NBA Player Archetypes: The Relationship Between Defense and 3-Point Shooting

2023-2024 Season | Four Player Archetypes Identified via K-means Clustering

30DayChartChallenge

2025

Exploring the relationship between defensive contributions and 3-point shooting ability in NBA players for the 2023-2024 season, using K-means clustering to identify four distinct player archetypes. This visualization reveals how elite defenders rarely excel at 3-point shooting, while the league’s best shooters often sacrifice defensive effort, highlighting the rarity and value of two-way stars like Victor Wembanyama.

Author

Steven Ponce

Published

April 13, 2025

Figure 1: A scatter plot showing the relationship between NBA players’ defensive contributions (steals plus blocks per game) and 3-point shooting percentages. Four distinct clusters identified through K-means clustering reveal player archetypes: Two-Way Stars (upper right), Defensive Specialists (upper left), Offensive Specialists (lower right), and Role Players (lower center). Notable players like Victor Wembanyama stand out as elite two-way performers. Dashed lines indicate league averages for both metrics, creating quadrants with annotations describing each player archetype’s characteristics.

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'
  cluster,        # "Finding Groups in Data": Cluster Analysis Extended Rousseeuw etal
  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 data for 2023-2024 season
players_2024_list <- nba_leaguedashplayerstats(season = "2023-24")

# Extract the data frame from the response
players_2024 <- players_2024_list$LeagueDashPlayerStats

3. Examine the Data

Show code

glimpse(players_2024)
skim(players_2024)

4. Tidy Data

Show code

### |- Tidy ----
player_stats <- players_2024 |>
  # Make sure numeric columns are actually numeric
  mutate(
    GP = as.numeric(GP),
    MIN = as.numeric(MIN),
    STL = as.numeric(STL),
    BLK = as.numeric(BLK),
    FG3_PCT = as.numeric(FG3_PCT),
    FT_PCT = as.numeric(FT_PCT)
  ) |>
  # Filter for players with meaningful minutes
  filter(GP >= 40, MIN >= 15) |>
  # Calculate key metrics
  mutate(
    STL_per_game = STL / GP,
    BLK_per_game = BLK / GP,
    defensive_contribution = STL_per_game + BLK_per_game
  ) |>
  # Select specific columns
  select(
    PLAYER_NAME, TEAM_ABBREVIATION, 
    GP, MIN, 
    STL_per_game, BLK_per_game, defensive_contribution,
    FG3_PCT, FT_PCT
  )

# Cluster df 
cluster_data <- player_stats |>
  filter(
    !is.na(defensive_contribution),
    !is.na(FG3_PCT),
    !is.na(FT_PCT)
  )

# Scale the variables for clustering
scaled_data <- cluster_data |>
  select(defensive_contribution, FG3_PCT, FT_PCT) |>
  scale()

# Apply k-means clustering with k=4
set.seed(123) 
k <- 4
km_result <- kmeans(scaled_data, centers = k, nstart = 25)

# Add cluster assignments back to the data
cluster_data$cluster <- as.factor(km_result$cluster)

# Descriptive cluster labels
centers <- as.data.frame(km_result$centers)
colnames(centers) <- c("defensive_contribution", "FG3_PCT", "FT_PCT")

# Create more meaningful cluster labels
cluster_labels <- c()
for(i in 1:k) {
  def_val <- centers[i, "defensive_contribution"]
  fg3_val <- centers[i, "FG3_PCT"]
  ft_val <- centers[i, "FT_PCT"]
  
  if(def_val > 0 && fg3_val > 0 && ft_val > 0) {
    label <- "Two-Way Stars" # Good at everything
  } else if(def_val > 0 && (fg3_val < 0 || ft_val < 0)) {
    label <- "Defensive Specialists" # Good defense, weaker shooting
  } else if(def_val < 0 && fg3_val > 0 && ft_val > 0) {
    label <- "Offensive Specialists" # Good shooting, weaker defense
  } else {
    label <- "Role Players" # Average or below average
  }
  cluster_labels[i] <- label
}

# Map the labels to clusters
cluster_mapping <- setNames(cluster_labels, 1:k)
cluster_data$cluster_label <- cluster_mapping[cluster_data$cluster]

# Identify top players in each cluster (for labeling in the plot)
top_players <- cluster_data |>    
  group_by(cluster) |>
  arrange(desc(defensive_contribution + FG3_PCT + FT_PCT)) |>
  slice_head(n = 3) |>
  ungroup()

5. Visualization Parameters

Show code

### |- plot aesthetics ---- 
colors <- get_theme_colors(
  palette = c(
    "#D55E00",
    "#CC79A7",
    "#009E73",
    "#0072B2"
    )  
  )

### |-  titles and caption ----
# text
title_text    <- str_wrap("NBA Player Archetypes: The Relationship Between Defense and 3-Point Shooting",
                          width = 55) 
subtitle_text <- str_wrap("2023-2024 Season | Four Player Archetypes Identified via K-means Clustering", 
                          width = 100)

# Create caption
caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 13,
  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)),

    # Grid elements
    panel.grid.minor = element_blank(),

    # Legend elements
    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(
  cluster_data, 
  aes(x = FG3_PCT, y = defensive_contribution, 
      color = cluster_label)
) +
  # Geoms
  geom_point(
    alpha = 0.5, 
    size = 3
  ) +
  geom_text_repel( # Label top players
    data = top_players,
    aes(label = PLAYER_NAME),
    size = 3,
    seed = 123,
    max.overlaps = 15,
    box.padding = 0.5,
    segment.color = "grey50"
  ) +
  geom_hline( # average lines 
    yintercept = mean(cluster_data$defensive_contribution),
    linetype = "dashed",
    color = "grey50",
    alpha = 0.5
  ) +
  geom_vline(
    xintercept = mean(cluster_data$FG3_PCT),
    linetype = "dashed",
    alpha = 0.5
  ) +
  # Scales
  scale_y_continuous(
    limits = c(-0.05, 5),
    breaks = pretty_breaks()
  ) +
  scale_x_continuous(
    limits = c(-0.05, 1),
    breaks = pretty_breaks()
  ) +
  scale_color_manual(values = colors$palette) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = "3-Point Field Goal Percentage (FG3%)",
    y = "Defensive Contribution (STL + BLK per game)",
    color = "Player Archetype"
  ) +
  # Annotate
  annotate("text", x = -0.05, y = 3.75, 
           label = "Elite defenders rarely\nshoot well from deep", 
           size = 3.5, fontface = "italic", color = colors$palette[1], hjust = 0) +
  
  annotate("text", x = 0.45, y = 3.65, 
           label = "Two-Way Stars are rare\nand highly valuable", 
           size = 3.5, fontface = "italic", color = colors$palette[2], hjust = 0) +
  
  annotate("text", x = 0.02, y = 0, 
           label = "Role players contribute\nin less measurable ways", 
           size = 3.5, fontface = "italic", color = colors$palette[3], hjust = 0) +
  
  annotate("text", x = 0.65, y = 1, 
           label = "Elite shooters often\nsacrifice defensive effort", 
           size = 3.5, fontface = "italic", color = colors$palette[4], hjust = 0) +
  
  annotate("text", x = 0.03, y = 4, label = "Defensive Specialists", 
           color = colors$palette[1], fontface = "bold", size = 4) +
  
  annotate("text", x = 0.35, y = 3.8, label = "Two-Way Stars", 
           color = colors$palette[2], fontface = "bold", size = 4) +
  
  annotate("text", x = 0.08, y = 0.25, label = "Role Players", 
           color = colors$palette[3], fontface = "bold", size = 4) +
  
  annotate("text", x = 0.75, y = 1.3, label = "Offensive Specialists", 
           color = colors$palette[4], fontface = "bold", size = 4) +
  
  # Theme
  theme(
    plot.title = element_text(
      size = rel(2),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_text(
      size = rel(0.95),
      family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.8),
      lineheight = 1.2,
      margin = margin(t = 5, b = 10)
    ),
    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 = 20, b = 5)
    ),
  )

7. Save

Show code

### |-  plot image ----  

save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2025, 
  day = 13, 
  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 cluster_2.1.6   ggrepel_0.9.6  
 [5] hoopR_2.1.0     scales_1.3.0    skimr_2.1.5     janitor_2.2.0  
 [9] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
[13] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[17] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[21] 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] 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       globals_0.16.3      cli_3.6.3          
[31] rmarkdown_2.29      ragg_1.3.3          generics_0.1.3     
[34] RcppParallel_5.1.10 rstudioapi_0.17.1   httr_1.4.7         
[37] tzdb_0.4.0          commonmark_1.9.2    chromote_0.4.0     
[40] rvest_1.0.4         parallel_4.4.0      base64enc_0.1-3    
[43] vctrs_0.6.5         jsonlite_1.8.9      hms_1.1.3          
[46] listenv_0.9.1       systemfonts_1.1.0   magick_2.8.5       
[49] glue_1.8.0          parallelly_1.43.0   gifski_1.32.0-1    
[52] codetools_0.2-20    ps_1.8.1            stringi_1.8.4      
[55] gtable_0.3.6        later_1.3.2         munsell_0.5.1      
[58] furrr_0.3.1         pillar_1.9.0        htmltools_0.5.8.1  
[61] R6_2.5.1            textshaping_0.4.0   rprojroot_2.0.4    
[64] evaluate_1.0.1      markdown_1.13       gridtext_0.1.5     
[67] snakecase_0.11.1    renv_1.0.3          Rcpp_1.0.13-1      
[70] svglite_2.1.3       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_13.qmd.

For the full repository, click here.

10. References

Expand for References

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

--- title: "NBA Player Archetypes: The Relationship Between Defense and 3-Point Shooting" subtitle: "2023-2024 Season | Four Player Archetypes Identified via K-means Clustering" description: "Exploring the relationship between defensive contributions and 3-point shooting ability in NBA players for the 2023-2024 season, using K-means clustering to identify four distinct player archetypes. This visualization reveals how elite defenders rarely excel at 3-point shooting, while the league's best shooters often sacrifice defensive effort, highlighting the rarity and value of two-way stars like Victor Wembanyama." author: "Steven Ponce" date: "2025-04-13" categories: ["30DayChartChallenge", "2025"] tags: [ "NBA", "Basketball", "Cluster Analysis", "K-means", "ggplot2", "hoopR", "Player Analysis", "Sports Analytics", "Relationships", "Clusters" ] image: "thumbnails/30dcc_2025_13.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_13.html" # description: "Day 13 of #30DayChartChallenge: Identifying NBA player archetypes through the relationship between defense and 3-point shooting using K-means clustering" # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ![A scatter plot showing the relationship between NBA players' defensive contributions (steals plus blocks per game) and 3-point shooting percentages. Four distinct clusters identified through K-means clustering reveal player archetypes: Two-Way Stars (upper right), Defensive Specialists (upper left), Offensive Specialists (lower right), and Role Players (lower center). Notable players like Victor Wembanyama stand out as elite two-way performers. Dashed lines indicate league averages for both metrics, creating quadrants with annotations describing each player archetype's characteristics.](30dcc_2025_13.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' cluster, # "Finding Groups in Data": Cluster Analysis Extended Rousseeuw etal 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 data for 2023-2024 season players_2024_list <- nba_leaguedashplayerstats(season = "2023-24") # Extract the data frame from the response players_2024 <- players_2024_list$LeagueDashPlayerStats ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(players_2024) skim(players_2024) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- Tidy ---- player_stats <- players_2024 |> # Make sure numeric columns are actually numeric mutate( GP = as.numeric(GP), MIN = as.numeric(MIN), STL = as.numeric(STL), BLK = as.numeric(BLK), FG3_PCT = as.numeric(FG3_PCT), FT_PCT = as.numeric(FT_PCT) ) |> # Filter for players with meaningful minutes filter(GP >= 40, MIN >= 15) |> # Calculate key metrics mutate( STL_per_game = STL / GP, BLK_per_game = BLK / GP, defensive_contribution = STL_per_game + BLK_per_game ) |> # Select specific columns select( PLAYER_NAME, TEAM_ABBREVIATION, GP, MIN, STL_per_game, BLK_per_game, defensive_contribution, FG3_PCT, FT_PCT ) # Cluster df cluster_data <- player_stats |> filter( !is.na(defensive_contribution), !is.na(FG3_PCT), !is.na(FT_PCT) ) # Scale the variables for clustering scaled_data <- cluster_data |> select(defensive_contribution, FG3_PCT, FT_PCT) |> scale() # Apply k-means clustering with k=4 set.seed(123) k <- 4 km_result <- kmeans(scaled_data, centers = k, nstart = 25) # Add cluster assignments back to the data cluster_data$cluster <- as.factor(km_result$cluster) # Descriptive cluster labels centers <- as.data.frame(km_result$centers) colnames(centers) <- c("defensive_contribution", "FG3_PCT", "FT_PCT") # Create more meaningful cluster labels cluster_labels <- c() for(i in 1:k) { def_val <- centers[i, "defensive_contribution"] fg3_val <- centers[i, "FG3_PCT"] ft_val <- centers[i, "FT_PCT"] if(def_val > 0 && fg3_val > 0 && ft_val > 0) { label <- "Two-Way Stars" # Good at everything } else if(def_val > 0 && (fg3_val < 0 || ft_val < 0)) { label <- "Defensive Specialists" # Good defense, weaker shooting } else if(def_val < 0 && fg3_val > 0 && ft_val > 0) { label <- "Offensive Specialists" # Good shooting, weaker defense } else { label <- "Role Players" # Average or below average } cluster_labels[i] <- label } # Map the labels to clusters cluster_mapping <- setNames(cluster_labels, 1:k) cluster_data$cluster_label <- cluster_mapping[cluster_data$cluster] # Identify top players in each cluster (for labeling in the plot) top_players <- cluster_data |> group_by(cluster) |> arrange(desc(defensive_contribution + FG3_PCT + FT_PCT)) |> slice_head(n = 3) |> ungroup() ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- colors <- get_theme_colors( palette = c( "#D55E00", "#CC79A7", "#009E73", "#0072B2" ) ) ### |- titles and caption ---- # text title_text <- str_wrap("NBA Player Archetypes: The Relationship Between Defense and 3-Point Shooting", width = 55) subtitle_text <- str_wrap("2023-2024 Season | Four Player Archetypes Identified via K-means Clustering", width = 100) # Create caption caption_text <- create_dcc_caption( dcc_year = 2025, dcc_day = 13, 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)), # Grid elements panel.grid.minor = element_blank(), # Legend elements 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( cluster_data, aes(x = FG3_PCT, y = defensive_contribution, color = cluster_label) ) + # Geoms geom_point( alpha = 0.5, size = 3 ) + geom_text_repel( # Label top players data = top_players, aes(label = PLAYER_NAME), size = 3, seed = 123, max.overlaps = 15, box.padding = 0.5, segment.color = "grey50" ) + geom_hline( # average lines yintercept = mean(cluster_data$defensive_contribution), linetype = "dashed", color = "grey50", alpha = 0.5 ) + geom_vline( xintercept = mean(cluster_data$FG3_PCT), linetype = "dashed", alpha = 0.5 ) + # Scales scale_y_continuous( limits = c(-0.05, 5), breaks = pretty_breaks() ) + scale_x_continuous( limits = c(-0.05, 1), breaks = pretty_breaks() ) + scale_color_manual(values = colors$palette) + # Labs labs( title = title_text, subtitle = subtitle_text, caption = caption_text, x = "3-Point Field Goal Percentage (FG3%)", y = "Defensive Contribution (STL + BLK per game)", color = "Player Archetype" ) + # Annotate annotate("text", x = -0.05, y = 3.75, label = "Elite defenders rarely\nshoot well from deep", size = 3.5, fontface = "italic", color = colors$palette[1], hjust = 0) + annotate("text", x = 0.45, y = 3.65, label = "Two-Way Stars are rare\nand highly valuable", size = 3.5, fontface = "italic", color = colors$palette[2], hjust = 0) + annotate("text", x = 0.02, y = 0, label = "Role players contribute\nin less measurable ways", size = 3.5, fontface = "italic", color = colors$palette[3], hjust = 0) + annotate("text", x = 0.65, y = 1, label = "Elite shooters often\nsacrifice defensive effort", size = 3.5, fontface = "italic", color = colors$palette[4], hjust = 0) + annotate("text", x = 0.03, y = 4, label = "Defensive Specialists", color = colors$palette[1], fontface = "bold", size = 4) + annotate("text", x = 0.35, y = 3.8, label = "Two-Way Stars", color = colors$palette[2], fontface = "bold", size = 4) + annotate("text", x = 0.08, y = 0.25, label = "Role Players", color = colors$palette[3], fontface = "bold", size = 4) + annotate("text", x = 0.75, y = 1.3, label = "Offensive Specialists", color = colors$palette[4], fontface = "bold", size = 4) + # Theme theme( plot.title = element_text( size = rel(2), family = fonts$title, face = "bold", color = colors$title, margin = margin(t = 5, b = 5) ), plot.subtitle = element_text( size = rel(0.95), family = fonts$subtitle, color = alpha(colors$subtitle, 0.8), lineheight = 1.2, margin = margin(t = 5, b = 10) ), plot.caption = element_markdown( size = rel(0.6), family = fonts$caption, color = colors$caption, lineheight = 0.65, hjust = 0.5, halign = 0.5, margin = margin(t = 20, b = 5) ), ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot( p, type = "30daychartchallenge", year = 2025, day = 13, 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_13.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2025/30dcc_2025_13.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) :::