Visualization of European basketball data

In BAwiR: Analysis of Basketball Data

This document contains all the needed R code to reproduce the results described in the paper A Web Application for Interactive Visualization of European Basketball Data (https://doi.org/10.1089/big.2018.0124{target="_blank"}), which presents the dashboard available at https://www.uv.es/vivigui/AppEuroACB.html{target="_blank"}. This dashboard belongs to the platform available at https://www.uv.es/vivigui/basketball_platform.html{target="_blank"}.

knitr::opts_chunk$set(echo = TRUE)

# Firstly, load BAwiR and other packages that will be used in the paper:
library(BAwiR) # 1.3
library(tidyverse) # 1.3.2
library(FSA) # 0.8.22
library(gridExtra) # 2.3

# Code for Figure 1:
# Load the data_app_acb file with the ACB games from the 1985-1986 season to the 2017-2018 season:
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")

# Create the data with games and players' info, add the advanced stats and compute the total numbers:
df0 <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)
df1 <- do_add_adv_stats(df0)
df2 <- do_stats(df1, "Total", "2017-2018", "ACB", "Regular Season")

# Code for Figure 2:
df3 <- df2[which(df2$Position == "Center"), c("MP", "PTS", "Name", "CombinID")]
df3 <- df3[df3$MP > 100,]
ggplot(df3, aes(x = c(df3[,1])[[1]], y = c(df3[,2])[[1]], group = Name)) +
  geom_point() +
  geom_text(aes(label = Name), size = 2, vjust = -0.8) +
  labs(x = colnames(df3)[1], y = colnames(df3)[2], 
       title = "ACB 2017-2018, Regular Season. Total stats. Centers.")

# Code for Table 2:
df4 <- df3 %>%
  mutate(Player_info = paste("http://www.acb.com/jugador.php?id=", CombinID, sep = "")) %>%
  select(-CombinID)
df5 <- df4[order(df4[,1][[1]], decreasing = TRUE),]
headtail(df5, 3)

# Code for Figure 3:
stats <- c("GP", "MP", "PTS", "FGPerc", "FTPerc", "TRB", "AST", "TOV", "PlusMinus", "PIR")
descr_stats <- c("Games played", "Minutes played", "Points", "Field goals percentage", 
                 "Free throws percentage", "Total rebounds", "Assists", "Turnovers", 
                 "Plus/minus", "Performance index rating")
df2_1 <- df2 %>% 
  select(1:5, stats, 46:49)

perc_plot_doncid <- get_bubble_plot(df2_1, "Doncic, Luka", descr_stats, 3, 7, 8) +
                        theme(strip.text.x = element_blank()) +
                        ggtitle(label = "Doncic, Luka",
                                subtitle = "ACB 2017-2018, Regular Season. Total stats.") +
                        theme(plot.title = element_text(size = 20))

perc_plot_abalde <- get_bubble_plot(df2_1, "Abalde, Alberto", descr_stats, 3, 7, 8) +
                        theme(strip.text.x = element_blank()) +
                        ggtitle(label = "Abalde, Alberto",
                                subtitle = "ACB 2017-2018, Regular Season. Total stats.") +
                        theme(plot.title = element_text(size = 20))

grid.arrange(perc_plot_doncid, perc_plot_abalde, ncol = 2)

# Code for Figure 4:
months <- c(df0 %>% 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]

df1_m <- df1 %>%
              filter(Player.x %in% c("Doncic, Luka", "Abalde, Alberto")) %>%
              group_by(Month) %>%
              do(do_stats(., "Average", "2017-2018", "ACB", "Regular Season")) %>%
              ungroup() %>%
              mutate(Month = factor(Month, levels = months_plot2)) %>%
              arrange(Month)

df1_m1 <- df1_m %>% 
  select(1:5, stats, 46:50) %>% 
  select(-EPS)
max_val <- max(df1_m1[,colnames(df1_m1) %in% stats])
min_val <- min(df1_m1[,colnames(df1_m1) %in% stats])
get_barplot_monthly_stats(df1_m1, "ACB 2017-2018, Regular Season. Monthly average stats.", 3) +
  scale_y_continuous(limits = c(min_val - 10, max_val + 10))

# Code for Figure 5:
df0$Compet <- "ACB"
plot_yearly <- get_stats_seasons(df0, "ACB", c("Doncic, Luka", "Abalde, Alberto"), 
                                 stats[1:4], "Regular Season", TRUE, FALSE)
plot_yearly$gg + 
  labs(title = "ACB 2017-2018, Regular Season. Yearly average stats.") +
  theme(strip.text.x = element_text(size = 15))

# Code for Figure 6:
levels_stats <- list("Offensive" = c("PTS", "FG", "FGA", "FGPerc", 
                                     "TwoP", "TwoPA", "TwoPPerc",
                                     "ThreeP", "ThreePA", "ThreePPerc",
                                     "FT", "FTA", "FTPerc", "ORB", "AST"),
                     "Defensive" = c("DRB", "STL", "PF"),
                     "Other" = c("GP", "MP", "TRB", "PlusMinus", "PIR"),
                     "Advanced" = c("EFGPerc", "PPS"))
get_heatmap_bb(df2, "Real_Madrid", levels_stats, "PlusMinus", 9, 
               paste("ACB", "2017-2018, Regular Season.", "Total stats.", sep = " "))

# Code for Figure 7:
get_shooting_plot(df2, "Real_Madrid", 3, 1, "ACB 2017-2018, Regular Season.", "en") +
  theme(plot.title = element_text(size = 15))

# Code for Figure 8:
df1_10 <- df1 %>% 
  filter(Day <= 10) 
teams <- as.character(rev(sort(unique(df2$Team))))
df_four_factors <- do_four_factors_df(df1_10, teams)
get_four_factors_plot(df_four_factors$df_rank, df_four_factors$df_no_rank, 
                      c("Real_Madrid", "Valencia"), "en") +
  ggtitle("ACB 2017-2018, Regular Season.")

# Code for Figure 9:
df0$Compet <- "ACB"
gg <- get_table_results(df0, "ACB", "2017-2018")
gg$plot_teams

# Code for Figure 10:
get_map_nats(df2) +
  ggtitle("ACB 2017-2018, Regular Season.")

sessionInfo()

BAwiR documentation built on Nov. 14, 2023, 5:08 p.m.