136: Bracket fun

gt
nanoplots
Published

March 19, 2025

Load data

Code 
library(rlang)
library(cbbplotR)
options(chromote.headless = "new")

# this data is used in the evidence app, saving as a csv for static generation 
# https://github.com/gallochris/evdev-byc/blob/main/data/college-basketball/wab_logs.R

team_sum_tbl <- readr::read_csv("ncaat_data.csv")

ncaat_teams <- team_sum_tbl |> 
  dplyr::pull(team)

cbbdata::cbd_torvik_game_stats(year = 2025) |> 
  dplyr::filter(team %in% ncaat_teams) |> 
  dplyr::group_by(team) |> 
  dplyr::summarise(
    record = paste0(sum(result == "W"), "-", sum(result == "L")),
    avg = mean(game_score),
    avg_last_10 = mean(head(game_score, 10)),
    sum_data = list(rev(game_score)),
    max_game = max(game_score),
    max_result = result[which.max(game_score)],
    max_opp = opp[which.max(game_score)],
    max_pts_scored =  pts_scored[which.max(game_score)],
    max_pts_allowed =  pts_allowed[which.max(game_score)],
    min_game = min(game_score),
    min_result = result[which.min(game_score)],
    min_opp = opp[which.min(game_score)],
    min_pts_scored =  pts_scored[which.min(game_score)],
    min_pts_allowed =  pts_allowed[which.min(game_score)],
  ) |> 
  dplyr::mutate(
    max_score = paste0(max_result, ", ", 
                       max_opp, " ",
                       max_pts_scored, "-",
                       max_pts_allowed),
    min_score = paste0(min_result, ", ", 
                       min_opp, " ",
                       min_pts_scored, "-",
                       min_pts_allowed),
  ) |> 
  dplyr::select(team, record, avg, avg_last_10, sum_data, max_score, max_game, min_score, min_game,) |> 
  dplyr::arrange(-avg) -> gscores


ncaat_gscores <- gscores |> 
  dplyr::left_join(team_sum_tbl, by = c("team", "record")) |> 
  dplyr::relocate(seed, .before = team) |> 
  dplyr::relocate(region, .after = team) |> 
  dplyr::mutate(
    region = dplyr::if_else(team == "North Carolina",
                             "South", region),
    seed = dplyr::if_else(team == "North Carolina",
                         11, seed),
    f4 = dplyr::if_else(team == "North Carolina",
                          0.022, f4),
    region = dplyr::if_else(team == "Alabama St.",
                            "South", region),
    seed = dplyr::if_else(team == "Alabama St.",
                          16, seed),
    f4 = dplyr::if_else(team == "Alabama St.",
                        0, f4),
  ) |> 
  dplyr::mutate(avg_rank = rowMeans(dplyr::across(c(trk, kp)))) |> 
  dplyr::select(region,
                seed, 
                team,
                record,
                f4,
                avg, 
                max_score,
                max_game,
                min_score,
                min_game,
                avg_last_10,
                sum_data
                )

Function for Game Scores

Code 
region_tbl <- function(region) {
  
  # Set the title of the plot
  table_title <- 
    paste0(region, " Region: Team Performance")
  
  custom_header <-glue::glue(
    "<div style='display: flex; justify-content: space-between; align-items: center;'>
      <div>
       <img src='https://a.espncdn.com/combiner/i?img=/redesign/assets/img/icons/ESPN-icon-basketball.png'
       style='height: 40px; width: auto; vertical-align: middle;'>
     </div>
     <div style='flex-grow:1; margin-left: 30px; margin-right: 30px'>
       <span style='display: block; font-weight: bold; text-align: center; font-size: 24px;'>{table_title}</span>
       <span style='font-size: 14px; font-weight: normal; display: block; text-align: center;'>Game Score (GS) is calculated by Bart Torvik on a per game basis.<br>Shows odds to win the region, the season average and average GS over the last 10 games, the best/worst games, and performance over time for every team.</span>
     </div>
     <div>
       <img src='https://a.espncdn.com/combiner/i?img=/redesign/assets/img/icons/ESPN-icon-basketball.png'
       style='height: 40px; width: auto; vertical-align: middle;'>
     </div>
   </div>
  <br>"
  )
  
  ncaat_gscores |>
    dplyr::filter(region == {{region}}) |>
    dplyr::mutate(
      pairs = dplyr::case_when(
        seed %in% c(1, 16) ~ 1,
        seed %in% c(8, 9) ~ 2,
        seed %in% c(5, 12) ~ 3,
        seed %in% c(4, 13) ~ 4,
        seed %in% c(6, 11) ~ 5,
        seed %in% c(3, 14) ~ 6,
        seed %in% c(7, 10) ~ 7,
        seed %in% c(2, 15) ~ 8,
      )
    ) |> 
    dplyr::arrange(pairs, seed) |> 
    cbbplotR::gt_cbb_teams(team, team, logo_height = 12.5) |> 
    gt::gt() |>
    gtUtils::gt_theme_gtutils() |> 
    gt::fmt_markdown(columns = c(team)) |>
    gt::cols_hide(columns = c(region, pairs)) |> 
    gtExtras::gt_merge_stack(
      col1 = team,
      col2 = record,
      palette = c("black", "#333333"),
      font_size = c("12px", "8px"),
      font_weight = c("bold", "normal")
    ) |> 
    gtExtras::gt_merge_stack(
      col1 = max_game,
      col2 = max_score,
      palette = c("black", "#333333"),
      font_size = c("12px", "6.5px"),
      font_weight = c("bold", "normal")
    ) |>
    gtExtras::gt_merge_stack(
      col1 = min_game,
      col2 = min_score,
      palette = c("black", "#333333"),
      font_size = c("12px", "6.5px"),
      font_weight = c("bold", "normal")
    ) |>
    gtUtils::gt_column_subheaders(
      f4 = list(heading = "Odds", subtitle = "% to win Region"),
      avg = list(heading = "Avg", subtitle = "Season"),
      max_game = list(heading = "Best", subtitle = "Game"),
      min_game = list(heading = "Worst", subtitle = "Game"),
      avg_last_10 = list(heading = "Last 10", subtitle = "Games"),
      heading_color = "black",
      subtitle_color = "gray"
    ) |> 
    gt::tab_spanner(
      columns = c(avg, max_game, min_game, avg_last_10,
                  sum_data),
      label = "Game Score"
    ) |> 
    gt::cols_label(
      seed = "Seed",
      team = ""
    ) |> 
    gt::cols_nanoplot(
      columns = sum_data,
      reference_line = "mean",
      autohide = TRUE,
      new_col_label = "Over time",
      options = gt::nanoplot_options(
        data_area_fill_color = "#a6d96a",
        reference_line_color = "#333333",
        show_data_area = TRUE,
        show_data_points = FALSE
      )
    ) |> 
    gt::cols_align(align = "left", columns = c(team, f4, max_score, min_score)) |>
    gt::cols_align(align = "right", columns = c(seed, avg, avg_last_10)) |>
    gtExtras::gt_add_divider(columns = c(f4), sides = "right", color = "black") |> 
    gt::tab_header(title = gt::html(custom_header)) |>
    gt::tab_source_note(source_note = gt::html("<hr><b>Table by Chris at Bless your Chart & Ryan at Fifth Factor | data from Bart Torvik and cbbplotR | March 19, 2025</b>")) |> 
    gt::tab_style(
      locations = gt::cells_column_labels(),
      style = gt::cell_text(
        font = gt::google_font("Signika Negative"),
        weight = "650",
        size = gt::px(14)
      )
    ) |>
    gtUtils::gt_color_pills(f4,
                            domain = c(0, .55), format_type = "percent", digits = 1,
                            palette = c("#d7191c", 
                                        "#ffffbf", 
                                        "#1a9641")
    ) |>
    gtUtils::gt_color_pills(avg,
                            domain = c(10, 100), format_type = "number", digits = 1,
                            palette = c("#d7191c",
                                        "#ffffbf", 
                                        "#1a9641")
    ) |> 
    gtUtils::gt_color_pills(avg_last_10,
                            domain = c(10, 100), format_type = "number", digits = 1,
                            palette = c("#d7191c", 
                                        "#ffffbf", 
                                        "#1a9641")
    ) |> 
    gtUtils::gt_border_bars_bottom(c("#636363", "#969696", "#cccccc")) |> 
    gt::tab_options(table.width = gt::px(750)) |>
    gt::tab_style(
      locations = gt::cells_row_groups(),
      style = list(
        gt::cell_text(
          font = gt::google_font("Signika Negative"),
          weight = 850,
          size = gt::px(11.5),
          color = "black",
          align = "left"
        ),
        gt::cell_fill(
          color = "#C5C5FF"
        )
      )
    ) |> 
    gt::tab_style(
      locations = gt::cells_column_spanners(),
      style = gt::cell_text(
        font = gt::google_font("Signika Negative"),
        weight = 850,
        size = gt::px(14)
      )
    ) |>
    gt::tab_style(
      locations = gt::cells_source_notes(),
      style = gt::cell_text(
        font = gt::google_font("Signika Negative"),
        size = gt::px(10),
        weight = 250
      )
    ) |>
    gt::tab_style(
      style = list(
        gt::cell_text(font = gt::google_font("Signika Negative"),
                      size = gt::px(10.5)
        )
      ),
      locations = gt::cells_body(
        rows = gt::everything(),
        columns = gt::everything()
      )
    ) |> 
    gtUtils::gt_border_grid(color = "black",
                            weight = 2,
                            include_labels = FALSE) -> ncaat_region_table
  
  ncaat_region_table
}

region_tbl(region = "South") -> south

gtUtils::gt_save_crop(
  south,
  file = "south_region_tbl.png",
  whitespace = 60,
  bg = "#FFFDF5"
)

south
South Region: Team Performance Game Score (GS) is calculated by Bart Torvik on a per game basis.
Shows odds to win the region, the season average and average GS over the last 10 games, the best/worst games, and performance over time for every team.

Seed
Odds
% to win Region
Game Score Over time
Avg
Season
Best
Game
Worst
Game
Last 10
Games
1

Auburn

28-5
46.3% 95.8
99.9
W, Ohio St. 91-53
74.7
L, Texas A&M 72-83
94.2
95.8 99.9 74.7 99.8 98.6 97.8 97.2 93.3 98.8 98.1 97.4 99.2 99.9 96.4 99.5 91.5 99.5 97.1 83.8 99.4 97.4 96.4 94.9 97.2 98.7 88.4 98.2 99.2 92.9 97.2 99.4 99.5 74.7 92.6 96.1 91.7
16

Alabama St.

19-15
0.0% 31.9
93.1
W, Nebraska Omaha 85-67
1.3
L, Norfolk St. 54-71
40.2
31.9 93.1 1.30 39.3 57.6 93.1 44.1 26.0 26.5 33.2 8.20 64.4 1.30 10.8 41.6 20.5 3.40 18.7 18.5 10.9 9.60 9.20 17.2 53.7 2.90 39.3 34.1 48.2 16.8 10.4 85.7 58.5 54.6 54.5 17.0 35.7 20.6
8

Louisville

27-7
4.4% 85.2
99.7
W, SMU 98-73
24.3
L, Mississippi 63-86
91.7
85.2 99.7 24.3 99.2 27.9 82.9 85.5 99.3 92.6 73.1 24.3 95.5 56.1 84.3 93.8 57.6 96.4 97.6 97.5 86.2 98.6 95.4 99.7 98.7 67.3 96.9 74.8 98.8 95.1 90.7 85.7 88.7 91.9 99.0 77.3 96.4 93.0
9

Creighton

24-10
1.0% 81.0
99.4
W, DePaul 73-49
13.9
L, Georgetown 57-81
80.3
81.0 99.4 13.9 62.3 75.9 98.9 96.4 22.8 22.9 91.9 87.6 98.9 96.1 86.2 13.9 92.9 91.5 82.4 88.6 94.6 95.3 99.4 85.8 93.6 92.0 86.5 96.0 82.6 92.8 83.5 86.8 32.8 94.4 91.2 57.9 97.6 83.4
5

Michigan

25-9
3.3% 85.2
99.5
W, Xavier 78-53
15.7
L, Purdue 64-91
80.1
85.2 99.5 15.7 99.5 78.9 92.3 93.5 89.8 76.5 99.5 95.9 85.7 83.5 87.3 99.1 99.4 95.4 99.3 91.3 77.6 82.5 15.7 84.2 91.2 92.9 96.4 89.0 92.9 77.7 90.3 70.1 57.2 79.4 42.4 99.2 95.8 95.9
12

UC San Diego

28-4
0.7% 78.3
99.6
W, Hawaii 83-44
7.5
L, Seattle 71-84
87.8
78.3 99.6 7.50 72.8 84.4 47.0 7.50 62.9 73.7 97.5 79.6 92.6 89.4 88.5 55.5 97.0 81.7 42.5 93.5 33.8 91.5 98.1 61.9 78.6 96.1 91.4 95.4 89.1 99.6 83.0 97.1 50.3 89.9 91.8 90.7
4

Texas A&M

22-10
6.6% 89.1
99.4
W, Auburn 83-72
58.3
W, Southern 71-54
87.1
89.1 99.4 58.3 84.4 97.4 96.0 98.2 58.3 80.8 88.9 83.4 93.9 98.1 96.8 97.4 98.8 97.0 85.2 86.1 82.2 91.3 89.1 93.8 91.1 93.1 98.0 96.0 92.7 60.9 90.9 73.8 84.6 99.4 96.2 78.5
13

Yale

21-7
0.1% 70.0
97.6
W, Harvard 84-55
15.6
L, Harvard 69-74
65.8
70.0 97.6 15.6 94.2 29.5 81.5 77.9 93.5 87.6 17.2 47.8 74.7 94.6 41.7 90.0 83.2 31.3 80.5 97.6 85.3 93.4 90.6 36.3 91.3 51.3 81.8 66.1 15.6 79.5 70.4 75.4
6

Mississippi

22-11
2.5% 83.9
99.7
W, Louisville 86-63
15.0
W, Grambling St. 66-64
82.2
83.9 99.7 15.0 90.3 15.0 87.9 99.3 83.6 96.4 85.8 99.7 81.5 96.9 81.1 82.6 36.2 97.5 94.2 91.0 98.9 79.1 93.0 85.6 82.0 91.2 99.1 75.9 93.0 63.1 69.3 68.6 89.6 94.0 81.7 92.9 93.5
11

North Carolina

23-13
2.2% 83.7
99.5
W, San Diego St. 95-68
49.7
W, Boston College 102-96
94.0
83.7 99.5 49.7 71.5 87.2 96.6 89.8 68.1 81.0 86.4 71.7 67.1 93.0 91.7 89.6 78.6 80.5 82.3 98.6 81.3 97.6 71.5 81.4 49.7 74.5 64.5 81.0 50.0 87.3 98.3 95.8 91.5 89.3 98.5 89.4 99.1 87.6 91.0 99.5
3

Iowa St.

24-9
15.5% 88.1
99.5
W, Arizona 84-67
30.5
L, Kansas St. 61-80
89.5
88.1 99.5 30.5 76.2 92.7 96.9 98.1 79.2 97.7 97.4 98.6 86.6 98.4 75.8 91.4 99.4 97.8 95.6 98.8 85.2 97.0 93.8 92.3 30.5 34.5 99.5 96.5 94.2 97.9 91.4 41.4 99.5 83.6 99.1 99.1 92.3
14

Lipscomb

23-9
0.0% 63.7
97.3
W, Alabama A&M 82-44
11.1
L, Eastern Kentucky 57-66
67.8
63.7 97.3 11.1 91.1 50.2 84.4 52.0 48.6 28.0 90.2 97.3 95.5 93.5 49.3 83.5 96.7 33.8 30.3 87.8 83.4 34.6 60.8 17.8 27.4 26.0 73.2 78.5 78.2 11.1 93.7 83.8 63.3 72.4 40.9 82.4
7

Marquette

23-10
2.6% 84.6
99.8
W, Providence 78-50
28.7
L, Villanova 66-81
82.7
84.6 99.8 28.7 89.7 95.0 79.1 96.8 98.9 97.5 91.0 85.6 81.0 97.7 74.1 84.9 95.3 99.8 93.4 72.0 75.5 50.2 94.1 95.9 87.8 38.7 90.6 73.2 90.1 95.3 28.7 98.4 98.7 84.9 90.2 86.6 81.1
10

New Mexico

25-7
0.9% 79.3
99.1
W, Boise St. 84-65
34.5
W, Wyoming 71-67
76.5
79.3 99.1 34.5 63.5 98.9 91.1 74.2 70.0 81.9 74.6 96.3 47.8 40.0 95.9 98.5 83.2 69.0 56.8 98.8 62.4 99.1 94.5 83.4 96.5 97.2 95.0 34.5 81.8 72.1 59.7 79.5 83.6 85.9 88.6 83.9
2

Michigan St.

27-6
13.9% 87.8
99.7
W, Washington 88-54
37.1
W, Bowling Green 86-72
93.5
87.8 99.7 37.1 92.0 83.7 82.8 37.1 68.8 97.3 50.5 95.5 97.7 99.5 87.4 92.1 70.1 96.9 99.7 98.6 89.3 89.8 93.7 99.0 58.5 90.1 91.0 72.7 97.2 96.6 97.8 97.8 96.1 86.7 99.5 97.3 93.0
15

Bryant

21-11
0.0% 50.8
95.6
W, Buffalo 87-64
1.9
L, Grand Canyon 66-112
50.5
50.8 95.6 1.90 25.6 95.6 56.7 25.1 20.3 72.1 36.2 76.5 33.2 71.8 25.4 36.2 1.90 92.2 82.5 69.2 91.9 57.4 39.6 33.6 68.4 7.50 41.5 60.7 21.1 36.4 33.7 64.9 50.5 46.4 60.5 89.5
Table by Chris at Bless your Chart & Ryan at Fifth Factor | data from Bart Torvik and cbbplotR | March 19, 2025

Yahoo and Torvik data

Code 
bodds <- readr::read_csv("yahoo.csv")

bodds_tbl <- bodds |> 
  dplyr::mutate(f4pick = f4pick * 100, 
                delta = F4 - f4pick) |> 
  dplyr::arrange(-f4pick) |> 
  dplyr::relocate(f4pick) |> 
  dplyr::filter(f4pick > 3) |> 
  dplyr::mutate(
    F4 = F4/100,
    f4pick = f4pick/100
  ) |> 
  dplyr::mutate(
    color_f4pick = dplyr::case_when(
      f4pick > .25 ~ "orange",
      f4pick <= .25 & f4pick > F4 ~ "yellow",
      f4pick <= .25 & f4pick <= F4 ~ "lightgreen"
    )
  ) |> 
  dplyr::select(F4, Team, Seed, Region, 
                f4pick, color_f4pick)


bodds_tbl |> 
  cbbplotR::gt_cbb_teams(Team, Team,
                         logo_height = 15) |> 
  dplyr::arrange(-F4, -f4pick) |> 
  gt::gt() |> 
  gt::cols_hide(color_f4pick) |> 
  gt::fmt_markdown(columns = Team) |> 
  gtExtras::gt_theme_dot_matrix() |>
  gt::cols_align(align = "left", columns = c("Team", "Region")) |> 
  gt::cols_align(align = "right", columns = c("F4", "Seed", 
                                              "f4pick")) |> 
  gtUtils::gt_color_pills(
    F4,
    domain = c(0, .7),
    format_type = "percent",
    digits = 1,
    palette = c("#8B0000", "#FF0000", "#FF4500", "#ADFF2F", "#008000"),
    pill_height = 15,
  ) |> 
  gt::fmt_percent(f4pick, decimals = 1) |> 
  gt::data_color(
    columns = f4pick,
    fn = function(x) {
      dplyr::case_when(
        x > 0.25 ~ "orange",
        x <= 0.25 & x > bodds_tbl$F4[match(x, bodds_tbl$f4pick)] ~ "yellow",
        x <= 0.25 & x <= bodds_tbl$F4[match(x, bodds_tbl$f4pick)] ~ "lightgreen",
        TRUE ~ "black"
      )
    },
    alpha = 0.6
  ) |> 
  gt::cols_label(
    F4 = "Odds", 
    Team = "",
    Seed = "Seed", 
    Region = "Region", 
    f4pick = "Pick %", 
  ) |> 
  gt::tab_header(
    title = gt::md("**Final Four Odds vs Yahoo Bracket Picks**"),
    subtitle = "Shows Final Four odds from Bart Torvik compared to Yahoo bracket picks by the public for the 2025 NCAA men's basketball tournament."
  ) |>
  gt::tab_source_note(
    source_note = gt::html(
      "<hr>Data through March 18, 2025 games<hr><br><b>Table by Ryan at Fifth Factor and Chris at Bless your chart <br> data via barttorvik.com, yahoo.com, + cbbplotR</b>"
    )
  ) |>
  gt::tab_options (
    source_notes.font.size = gt::px(10),
    row.striping.background_color = '#EEEEEE',
    table.font.size = gt::px(12),
    column_labels.text_transform = 'uppercase'
  ) |> 
  gt::tab_style(
    locations = gt::cells_column_labels(),
    style = gt::cell_text(
      weight = 850,
    )
  ) |> 
  gt::tab_options(table.width = gt::px(385)) -> f4_tbl 


gtUtils::gt_save_crop(
  f4_tbl,
  file = "f4_tbl.png",
  whitespace = 40,
  bg = "white"
)

f4_tbl

Final Four Odds vs Yahoo Bracket Picks
Shows Final Four odds from Bart Torvik compared to Yahoo bracket picks by the public for the 2025 NCAA men's basketball tournament.
Odds Seed Region Pick %
50.7%

Houston

 1 Midwest 46.0%
49.3%

Duke

 1 East 66.0%
46.1%

Auburn

 1 South 52.0%
40.3%

Florida

 1 West 58.0%
19.2%

Alabama

 2 East 18.0%
17.9%

Texas Tech

 3 West 7.0%
17.6%

Tennessee

 2 Midwest 26.0%
15.8%

Iowa St.

 3 South 6.0%
13.9%

Maryland

 4 West 4.0%
13.8%

Michigan St.

 2 South 27.0%
10.8%

Arizona

 4 East 4.0%
10.2%

St. John’s

 2 West 19.0%
8.2%

Wisconsin

 3 East 6.0%
6.9%

Kentucky

 3 Midwest 11.0%
4.6%

Clemson

 5 Midwest 4.0%
3.2%

Michigan

 5 South 5.0%
Data through March 18, 2025 games

Table by Ryan at Fifth Factor and Chris at Bless your chart
data via barttorvik.com, yahoo.com, + cbbplotR