Birds of a Feather: How NBA’s Bird Teams Show Identical Home Nest Advantage

Both Hawks and Pelicans win exactly 54.3% of home games and 45.7% of away games

30DayChartChallenge

2025

Analyzing NBA’s two bird-themed teams reveals a surprising statistical symmetry: both the Atlanta Hawks and New Orleans Pelicans share identical home and away win percentages during the 2023-24 season, demonstrating a perfect mirror image of home court advantage patterns.

Published

April 17, 2025

Figure 1: Bar chart titled “Birds of a Feather: How NBA’s Bird Teams Show Identical Home Nest Advantage” comparing Hawks and Pelicans win percentages. Both teams win exactly 54.3% of home games (teal bars) and 45.7% of away games (red bars). Arrows connect the identical percentages between teams, highlighting the perfect mirroring of their home court advantage pattern.

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  = 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

# Define the bird teams using their NBA team IDs
pelicans_id <- "1610612740"  # New Orleans Pelicans 
hawks_id <- "1610612737"     # Atlanta Hawks 

# Get team game logs for the 2023-2024 season
pelicans_games <- hoopR::nba_leaguegamelog(season = "2023-24", team_id_nullable = pelicans_id)
hawks_games <- hoopR::nba_leaguegamelog(season = "2023-24", team_id_nullable = hawks_id)
  
# Extract and prepare data
pelicans_data <- pelicans_games$LeagueGameLog |> mutate(team = "Pelicans")
hawks_data <- hawks_games$LeagueGameLog |> mutate(team = "Hawks")

# Combine datasets
all_games <- bind_rows(pelicans_data, hawks_data)

3. Examine the Data

Show code

glimpse(all_games)
skim(all_games)

4. Tidy Data

Show code

### |- Tidy ----
# Add home/away indicator
all_games <- all_games |>
  mutate(location = if_else(MATCHUP |> str_detect("@"), "Away", "Home"))

# Calculate metrics 
bird_analysis <- all_games |>
  mutate(
    game_date = as.Date(GAME_DATE, format = "%m/%d/%Y"),
    FGA_num = as.numeric(FGA),
    FTA_num = as.numeric(FTA),
    TOV_num = as.numeric(TOV),
    OREB_num = as.numeric(OREB),
    MIN_num = as.numeric(gsub(":", ".", MIN)),
    PTS_num = as.numeric(PTS),
    
    pace = case_when(
      is.na(MIN_num) | MIN_num == 0 ~ NA_real_,
      TRUE ~ (FGA_num + TOV_num + 0.44 * FTA_num - OREB_num) / MIN_num * 48
    ),
    
    efficiency = case_when(
      is.na(FGA_num) | FGA_num == 0 ~ NA_real_,
      TRUE ~ PTS_num / FGA_num
    )
  ) |>
  filter(!is.na(pace), !is.na(efficiency), !is.infinite(pace), !is.infinite(efficiency))

# Calculate home vs away statistics
home_away_summary <- bird_analysis |>
  group_by(team, location) |>
  summarize(
    avg_pace = mean(pace, na.rm = TRUE),
    avg_efficiency = mean(efficiency, na.rm = TRUE),
    games_played = n(),
    wins = sum(WL == "W", na.rm = TRUE),
    win_pct = wins / games_played,
    .groups = "drop"
  )

5. Visualization Parameters

Show code

### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = c(
    "Home" = "#008080", 
    "Away" = "#E03A3E"
  )
)

### |-  titles and caption ----
# text
title_text    <- str_wrap("Birds of a Feather: How NBA's Bird Teams Show Identical Home Nest Advantage",
                          width = 55) 

subtitle_text <- str_wrap("Both Hawks and Pelicans win exactly 54.3% of home games and 45.7% of away games",
                          width = 85)

caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 17,
  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 = "bottom",
    legend.title = element_blank(),
    # 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(home_away_summary, aes(x = team, y = win_pct, fill = location)) +
  # Geoms
  geom_col(
    position = "dodge", 
    width = 0.7
    ) +
  geom_text(
    aes(label = scales::percent(win_pct, accuracy = 0.1)), 
    position = position_dodge(
      width = 0.7), 
    vjust = -0.7, 
    size = 4, 
    fontface = "bold"
    ) +
  # Scales
  scale_y_continuous(
    labels = scales::percent, limits = c(0, 0.8), 
    breaks = seq(0, 0.8, 0.1)
    ) +
  scale_fill_manual(
    values = colors$palette
    ) +
  # Annotate
  annotate(
    "text", x = 1.5, y = 0.72, 
    label = "Both bird teams show identical\nhome vs. away performance patterns",
    size = 4, fontface = "italic", color = "gray30"
    ) +
  annotate(
    "segment", x = 1.25, xend = 1.75, y = 0.57, yend = 0.57, 
    arrow = arrow(ends = "both", length = unit(0.1, "inches")), 
    color = colors$palette[1]
    ) +
  annotate(
    "segment", x = 1.25, xend = 1.75, y = 0.48, yend = 0.48, 
    arrow = arrow(ends = "both", length = unit(0.1, "inches")), 
    olor = colors$palette[2]
    ) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    y = "Win Percentage", 
    x = NULL,
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.6),
      family = fonts$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 = 17, 
  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 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       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_17.qmd.

For the full repository, click here.

10. References

Expand for References

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

--- title: "Birds of a Feather: How NBA's Bird Teams Show Identical Home Nest Advantage" subtitle: "Both Hawks and Pelicans win exactly 54.3% of home games and 45.7% of away games" description: "Analyzing NBA's two bird-themed teams reveals a surprising statistical symmetry: both the Atlanta Hawks and New Orleans Pelicans share identical home and away win percentages during the 2023-24 season, demonstrating a perfect mirror image of home court advantage patterns." date: "2025-04-17" categories: ["30DayChartChallenge", "2025"] tags: [ "ggplot2", "hoopR", "NBA", "Sports Analytics", "Home Court Advantage", "Atlanta Hawks", "New Orleans Pelicans", "Bird Teams", "Relationships", "Win Percentage" ] image: "thumbnails/30dcc_2025_17.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_17.html" # description: "Day 17 of #30DayChartChallenge: Birds of a Feather - Discovering the identical home court advantage patterns shared by NBA's bird-themed teams" # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ![Bar chart titled "Birds of a Feather: How NBA's Bird Teams Show Identical Home Nest Advantage" comparing Hawks and Pelicans win percentages. Both teams win exactly 54.3% of home games (teal bars) and 45.7% of away games (red bars). Arrows connect the identical percentages between teams, highlighting the perfect mirroring of their home court advantage pattern.](30dcc_2025_17.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 = 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 # Define the bird teams using their NBA team IDs pelicans_id <- "1610612740" # New Orleans Pelicans hawks_id <- "1610612737" # Atlanta Hawks # Get team game logs for the 2023-2024 season pelicans_games <- hoopR::nba_leaguegamelog(season = "2023-24", team_id_nullable = pelicans_id) hawks_games <- hoopR::nba_leaguegamelog(season = "2023-24", team_id_nullable = hawks_id) # Extract and prepare data pelicans_data <- pelicans_games$LeagueGameLog |> mutate(team = "Pelicans") hawks_data <- hawks_games$LeagueGameLog |> mutate(team = "Hawks") # Combine datasets all_games <- bind_rows(pelicans_data, hawks_data) ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(all_games) skim(all_games) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- Tidy ---- # Add home/away indicator all_games <- all_games |> mutate(location = if_else(MATCHUP |> str_detect("@"), "Away", "Home")) # Calculate metrics bird_analysis <- all_games |> mutate( game_date = as.Date(GAME_DATE, format = "%m/%d/%Y"), FGA_num = as.numeric(FGA), FTA_num = as.numeric(FTA), TOV_num = as.numeric(TOV), OREB_num = as.numeric(OREB), MIN_num = as.numeric(gsub(":", ".", MIN)), PTS_num = as.numeric(PTS), pace = case_when( is.na(MIN_num) | MIN_num == 0 ~ NA_real_, TRUE ~ (FGA_num + TOV_num + 0.44 * FTA_num - OREB_num) / MIN_num * 48 ), efficiency = case_when( is.na(FGA_num) | FGA_num == 0 ~ NA_real_, TRUE ~ PTS_num / FGA_num ) ) |> filter(!is.na(pace), !is.na(efficiency), !is.infinite(pace), !is.infinite(efficiency)) # Calculate home vs away statistics home_away_summary <- bird_analysis |> group_by(team, location) |> summarize( avg_pace = mean(pace, na.rm = TRUE), avg_efficiency = mean(efficiency, na.rm = TRUE), games_played = n(), wins = sum(WL == "W", na.rm = TRUE), win_pct = wins / games_played, .groups = "drop" ) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- colors <- get_theme_colors( palette = c( "Home" = "#008080", "Away" = "#E03A3E" ) ) ### |- titles and caption ---- # text title_text <- str_wrap("Birds of a Feather: How NBA's Bird Teams Show Identical Home Nest Advantage", width = 55) subtitle_text <- str_wrap("Both Hawks and Pelicans win exactly 54.3% of home games and 45.7% of away games", width = 85) caption_text <- create_dcc_caption( dcc_year = 2025, dcc_day = 17, 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 = "bottom", legend.title = element_blank(), # 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(home_away_summary, aes(x = team, y = win_pct, fill = location)) + # Geoms geom_col( position = "dodge", width = 0.7 ) + geom_text( aes(label = scales::percent(win_pct, accuracy = 0.1)), position = position_dodge( width = 0.7), vjust = -0.7, size = 4, fontface = "bold" ) + # Scales scale_y_continuous( labels = scales::percent, limits = c(0, 0.8), breaks = seq(0, 0.8, 0.1) ) + scale_fill_manual( values = colors$palette ) + # Annotate annotate( "text", x = 1.5, y = 0.72, label = "Both bird teams show identical\nhome vs. away performance patterns", size = 4, fontface = "italic", color = "gray30" ) + annotate( "segment", x = 1.25, xend = 1.75, y = 0.57, yend = 0.57, arrow = arrow(ends = "both", length = unit(0.1, "inches")), color = colors$palette[1] ) + annotate( "segment", x = 1.25, xend = 1.75, y = 0.48, yend = 0.48, arrow = arrow(ends = "both", length = unit(0.1, "inches")), olor = colors$palette[2] ) + # Labs labs( title = title_text, subtitle = subtitle_text, caption = caption_text, y = "Win Percentage", x = NULL, ) + # Theme theme( plot.title = element_text( size = rel(1.6), family = fonts$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 = 17, 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_17.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2025/30dcc_2025_17.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) :::