124: Slope Net

Load data

library(cbbplotR)
library(rlang)

# majority of data wrangling done via evdevbyc repo
# see https://github.com/gallochris/evdev-byc/blob/main/data/college-basketball/

twenty <- readr::read_csv("twenty_plus.csv")

# Prepare data for plotting
data_for_plot <- twenty |> 
  dplyr::mutate(raw_net_eff = round(raw_net_eff, 1)) |> 
  tidyr::pivot_longer(
    cols = c(pts_dif, raw_net_eff),
    names_to = "metric",
    values_to = "value"
  )  |> 
  dplyr::mutate(color_line = 
            dplyr::if_else(
              row_num %in% c(15, 30, 27), "label-games", "games" 
            )
      )


# prep conference summary data too 
conf_summary <- readr::read_csv("conf-sum.csv") |> 
  dplyr::select(-1)

Custom theme

theme_me <- function() {
  # Create a base theme with minimal style
  base_theme <- ggplot2::theme_minimal(base_size = 10, base_family = "RobotoCondensed-Regular")
  
  # Customize the base theme with additional modifications
  custom_theme <- base_theme +
    ggplot2::theme(
      plot.title = ggplot2::element_text(
        hjust = 0.5,
        size = 24,
        face = "bold"
      ),
      plot.subtitle = ggplot2::element_text(
        hjust = 0.5,
        size = 10,
        lineheight = 0.25,
        vjust = -0.5
      ),
      plot.caption = ggplot2::element_text(
        hjust = 1,
        size = 6,
        lineheight = 0.35,
        margin = ggplot2::margin(t = 20)
      ),
      panel.grid.minor = ggplot2::element_line(color = "gray", linetype = "dotted"),  # Customize minor grid lines
      plot.background = ggplot2::element_rect(fill = "floralwhite", color = "floralwhite")
    )
  
  return(custom_theme)
} 

Slope chart

slope_plot <- data_for_plot |> 
  ggplot2::ggplot(ggplot2::aes(x = metric, y = value, 
                               group = row_num)) +
  ggplot2::geom_line(ggplot2::aes(color = color_line, 
                                  size = color_line)) +
  ggplot2::geom_point(ggplot2::aes(color = color_line, 
                                   size = color_line)) +
  ggplot2::scale_color_manual(values = c("games" = "lightpink", 
                                         "label-games" = "lightgreen")) +
  ggplot2::scale_size_manual(values = c("games" = 0.5, 
                                        "label-games" = 1.5)) +
  ggplot2::scale_x_discrete(labels = 
                              c("pts_dif" = "Points Difference", 
                                "raw_net_eff" = "Raw Net Efficiency")) +
  ggplot2::labs(
    title = "Are all 20-point  \nwins the same?",
    x = "", y = "",
    caption = "Data via barttorvik.com | Viz by Chris at Bless your chart | January 10, 2025"
  ) +
  ggplot2::scale_y_continuous(limits= c(15, 40)) +
  theme_me() +
  ggplot2::theme(
    legend.position = "none",
    plot.title = ggtext::element_markdown(family = "mono"),
    axis.text.y = ggplot2::element_blank(),
    axis.text.x = ggplot2::element_blank(),
    axis.title.x = ggplot2::element_text(size = 14, face = "bold"),
    plot.caption = ggtext::element_markdown(family = "mono")
  ) +
  ggplot2::annotate(
    "text", 
    x = 1,
    y = 17.5,
    label = "Margin of  \nVictory",
    color = "#333333",
    size = 4,
    fontface = 'bold',
    family = 'mono',
    hjust = 0.5
  ) +
  ggplot2::annotate(
    "text", 
    x = 2,
    y = 17.5,
    label = "Raw Net Efficiency  \nper 100 possessions",
    color = "#333333",
    size = 4,
    fontface = 'bold',
    family = 'mono',
    hjust = 0.5
  ) +
  ggplot2::annotate(
    "text", 
    x = 0.94,
    y = 20,
    label = "+20",
    color = "#333333",
    size = 3.5,
    fontface = 'bold',
    family = 'mono',
    hjust = 0.5
  ) +
  ggplot2::annotate(
    "text", 
    x = 2.09,
    y = 37.7,
    label = "+37.7",
    color = "#333333",
    size = 3.5,
    fontface = 'bold',
    family = 'mono',
    hjust = 0.5
  ) +
  ggplot2::annotate(
    "text", 
    x = 2.09,
    y = 29.7,
    label = "+29.7",
    color = "#333333",
    size = 3.5,
    fontface = 'bold',
    family = 'mono',
    hjust = 0.5
  ) +
  ggplot2::annotate(
    "text", 
    x = 2.09,
    y = 23.5,
    label = "+23.5",
    color = "#333333",
    size = 3.5,
    fontface = 'bold',
    family = 'mono',
    hjust = 0.5
  ) +
  ggplot2::annotate(
    "label",
    x = 2.39,
    y = 37.7,
    color = "#333333",
    fill = "lightgreen",
    size = 2.5,
    fontface = 'bold',
    family = 'mono',
    alpha = .3,
    hjust = 0.5,
    label = "Houston 62, Troy 42  \n~55 possessions"
  ) +
  ggplot2::annotate(
    "label",
    x = 2.39,
    y = 29.7,
    color = "#333333",
    fill = "lightgreen",
    size = 2.5,
    fontface = 'bold',
    family = 'mono',
    alpha = .3,
    hjust = 0.5,
    label = "Texas A&M 80, Texas 60  \n~68 possessions"
  ) +
  ggplot2::annotate(
    "label",
    x = 2.39,
    y = 23.6,
    color = "#333333",
    fill = "lightgreen",
    size = 2.5,
    fontface = 'bold',
    family = 'mono',
    alpha = .3,
    hjust = 0.5,
    label = "Ohio St. 103, ISU 83  \n79 possessions"
  ) +
  ggplot2::annotate(
    geom = "curve",
    color = "#333333",
    x = 1.3,
    y = 35,
    xend = 1.5,
    yend = 25,
    curvature = -.6,
    arrow = ggplot2::arrow(length = ggplot2::unit(2, "mm"), )
  ) +
  ggplot2::annotate(
    "label",
    x = 1.1,
    y = 36.6,
    color = "#333333",
    fill = "lightpink",
    size = 3,
    fontface = 'bold',
    family = 'mono',
    alpha = .3,
    hjust = 0.5,
    label = "51 different games  \nplayed at a different pace  \nwith different efficiencies"
  ) 



ggplot2::ggsave(
    "slope_plot.png",
    slope_plot ,
    h = 8.25,
    w = 8.25,
    dpi = 600,
    type = 'cairo'
)

slope_plot

Conference summary

conf_summary |> 
  dplyr::filter(conf %in% c("SEC", "Big Ten", 
                            "Big 12", "Big East", 
                            "ACC", "Mountain West", 
                            "WCC", "Atlantic 10", "CUSA", "American")) |> 
  dplyr::select(-home_wins, -home_games) |> 
  dplyr::mutate(
    conf = dplyr::case_match(
      conf,
      "Mountain West" ~ "MWC",
      "Big Ten" ~ "B10",
      "Big 12" ~ "B12",
      "American" ~ "Amer",
      "Big East" ~ "BE",
      "Atlantic 10" ~ "A10",
      conf ~ conf
    )
  ) |> 
  cbbplotR::gt_cbb_conferences(conf, logo_height = 14) |>
  dplyr::arrange(-avg_point_diff) |> 
  gt::gt() |> 
  gt::fmt_markdown(conf) |>
  gtUtils::gt_theme_gtutils() |> 
  gt::cols_align(conf, align = "left") |>
  gtUtils::gt_column_subheaders(
    teams = list(heading = "Teams", subtitle = "Total"),
    total_games = list(heading = "Games", subtitle = "Total"),
    avg_point_diff = list(heading = "AVG", subtitle = "Margin of victory"),
    close_games_pct = list(heading = "Close", subtitle = "< 5.5 points"),
    blowout_games_pct = list(heading = "Blowout", subtitle = "> 15.5 points"),
    home_win_pct = list(heading = "Home", subtitle = "Win %"),
    heading_color = "black",
    subtitle_color = "gray"
  ) |> 
  gt::cols_label(
    conf = ""
  ) |> 
  gtUtils::gt_color_pills(avg_point_diff,
                          domain = c(0, 20), format_type = "number", digits = 1,
                          palette = c("#2ECC71", "#82E0AA", "#F5B041", "#E74C3C")
  ) |> 
  gtUtils::gt_color_pills(home_win_pct,
                          domain = c(0, 1), format_type = "percent", digits = 1,
                          palette = c("#2ECC71", "#82E0AA", "#F5B041", "#E74C3C"),
                          reverse = TRUE
  ) |> 
  gtUtils::gt_color_pills(close_games_pct,
                          domain = c(0, .5), format_type = "percent", digits = 1,
                          palette = c("#2ECC71", "#82E0AA", "#F5B041", "#E74C3C"),
                          reverse = TRUE
  ) |> 
  gtUtils::gt_color_pills(blowout_games_pct,
                          domain = c(0, .65), format_type = "percent", digits = 1,
                          palette = c("#2ECC71", "#82E0AA", "#F5B041", "#E74C3C")
  ) |> 
  gt::tab_header(
    title = gt::html("2024-25 Conference Summary through January 9 Games"),
    subtitle = gt::html(
      "Total games, average margin of victory, home win percentage, close game and blowout percentages for the top 10 leagues per kenpom.com. Data sorted from highest to lowest margin of victory."
    )
  ) |>
  gt::tab_source_note(
    source_note =
      gt::html(
        "<hr>Data via barttorvik.com, logos via ESPN, and theme via {gtUtils}<hr>
        <b>Table by Chris at Bless your chart</b>"
      )
  ) |>
  gt::tab_style(
    locations = gt::cells_column_spanners(),
    style = gt::cell_text(
      font = gt::google_font("Signika Negative"),
      weight = 850,
      size = gt::px(15)
    )
  ) |>
  gt::tab_style(
    locations = gt::cells_source_notes(),
    style = gt::cell_text(
      font = gt::google_font("Signika Negative"),
      size = gt::px(11.5),
      weight = 250
    )
  ) |>
  gt::tab_options(table.width = gt::px(600)) |>
  gtUtils::gt_border_bars_bottom(c("#636363", "#969696", "#cccccc")) -> conf_tbl 

gtUtils::gt_save_crop(
  conf_tbl,
  file = "conf_chart.png",
  whitespace = 40,
  bg = "#FFFDF5"
)

conf_tbl

2024-25 Conference Summary through January 9 Games
Total games, average margin of victory, home win percentage, close game and blowout percentages for the top 10 leagues per kenpom.com. Data sorted from highest to lowest margin of victory.
	Teams Total	Games Total	AVG Margin of victory	Home Win %	Close < 5.5 points	Blowout > 15.5 points
	16	32	15.9	62.5%	12.5%	50.0%
	16	48	15.8	62.5%	29.2%	54.2%
	11	48	14.2	66.7%	16.7%	37.5%
	18	80	12.9	60.0%	30.0%	32.5%
	13	34	12.6	58.8%	29.4%	29.4%
	18	76	12.5	68.4%	18.4%	23.7%
	11	52	11.0	65.4%	34.6%	23.1%
	15	42	10.5	71.4%	28.6%	23.8%
	11	50	10.5	44.0%	32.0%	28.0%
	10	26	10.4	69.2%	30.8%	23.1%
Data via barttorvik.com, logos via ESPN, and theme via {gtUtils} Table by Chris at Bless your chart