Comparison of NBA Stars’ Shooting Efficiency (2023-2024)

Inspired by Florence Nightingale’s 1858 rose diagrams that revolutionized statistical visualization

30DayChartChallenge

2025

A Florence Nightingale-inspired rose diagram visualizing shooting efficiency metrics across four categories for NBA stars Joel Embiid, Nikola Jokic, and Stephen Curry. This visualization compares Field Goal %, 3-Point %, Free Throw %, and True Shooting % using the hoopR package for the 2023-2024 NBA season data.

Author

Steven Ponce

Published

April 6, 2025

Figure 1: A Florence Nightingale-inspired rose diagram comparing shooting efficiency metrics (Field Goal %, 3-Point %, Free Throw %, and True Shooting %) among three NBA stars: Joel Embiid (red), Nikola Jokic (blue), and Stephen Curry (teal). The diagram highlights Curry’s superior free throw percentage (92.3%), Jokic’s leading field goal percentage (57.8%) and true shooting percentage (64.7%), and Curry’s top 3-point percentage (40.6%).

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
  ggrepel,        # Automatically Position Non-Overlapping Text Labels with ggplot2
  camcorder       # Record Your Plot History
)
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 6,
    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 player stats for the 2023-2024 NBA season
nba_players <- load_nba_player_box(seasons = 2024)

3. Examine the Data

Show code

glimpse(nba_players)
skim(nba_players)

4. Tidy Data

Show code

### |- Tidy ----
# Select our 3 players 
selected_players <- c("Joel Embiid", "Nikola Jokic", "Stephen Curry")

# Calculate player stats
player_stats <- nba_players |>
  filter(athlete_display_name %in% selected_players) |>
  group_by(athlete_display_name) |>
  summarize(
    games = n(),
    across(
      .cols = c(
        field_goals_made, field_goals_attempted,
        three_point_field_goals_made, three_point_field_goals_attempted,
        free_throws_made, free_throws_attempted,
        points
      ),
      .fns = ~ sum(.x, na.rm = TRUE),
      .names = "{.col}_sum"
    ),
    fg_pct = field_goals_made_sum / field_goals_attempted_sum,
    three_pct = three_point_field_goals_made_sum / three_point_field_goals_attempted_sum,
    ft_pct = free_throws_made_sum / free_throws_attempted_sum,
    total_pts = points_sum
  ) |>
  mutate(
    true_shooting = total_pts / (2 * (field_goals_attempted_sum + 0.44 * free_throws_attempted_sum))
  ) |> 
  select(athlete_display_name, fg_pct, three_pct, ft_pct, true_shooting)

# rose diagram data
nightingale_data <- player_stats |>
  pivot_longer(
    cols = -athlete_display_name,
    names_to = "stat",
    values_to = "value"
  ) |>
  mutate(
    stat_label = case_when(
      stat == "fg_pct" ~ "Field Goal %",
      stat == "three_pct" ~ "3-Point %",
      stat == "ft_pct" ~ "Free Throw %",
      stat == "true_shooting" ~ "True Shooting %"
    ),
    # Set factor levels to control order
    stat_label = factor(
      stat_label, 
      levels = c("Field Goal %", "3-Point %", "Free Throw %", "True Shooting %")
      )
  )

label_data <- nightingale_data |>  
  filter(
    (athlete_display_name == "Nikola Jokic" & stat_label == "Field Goal %") |
      (athlete_display_name == "Nikola Jokic" & stat_label == "True Shooting %") |
      (athlete_display_name == "Stephen Curry" & stat_label == "3-Point %") |
      (athlete_display_name == "Stephen Curry" & stat_label == "Free Throw %")
  ) |>
  mutate(
    # Format percentage for label
    label = paste0(round(value * 100, 1), "%")
  )

5. Visualization Parameters

Show code

### |- plot aesthetics ---- 
colors <- get_theme_colors(palette = c(
  "Joel Embiid" = "#BF212E",     
  "Nikola Jokic" = "#1D3E85",    
  "Stephen Curry" = "#008080"   
  )
)

### |-  titles and caption ----
# text
title_text    <- str_wrap("Comparison of NBA Stars' Shooting Efficiency (2023-2024)", 
                          width = 50) 
subtitle_text <- str_wrap("Inspired by Florence Nightingale's 1858 rose diagrams that revolutionized statistical visualization",
                          width = 80)

# Create caption
caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 06,
  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, face = "italic", color = colors$text, size = rel(0.78), margin = margin(b = 20)),
    
    # Axis elements
    axis.title.y = element_text(color = colors$text, size = rel(0.8),
                              hjust = 1, vjust = 0.95, angle = 0),
    axis.title.x = element_blank(),
    
    axis.text.x = element_text(color = colors$text, face = "bold", size = rel(0.7)),
    axis.text.y = element_text(color = colors$text, size = rel(0.57)),

    # Grid elements
    panel.grid.major = element_line(color = "gray85", linewidth = 0.25),
    panel.grid.minor = element_blank(),
 
    # Legend elements
    legend.position = "bottom",
    legend.title = element_text(family = fonts$text, face = "bold", size = rel(0.8)),
    legend.text = element_text(family = fonts$text, size = rel(0.7)),
    
    # Plot margins 
    plot.margin = margin(t = 10, r = 10, b = 10, l = 10),
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code

### |-  Plot ----
p <- ggplot() +
  # Geoms
  geom_col(data = nightingale_data, 
           aes(x = stat_label, y = value, fill = athlete_display_name),
           position = position_dodge(width = 0.9), width = 0.8) +
  # Annotate
  annotate(  # Nikola Jokic FG% label
    "text",  
    x = 1, 
    y = 0.61, 
    label = "57.8%", 
    hjust = 0, 
    size = 3, 
    fontface = "bold", 
    color = colors$palette[2]
    ) +
  annotate( # Stephen Curry 3PT% label
    "text", 
    x = 2, 
    y = 0.6, 
    label = "40.6%", 
    hjust = 2.2, 
    vjust = 1, 
    size = 3, 
    fontface = "bold",
    color = colors$palette[3]
    ) +
  annotate( # Stephen Curry FT% label
    "text", 
    x = 3, 
    y = 0.96, 
    label = "92.3%", 
    hjust = 2.5, 
    vjust = -8, 
    size = 3, 
    fontface = "bold", 
    color = colors$palette[3]
    ) +
  annotate( # Nikola Jokic TS% label
    "text", 
    x = 4, 
    y = 0.68, 
    label = "64.7%", 
    hjust = 1, 
    size = 3, 
    fontface = "bold", 
    color = colors$palette[2]
    ) +
  # Scales
  scale_y_continuous(
    limits = c(0, 1),
    breaks = c(0, 0.25, 0.50, 0.75)
  ) +
  scale_fill_manual(
    values = colors$palette
  ) +
  coord_polar(clip = 'off') +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = NULL,
    y = NULL,
    fill = "Player"
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.5),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_text(
      size = rel(0.8),
      family = fonts$subtitle,
      color = colors$subtitle,
      lineheight = 1.2,
      margin = margin(t = 15, b = 5)
    ),
    plot.caption = element_markdown(
      size = rel(0.55),
      family = fonts$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 = 06, 
  width = 06, 
  height = 08
  )

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 ggrepel_0.9.6   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_06.qmd.

For the full repository, click here.

10. References

Expand for References

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

--- title: "Comparison of NBA Stars' Shooting Efficiency (2023-2024)" subtitle: "Inspired by Florence Nightingale's 1858 rose diagrams that revolutionized statistical visualization" description: "A Florence Nightingale-inspired rose diagram visualizing shooting efficiency metrics across four categories for NBA stars Joel Embiid, Nikola Jokic, and Stephen Curry. This visualization compares Field Goal %, 3-Point %, Free Throw %, and True Shooting % using the hoopR package for the 2023-2024 NBA season data." author: "Steven Ponce" date: "2025-04-06" categories: ["30DayChartChallenge", "2025"] tags: [ "ggplot2", "NBA statistics", "hoopR", "sports analytics", "polar coordinates", "Florence Nightingale", "rose diagram", "shooting efficiency", "data visualization", "basketball", "comparative analysis", "Stephen Curry", "Nikola Jokic", "Joel Embiid" ] image: "thumbnails/30dcc_2025_06.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_06.html" # description: "Day 6 of #30DayChartChallenge: A Florence Nightingale-inspired rose diagram comparing shooting efficiency among NBA stars Embiid, Jokic, and Curry. See who leads in Field Goal %, 3-Point %, Free Throw %, and True Shooting % for the 2023-2024 season. #rstats #dataviz #NBA" # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ![A Florence Nightingale-inspired rose diagram comparing shooting efficiency metrics (Field Goal %, 3-Point %, Free Throw %, and True Shooting %) among three NBA stars: Joel Embiid (red), Nikola Jokic (blue), and Stephen Curry (teal). The diagram highlights Curry's superior free throw percentage (92.3%), Jokic's leading field goal percentage (57.8%) and true shooting percentage (64.7%), and Curry's top 3-point percentage (40.6%).](30dcc_2025_06.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 ggrepel, # Automatically Position Non-Overlapping Text Labels with ggplot2 camcorder # Record Your Plot History ) }) ### |- figure size ---- gg_record( dir = here::here("temp_plots"), device = "png", width = 6, 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 player stats for the 2023-2024 NBA season nba_players <- load_nba_player_box(seasons = 2024) ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(nba_players) skim(nba_players) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- Tidy ---- # Select our 3 players selected_players <- c("Joel Embiid", "Nikola Jokic", "Stephen Curry") # Calculate player stats player_stats <- nba_players |> filter(athlete_display_name %in% selected_players) |> group_by(athlete_display_name) |> summarize( games = n(), across( .cols = c( field_goals_made, field_goals_attempted, three_point_field_goals_made, three_point_field_goals_attempted, free_throws_made, free_throws_attempted, points ), .fns = ~ sum(.x, na.rm = TRUE), .names = "{.col}_sum" ), fg_pct = field_goals_made_sum / field_goals_attempted_sum, three_pct = three_point_field_goals_made_sum / three_point_field_goals_attempted_sum, ft_pct = free_throws_made_sum / free_throws_attempted_sum, total_pts = points_sum ) |> mutate( true_shooting = total_pts / (2 * (field_goals_attempted_sum + 0.44 * free_throws_attempted_sum)) ) |> select(athlete_display_name, fg_pct, three_pct, ft_pct, true_shooting) # rose diagram data nightingale_data <- player_stats |> pivot_longer( cols = -athlete_display_name, names_to = "stat", values_to = "value" ) |> mutate( stat_label = case_when( stat == "fg_pct" ~ "Field Goal %", stat == "three_pct" ~ "3-Point %", stat == "ft_pct" ~ "Free Throw %", stat == "true_shooting" ~ "True Shooting %" ), # Set factor levels to control order stat_label = factor( stat_label, levels = c("Field Goal %", "3-Point %", "Free Throw %", "True Shooting %") ) ) label_data <- nightingale_data |> filter( (athlete_display_name == "Nikola Jokic" & stat_label == "Field Goal %") | (athlete_display_name == "Nikola Jokic" & stat_label == "True Shooting %") | (athlete_display_name == "Stephen Curry" & stat_label == "3-Point %") | (athlete_display_name == "Stephen Curry" & stat_label == "Free Throw %") ) |> mutate( # Format percentage for label label = paste0(round(value * 100, 1), "%") ) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- colors <- get_theme_colors(palette = c( "Joel Embiid" = "#BF212E", "Nikola Jokic" = "#1D3E85", "Stephen Curry" = "#008080" ) ) ### |- titles and caption ---- # text title_text <- str_wrap("Comparison of NBA Stars' Shooting Efficiency (2023-2024)", width = 50) subtitle_text <- str_wrap("Inspired by Florence Nightingale's 1858 rose diagrams that revolutionized statistical visualization", width = 80) # Create caption caption_text <- create_dcc_caption( dcc_year = 2025, dcc_day = 06, 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, face = "italic", color = colors$text, size = rel(0.78), margin = margin(b = 20)), # Axis elements axis.title.y = element_text(color = colors$text, size = rel(0.8), hjust = 1, vjust = 0.95, angle = 0), axis.title.x = element_blank(), axis.text.x = element_text(color = colors$text, face = "bold", size = rel(0.7)), axis.text.y = element_text(color = colors$text, size = rel(0.57)), # Grid elements panel.grid.major = element_line(color = "gray85", linewidth = 0.25), panel.grid.minor = element_blank(), # Legend elements legend.position = "bottom", legend.title = element_text(family = fonts$text, face = "bold", size = rel(0.8)), legend.text = element_text(family = fonts$text, size = rel(0.7)), # Plot margins plot.margin = margin(t = 10, r = 10, b = 10, l = 10), ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Plot ---- p <- ggplot() + # Geoms geom_col(data = nightingale_data, aes(x = stat_label, y = value, fill = athlete_display_name), position = position_dodge(width = 0.9), width = 0.8) + # Annotate annotate( # Nikola Jokic FG% label "text", x = 1, y = 0.61, label = "57.8%", hjust = 0, size = 3, fontface = "bold", color = colors$palette[2] ) + annotate( # Stephen Curry 3PT% label "text", x = 2, y = 0.6, label = "40.6%", hjust = 2.2, vjust = 1, size = 3, fontface = "bold", color = colors$palette[3] ) + annotate( # Stephen Curry FT% label "text", x = 3, y = 0.96, label = "92.3%", hjust = 2.5, vjust = -8, size = 3, fontface = "bold", color = colors$palette[3] ) + annotate( # Nikola Jokic TS% label "text", x = 4, y = 0.68, label = "64.7%", hjust = 1, size = 3, fontface = "bold", color = colors$palette[2] ) + # Scales scale_y_continuous( limits = c(0, 1), breaks = c(0, 0.25, 0.50, 0.75) ) + scale_fill_manual( values = colors$palette ) + coord_polar(clip = 'off') + # Labs labs( title = title_text, subtitle = subtitle_text, caption = caption_text, x = NULL, y = NULL, fill = "Player" ) + # Theme theme( plot.title = element_text( size = rel(1.5), family = fonts$title, face = "bold", color = colors$title, margin = margin(t = 5, b = 5) ), plot.subtitle = element_text( size = rel(0.8), family = fonts$subtitle, color = colors$subtitle, lineheight = 1.2, margin = margin(t = 15, b = 5) ), plot.caption = element_markdown( size = rel(0.55), family = fonts$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 = 06, width = 06, height = 08 ) ``` #### 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_06.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2025/30dcc_2025_06.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) :::