Win Percentage by Player Combinations and Playstyle in 2023-2024 Season

Top-performing duos organized by their playing chemistry, with team affiliations

30DayChartChallenge

2025

Visualization exploring the relationship between NBA player partnerships and win percentages in the 2023-2024 season, categorized by playstyle groups. The chart reveals which player combinations have the strongest on-court chemistry and how playing styles influence team success.

Author

Steven Ponce

Published

April 14, 2025

Figure 1: A lollipop chart showing NBA player combinations organized by playstyle groups (Offensive-Minded, Developing Chemistry, Two-Way Elite, and Defensive-Minded). Each horizontal line represents a player pair with team affiliations in parentheses, extending to points indicating their win percentage. The Offensive-Minded category tops the chart with the highest win rates (up to 90%), dominated by New York Knicks players. Colors differentiate the playstyle groups: yellow for Offensive-Minded, blue for Developing Chemistry, red for Two-Way Elite, and purple for Defensive-Minded. The visualization demonstrates which player relationships produce the most wins across different playing styles and teams.

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
  paletteer,      # Comprehensive Collection of Color Palettes
  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 the lineup data
lineups_2man <- nba_leaguedashlineups(
  season = "2023-24",
  measure_type = "Advanced",
  group_quantity = 2, # 2-player combinations
  season_type = "Regular Season"
)

lineups_df <- lineups_2man$Lineups

3. Examine the Data

Show code

glimpse(lineups_df)
skim(lineups_df)

4. Tidy Data

Show code

### |- Tidy ----
lineups_clean <- lineups_df |>
  select(
    player_combo = GROUP_NAME,
    min = MIN,
    net_rating = NET_RATING,
    off_rating = OFF_RATING, 
    def_rating = DEF_RATING,
    gp = GP,
    w = W,
    l = L,
    win_pct = W_PCT
  ) |>
  mutate(
    across(c(min, net_rating, off_rating, def_rating, gp, w, l, win_pct), as.numeric),
    win_loss_diff = w - l,
    combo_effectiveness = (win_pct * 100) * (net_rating / 20) # Scale 
  ) |>
  # Filter for meaningful playing time
  filter(min >= 300) |>
  # Group player combinations based on their effectiveness
  mutate(
    playstyle_group = case_when(
      off_rating > median(off_rating) & def_rating < median(def_rating) ~ "Offensive-Minded",
      off_rating < median(off_rating) & def_rating > median(def_rating) ~ "Defensive-Minded",
      off_rating > median(off_rating) & def_rating > median(def_rating) ~ "Two-Way Elite",
      TRUE ~ "Developing Chemistry"
    )
  )

# Top player combinations by effectiveness
top_combos <- lineups_clean |>
  group_by(playstyle_group) |>
  arrange(desc(combo_effectiveness)) |>
  slice_max(order_by = combo_effectiveness, n = 8) |>
  ungroup()

# Facet order (levels)
playstyle_order <- top_combos |>
  group_by(playstyle_group) |>
  summarize(avg_win = mean(win_pct, na.rm = TRUE)) |>
  arrange(desc(avg_win)) |>
  pull(playstyle_group)

# Housekeeping
rm(lineups_2man)

5. Visualization Parameters

Show code

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(
  palette = paletteer:::paletteer_d(
    "ggsci::default_aaas",
    type = 'discrete', 
    n = 4)
)

### |-  titles and caption ----
# text
title_text    <- str_wrap("Win Percentage by Player Combinations and Playstyle in 2023-2024 Season",
                          width = 60) 
subtitle_text <- str_wrap("Top-performing duos organized by their playing chemistry, with team affiliations", 
                          width = 100)

caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 14,
  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 = 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(0.75)),

    # Grid elements
    panel.grid.major.y = 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)),
    
    # Strip
    strip.text = element_text(family = fonts$text, color = colors$text, face = "bold", size = rel(0.92)),
    
    # 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(top_combos,
       aes(
         x = win_pct * 100, 
         y = fct_reorder(player_combo, win_pct))
) +
  # Geom
  geom_segment(aes(
    x = 0,
    xend = win_pct * 100,
    y = fct_reorder(player_combo, win_pct),
    yend = fct_reorder(player_combo, win_pct),
    color = playstyle_group
  ), 
  linewidth = 1, 
  alpha = 0.8
  ) +
  geom_point(aes(
    color = playstyle_group), 
    size = 3.5
  ) +
  geom_text(aes(
    label = sprintf("%.0f%%", win_pct * 100)), 
    hjust = -0.5, size = 3
  ) +
  # Scales
  scale_x_continuous(    
    limits = c(0, 100)
  ) +
  scale_y_discrete() +
  scale_color_manual(values = colors$palette) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = "Win Rate (%)",
    y = NULL,
    color = "Playstyle",
  ) +
  # Facets 
  facet_wrap(~ factor(
    playstyle_group, levels = playstyle_order), 
    scales = "free_y", ncol = 1
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.6),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_text(
      size = rel(0.9),
      family = fonts$subtitle,
      color = colors$subtitle,
      lineheight = 1.2,
      margin = margin(t = 5, b = 15)
    ),
    plot.caption = element_markdown(
      size = rel(0.6),
      family = fonts$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 = 14, 
  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 paletteer_1.6.0 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] 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       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     rematch2_2.1.2      codetools_0.2-20   
[55] ps_1.8.1            stringi_1.8.4       gtable_0.3.6       
[58] later_1.3.2         prismatic_1.1.2     munsell_0.5.1      
[61] furrr_0.3.1         pillar_1.9.0        htmltools_0.5.8.1  
[64] R6_2.5.1            textshaping_0.4.0   rprojroot_2.0.4    
[67] evaluate_1.0.1      markdown_1.13       gridtext_0.1.5     
[70] snakecase_0.11.1    renv_1.0.3          Rcpp_1.0.13-1      
[73] 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_14.qmd.

For the full repository, click here.

10. References

Expand for References

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

--- title: "Win Percentage by Player Combinations and Playstyle in 2023-2024 Season" subtitle: "Top-performing duos organized by their playing chemistry, with team affiliations" description: "Visualization exploring the relationship between NBA player partnerships and win percentages in the 2023-2024 season, categorized by playstyle groups. The chart reveals which player combinations have the strongest on-court chemistry and how playing styles influence team success." author: "Steven Ponce" date: "2025-04-14" categories: ["30DayChartChallenge", "2025"] tags: [ "NBA", "Basketball", "Player Analytics", "Sports Visualization", "hoopR", "ggplot2", "Player Chemistry", "Win Rate", "Team Performance", "Relationships", "Kinship", "Player Partnerships", "Playstyle Analysis" ] image: "thumbnails/30dcc_2025_14.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_14.html" # description: "Day 14 of #30DayChartChallenge: Visualizing NBA player relationships and how different playstyle combinations impact win percentages during the 2023-2024 season" # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ![A lollipop chart showing NBA player combinations organized by playstyle groups (Offensive-Minded, Developing Chemistry, Two-Way Elite, and Defensive-Minded). Each horizontal line represents a player pair with team affiliations in parentheses, extending to points indicating their win percentage. The Offensive-Minded category tops the chart with the highest win rates (up to 90%), dominated by New York Knicks players. Colors differentiate the playstyle groups: yellow for Offensive-Minded, blue for Developing Chemistry, red for Two-Way Elite, and purple for Defensive-Minded. The visualization demonstrates which player relationships produce the most wins across different playing styles and teams.](30dcc_2025_14.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 paletteer, # Comprehensive Collection of Color Palettes 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 the lineup data lineups_2man <- nba_leaguedashlineups( season = "2023-24", measure_type = "Advanced", group_quantity = 2, # 2-player combinations season_type = "Regular Season" ) lineups_df <- lineups_2man$Lineups ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(lineups_df) skim(lineups_df) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- Tidy ---- lineups_clean <- lineups_df |> select( player_combo = GROUP_NAME, min = MIN, net_rating = NET_RATING, off_rating = OFF_RATING, def_rating = DEF_RATING, gp = GP, w = W, l = L, win_pct = W_PCT ) |> mutate( across(c(min, net_rating, off_rating, def_rating, gp, w, l, win_pct), as.numeric), win_loss_diff = w - l, combo_effectiveness = (win_pct * 100) * (net_rating / 20) # Scale ) |> # Filter for meaningful playing time filter(min >= 300) |> # Group player combinations based on their effectiveness mutate( playstyle_group = case_when( off_rating > median(off_rating) & def_rating < median(def_rating) ~ "Offensive-Minded", off_rating < median(off_rating) & def_rating > median(def_rating) ~ "Defensive-Minded", off_rating > median(off_rating) & def_rating > median(def_rating) ~ "Two-Way Elite", TRUE ~ "Developing Chemistry" ) ) # Top player combinations by effectiveness top_combos <- lineups_clean |> group_by(playstyle_group) |> arrange(desc(combo_effectiveness)) |> slice_max(order_by = combo_effectiveness, n = 8) |> ungroup() # Facet order (levels) playstyle_order <- top_combos |> group_by(playstyle_group) |> summarize(avg_win = mean(win_pct, na.rm = TRUE)) |> arrange(desc(avg_win)) |> pull(playstyle_group) # Housekeeping rm(lineups_2man) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- # Get base colors with custom palette colors <- get_theme_colors( palette = paletteer:::paletteer_d( "ggsci::default_aaas", type = 'discrete', n = 4) ) ### |- titles and caption ---- # text title_text <- str_wrap("Win Percentage by Player Combinations and Playstyle in 2023-2024 Season", width = 60) subtitle_text <- str_wrap("Top-performing duos organized by their playing chemistry, with team affiliations", width = 100) caption_text <- create_dcc_caption( dcc_year = 2025, dcc_day = 14, 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 = 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(0.75)), # Grid elements panel.grid.major.y = 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)), # Strip strip.text = element_text(family = fonts$text, color = colors$text, face = "bold", size = rel(0.92)), # 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(top_combos, aes( x = win_pct * 100, y = fct_reorder(player_combo, win_pct)) ) + # Geom geom_segment(aes( x = 0, xend = win_pct * 100, y = fct_reorder(player_combo, win_pct), yend = fct_reorder(player_combo, win_pct), color = playstyle_group ), linewidth = 1, alpha = 0.8 ) + geom_point(aes( color = playstyle_group), size = 3.5 ) + geom_text(aes( label = sprintf("%.0f%%", win_pct * 100)), hjust = -0.5, size = 3 ) + # Scales scale_x_continuous( limits = c(0, 100) ) + scale_y_discrete() + scale_color_manual(values = colors$palette) + # Labs labs( title = title_text, subtitle = subtitle_text, caption = caption_text, x = "Win Rate (%)", y = NULL, color = "Playstyle", ) + # Facets facet_wrap(~ factor( playstyle_group, levels = playstyle_order), scales = "free_y", ncol = 1 ) + # Theme theme( plot.title = element_text( size = rel(1.6), family = fonts$title, face = "bold", color = colors$title, margin = margin(t = 5, b = 5) ), plot.subtitle = element_text( size = rel(0.9), family = fonts$subtitle, color = colors$subtitle, lineheight = 1.2, margin = margin(t = 5, b = 15) ), plot.caption = element_markdown( size = rel(0.6), family = fonts$caption, color = colors$caption, lineheight = 0.65, hjust = 0.5, halign = 0.5, margin = margin(t = 5, b = 5) ), ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot( p, type = "30daychartchallenge", year = 2025, day = 14, 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_14.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2025/30dcc_2025_14.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) :::