NBA 3-Point Shooting Efficiency: Top 4 Teams

7-Game rolling averages showing shooting trends throughout the 2023-2024 season

30DayChartChallenge

2025

Exploring NBA 3-point shooting efficiency through smoothed time series visualization, showing how top teams’ performance fluctuated throughout the 2023-2024 season. Created for Day 19 of the #30DayChartChallenge focusing on time series data with smoothing techniques.

Published

April 19, 2025

Figure 1: A 2x2 grid showing 7-game rolling averages of 3-point shooting percentages for four NBA teams (Celtics, Suns, Thunder, and Timberwolves) during the 2023-2024 season. Each panel displays individual game data as gray dots with a colored trend line representing the rolling average.

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
  zoo,            # S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations)
  ggh4x,          # Hacks for 'ggplot2'
  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

# Load player box scores
player_boxes <- load_nba_player_box(seasons = 2024)

3. Examine the Data

Show code

glimpse(player_boxes)
skim(player_boxes)

4. Tidy Data

Show code

### |- Tidy ----
# Calculate team season averages
team_season_stats <- player_boxes |>        
  group_by(team_name) |>
  summarize(
    games = n_distinct(game_id),
    three_pt_made = sum(three_point_field_goals_made, na.rm = TRUE),
    three_pt_attempts = sum(three_point_field_goals_attempted, na.rm = TRUE),
    three_pt_pct = (three_pt_made / three_pt_attempts) * 100,
    .groups = "drop"
  ) |>
  filter(games >= 70) |>  # Ensure sufficient games 
  arrange(desc(three_pt_pct))

# Top 4 teams by 3PT percentage
top4_teams <- team_season_stats |> 
  slice_head(n = 4) |> 
  pull(team_name)

# Process game-by-game data for 
team_shooting <- player_boxes |>
  filter(team_name %in% top4_teams) |>
  group_by(team_name, game_date) |>
  summarize(
    three_point_made = sum(three_point_field_goals_made, na.rm = TRUE),
    three_point_attempts = sum(three_point_field_goals_attempted, na.rm = TRUE),
    .groups = "drop"
  ) |>
  mutate(
    three_pt_pct = (three_point_made / three_point_attempts) * 100,
    game_date = as.Date(game_date)
  ) |>
  filter(!is.na(three_pt_pct), !is.infinite(three_pt_pct))

# Calculate team averages for reference lines
team_averages <- team_shooting |>
  group_by(team_name) |>
  summarize(
    avg_pct = mean(three_pt_pct, na.rm = TRUE),
    .groups = "drop"
    )

# Calculate 7-game rolling average
team_rolling_data <- team_shooting |>
  arrange(team_name, game_date) |>
  group_by(team_name) |>
  mutate(
    rolling_avg_pct = rollmean(three_pt_pct, k = 7, fill = NA, align = "right"),
    .groups = "drop"
    )

5. Visualization Parameters

Show code

### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = c(
    "Celtics" = "#007932",
    "Suns" = "#1D1160", 
    "Thunder" =  "#007cc2",
    "Timberwolves" = "#236192"
  )
)

### |-  titles and caption ----
# text
title_text    <- str_wrap("NBA 3-Point Shooting Efficiency: Top 4 Teams",
                          width = 55) 

subtitle_text <- str_wrap("7-Game rolling averages showing shooting trends throughout the 2023-2024 season",
                          width = 85)

caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 19,
  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(

    # Axis elements
    axis.title.y = element_text(color = colors$text, size = rel(0.8), margin = margin(r = 10)),
    axis.text = element_text(color = colors$text, size = rel(0.7)),

    # Grid elements
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank(),
    panel.grid.major.y = element_line(color = "gray92"),

    # Legend elements
    legend.position = "plot",
    legend.title = element_blank(),
    legend.text = element_text(family = fonts$text, size = rel(0.7)),
    
    # Facet elements
    strip.background = element_rect(fill = "gray90", color = NA),
    strip.text = element_text(face = "bold", size = rel(1), margin = margin(10, 0, 10, 0)),
    
    # 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(team_rolling_data, aes(x = game_date)) +
  # Geoms
  geom_point(aes(y = three_pt_pct), alpha = 0.3, size = 2, color = "gray50") +
  geom_line(aes(y = rolling_avg_pct, color = team_name), linewidth = 2) +
  geom_hline(
    data = team_averages, aes(yintercept = avg_pct),
    linetype = "dashed", color = "gray30", linewidth = 0.7
  ) +
  # Scales
  scale_x_date(
    date_breaks = "2 month",
    date_labels = "%b",
    limits = c(min(team_shooting$game_date), max(team_shooting$game_date))
  ) +
  scale_y_continuous(
    limits = c(20, 60),
    breaks = seq(20, 60, by = 10),
    labels = function(x) paste0(x, "%")
  ) +
  scale_color_manual(values = colors$palette) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = NULL,
    y = "3-Point Shooting %",
  ) +
  # Facet
  facet_wrap2(
    ~team_name,
    ncol = 2,
    strip = strip_themed(
      background_x = elem_list_rect(fill = colors$palette[levels(factor(team_rolling_data$team_name))]),
      text_x = elem_list_text(color = "white", face = "bold", size = 14)
    )
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.8),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_text(
      size = rel(0.85),
      family = fonts$subtitle,
      color = colors$subtitle,
      lineheight = 1.2,
      margin = margin(t = 5, b = 10)
    ),
    plot.caption = element_markdown(
      size = rel(0.6),
      family = fonts$caption,
      color = colors$caption,
      lineheight = 0.65,
      hjust = 0.5,
      halign = 0.5,
      margin = margin(t = 10, b = 5)
    ),
  )

7. Save

Show code

### |-  plot image ----  

save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2025, 
  day = 19, 
  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 ggh4x_0.2.8     zoo_1.8-12     
 [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       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      lattice_0.22-6     
[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_19.qmd.

For the full repository, click here.

10. References

Expand for References

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

--- title: "NBA 3-Point Shooting Efficiency: Top 4 Teams" subtitle: "7-Game rolling averages showing shooting trends throughout the 2023-2024 season" description: "Exploring NBA 3-point shooting efficiency through smoothed time series visualization, showing how top teams' performance fluctuated throughout the 2023-2024 season. Created for Day 19 of the #30DayChartChallenge focusing on time series data with smoothing techniques." date: "2025-04-19" categories: ["30DayChartChallenge", "2025"] tags: [ "ggplot2", "hoopR", "timeseries", "smooth", "NBA", "basketball", "sports analytics", "rolling average", "facets", "ggh4x" ] image: "thumbnails/30dcc_2025_19.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_19.html" # description: "Day 19 of #30DayChartChallenge: Time Series & Smooth - Visualizing NBA 3-point shooting efficiency with 7-game rolling averages for the top 4 teams in the 2023-2024 season" # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ![A 2x2 grid showing 7-game rolling averages of 3-point shooting percentages for four NBA teams (Celtics, Suns, Thunder, and Timberwolves) during the 2023-2024 season. Each panel displays individual game data as gray dots with a colored trend line representing the rolling average. ](30dcc_2025_19.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 zoo, # S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations) ggh4x, # Hacks for 'ggplot2' 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 # Load player box scores player_boxes <- load_nba_player_box(seasons = 2024) ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(player_boxes) skim(player_boxes) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- Tidy ---- # Calculate team season averages team_season_stats <- player_boxes |> group_by(team_name) |> summarize( games = n_distinct(game_id), three_pt_made = sum(three_point_field_goals_made, na.rm = TRUE), three_pt_attempts = sum(three_point_field_goals_attempted, na.rm = TRUE), three_pt_pct = (three_pt_made / three_pt_attempts) * 100, .groups = "drop" ) |> filter(games >= 70) |> # Ensure sufficient games arrange(desc(three_pt_pct)) # Top 4 teams by 3PT percentage top4_teams <- team_season_stats |> slice_head(n = 4) |> pull(team_name) # Process game-by-game data for team_shooting <- player_boxes |> filter(team_name %in% top4_teams) |> group_by(team_name, game_date) |> summarize( three_point_made = sum(three_point_field_goals_made, na.rm = TRUE), three_point_attempts = sum(three_point_field_goals_attempted, na.rm = TRUE), .groups = "drop" ) |> mutate( three_pt_pct = (three_point_made / three_point_attempts) * 100, game_date = as.Date(game_date) ) |> filter(!is.na(three_pt_pct), !is.infinite(three_pt_pct)) # Calculate team averages for reference lines team_averages <- team_shooting |> group_by(team_name) |> summarize( avg_pct = mean(three_pt_pct, na.rm = TRUE), .groups = "drop" ) # Calculate 7-game rolling average team_rolling_data <- team_shooting |> arrange(team_name, game_date) |> group_by(team_name) |> mutate( rolling_avg_pct = rollmean(three_pt_pct, k = 7, fill = NA, align = "right"), .groups = "drop" ) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- colors <- get_theme_colors( palette = c( "Celtics" = "#007932", "Suns" = "#1D1160", "Thunder" = "#007cc2", "Timberwolves" = "#236192" ) ) ### |- titles and caption ---- # text title_text <- str_wrap("NBA 3-Point Shooting Efficiency: Top 4 Teams", width = 55) subtitle_text <- str_wrap("7-Game rolling averages showing shooting trends throughout the 2023-2024 season", width = 85) caption_text <- create_dcc_caption( dcc_year = 2025, dcc_day = 19, 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( # Axis elements axis.title.y = element_text(color = colors$text, size = rel(0.8), margin = margin(r = 10)), axis.text = element_text(color = colors$text, size = rel(0.7)), # Grid elements panel.grid.minor = element_blank(), panel.grid.major.x = element_blank(), panel.grid.major.y = element_line(color = "gray92"), # Legend elements legend.position = "plot", legend.title = element_blank(), legend.text = element_text(family = fonts$text, size = rel(0.7)), # Facet elements strip.background = element_rect(fill = "gray90", color = NA), strip.text = element_text(face = "bold", size = rel(1), margin = margin(10, 0, 10, 0)), # Plot margins plot.margin = margin(t = 10, r = 20, b = 10, l = 20), ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Plot ---- p <- ggplot(team_rolling_data, aes(x = game_date)) + # Geoms geom_point(aes(y = three_pt_pct), alpha = 0.3, size = 2, color = "gray50") + geom_line(aes(y = rolling_avg_pct, color = team_name), linewidth = 2) + geom_hline( data = team_averages, aes(yintercept = avg_pct), linetype = "dashed", color = "gray30", linewidth = 0.7 ) + # Scales scale_x_date( date_breaks = "2 month", date_labels = "%b", limits = c(min(team_shooting$game_date), max(team_shooting$game_date)) ) + scale_y_continuous( limits = c(20, 60), breaks = seq(20, 60, by = 10), labels = function(x) paste0(x, "%") ) + scale_color_manual(values = colors$palette) + # Labs labs( title = title_text, subtitle = subtitle_text, caption = caption_text, x = NULL, y = "3-Point Shooting %", ) + # Facet facet_wrap2( ~team_name, ncol = 2, strip = strip_themed( background_x = elem_list_rect(fill = colors$palette[levels(factor(team_rolling_data$team_name))]), text_x = elem_list_text(color = "white", face = "bold", size = 14) ) ) + # Theme theme( plot.title = element_text( size = rel(1.8), family = fonts$title, face = "bold", color = colors$title, margin = margin(t = 5, b = 5) ), plot.subtitle = element_text( size = rel(0.85), family = fonts$subtitle, color = colors$subtitle, 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 = 10, b = 5) ), ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot( p, type = "30daychartchallenge", year = 2025, day = 19, 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_19.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2025/30dcc_2025_19.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) :::