sale de la pista") %>%
  # Avoid actions that are assigned to teams: 
  filter(player != team_sel) %>%
  distinct(player) %>%
  pull()

# Identify when the possessions start:
data2_rival <- data %>%
  filter(team == team_opp) %>%
  filter(between(row_num, data2$row_num[1], data2$row_num[nr]))

data3 <- rbind(data2, data2_rival) %>%
  arrange(row_num) %>%
  na.omit()

data4 <- do_possession(data3, NULL, "10:00") 

data4[data4$action == "Mate", "action"] <- "Tiro de 2 anotado"

data4_usg <- do_usage(data4, team_sel, lineup, "2022-2023")

## End(Not run)

Description

Create violin plots and boxplots to analyze the distribution of the two-point, three-point and total shots. Violin plots show the distribution shape, while boxplots give a compact statistical summary.

Usage

do_violin_box_plots(data_shots, data_players)

Arguments

Value

A plot.

Author(s)

Guillermo Vinue

Examples

## Not run: 
df0 <- do_divide_court_zones(acb_shooting_data_2425)

df1 <- do_filter_data(df0, "2024-2025", "", "", "", "", "")

do_violin_box_plots(df1, acb_players_2425)

## End(Not run)

Description

Create a visualization of the left half of the court and compare either the field goal percentage or the points per shot of a given player with respect to the league. In addition, it can also show a heatmap with the zones where the player takes the shots.

Usage

do_viz_shots_gradient(data_filter, type, metric, data_shots_zones, language = "English")

Arguments

Value

A plot.

Author(s)

Guillermo Vinue

See Also

do_divide_court_zones, do_filter_data, do_shots_stats, do_prepare_data_gradient

Examples

## Not run: 
df0 <- do_divide_court_zones(acb_shooting_data_2425)

df1 <- do_filter_data(df0, "2024-2025", "", "", "", "", "")

do_viz_shots_gradient(df1, "all", "none", df0)

df1 <- do_filter_data(df0, "2024-2025", "", "", "", "", "D. Ennis")

do_viz_shots_gradient(df1, "player", "none", df0)
do_viz_shots_gradient(df1, "player", "fg", df0)

df1 <- do_filter_data(df0, "2024-2025", "Valencia Basket", "", "", "", "")

do_viz_shots_gradient(df1, "team", "none", df0)

## End(Not run)

Description

Create a visualization of the left half of the court and annotates both the total and by zone shooting statistics. It can also show the location of each individual shot, with color-coding for makes and misses.

Usage

do_viz_shots_scatter(shots_stats, type, draw, size_lab_box = 2.8, size_lab_court = 3, 
                     size_point = 3, language = "English")

Arguments

Value

A plot.

Author(s)

Guillermo Vinue

See Also

do_divide_court_zones, do_filter_data, do_shots_stats

Examples

## Not run: 
df0 <- do_divide_court_zones(acb_shooting_data_2425)

df1 <- do_filter_data(df0, "2024-2025", "", "", "", "", "")

shots_stats <- do_shots_stats(df1, df0) 

do_viz_shots_scatter(shots_stats, "all", FALSE) 
do_viz_shots_scatter(shots_stats, "all", TRUE)

df1 <- do_filter_data(df0, "2024-2025", "", "", "", "", "D. Ennis")

shots_stats <- do_shots_stats(df1, df0) 

do_viz_shots_scatter(shots_stats, "player", FALSE) 
do_viz_shots_scatter(shots_stats, "player", TRUE)
do_viz_shots_scatter(shots_stats, "player", TRUE, language = "Spanish")

## End(Not run)

Description

This function computes the three-point shots volume, both in offense in defense. This volume is defined as the percentage of three-point shots attempted with respect to the total field-goal attempts.

Usage

do_volume_threes(df)

Arguments

Value

A data frame with the volume statistics.

Author(s)

Guillermo Vinue

Examples

library(dplyr)

df0 <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718) 

df1 <- df0 %>% rename(game_code = Game)

data_volume <- do_volume_threes(df1)

data_volume$data_volume_threes
data_volume$data_volume_threes_comp

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/

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/

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/

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/

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)

Description

This function creates a donut chart that displays the percentage of possessions that each player of a team ends while being on the court.

Usage

get_donut_usage(data_usage, team_sel, size_play, size_perc)

Arguments

Details

Donut charts are an alternative for pie charts, which have a hole in the middle, making them cleaner to read than pie charts.

Value

A donut chart.

Author(s)

Guillermo Vinue with the help of ChatGPT.

See Also

get_donut_usage_action

Examples

## Not run: 
get_donut_usage(acb_usage_data_2526, "Valencia Basket", 3, 4)

## End(Not run)

Description

This function creates a donut chart that displays the percentage of possessions that each player of a team ends with a particular action (turnover, field goal attempted or free throw attempted) while being on the court.

Usage

get_donut_usage_action(data_usage_act, team_sel, type_play, 
                       language, min_poss, min_perc, size_orl,
                       size_irl = 4, vjust_title = 1)

Arguments

Details

Donut charts are an alternative for pie charts, which have a hole in the middle, making them cleaner to read than pie charts.

Value

A donut chart.

Author(s)

Guillermo Vinue with the help of ChatGPT.

See Also

get_donut_usage

Examples

## Not run: 
get_donut_usage_action(acb_usage_act_data_2526, "Valencia Basket", "two", "English", 1, 1, 3)
# For example, the interpretation here is that Sako finishes the 66.67% of his possessions
# scoring a two-point shot.

get_donut_usage_action(acb_usage_act_data_2526, "Valencia Basket", "tov", "English", 1, 1, 5)

## End(Not run)

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, scope = "def_off")

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, NULL)

# 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")

# Example with only defense:
get_four_factors_plot(df_four_factors$df_rank, df_four_factors$df_no_rank, team, 
                      "en", "def") + 
 ggplot2::theme(legend.position = "none")

## End(Not run)

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 = "[email protected]", 
                              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 = "[email protected]", 
                              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 = "[email protected]", 
                              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 = "[email protected]", 
                              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 = "[email protected]", 
                              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 = "[email protected]", 
                              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)

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)

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)

Description

The Net Rating On/Off measures a team's net rating when a specific player is on the court versus when they are on the bench. It highlights the player's impact by comparing the team's efficiency both in offense and defense. A positive differential indicates a positive, higher-impact player.

Usage

get_net_rtg_on_off(data_combs, team_name, type_lineup, type_period, type_opponent, 
                   filter_players, language, asp_ratio = 1)

Arguments

Details

• Net Rating: difference between Offensive Rating and Defensive Rating.

• On-Court Rating: team's net rating while the player is in the game.

• Off-Court Rating: team's net rating while the player is on the bench.

• On/Off Differential: difference between the on-court and off-court net ratings, which gives a first idea of how much better/worse the team performs with that player.

Value

A plot with the players' net ratings and differentials.

Author(s)

Guillermo Vinue

See Also

do_possession_stats_lineup

Examples

## Not run: 
get_net_rtg_on_off(acb_combs, "Unicaja", "single", NULL, NULL, NULL, "Spanish")

## End(Not run)

Description

In all the available basketball websites, the stats are presented for the whole number of games played. This function represents the players' stats for each month, which is very useful to analyze the players' evolution. The plot can be either a bar plot or a line plot.

Usage

get_plot_monthly_stats(df_stats, title, size_text = 2.5, type_plot, language, 
                       same_team = FALSE, hjust_val = 2, vjust_val = 0.5)

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, all_of(stats), 46:50) %>%
  mutate(Month = plyr::mapvalues(Month, 
                                 from = c("octubre", "noviembre", "diciembre", "enero"),
                                 to = c("October", "November", "December", "January")))
  
get_plot_monthly_stats(df3_m1, paste("ACB", "2017-2018", "Average", sep = " ; "), 
                       2.5, "bar_plot", "English")
                       
get_plot_monthly_stats(df3_m1, paste("ACB", "2017-2018", "Average", sep = " ; "), 
                       2.5, "line_plot", "English")

## End(Not run)

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)

Description

For the players of the same team, show their age at time of joining the team, their current year and how many years they have spent in the team.

Usage

get_roster_age_profile(data_age_team, team_sel, language)

Arguments

Value

Graphical device.

Author(s)

Guillermo Vinue

Examples

## Not run: 
get_roster_age_profile(acb_age_profile_data_2526, "Valencia Basket", "es")

## End(Not run)

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, 
                         size_summ = 5, size_add = 16)

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)

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

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)

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)

Description

This function creates players' cards (a kind of sticker) that bring together for each player three of the concepts considered most important in basketball analytics. These are efficiency (how many points the team scores and receives per 100 possessions with the player on the floor), the shooting context (from where and with what percentage they shoot) and the use of possessions (how they end the possessions they execute).

Usage

get_sticker(data_player_eff, data_team, player_sel, language = "English",
            change_hjust_perc = FALSE, size_head = c(0.4, 0.3, 5.6, 3.5), 
            size_eff = 2, size_cont_us = c(2.3, 1.7), size_plot_tit = 8,
            hjust_title = 0.1, xjust_vt = c(0.392, 0.3), size_netrtg = 20)

Arguments

Value

A plot.

Author(s)

Guillermo Vinue

Examples

## Not run: 
# The efficiency data frame must have this type of structure:
data_player_eff <- data.frame(team = "Real Madrid", 
                              win_perc = "83.3% (5/6)",
                              pts_poss = 114,
                              pts_poss_opp = 104,
                              net_rtg = 10)

get_sticker(data_player_eff, acb_sticker_data_2526, "A. Abalde", language = "English")
get_sticker(data_player_eff, acb_sticker_data_2526, "A. Abalde", language = "Spanish", TRUE)

## End(Not run)

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)

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)

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)

Description

Metrics (total shots, field goal percentages and points per shot) at some of the court zones for some of the 30 players contained in the data frame acb_players_2425.

Usage

metrics_player_zone

Format

Data frame with 8 rows and 5 columns.

Source

https://www.acb.com/

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)

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 = "[email protected]")

Arguments