Shooting Performance Variability Across NBA Teams (2023-24)

Field goal percentage distribution with statistical outliers highlighted in red

This visualization explores the distribution of field goal percentages across NBA teams during the 2023-24 season, with a focus on identifying statistical outliers. Using boxplots with overlaid data points, the chart reveals which teams maintained consistent shooting performance versus those with more variable results. Statistical outliers (defined using the 1.5×IQR rule) are highlighted in red, providing insights into exceptional shooting performances and unusually poor shooting nights.

Author

Steven Ponce

Published

April 7, 2025

Figure 1: A boxplot showing field goal percentage distribution across NBA teams for the 2023-24 season. Teams are ordered vertically by median percentage, with Indiana at the top. Statistical outliers are highlighted as red dots. A dashed blue vertical line indicates the league mean FG%. The chart reveals which teams have more consistent shooting performance versus those with extreme outlier games.

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 = 10,
    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_teams_2024 <- load_nba_team_box(seasons = 2024)

3. Examine the Data

Show code

glimpse(nba_teams_2024)
skim(nba_teams_2024)

4. Tidy Data

Show code

### |- Tidy ----
nba_teams_2024 <- nba_teams_2024 |> 
  filter(team_abbreviation != "EAST" & team_abbreviation != "WEST") |> 
  mutate(
    team_abbreviation = fct_reorder(team_abbreviation, field_goal_pct, median)
  ) 

# outliers df        
outliers_df <- nba_teams_2024 |> 
  mutate(
    q1 = quantile(field_goal_pct, 0.25, na.rm = TRUE),
    q3 = quantile(field_goal_pct, 0.75, na.rm = TRUE),
    iqr = q3 - q1,
    is_outlier = field_goal_pct < (q1 - 1.5 * iqr) | 
    field_goal_pct > (q3 + 1.5 * iqr),
    .by = team_abbreviation
    ) |>
  filter(is_outlier)

5. Visualization Parameters

Show code

### |- plot aesthetics ---- 
colors <- get_theme_colors(palette = NULL)

### |-  titles and caption ----
# text
title_text    <- str_glue("Shooting Performance Variability Across NBA Teams (2023-24)") 
subtitle_text <- str_glue("Field goal percentage distribution with statistical outliers highlighted in red")

# Create caption
caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 07,
  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 = "plot",
    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(
  data = nba_teams_2024,
  aes(y = team_abbreviation, x = field_goal_pct)
) +
  # Geoms
  geom_boxplot(fill = "lightgray", alpha = 0.2, outlier.shape = NA) +
  geom_jitter(height = 0.2, alpha = 0.2, color = "darkgray", size = 1.5) +
  geom_point(
    data = outliers_df,
    color = "red", size = 2.5
  ) +
  geom_vline(
    xintercept = mean(nba_teams_2024$field_goal_pct, na.rm = TRUE),
    linetype = "dashed", color = "blue", alpha = 0.8
  ) +
  annotate(
    "text", 
    x = mean(nba_teams_2024$field_goal_pct, na.rm = TRUE), 
    y = levels(nba_teams_2024$team_abbreviation)[length(levels(nba_teams_2024$team_abbreviation))],
    label = "League Mean FG%",  
    vjust = -2.1,
    color = "blue", 
    size = 4, 
    alpha = 0.8,
    fontface = "bold"
  ) +
  # Scales
  scale_x_continuous(labels = scales::number_format(suffix ="%")) +
  scale_y_discrete(
    expand = expansion(0.05, 0.0)
  ) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    y = "Team",
    x = "Field Goal Percentage"
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.4),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_text(
      size = rel(0.95),
      family = fonts$subtitle,
      color = colors$subtitle,
      lineheight = 1.2,
      margin = margin(t = 5, b = 30)
    ),
    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 = 07, 
  width = 8, 
  height = 10
  )

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_07.qmd.

For the full repository, click here.

10. References

Expand for References

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

--- title: "Shooting Performance Variability Across NBA Teams (2023-24)" subtitle: "Field goal percentage distribution with statistical outliers highlighted in red" description: "This visualization explores the distribution of field goal percentages across NBA teams during the 2023-24 season, with a focus on identifying statistical outliers. Using boxplots with overlaid data points, the chart reveals which teams maintained consistent shooting performance versus those with more variable results. Statistical outliers (defined using the 1.5×IQR rule) are highlighted in red, providing insights into exceptional shooting performances and unusually poor shooting nights." author: "Steven Ponce" date: "2025-04-07" categories: ["30DayChartChallenge", "Data Visualization", "R Programming", "2025"] tags: [ "ggplot2", "boxplot", "hoopR", "NBA", "sports analytics", "outlier detection", "statistical distribution", "basketball", "data storytelling", "exploratory data analysis" ] image: "thumbnails/30dcc_2025_07.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" self-contained: true theme: light: [flatly, assets/styling/custom_styles.scss] dark: [darkly, assets/styling/custom_styles_dark.scss] 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_07.html" # description: "Exploring NBA shooting performance variability across teams in the 2023-24 season. Which teams show consistent field goal percentages, and which teams have the most outlier games? Check out this visualization highlighting statistical distribution and outliers in NBA team shooting." # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ![ A boxplot showing field goal percentage distribution across NBA teams for the 2023-24 season. Teams are ordered vertically by median percentage, with Indiana at the top. Statistical outliers are highlighted as red dots. A dashed blue vertical line indicates the league mean FG%. The chart reveals which teams have more consistent shooting performance versus those with extreme outlier games.](30dcc_2025_07.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 = 10, 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_teams_2024 <- load_nba_team_box(seasons = 2024) ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(nba_teams_2024) skim(nba_teams_2024) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- Tidy ---- nba_teams_2024 <- nba_teams_2024 |> filter(team_abbreviation != "EAST" & team_abbreviation != "WEST") |> mutate( team_abbreviation = fct_reorder(team_abbreviation, field_goal_pct, median) ) # outliers df outliers_df <- nba_teams_2024 |> mutate( q1 = quantile(field_goal_pct, 0.25, na.rm = TRUE), q3 = quantile(field_goal_pct, 0.75, na.rm = TRUE), iqr = q3 - q1, is_outlier = field_goal_pct < (q1 - 1.5 * iqr) | field_goal_pct > (q3 + 1.5 * iqr), .by = team_abbreviation ) |> filter(is_outlier) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- colors <- get_theme_colors(palette = NULL) ### |- titles and caption ---- # text title_text <- str_glue("Shooting Performance Variability Across NBA Teams (2023-24)") subtitle_text <- str_glue("Field goal percentage distribution with statistical outliers highlighted in red") # Create caption caption_text <- create_dcc_caption( dcc_year = 2025, dcc_day = 07, 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$text, size = rel(0.7)), axis.line.x = element_line(color = "gray50", linewidth = .2), # Grid elements panel.grid.major = element_blank(), panel.grid.minor = element_blank(), # Legend elements legend.position = "plot", legend.title = element_text(family = fonts$text, size = rel(0.8)), legend.text = element_text(family = fonts$text, size = rel(0.7)), # Plot margins plot.margin = margin(t = 10, r = 20, b = 10, l = 20), ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Plot ---- p <- ggplot( data = nba_teams_2024, aes(y = team_abbreviation, x = field_goal_pct) ) + # Geoms geom_boxplot(fill = "lightgray", alpha = 0.2, outlier.shape = NA) + geom_jitter(height = 0.2, alpha = 0.2, color = "darkgray", size = 1.5) + geom_point( data = outliers_df, color = "red", size = 2.5 ) + geom_vline( xintercept = mean(nba_teams_2024$field_goal_pct, na.rm = TRUE), linetype = "dashed", color = "blue", alpha = 0.8 ) + annotate( "text", x = mean(nba_teams_2024$field_goal_pct, na.rm = TRUE), y = levels(nba_teams_2024$team_abbreviation)[length(levels(nba_teams_2024$team_abbreviation))], label = "League Mean FG%", vjust = -2.1, color = "blue", size = 4, alpha = 0.8, fontface = "bold" ) + # Scales scale_x_continuous(labels = scales::number_format(suffix ="%")) + scale_y_discrete( expand = expansion(0.05, 0.0) ) + # Labs labs( title = title_text, subtitle = subtitle_text, caption = caption_text, y = "Team", x = "Field Goal Percentage" ) + # Theme theme( plot.title = element_text( size = rel(1.4), family = fonts$title, face = "bold", color = colors$title, margin = margin(t = 5, b = 5) ), plot.subtitle = element_text( size = rel(0.95), family = fonts$subtitle, color = colors$subtitle, lineheight = 1.2, margin = margin(t = 5, b = 30) ), plot.caption = element_markdown( size = rel(0.6), family = fonts$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 = 07, width = 8, height = 10 ) ``` #### 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_07.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2025/30dcc_2025_07.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) :::