| Title: | 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>. |
| Authors: | Guillermo Vinue |
| Maintainer: | Guillermo Vinue <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.3.2 |
| Built: | 2024-12-05 07:07:50 UTC |
| Source: | CRAN |
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>.
| Package: BAwiR |
| Type: Package |
| Version: 1.3.2 |
| Date: 2024-01-09 |
| License: GPL-2 |
| LazyLoad: yes |
| LazyData: yes |
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.
Guillermo Vinue <[email protected]>, <[email protected]>
Vinue, G., (2020). A Web Application for Interactive Visualization of European Basketball Data, Big Data 8(1), 70-86. http://doi.org/10.1089/big.2018.0124, https://www.uv.es/vivigui/AppEuroACB.html
Games of the first seventeen days of the ACB 2017-2018 season.
acb_games_1718acb_games_1718
Data frame with 3939 rows and 38 columns.
Coach for each team in all the games of the ACB 2022-2023 season.
acb_games_2223_coachacb_games_2223_coach
Data frame with 612 rows and 4 columns.
The game_code column allows us to detect the source website, for example, https://jv.acb.com/es/103389/jugadas.
Game codes, games and days from the ACB 2022-2023 season.
acb_games_2223_infoacb_games_2223_info
Data frame with 306 rows and 3 columns.
The game_code column allows us to detect the source website, for example, https://jv.acb.com/es/103389/jugadas.
Players corresponding to the games of the first seventeen days of the ACB 2017-2018 season.
acb_players_1718acb_players_1718
Data frame with 255 rows and 7 columns.
Links to the official shields of the ACB teams.
acb_shieldsacb_shields
Data frame with 20 rows and 2 columns.
Play-by-play data from the game Valencia Basket-Casademont Zaragoza from the ACB 2022-2023 season.
acb_vbc_cz_pbp_2223acb_vbc_cz_pbp_2223
Data frame with 466 rows and 9 columns.
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.
Starting lineups in each period from the game Valencia Basket-Casademont Zaragoza from the ACB 2022-2023 season.
acb_vbc_cz_sl_2223acb_vbc_cz_sl_2223
Data frame with 40 rows and 9 columns.
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.
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.
capit_two_words(two_word_string)capit_two_words(two_word_string)
two_word_string |
Two-word string. |
Vector with the two words capitalized.
Guillermo Vinue
capit_two_words("valencia basket")capit_two_words("valencia basket")
This function adds to the whole data frame the advanced statistics for every player in every game.
do_add_adv_stats(df)do_add_adv_stats(df)
df |
Data frame with the games and the players info. |
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/.
Data frame.
Guillermo Vinue
df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718) df1 <- do_add_adv_stats(df)df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718) df1 <- do_add_adv_stats(df)
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.
do_clutch_time(data)do_clutch_time(data)
data |
Source play-by-play data. |
Data frame of the game that has clutch time.
Guillermo Vinue
df0 <- do_clutch_time(acb_vbc_cz_pbp_2223) #df0 # If no rows, that means that the game did not have clutch time.df0 <- do_clutch_time(acb_vbc_cz_pbp_2223) #df0 # If no rows, that means that the game did not have clutch time.
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.
do_EPS(df)do_EPS(df)
df |
Data frame with the games and the players info. |
EPS values.
Guillermo Vinue
Shea, S., Baker, C., (2013). Basketball Analytics: Objective and Efficient Strategies for Understanding How Teams Win. Lake St. Louis, MO: Advanced Metrics, LLC.
df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718) df1 <- do_add_adv_stats(df) do_EPS(df1)[1]df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718) df1 <- do_add_adv_stats(df) do_EPS(df1)[1]
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.
do_four_factors_df(df_games, teams)do_four_factors_df(df_games, teams)
df_games |
Data frame with the games, players info, advanced stats and eventually recoded teams names. |
teams |
Teams names. |
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.
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.
Guillermo Vinue
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")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 how many 1-,2- and 3-free throw fouls has committed or received every player.
do_ft_fouls(data, type)do_ft_fouls(data, type)
data |
Play-by-play data. |
type |
Either 'comm' (for committed) or 'rec' (for received). |
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.
Guillermo Vinue
df01 <- do_ft_fouls(acb_vbc_cz_pbp_2223, "comm") #df01 df02 <- do_ft_fouls(acb_vbc_cz_pbp_2223, "rec") #df02df01 <- do_ft_fouls(acb_vbc_cz_pbp_2223, "comm") #df01 df02 <- do_ft_fouls(acb_vbc_cz_pbp_2223, "rec") #df02
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.
do_join_games_bio(competition, df_games, df_rosters)do_join_games_bio(competition, df_games, df_rosters)
competition |
String. Options are "ACB", "Euroleague" and "Eurocup". |
df_games |
Data frame with the games. |
df_rosters |
Data frame with the biography of the roster players. |
Data frame.
Guillermo Vinue
join_players_bio_age_acb, join_players_bio_age_euro
df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)
Compute all the lineups that a given team shows during a game.
do_lineup(data, day_num, game_code, team_sel, verbose)do_lineup(data, day_num, game_code, team_sel, verbose)
data |
Play-by-play prepared data from a given game. |
day_num |
Day number. |
game_code |
Game code. |
team_sel |
One of the teams' names involved in the game. |
verbose |
Logical. Decide if information of the computations must be provided or not. |
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.
A possession lasts 24 seconds in the ACB league.
Guillermo Vinue
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) #df2library(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
This function prepares the data frame with the nationalities
to be mapped with get_map_nats. It is used inside it.
do_map_nats(df_stats)do_map_nats(df_stats)
df_stats |
Data frame with the statistics and the corrected nationalities. |
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.
Guillermo Vinue
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.
do_OE(df)do_OE(df)
df |
Data frame with the games and the players info. |
OE values.
When either both the numerator and denominator of the OE expression are 0 or just the denominator is 0, the function returns a 0.
Guillermo Vinue
Shea, S., Baker, C., (2013). Basketball Analytics: Objective and Efficient Strategies for Understanding How Teams Win. Lake St. Louis, MO: Advanced Metrics, LLC.
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,])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,])
Compute how many offensive fouls has committed or received every player.
do_offensive_fouls(data, type)do_offensive_fouls(data, type)
data |
Play-by-play data. |
type |
Either 'comm' (for committed) or 'rec' (for received). |
Data frame with the following columns:
team: Name of the team. player: Name of the player. n_offensive_fouls_x: Number of offensive fouls.
Guillermo Vinue
df01 <- do_offensive_fouls(acb_vbc_cz_pbp_2223, "comm") #df01 df02 <- do_offensive_fouls(acb_vbc_cz_pbp_2223, "rec") #df02df01 <- do_offensive_fouls(acb_vbc_cz_pbp_2223, "comm") #df01 df02 <- do_offensive_fouls(acb_vbc_cz_pbp_2223, "rec") #df02
Compute when the possession starts for each team during each period of a game.
do_possession(data, period_sel)do_possession(data, period_sel)
data |
Play-by-play prepared data from a given game. |
period_sel |
Period of interest. Options can be "xC", where x=1,2,3,4. |
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.
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.
Guillermo Vinue
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") #df2library(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 the ACB play-by-play data to be analyzed in further steps. It involves correcting some inconsistencies and filtering some unnecessary information.
do_prepare_data(data, day_num, data_gsl, data_ginfo, game_code_excel)do_prepare_data(data, day_num, data_gsl, data_ginfo, game_code_excel)
data |
Source play-by-play data from a given game. |
day_num |
Day number. |
data_gsl |
Games' starting lineups. |
data_ginfo |
Games' basic information. |
game_code_excel |
Game code. |
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.
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.
Guillermo Vinue
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) #df1library(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
The computation of the scoring after offensive rebounds requires a specifical data preparation. This function does this data processing.
do_prepare_data_or(data, rm_overtime, data_ginfo)do_prepare_data_or(data, rm_overtime, data_ginfo)
data |
Source play-by-play data from a given game. |
rm_overtime |
Logical. Decide to remove overtimes or not. |
data_ginfo |
Games' basic information. |
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.
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.
Guillermo Vinue
df0 <- acb_vbc_cz_pbp_2223 df1 <- do_prepare_data_or(df0, TRUE, acb_games_2223_info) #df1df0 <- acb_vbc_cz_pbp_2223 df1 <- do_prepare_data_or(df0, TRUE, acb_games_2223_info) #df1
The computation of the successful timeouts requires a specific data preparation. This function does this data processing.
do_prepare_data_to(data, rm_overtime, data_ginfo, data_gcoach)do_prepare_data_to(data, rm_overtime, data_ginfo, data_gcoach)
data |
Source play-by-play data from a given game. |
rm_overtime |
Logical. Decide to remove overtimes or not. |
data_ginfo |
Games' basic information. |
data_gcoach |
Coach of each team in each day. |
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 .
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.
Guillermo Vinue
df0 <- acb_vbc_cz_pbp_2223 df1 <- do_prepare_data_to(df0, TRUE, acb_games_2223_info, acb_games_2223_coach) #df1df0 <- acb_vbc_cz_pbp_2223 df1 <- do_prepare_data_to(df0, TRUE, acb_games_2223_info, acb_games_2223_coach) #df1
This function disentangles the play-by-play data coming from the ACB website and creates a common data structure in R.
do_process_acb_pbp(game_elem, day, game_code, period, acb_shields, verbose)do_process_acb_pbp(game_elem, day, game_code, period, acb_shields, verbose)
game_elem |
Character with the tangled play-by-play data. |
day |
Day of the game. |
game_code |
Game code. |
period |
Period of the game. |
acb_shields |
Data frame with the links to the shields of the ACB teams. |
verbose |
Logical to display processing information. |
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.
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.
Guillermo Vinue
## 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$stop() period <- "1C" data_game <- do_process_acb_pbp(game_elem, day, game_code, period, acb_shields, FALSE) ## End(Not run)## Not run: # Load packages required: library(RSelenium) # Provide the day and game code: day <- "24" game_code <- "103170" # Open an Internet server: rD <- rsDriver(browser = "firefox", chromever = NULL) # Follow this procedure on the server: # 1. Copy and paste the game link https://jv.acb.com/es/103170/jugadas # 2. Click on each period, starting with 1C. # 3. Scroll down to the first row of data. # 4. Go back to R and run the following code: # Set the remote driver: remDr <- rD$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)
For each team and player, locate the position of offensive rebounds and check if they resulted in scoring points.
do_reb_off_success(data, day_num, game_code, team_sel, verbose)do_reb_off_success(data, day_num, game_code, team_sel, verbose)
data |
Play-by-play prepared data from a given game. |
day_num |
Day number. |
game_code |
Game code. |
team_sel |
One of the teams' names involved in the game. |
verbose |
Logical. Decide if information of the computations must be provided or not. |
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.
Guillermo Vinue
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) #df2df0 <- 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
This function calls the needed ancillary functions to scrape the player game finder data for the desired competition (currently, ACB, Euroleague and Eurocup).
do_scraping_games(competition, type_league, nums, year, verbose, accents, r_user)do_scraping_games(competition, type_league, nums, year, verbose, accents, r_user)
competition |
String. Options are "ACB", "Euroleague" and "Eurocup". |
type_league |
String. If |
nums |
Numbers corresponding to the website from which scraping. |
year |
If |
verbose |
Should R report information on progress? Default TRUE. |
accents |
If |
r_user |
Email to identify the user when doing web scraping. This is a polite way to do web scraping and to certify that the user is working as transparently as possible with a research purpose. |
A data frame with the player game finder data for the competition selected.
Guillermo Vinue
scraping_games_acb, scraping_games_euro
## 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)## 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)
This function calls the needed ancillary functions to scrape the players' profile data for the desired competition (currently, ACB, Euroleague and Eurocup).
do_scraping_rosters(competition, pcode, verbose, accents, year, r_user)do_scraping_rosters(competition, pcode, verbose, accents, year, r_user)
competition |
String. Options are "ACB", "Euroleague" and "Eurocup". |
pcode |
Code corresponding to the player's website to scrape. |
verbose |
Should R report information on progress? Default TRUE. |
accents |
If |
year |
If |
r_user |
Email to identify the user when doing web scraping. This is a polite way to do web scraping and to certify that the user is working as transparently as possible with a research purpose. |
A data frame with the players' information.
Guillermo Vinue
scraping_games_acb, scraping_rosters_euro
## 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)## 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)
This function computes either the total or the average statistics.
do_stats(df_games, type_stats = "Total", season, competition, type_season)do_stats(df_games, type_stats = "Total", season, competition, type_season)
df_games |
Data frame with the games, players info, advanced stats and eventually recoded teams names. |
type_stats |
String. In English, the options are "Total" and "Average" and in Spanish, the options are "Totales" and "Promedio". |
season |
String indicating the season, for example, 2017-2018. |
competition |
String. Options are "ACB", "Euroleague" and "Eurocup". |
type_season |
String with the round of competition, for example regular season or playoffs and so on. |
Data frame.
Guillermo Vinue
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")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 time played and points scored for a player of interest in any period of the game.
do_stats_per_period(data, day_num, game_code, team_sel, period_sel, player_sel)do_stats_per_period(data, day_num, game_code, team_sel, period_sel, player_sel)
data |
Prepared data from a given game. |
day_num |
Day number. |
game_code |
Game code. |
team_sel |
One of the teams' names involved in the game. |
period_sel |
Period of interest. Options can be "xC", where x=1,2,3,4. |
player_sel |
Player of interest. |
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.
The game_code column allows us to detect the source website, for example, https://jv.acb.com/es/103389/jugadas.
Guillermo Vinue
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) #df4library(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
This function computes the total and average statistics for every team.
do_stats_teams(df_games, season, competition, type_season)do_stats_teams(df_games, season, competition, type_season)
df_games |
Data frame with the games, players info, advanced stats and eventually recoded teams names. |
season |
String indicating the season, for example, 2017-2018. |
competition |
String. Options are "ACB", "Euroleague" and "Eurocup". |
type_season |
String with the round of competition, for example regular season or playoffs and so on. |
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.
Guillermo Vinue
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$df_team_meancompet <- "ACB" df <- do_join_games_bio(compet, acb_games_1718, acb_players_1718) df$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 all the sub-lineups that a given team shows during a game. They can be made up of four, three or two players.
do_sub_lineup(data, elem_choose)do_sub_lineup(data, elem_choose)
data |
Data frame with the lineups (quintets). |
elem_choose |
Numeric: 4, 3 or 2. |
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.
A possession lasts 24 seconds in the ACB league.
Guillermo Vinue
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) #df3library(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
For each team, locate the position of timeouts and check if they resulted in scoring points.
do_time_out_success(data, day_num, game_code, team_sel, verbose)do_time_out_success(data, day_num, game_code, team_sel, verbose)
data |
Prepared data from a given game. |
day_num |
Day number. |
game_code |
Game code. |
team_sel |
One of the teams' names involved in the game. |
verbose |
Logical. Decide if information of the computations must be provided or not. |
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.
Guillermo Vinue
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$team)) # "Casademont Zaragoza_Porfirio Fisac" "Valencia Basket_Alex Mumbru" df2 <- do_time_out_success(df1, day_num, game_code, "Casademont Zaragoza_Porfirio Fisac", FALSE) #df2df0 <- acb_vbc_cz_pbp_2223 day_num <- unique(acb_vbc_cz_pbp_2223$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
Games of the ten days of regular season and the first three days of top 16 of the Eurocup 2017-2018 season.
eurocup_games_1718eurocup_games_1718
Data frame with 3604 rows and 38 columns.
https://www.euroleaguebasketball.net/eurocup/
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.
eurocup_players_1718eurocup_players_1718
Data frame with 351 rows and 7 columns.
https://www.euroleaguebasketball.net/eurocup/
Games of the first nineteen days of the Euroleague 2017-2018 season.
euroleague_games_1718euroleague_games_1718
Data frame with 3932 rows and 38 columns.
https://www.euroleaguebasketball.net/euroleague/
Players corresponding to the games of the first nineteen days of the Euroleague 2017-2018 season.
euroleague_players_1718euroleague_players_1718
Data frame with 245 rows and 7 columns.
https://www.euroleaguebasketball.net/euroleague/
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.
get_barplot_monthly_stats(df_stats, title, size_text = 2.5)get_barplot_monthly_stats(df_stats, title, size_text = 2.5)
df_stats |
Data frame with the statistics. |
title |
Plot title. |
size_text |
Label size for each bar. Default 2.5. |
Graphical device.
Guillermo Vinue
## 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))$Month months_order <- c("September", "October", "November", "December", "January", "February", "March", "April", "May", "June") months_plot <- match(months_order, months) months_plot1 <- months_plot[!is.na(months_plot)] months_plot2 <- months[months_plot1] df3_m <- df1 %>% filter(Team == "Real_Madrid", Player.x == "Doncic, Luka") %>% group_by(Month) %>% do(do_stats(., "Average", "2017-2018", "ACB", "Regular Season")) %>% ungroup() %>% mutate(Month = factor(Month, levels = months_plot2)) %>% arrange(Month) stats <- c("GP", "MP", "PTS", "FGA", "FGPerc", "ThreePA", "ThreePPerc", "FTA", "FTPerc", "TRB", "ORB", "AST", "TOV", "STL") df3_m1 <- df3_m %>% select(1:5, stats, 46:50) get_barplot_monthly_stats(df3_m1, paste("; ACB", "2017-2018", "Average", sep = " ; "), 2.5) # For all teams and players: teams <- as.character(sort(unique(df1$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) for (i in unique(df3_m1$Name)) { print(i) print(get_barplot_monthly_stats(df3_m1 %>% filter(Name == i), paste(" ; ACB", "2017-2018", "Average", sep = " ; "), 2.5)) } ## End(Not run)## Not run: library(dplyr) compet <- "ACB" df <- do_join_games_bio(compet, acb_games_1718, acb_players_1718) df1 <- do_add_adv_stats(df) months <- c(df %>% distinct(Month))$Month months_order <- c("September", "October", "November", "December", "January", "February", "March", "April", "May", "June") 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) 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$Team))) df3_m <- df1 %>% filter(Team == teams[13]) %>% group_by(Month) %>% do(do_stats(., "Average", "2017-2018", "ACB", "Regular Season")) %>% ungroup() %>% mutate(Month = factor(Month, levels = months_plot2)) %>% arrange(Month) df3_m1 <- df3_m %>% select(1:5, stats, 46:50) for (i in unique(df3_m1$Name)) { print(i) print(get_barplot_monthly_stats(df3_m1 %>% filter(Name == i), paste(" ; ACB", "2017-2018", "Average", sep = " ; "), 2.5)) } ## End(Not run)
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.
get_bubble_plot(df_stats, player, descr_stats, size_text, size_text_x, size_legend)get_bubble_plot(df_stats, player, descr_stats, size_text, size_text_x, size_legend)
df_stats |
Data frame with the statistics. |
player |
Player. |
descr_stats |
Description of the statistics for the legend. |
size_text |
Text size inside each box. |
size_text_x |
Stats labels size. |
size_legend |
Legend size. |
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.
Graphical device.
This function has been created using the code from this website: https://www.r-bloggers.com/2017/01/visualizing-the-best/.
## 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)## 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)
Once computed the team's factors and its rankings with
do_four_factors_df, this function represents them.
get_four_factors_plot(df_rank, df_no_rank, team, language)get_four_factors_plot(df_rank, df_no_rank, team, language)
df_rank |
Data frame with the team's offense and defense four factors and its ranking labels. |
df_no_rank |
Data frame with the team's offense and defense four factors. |
team |
Team name. Multiple teams can be chosen. |
language |
Language labels. Current options are 'en' for English and 'es' for Spanish. |
Graphical device.
Guillermo Vinue
## 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)## 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)
This function is to get all the games and rosters of the competition selected.
get_games_rosters(competition, type_league, nums, verbose = TRUE, accents = FALSE, r_user, df0, df_bio0)get_games_rosters(competition, type_league, nums, verbose = TRUE, accents = FALSE, r_user, df0, df_bio0)
competition |
String. Options are "ACB", "Euroleague" and "Eurocup". |
type_league |
String. If |
nums |
Numbers corresponding to the website from which scraping. |
verbose |
Should R report information on progress? Default TRUE. |
accents |
If |
r_user |
Email to identify the user when doing web scraping. This is a polite way to do web scraping and to certify that the user is working as transparently as possible with a research purpose. |
df0 |
Data frame to save the games data. |
df_bio0 |
Data frame to save the rosters data. |
Data frame.
Guillermo Vinue
## 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)## 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)
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.
get_heatmap_bb(df_stats, team, levels_stats = NULL, stat_ord, base_size = 9, title)get_heatmap_bb(df_stats, team, levels_stats = NULL, stat_ord, base_size = 9, title)
df_stats |
Data frame with the statistics. |
team |
Team. |
levels_stats |
Statistics classified in several categories to plot. If this is NULL, all the statistics are included in the data frame. Otherwise, the user can define a vector with the variables to represent. |
stat_ord |
To sort the heatmap on one particular statistic. |
base_size |
Sets the font size in the theme used. Default 9. |
title |
Plot title. |
Graphical device.
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
## 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$Team)))) get_heatmap_bb(df2, teams[6], NULL, "MP", 9, paste(compet, "2017-2018", "Total", sep = " ")) ## End(Not run)## Not run: compet <- "ACB" df <- do_join_games_bio(compet, acb_games_1718, acb_players_1718) df1 <- do_add_adv_stats(df) df2 <- do_stats(df1, "Total", "2017-2018", compet, "Regular Season") teams <- as.character(rev(sort(unique(df2$Team)))) get_heatmap_bb(df2, teams[6], NULL, "MP", 9, paste(compet, "2017-2018", "Total", sep = " ")) ## End(Not run)
A world map is represented. The countries from where there are players in the competition selected are in green color.
get_map_nats(df_stats)get_map_nats(df_stats)
df_stats |
Data frame with the statistics and the corrected nationalities. |
Graphical device.
Guillermo Vinue
## 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)## 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)
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.
get_pop_pyramid(df, title, language)get_pop_pyramid(df, title, language)
df |
Data frame that contains the ACB players' nationality. |
title |
Title of the plot |
language |
String, "eng" for English labels; "esp" for Spanish labels. |
Graphical device.
Guillermo Vinue
## 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)## 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)
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.
get_shooting_plot(df_stats, team, type_shot, min_att, title, language)get_shooting_plot(df_stats, team, type_shot, min_att, title, language)
df_stats |
Data frame with the statistics. |
team |
Team. |
type_shot |
Numeric with values 1-2-3: 1 refers to free throws, 2 refers to two point shots and 3 refers to three points shots. |
min_att |
Minimum number of attempts by the player to be represented in the plot. |
title |
Plot title. |
language |
Language labels. Current options are 'en' for English and 'es' for Spanish. |
Graphical device.
Guillermo Vinue
## 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)## 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 the archetypoids computed with
archetypoids according to a similarity threshold.
get_similar_players(atype, threshold, alphas, cases, data, variables, compet, season)get_similar_players(atype, threshold, alphas, cases, data, variables, compet, season)
atype |
Number assigned to the archetypoid (1:length( |
threshold |
Similarity threshold. |
alphas |
Alpha values of all the players. |
cases |
Archetypoids. |
data |
Data frame with the statistics. |
variables |
Statistics used to compute the archetypoids. |
compet |
Competition. |
season |
Season. |
Data frame with the features of the similar players.
Guillermo Vinue
(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$Season)) s1 <- Sys.time() - s0 s1(s0 <- Sys.time()) # Turn off temporarily some negligible warnings from the # archetypes package to avoid missunderstandings. The code works well. library(Anthropometry) df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718) df1 <- do_add_adv_stats(df) df2 <- do_stats(df1, "Total", "2017-2018", "ACB", "Regular Season") df3 <- df2[which(df2$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 the archetypoids computed with
archetypoids according to a similarity threshold.
get_similar_teams(atype, threshold, alphas, cases, data, variables)get_similar_teams(atype, threshold, alphas, cases, data, variables)
atype |
Number assigned to the archetypoid (1:length( |
threshold |
Similarity threshold. |
alphas |
Alpha values of all the players. |
cases |
Archetypoids. |
data |
Data frame with the statistics. |
variables |
Statistics used to compute the archetypoids. |
Data frame with the features of the similar teams.
Guillermo Vinue
## 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$alphas, 4) get_similar_teams(1, 0.95, alphas, cases, df_team_total, c("PTS", "PTSrv")) s1 <- Sys.time() - s0 s1 ## End(Not run)## Not run: (s0 <- Sys.time()) library(Anthropometry) df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718) df$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)
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.
get_stats_seasons(df, competition, player, variabs, type_season, add_text, show_x_axis)get_stats_seasons(df, competition, player, variabs, type_season, add_text, show_x_axis)
df |
Data frame with the games and the players info. |
competition |
Competition. |
player |
Players's names. |
variabs |
Vector with the statistics to plot. |
type_season |
String with the round of competition, for example regular season or playoffs and so on. |
add_text |
Boolean. Should text be added to the plot points? |
show_x_axis |
Boolean. Should x-axis labels be shown in the plot? |
List with two elements:
gg Graphical device.
df_gg Data frame associated with the plot.
Guillermo Vinue
## 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$gg ## End(Not run)## Not run: competition <- "ACB" df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718) df$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)
The league results are represented with a cross table.
get_table_results(df, competition, season)get_table_results(df, competition, season)
df |
Data frame with the games and the players info. |
competition |
Competition. |
season |
Season. |
List with these two elements:
plot_teams Graphical device with the cross table.
wins_teams Vector with the team wins.
Guillermo Vinue
## 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$plot_teams ## End(Not run)## Not run: df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718) df$Compet <- "ACB" gg <- get_table_results(df, "ACB", "2017-2018") gg$wins_teams gg$plot_teams ## End(Not run)
This function joins the ACB games with the players' bio and computes the players' age at each game.
join_players_bio_age_acb(df_games, df_rosters)join_players_bio_age_acb(df_games, df_rosters)
df_games |
Data frame with the games. |
df_rosters |
Data frame with the biography of the roster players. |
Data frame.
Guillermo Vinue
df <- join_players_bio_age_acb(acb_games_1718, acb_players_1718)df <- join_players_bio_age_acb(acb_games_1718, acb_players_1718)
This function joins the Euroleague/Eurocup games with the players' bio and computes the players' age at each game.
join_players_bio_age_euro(df_games, df_rosters)join_players_bio_age_euro(df_games, df_rosters)
df_games |
Data frame with the games. |
df_rosters |
Data frame with the biography of the roster players. |
Data frame.
Guillermo Vinue
df <- join_players_bio_age_euro(euroleague_games_1718, euroleague_players_1718)df <- join_players_bio_age_euro(euroleague_games_1718, euroleague_players_1718)
This is the new function to obtain the ACB box score data.
scraping_games_acb(code, game_id, season = "2020-2021", type_season = "Regular Season", user_email, user_agent_goo)scraping_games_acb(code, game_id, season = "2020-2021", type_season = "Regular Season", user_email, user_agent_goo)
code |
Game code. |
game_id |
Game id. |
season |
Season, e.g. 2022-2023. |
type_season |
Type of season, e.g. 'Regular season'. |
user_email |
Email's user to identify the user when doing web scraping. This is a polite way to do web scraping and to certify that the user is working as transparently as possible with a research purpose. |
user_agent_goo |
User-agent to identify the user when doing web scraping. This is a polite way to do web scraping and to certify that the user is working as transparently as possible with a research purpose. |
A data frame with the player game finder data (box score data).
Guillermo Vinue
## 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)## 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)
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.
scraping_games_acb_old(type_league, nums, year, verbose = TRUE, accents = FALSE, r_user = "[email protected]")scraping_games_acb_old(type_league, nums, year, verbose = TRUE, accents = FALSE, r_user = "[email protected]")
type_league |
String. If |
nums |
Numbers corresponding to the website to scrape. |
year |
Season, e.g. 2017-2018. |
verbose |
Should R report information on progress? Default TRUE. |
accents |
Should we keep the Spanish accents? The recommended option is to remove them, so default FALSE. |
r_user |
Email to identify the user when doing web scraping. This is a polite way to do web scraping and to certify that the user is working as transparently as possible with a research purpose. |
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.
A data frame with the player game finder data.
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.
Guillermo Vinue
## 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 = "[email protected]") ## End(Not run)## 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 = "[email protected]") ## End(Not run)
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.
scraping_games_euro(competition, nums, year, verbose = TRUE, r_user = "[email protected]")scraping_games_euro(competition, nums, year, verbose = TRUE, r_user = "[email protected]")
competition |
String. Options are "Euroleague" and "Eurocup". |
nums |
Numbers corresponding to the website from which scraping. |
year |
Year when the season starts. 2017 refers to 2017-2018 and so on. |
verbose |
Should R report information on progress? Default TRUE. |
r_user |
Email to identify the user when doing web scraping. This is a polite way to do web scraping and to certify that the user is working as transparently as possible with a research purpose. |
See the examples in get_games_rosters to see the game numbers
to scrape in each season.
A data frame with the player game finder data.
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).
Guillermo Vinue
## 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 = "[email protected]") ## End(Not run)## 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 = "[email protected]") ## End(Not run)
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.
scraping_rosters_acb(pcode, verbose = TRUE, accents = FALSE, r_user = "[email protected]")scraping_rosters_acb(pcode, verbose = TRUE, accents = FALSE, r_user = "[email protected]")
pcode |
Code corresponding to the player's website to scrape. |
verbose |
Should R report information on progress? Default TRUE. |
accents |
Should we keep the Spanish accents? The recommended option is to remove them, so default FALSE. |
r_user |
Email user to identify the user when doing web scraping. This is a polite way to do web scraping and to certify that the user is working as transparently as possible with a research purpose. |
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.
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.
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.
Guillermo Vinue
## 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 = "[email protected]") ## End(Not run)## 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 = "[email protected]") ## End(Not run)
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/.
scraping_rosters_euro(competition, pcode, year, verbose = TRUE, r_user = "[email protected]")scraping_rosters_euro(competition, pcode, year, verbose = TRUE, r_user = "[email protected]")
competition |
String. Options are "Euroleague" and "Eurocup". |
pcode |
Code corresponding to the player's website to scrape. |
year |
Year when the season starts. 2017 refers to 2017-2018 and so on. |
verbose |
Should R report information on progress? Default TRUE. |
r_user |
Email user to identify the user when doing web scraping. This is a polite way to do web scraping and to certify that the user is working as transparently as possible with a research purpose. |
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.
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).
Guillermo Vinue
## 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 = "[email protected]") ## End(Not run)## 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 = "[email protected]") ## End(Not run)