Distribution of Points Per Game: Starters vs. Bench Players

Players with at least 10 games played in the 2023-24 NBA Season

30DayChartChallenge

2025

Visualizing the stark contrast in scoring distributions between NBA starters and bench players using data from the hoopR package for the 2023-24 season.

Author

Steven Ponce

Published

April 8, 2025

Figure 1: A histogram showing the distribution of points per game for NBA players in the 2023-24 season, comparing starters versus bench players. Bench players (orange bars) are concentrated at lower scoring ranges with a mean of 5.3 PPG, while starters (blue bars) show a broader distribution with higher scoring averages and a mean of 14.5 PPG. Vertical dashed lines indicate the mean values for each group.

Steps to Create this Graphic

1. Load Packages & Setup

Show code

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
pacman::p_load(
tidyverse,      # Easily Install and Load the 'Tidyverse'
  ggtext,         # Improved Text Rendering Support for 'ggplot2'
  showtext,       # Using Fonts More Easily in R Graphs
  janitor,        # Simple Tools for Examining and Cleaning Dirty Data
  skimr,          # Compact and Flexible Summaries of Data
  scales,         # Scale Functions for Visualization
  lubridate,      # Make Dealing with Dates a Little Easier
  hoopR,          # Access Men's Basketball Play by Play Data
  camcorder       # Record Your Plot History
)
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 8,
    height = 8,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

Show code

nba_players_2024 <- load_nba_player_box(seasons = 2024)

3. Examine the Data

Show code

glimpse(nba_players_2024)
skim(nba_players_2024)

4. Tidy Data

Show code

### |- Tidy ----
# Add starter status  
player_ppg_role <- nba_players_2024 |>
  mutate(starter = ifelse(starter, "Starter", "Bench")) |>
  summarise(
    ppg = mean(points, na.rm = TRUE),
    games_played = n(),
    .by = c(athlete_display_name, starter)
  ) |>
  filter(games_played >= 10)

# Calculate mean PPG for each role
role_means <- player_ppg_role |>
  summarise(
    mean_ppg = mean(ppg, na.rm = TRUE), 
    .by = starter
    )

5. Visualization Parameters

Show code

### |- plot aesthetics ---- 
colors <- get_theme_colors(
  palette = c("Starter" = "#1F77B4", "Bench" = "#FF7F0E")
  )

colors2 <- get_theme_colors(
  palette = c("Starter" = "#0D3B66", "Bench" = "#C1292E")
)

### |-  titles and caption ----
# text
title_text    <- str_glue("Distribution of Points Per Game: Starters vs. Bench Players") 
subtitle_text <- str_glue("Players with at least 10 games played in the 2023-24 NBA Season")

# Create caption
caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 08,
  source_text =  "ESPN via { hoopR } package" 
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Text styling 
    plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)),
    plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)),
    
    # Axis elements
    axis.title = element_text(color = colors$text, size = rel(0.8)),
    axis.text = element_text(color = colors$text, size = rel(0.7)),
    
    axis.line.x = element_line(color = "gray50", linewidth = .2),

    # Grid elements
    # panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
 
    # Legend elements
    legend.position = "inside",
    legend.position.inside = c(0.95, 0.95),
    legend.justification = c(1, 1),
    legend.title = element_text(family = fonts$text, size = rel(0.8)),
    legend.text = element_text(family = fonts$text, size = rel(0.7)),
    
    # Plot margins 
    plot.margin = margin(t = 10, r = 20, b = 10, l = 20),
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code

### |-  Plot ----
p <- ggplot(player_ppg_role, aes(x = ppg, fill = starter)) +
  # Geoms
  geom_histogram(binwidth = 1.5, position = "identity", alpha = 0.7) +
  geom_vline(
    data = role_means, 
    aes(xintercept = mean_ppg, color = starter),
    linetype = "dashed", linewidth = 0.5, show.legend = FALSE
    ) +
  # Annotate
  annotate(
    "text", 
    x = role_means$mean_ppg[role_means$starter == "Starter"] + 4.2,
    y = max(table(cut(player_ppg_role$ppg, breaks = seq(0, 40, by = 1.5)))) * 0.9 ,
    label = paste0("Starter Mean: ", round(role_means$mean_ppg[role_means$starter == "Starter"], 1), " PPG"),
    color = colors2$palette[1],
    size = 3.2, 
    alpha = 0.8,
    fontface = "bold"
  ) +
  annotate(
    "text", 
    x = role_means$mean_ppg[role_means$starter == "Bench"] + 4, 
    y = max(table(cut(player_ppg_role$ppg, breaks = seq(0, 40, by = 1.5)))) * 1,
    label = paste0("Bench Mean: ", round(role_means$mean_ppg[role_means$starter == "Bench"], 1), " PPG"),
    color = colors2$palette[2],
    size = 3.2, 
    alpha = 0.8,
    fontface = "bold"
    ) +
  # Scales
  scale_fill_manual(values = colors$palette) +
  scale_color_manual(values = colors2$palette) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = "Points Per Game",
    y = "Number of Players",
    fill = "Role"
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.5),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_text(
      size = rel(1),
      family = fonts$subtitle,
      color = colors$subtitle,
      lineheight = 1.2,
      margin = margin(t = 5, b = 20)
    ),
    plot.caption = element_markdown(
      size = rel(0.6),
      family = fonts$caption,
      color = colors$caption,
      lineheight = 0.65,
      hjust = 0.5,
      halign = 0.5,
      margin = margin(t = 10, b = 5)
    ),
  )

7. Save

Show code

### |-  plot image ----  

save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2025, 
  day = 08, 
  width = 8, 
  height = 8
  )

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      camcorder_0.1.0 hoopR_2.1.0     scales_1.3.0   
 [5] skimr_2.1.5     janitor_2.2.0   showtext_0.9-7  showtextdb_3.0 
 [9] sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0  
[13] stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2     readr_2.1.5    
[17] tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1    farver_2.1.2        fastmap_1.2.0      
 [4] pacman_0.5.1        promises_1.3.0      digest_0.6.37      
 [7] timechange_0.3.0    lifecycle_1.0.4     rsvg_2.6.1         
[10] processx_3.8.4      magrittr_2.0.3      compiler_4.4.0     
[13] rlang_1.1.4         tools_4.4.0         utf8_1.2.4         
[16] yaml_2.3.10         data.table_1.16.2   knitr_1.49         
[19] labeling_0.4.3      htmlwidgets_1.6.4   curl_6.0.0         
[22] xml2_1.3.6          repr_1.1.7          websocket_1.4.2    
[25] withr_3.0.2         grid_4.4.0          fansi_1.0.6        
[28] colorspace_2.1-1    future_1.34.0       progressr_0.15.1   
[31] globals_0.16.3      cli_3.6.3           rmarkdown_2.29     
[34] ragg_1.3.3          generics_0.1.3      RcppParallel_5.1.10
[37] rstudioapi_0.17.1   httr_1.4.7          tzdb_0.4.0         
[40] commonmark_1.9.2    chromote_0.4.0      rvest_1.0.4        
[43] parallel_4.4.0      base64enc_0.1-3     vctrs_0.6.5        
[46] jsonlite_1.8.9      hms_1.1.3           listenv_0.9.1      
[49] systemfonts_1.1.0   magick_2.8.5        glue_1.8.0         
[52] parallelly_1.43.0   gifski_1.32.0-1     codetools_0.2-20   
[55] ps_1.8.1            stringi_1.8.4       gtable_0.3.6       
[58] later_1.3.2         munsell_0.5.1       furrr_0.3.1        
[61] pillar_1.9.0        htmltools_0.5.8.1   R6_2.5.1           
[64] textshaping_0.4.0   rprojroot_2.0.4     evaluate_1.0.1     
[67] markdown_1.13       gridtext_0.1.5      snakecase_0.11.1   
[70] renv_1.0.3          Rcpp_1.0.13-1       svglite_2.1.3      
[73] xfun_0.49           pkgconfig_2.0.3

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in 30dcc_2025_08.qmd.

For the full repository, click here.

10. References

Expand for References

Data Sources:
- ESPN via { hoopR } package: hoopR

--- title: "Distribution of Points Per Game: Starters vs. Bench Players" subtitle: "Players with at least 10 games played in the 2023-24 NBA Season" description: "Visualizing the stark contrast in scoring distributions between NBA starters and bench players using data from the hoopR package for the 2023-24 season." author: "Steven Ponce" date: "2025-04-08" categories: ["30DayChartChallenge", "2025"] tags: [ "NBA", "basketball", "hoopR", "ggplot2", "histogram", "sports analytics", "scoring distribution", "bench players", "starters", "tidyverse", "Day 8" ] image: "thumbnails/30dcc_2025_08.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" editor_options: chunk_output_type: inline execute: freeze: true cache: true error: false message: false warning: false eval: true # filters: # - social-share # share: # permalink: "https://stevenponce.netlify.app/data_visualizations/30DayChartChallenge/2025/30dcc_2025_08.html" # description: "See how NBA starters and bench players differ in scoring distributions, with starters averaging nearly three times more points per game than bench players in the 2023-24 season. #30DayChartChallenge #DataViz #NBA" # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ![A histogram showing the distribution of points per game for NBA players in the 2023-24 season, comparing starters versus bench players. Bench players (orange bars) are concentrated at lower scoring ranges with a mean of 5.3 PPG, while starters (blue bars) show a broader distribution with higher scoring averages and a mean of 14.5 PPG. Vertical dashed lines indicate the mean values for each group.](30dcc_2025_08.png){#fig-1} ### <mark> **Steps to Create this Graphic** </mark> #### 1. Load Packages & Setup ```{r} #| label: load #| warning: false #| message: false #| results: "hide" ## 1. LOAD PACKAGES & SETUP ---- suppressPackageStartupMessages({ pacman::p_load( tidyverse, # Easily Install and Load the 'Tidyverse' ggtext, # Improved Text Rendering Support for 'ggplot2' showtext, # Using Fonts More Easily in R Graphs janitor, # Simple Tools for Examining and Cleaning Dirty Data skimr, # Compact and Flexible Summaries of Data scales, # Scale Functions for Visualization lubridate, # Make Dealing with Dates a Little Easier hoopR, # Access Men's Basketball Play by Play Data camcorder # Record Your Plot History ) }) ### |- figure size ---- gg_record( dir = here::here("temp_plots"), device = "png", width = 8, height = 8, units = "in", dpi = 320 ) # Source utility functions suppressMessages(source(here::here("R/utils/fonts.R"))) source(here::here("R/utils/social_icons.R")) source(here::here("R/utils/image_utils.R")) source(here::here("R/themes/base_theme.R")) ``` #### 2. Read in the Data ```{r} #| label: read #| include: true #| eval: true #| warning: false nba_players_2024 <- load_nba_player_box(seasons = 2024) ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(nba_players_2024) skim(nba_players_2024) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- Tidy ---- # Add starter status player_ppg_role <- nba_players_2024 |> mutate(starter = ifelse(starter, "Starter", "Bench")) |> summarise( ppg = mean(points, na.rm = TRUE), games_played = n(), .by = c(athlete_display_name, starter) ) |> filter(games_played >= 10) # Calculate mean PPG for each role role_means <- player_ppg_role |> summarise( mean_ppg = mean(ppg, na.rm = TRUE), .by = starter ) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- colors <- get_theme_colors( palette = c("Starter" = "#1F77B4", "Bench" = "#FF7F0E") ) colors2 <- get_theme_colors( palette = c("Starter" = "#0D3B66", "Bench" = "#C1292E") ) ### |- titles and caption ---- # text title_text <- str_glue("Distribution of Points Per Game: Starters vs. Bench Players") subtitle_text <- str_glue("Players with at least 10 games played in the 2023-24 NBA Season") # Create caption caption_text <- create_dcc_caption( dcc_year = 2025, dcc_day = 08, source_text = "ESPN via { hoopR } package" ) ### |- fonts ---- setup_fonts() fonts <- get_font_families() ### |- plot theme ---- # Start with base theme base_theme <- create_base_theme(colors) # Add weekly-specific theme elements weekly_theme <- extend_weekly_theme( base_theme, theme( # Text styling plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)), plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)), # Axis elements axis.title = element_text(color = colors$text, size = rel(0.8)), axis.text = element_text(color = colors$text, size = rel(0.7)), axis.line.x = element_line(color = "gray50", linewidth = .2), # Grid elements # panel.grid.major = element_blank(), panel.grid.minor = element_blank(), # Legend elements legend.position = "inside", legend.position.inside = c(0.95, 0.95), legend.justification = c(1, 1), legend.title = element_text(family = fonts$text, size = rel(0.8)), legend.text = element_text(family = fonts$text, size = rel(0.7)), # Plot margins plot.margin = margin(t = 10, r = 20, b = 10, l = 20), ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Plot ---- p <- ggplot(player_ppg_role, aes(x = ppg, fill = starter)) + # Geoms geom_histogram(binwidth = 1.5, position = "identity", alpha = 0.7) + geom_vline( data = role_means, aes(xintercept = mean_ppg, color = starter), linetype = "dashed", linewidth = 0.5, show.legend = FALSE ) + # Annotate annotate( "text", x = role_means$mean_ppg[role_means$starter == "Starter"] + 4.2, y = max(table(cut(player_ppg_role$ppg, breaks = seq(0, 40, by = 1.5)))) * 0.9 , label = paste0("Starter Mean: ", round(role_means$mean_ppg[role_means$starter == "Starter"], 1), " PPG"), color = colors2$palette[1], size = 3.2, alpha = 0.8, fontface = "bold" ) + annotate( "text", x = role_means$mean_ppg[role_means$starter == "Bench"] + 4, y = max(table(cut(player_ppg_role$ppg, breaks = seq(0, 40, by = 1.5)))) * 1, label = paste0("Bench Mean: ", round(role_means$mean_ppg[role_means$starter == "Bench"], 1), " PPG"), color = colors2$palette[2], size = 3.2, alpha = 0.8, fontface = "bold" ) + # Scales scale_fill_manual(values = colors$palette) + scale_color_manual(values = colors2$palette) + # Labs labs( title = title_text, subtitle = subtitle_text, caption = caption_text, x = "Points Per Game", y = "Number of Players", fill = "Role" ) + # Theme theme( plot.title = element_text( size = rel(1.5), family = fonts$title, face = "bold", color = colors$title, margin = margin(t = 5, b = 5) ), plot.subtitle = element_text( size = rel(1), family = fonts$subtitle, color = colors$subtitle, lineheight = 1.2, margin = margin(t = 5, b = 20) ), plot.caption = element_markdown( size = rel(0.6), family = fonts$caption, color = colors$caption, lineheight = 0.65, hjust = 0.5, halign = 0.5, margin = margin(t = 10, b = 5) ), ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot( p, type = "30daychartchallenge", year = 2025, day = 08, width = 8, height = 8 ) ``` #### 8. Session Info ::: {.callout-tip collapse="true"} ##### Expand for Session Info ```{r, echo = FALSE} #| eval: true #| warning: false sessionInfo() ``` ::: #### 9. GitHub Repository ::: {.callout-tip collapse="true"} ##### Expand for GitHub Repo The complete code for this analysis is available in [`30dcc_2025_08.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2025/30dcc_2025_08.qmd). For the full repository, [click here](https://github.com/poncest/personal-website/). ::: #### 10. References ::: {.callout-tip collapse="true"} ##### Expand for References 1. Data Sources: - ESPN via { hoopR } package: [hoopR](https://github.com/sportsdataverse/hoopR) :::