Distribution of Offensive Rebounding Specialization by Position (2024 NBA Season)

Offensive rebounds as a proportion of total rebounds - Notice the multi-modal patterns

30DayChartChallenge

2025

An analysis of NBA player rebounding specialization patterns using ridge plots to visualize multi-modal distributions. This visualization for Day 10 of the #30DayChartChallenge explores how players within the same position show different rebounding tendencies, with particular focus on offensive rebounding ratios.

Author

Steven Ponce

Published

April 10, 2025

Figure 1: Ridge plot showing the distribution of offensive rebounding ratios across NBA positions. Multi-modal patterns are visible, with annotations highlighting different player types: Defensive-focused and Balanced Power Forwards, Perimeter and Inside Forwards, and Traditional and Offensive-focused Centers. Centers show highest ratios around 0.4, while guards have lower ratios centered around 0.2.

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'
  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
  ggridges,       # Ridgeline Plots in 'ggplot2'  
  camcorder       # Record Your Plot History
)
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 10,
    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

# Get NBA player stats for 2024 season
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 ----
# Season stats
player_season_stats <- nba_players_2024 |>
  group_by(athlete_id, athlete_display_name, athlete_position_abbreviation) |>
  summarize(
    games_played = n(),
    across(
      c(minutes, points, field_goals_attempted, field_goals_made,
        three_point_field_goals_attempted, three_point_field_goals_made,
        free_throws_attempted, free_throws_made, offensive_rebounds,
        defensive_rebounds, rebounds, assists, turnovers, steals, blocks),
      ~ sum(.x, na.rm = TRUE),
      .names = "total_{.col}"
    ),
    .groups = "drop"
  )

# Players stats
players_stats <- player_season_stats |>
  filter(total_minutes > 0, games_played >= 10) |>
  mutate(
    # metrics
    min_per_game = total_minutes / games_played,
    three_pt_rate = total_three_point_field_goals_attempted / total_minutes,
    
    # Free throw rate (FT attempts per minute)
    ft_rate = total_free_throws_attempted / total_minutes,
    
    # Offensive rebounding ratio (percentage of total rebounds that are offensive)
    orb_ratio = total_offensive_rebounds / (total_offensive_rebounds + total_defensive_rebounds + 0.001),
    
    # Shooting distance preference (proxy using 3PT to total FG ratio)
    three_pt_preference = total_three_point_field_goals_attempted / (total_field_goals_attempted + 0.001)
  ) |>
  filter(min_per_game >= 10)

5. Visualization Parameters

Show code

### |- plot aesthetics ---- 
colors <- get_theme_colors(
  palette = c("#3182bd", "Positive Impact" = "#333333")  
  )

### |-  titles and caption ----
# text
title_text    <- str_wrap("Distribution of Offensive Rebounding Specialization by Position (2024 NBA Season)",
                          width = 60) 
subtitle_text <- str_wrap("Offensive rebounds as a proportion of total rebounds - Notice the multi-modal patterns", 
                          width = 100)

# Create caption
caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 10,
  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.text.y = element_text(color = colors$text, size = rel(0.68)),
    
    axis.line.x = element_line(color = "gray50", linewidth = .2),

    # Grid elements
    panel.grid.minor.x = element_blank(),
    panel.grid.major.x = element_blank(), 
 
    # Legend elements
    legend.position = "top",
    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(players_stats, aes(x = orb_ratio, y = athlete_position_abbreviation)) +
  # Geoms
  geom_density_ridges(alpha = 0.7, scale = 1.2, rel_min_height = 0.01, fill = colors$palette[1]) +
  # Annotation
  annotate("text", x = 0.25, y = 4.4, label = "Defensive-focused PFs", size = 3.5, fontface = "italic", color = colors$palette[2]) +
  annotate("text", x = 0.35, y = 4.4, label = "Balanced PFs", size = 3.5, fontface = "italic", color = colors$palette[2]) +
  annotate("segment", x = 0.25, xend = 0.25, y = 4.2, yend = 4, arrow = arrow(length = unit(0.2, "cm")), color = colors$palette[2]) +
  annotate("segment", x = 0.35, xend = 0.35, y = 4.2, yend = 4, arrow = arrow(length = unit(0.2, "cm")), color = colors$palette[2]) +
  
  annotate("text", x = 0.20, y = 2.4, label = "Perimeter Fs", size = 3.5, fontface = "italic", color = colors$palette[2]) +
  annotate("text", x = 0.40, y = 2.4, label = "Inside Fs", size = 3.5, fontface = "italic", color = colors$palette[2]) +
  annotate("segment", x = 0.20, xend = 0.20, y = 2.2, yend = 2, arrow = arrow(length = unit(0.2, "cm")), color = colors$palette[2]) +
  annotate("segment", x = 0.40, xend = 0.40, y = 2.2, yend = 2, arrow = arrow(length = unit(0.2, "cm")), color = colors$palette[2]) +
  
  annotate("text", x = 0.35, y = 1.4, label = "Traditional Cs", size = 3.5, fontface = "italic", color = colors$palette[2]) +
  annotate("text", x = 0.48, y = 1.4, label = "Offensive-focused Cs", size = 3.5, fontface = "italic", color = colors$palette[2]) +
  annotate("segment", x = 0.35, xend = 0.35, y = 1.2, yend = 1, arrow = arrow(length = unit(0.2, "cm")), color = colors$palette[2]) +
  annotate("segment", x = 0.48, xend = 0.47, y = 1.2, yend = 1, arrow = arrow(length = unit(0.2, "cm")), color = colors$palette[2]) +
  # Scales
  scale_x_continuous(
    limits = c(0, 0.6), 
    breaks = seq(0, 0.6, by = 0.1)
    ) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = "Offensive Rebounding Ratio",
    y = "Position"
    ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.9),
      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 = 10, 
  width = 10, 
  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 ggridges_0.5.6  hoopR_2.1.0    
 [5] scales_1.3.0    skimr_2.1.5     janitor_2.2.0   showtext_0.9-7 
 [9] showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.3
[13] forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2    
[17] readr_2.1.5     tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1  
[21] 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] htmlwidgets_1.6.4   curl_6.0.0          xml2_1.3.6         
[22] repr_1.1.7          websocket_1.4.2     withr_3.0.2        
[25] grid_4.4.0          fansi_1.0.6         colorspace_2.1-1   
[28] future_1.34.0       progressr_0.15.1    globals_0.16.3     
[31] cli_3.6.3           rmarkdown_2.29      ragg_1.3.3         
[34] generics_0.1.3      RcppParallel_5.1.10 rstudioapi_0.17.1  
[37] httr_1.4.7          tzdb_0.4.0          commonmark_1.9.2   
[40] chromote_0.4.0      rvest_1.0.4         parallel_4.4.0     
[43] base64enc_0.1-3     vctrs_0.6.5         jsonlite_1.8.9     
[46] hms_1.1.3           listenv_0.9.1       systemfonts_1.1.0  
[49] magick_2.8.5        glue_1.8.0          parallelly_1.43.0  
[52] gifski_1.32.0-1     codetools_0.2-20    ps_1.8.1           
[55] stringi_1.8.4       gtable_0.3.6        later_1.3.2        
[58] munsell_0.5.1       furrr_0.3.1         pillar_1.9.0       
[61] htmltools_0.5.8.1   R6_2.5.1            textshaping_0.4.0  
[64] rprojroot_2.0.4     evaluate_1.0.1      markdown_1.13      
[67] gridtext_0.1.5      snakecase_0.11.1    renv_1.0.3         
[70] Rcpp_1.0.13-1       svglite_2.1.3       xfun_0.49          
[73] pkgconfig_2.0.3

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in 30dcc_2025_10.qmd.

For the full repository, click here.

10. References

Expand for References

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

--- title: "Distribution of Offensive Rebounding Specialization by Position (2024 NBA Season)" subtitle: "Offensive rebounds as a proportion of total rebounds - Notice the multi-modal patterns" description: "An analysis of NBA player rebounding specialization patterns using ridge plots to visualize multi-modal distributions. This visualization for Day 10 of the #30DayChartChallenge explores how players within the same position show different rebounding tendencies, with particular focus on offensive rebounding ratios." author: "Steven Ponce" date: "2025-04-10" categories: ["30DayChartChallenge", "2025"] tags: [ "ggplot2", "ggridges", "basketball", "hoopR", "NBA", "sports analytics", "multi-modal", "distribution", "ridge plot", "offensive rebounding", "player specialization" ] image: "thumbnails/30dcc_2025_10.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_10.html" # description: "Exploring multi-modal distributions in NBA player rebounding patterns. See how positions like centers and forwards show distinct specialization types in their offensive rebounding approach. #30DayChartChallenge #DataViz #NBA" # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ![Ridge plot showing the distribution of offensive rebounding ratios across NBA positions. Multi-modal patterns are visible, with annotations highlighting different player types: Defensive-focused and Balanced Power Forwards, Perimeter and Inside Forwards, and Traditional and Offensive-focused Centers. Centers show highest ratios around 0.4, while guards have lower ratios centered around 0.2.](30dcc_2025_10.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' 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 ggridges, # Ridgeline Plots in 'ggplot2' camcorder # Record Your Plot History ) }) ### |- figure size ---- gg_record( dir = here::here("temp_plots"), device = "png", width = 10, 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 # Get NBA player stats for 2024 season 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 ---- # Season stats player_season_stats <- nba_players_2024 |> group_by(athlete_id, athlete_display_name, athlete_position_abbreviation) |> summarize( games_played = n(), across( c(minutes, points, field_goals_attempted, field_goals_made, three_point_field_goals_attempted, three_point_field_goals_made, free_throws_attempted, free_throws_made, offensive_rebounds, defensive_rebounds, rebounds, assists, turnovers, steals, blocks), ~ sum(.x, na.rm = TRUE), .names = "total_{.col}" ), .groups = "drop" ) # Players stats players_stats <- player_season_stats |> filter(total_minutes > 0, games_played >= 10) |> mutate( # metrics min_per_game = total_minutes / games_played, three_pt_rate = total_three_point_field_goals_attempted / total_minutes, # Free throw rate (FT attempts per minute) ft_rate = total_free_throws_attempted / total_minutes, # Offensive rebounding ratio (percentage of total rebounds that are offensive) orb_ratio = total_offensive_rebounds / (total_offensive_rebounds + total_defensive_rebounds + 0.001), # Shooting distance preference (proxy using 3PT to total FG ratio) three_pt_preference = total_three_point_field_goals_attempted / (total_field_goals_attempted + 0.001) ) |> filter(min_per_game >= 10) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- colors <- get_theme_colors( palette = c("#3182bd", "Positive Impact" = "#333333") ) ### |- titles and caption ---- # text title_text <- str_wrap("Distribution of Offensive Rebounding Specialization by Position (2024 NBA Season)", width = 60) subtitle_text <- str_wrap("Offensive rebounds as a proportion of total rebounds - Notice the multi-modal patterns", width = 100) # Create caption caption_text <- create_dcc_caption( dcc_year = 2025, dcc_day = 10, 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$text, size = rel(0.7)), axis.text.y = element_text(color = colors$text, size = rel(0.68)), axis.line.x = element_line(color = "gray50", linewidth = .2), # Grid elements panel.grid.minor.x = element_blank(), panel.grid.major.x = element_blank(), # Legend elements legend.position = "top", 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(players_stats, aes(x = orb_ratio, y = athlete_position_abbreviation)) + # Geoms geom_density_ridges(alpha = 0.7, scale = 1.2, rel_min_height = 0.01, fill = colors$palette[1]) + # Annotation annotate("text", x = 0.25, y = 4.4, label = "Defensive-focused PFs", size = 3.5, fontface = "italic", color = colors$palette[2]) + annotate("text", x = 0.35, y = 4.4, label = "Balanced PFs", size = 3.5, fontface = "italic", color = colors$palette[2]) + annotate("segment", x = 0.25, xend = 0.25, y = 4.2, yend = 4, arrow = arrow(length = unit(0.2, "cm")), color = colors$palette[2]) + annotate("segment", x = 0.35, xend = 0.35, y = 4.2, yend = 4, arrow = arrow(length = unit(0.2, "cm")), color = colors$palette[2]) + annotate("text", x = 0.20, y = 2.4, label = "Perimeter Fs", size = 3.5, fontface = "italic", color = colors$palette[2]) + annotate("text", x = 0.40, y = 2.4, label = "Inside Fs", size = 3.5, fontface = "italic", color = colors$palette[2]) + annotate("segment", x = 0.20, xend = 0.20, y = 2.2, yend = 2, arrow = arrow(length = unit(0.2, "cm")), color = colors$palette[2]) + annotate("segment", x = 0.40, xend = 0.40, y = 2.2, yend = 2, arrow = arrow(length = unit(0.2, "cm")), color = colors$palette[2]) + annotate("text", x = 0.35, y = 1.4, label = "Traditional Cs", size = 3.5, fontface = "italic", color = colors$palette[2]) + annotate("text", x = 0.48, y = 1.4, label = "Offensive-focused Cs", size = 3.5, fontface = "italic", color = colors$palette[2]) + annotate("segment", x = 0.35, xend = 0.35, y = 1.2, yend = 1, arrow = arrow(length = unit(0.2, "cm")), color = colors$palette[2]) + annotate("segment", x = 0.48, xend = 0.47, y = 1.2, yend = 1, arrow = arrow(length = unit(0.2, "cm")), color = colors$palette[2]) + # Scales scale_x_continuous( limits = c(0, 0.6), breaks = seq(0, 0.6, by = 0.1) ) + # Labs labs( title = title_text, subtitle = subtitle_text, caption = caption_text, x = "Offensive Rebounding Ratio", y = "Position" ) + # Theme theme( plot.title = element_text( size = rel(1.9), family = fonts$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 = 10, width = 10, 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_10.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2025/30dcc_2025_10.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) :::