NBA Teams: Distribution of Assist-to-Turnover Ratios

2024 NBA Season | Teams sorted by median ratio | Circle size represents number of players

30DayChartChallenge
Data Visualization
R Programming
2025
Visualizing NBA players’ ball-handling efficiency through assist-to-turnover ratios. This dotplot combines distribution patterns with a striped design to show how players across different teams balance assists against turnovers, with teams ranked by median performance.
Author

Steven Ponce

Published

April 11, 2025

Figure 1: A dotplot showing the distribution of assist-to-turnover ratios for NBA teams in the 2024 season. Teams are sorted vertically by median ratio, with Jazz at the top and Celtics at the bottom. Purple circles represent players, with larger circles indicating more players with that specific ratio value. Most players cluster between ratios of 1.0-2.5. Alternating gray background stripes highlight each team row. A vertical dashed line marks the league median.

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  = 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 player stats for 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 ----
# Aggregate to get season stats per player
player_season_stats <- nba_players |>
  filter(!did_not_play) |>
  group_by(athlete_id, athlete_display_name, team_id, team_name) |>
  summarize(
    games_played = n(),
    total_minutes = sum(minutes, na.rm = TRUE),
    avg_minutes = total_minutes / games_played,
    total_assists = sum(assists, na.rm = TRUE),
    total_turnovers = sum(turnovers, na.rm = TRUE),
    .groups = "drop"
  )

# Filter out players with minimal playing time
filtered_players <- player_season_stats |>             
  filter(games_played >= 20, avg_minutes >= 10)

# Calculate Assist-to-Turnover Ratio (capped at 5)
filtered_players <- filtered_players |>
  mutate(AST_TO_Ratio = total_assists / pmax(total_turnovers, 1)) |>
  mutate(AST_TO_Ratio = pmin(AST_TO_Ratio, 5))

# Get team names to use for grouping
filtered_players$Team <- filtered_players$team_name

# Create team summary stats
team_stats <- filtered_players |>
  group_by(Team) |>
  summarize(
    median_ratio = median(AST_TO_Ratio, na.rm = TRUE),
    mean_ratio = mean(AST_TO_Ratio, na.rm = TRUE),
    min_ratio = min(AST_TO_Ratio, na.rm = TRUE),
    max_ratio = max(AST_TO_Ratio, na.rm = TRUE),
    q25 = quantile(AST_TO_Ratio, 0.25),
    q75 = quantile(AST_TO_Ratio, 0.75),
    players = n(),
    .groups = "drop"
  ) |>
  arrange(desc(median_ratio))

# Order teams by median ratio
team_order <- team_stats$Team
filtered_players$Team <- factor(filtered_players$Team, levels = team_order)

# Create background stripes df
background_stripes <- data.frame(
  Team = team_order[seq(1, length(team_order), by = 2)],
  xmin = rep(-Inf, length(team_order[seq(1, length(team_order), by = 2)])),
  xmax = rep(Inf, length(team_order[seq(1, length(team_order), by = 2)])),
  ymin = seq(1, length(team_order), by = 2) - 0.5,
  ymax = seq(1, length(team_order), by = 2) + 0.5
)

# Prepare data for dot plot
dotplot_data <- filtered_players |>
  mutate(ratio_rounded = round(AST_TO_Ratio * 2) / 2) |>
  group_by(Team, ratio_rounded) |>
  summarize(count = n(), .groups = "drop")

dotplot_data$Team <- factor(dotplot_data$Team, levels = team_order)

# Create median indicators df
median_indicators <- team_stats |>
  select(Team, median_ratio) |>
  mutate(Team = factor(Team, levels = team_order))

5. Visualization Parameters

Show code 
### |- plot aesthetics ---- 
colors <- get_theme_colors(
  palette = c("#6A0DAD")  
  )

### |-  titles and caption ----
# text
title_text    <- str_wrap("NBA Teams: Distribution of Assist-to-Turnover Ratios",
                          width = 60) 
subtitle_text <- str_wrap("2024 NBA Season | Teams sorted by median ratio | Circle size represents number of players", 
                          width = 100)

# Create caption
caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 11,
  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.major.y = element_blank(),
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank(),
 
    # Legend elements
    legend.position = "bottom",
    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() +
  # Geoms
  geom_rect(
    data = background_stripes,                                                  # Stripes
    aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
    fill = "gray80", alpha = 0.4
    ) +
  geom_vline(
    xintercept = seq(0, 5, by = 0.5), 
    color = "gray90", linewidth = 0.3
    ) +
  geom_vline(xintercept = median(team_stats$median_ratio), 
             linetype = "dashed", color = "gray50", linewidth = 0.5) +
  geom_point(
    data = dotplot_data,
    aes(x = ratio_rounded, y = as.numeric(Team), 
    size = count),
    color = colors$palette,
    alpha = 0.6
    ) +
  # Annotations
  annotate(
    "richtext", x = 4.6, y = 3, label = "Top-ranked<br>teams have<br>more players<br>with higher<br>assist-to-<br>turnover ratios", 
    hjust = 0, vjust = 0.5, size = 3, fill = NA, label.color = NA
    ) +
  annotate(
    "richtext", x = 0, y = 1, 
    label = "Most teams have <span style='color:#2D708EFF;'>clusters</span> of<br>players with ratios between 1.0-2.5",
    hjust = 0, vjust = 0.5, size = 3, fill = NA, label.color = NA
    ) +
  annotate(
    "richtext", x = 4.2, y = length(team_order) - 3.5,
    label = "Larger circles indicate<br>more players with that<br>specific ratio value",
    hjust = 0, vjust = 0, size = 3, fill = NA, label.color = NA
    ) +
  annotate(
    "text", x = median(team_stats$median_ratio) + 0.05, y = length(team_order) + 1.1,
    label = "League median", hjust = 0, size = 3, lineheight = 0.9
    ) +
  # Scales
  scale_x_continuous(
    breaks = seq(0, 5, 0.5),
    limits = c(0, 5.2),
    expand = expansion(add = c(0.1, 0))
  ) +
  scale_y_continuous(
    breaks = 1:length(team_order), 
    labels = team_order,
    expand = expansion(add = c(0.8, 0.8))
    ) +
  scale_size_continuous(
    range = c(1, 9), 
    breaks = c(1, 3, 5, 7),
    name = "Number of Players"
    ) +
  # Labs
  labs(title = title_text,
       subtitle = subtitle_text,
       caption = caption_text,
       x = "Assist-to-Turnover Ratio (rounded to nearest 0.5)",
       y = NULL
    ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(2),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_text(
      size = rel(1),
      family = fonts$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 = 5, b = 5)
    ),
  )

7. Save

Show code 
### |-  plot image ----  

save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2025, 
  day = 11, 
  width = 10, 
  height = 8
  )

8. 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] 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

The complete code for this analysis is available in 30dcc_2025_11.qmd.

For the full repository, click here.

10. References

  1. Data Sources:
    • ESPN via { hoopR } package: hoopR
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