115: ACC standings with diffs

gtUtils
indicator_boxes
Published

October 29, 2024

Fetch data from cfbfastR

Code 
library(cbbplotR)
library(rlang)

final_standings <- readRDS("conf_standings.RDS")

# saved this in a RDS file avoid fetching it each time 

# commenting out the code below, these uses existing code from the byc app
# https://github.com/gallochris/evdev-byc/blob/main/data/conference_standings.R

# fetch schedule 
# conf_sched <- cfbfastR::cfbd_game_info(year = 2024) |>
#  dplyr::filter(home_conference == away_conference) |>
#  dplyr::filter(!game_id %in% c("401636618", "401636864")) |>  
  # Arizona at Kansas St is not a conference game
  # Baylor and Utah also not a conference game *shakes fist at Big 12*
#  dplyr::mutate(conf = home_conference) |>
#  dplyr::select(game_id, 
#                season,
#                week,
#                conf,
#                home_team,
#                home_points,
#                away_team,
#                away_points)

# function to pivot data and get one row per opponent
# pivot_sched <- function(df) {
#  home_games <- df |>
#    dplyr::mutate(
#      team = home_team,
#      opponent = away_team,
#      points_for = home_points,
#      points_against = away_points,
#      differential = home_points - away_points
#    )
  
#  away_games <- df |>
#    dplyr::mutate(
#      team = away_team,
#      opponent = home_team,
#      points_for = away_points,
#      points_against = home_points,
#      differential = away_points - home_points
#    )
  
#  all_games <- dplyr::bind_rows(home_games, away_games) |>
#    dplyr::mutate(
#      status = dplyr::case_when(
#        is.na(points_for) | is.na(points_against) ~ "remaining",
#        .default = "played"
#      )
#    ) |>
#    dplyr::select(team, opponent, week, conf, status, points_for, points_against, differential) |>
#    dplyr::arrange(team, week)
  
#  return(all_games)
# }

# pivot_sched(conf_sched) -> sched_for_teams


# standings_with_win_pct <- conference_standings |> 
#  dplyr::mutate(conf_win_pct = conf_win / (conf_win + conf_loss)) |> 
#  dplyr::select(
#    conf, 
#    full_diff,
#    team,
#    conf_win,
#    conf_loss,
#    conf_win_pct
#  )

# played_win_pct <- sched_for_teams |> 
#  dplyr::left_join(standings_with_win_pct, by = c("opponent" = "team")) |>
#  dplyr::filter(status == "played") |>
#  dplyr::group_by(team) |>
#  dplyr::summarise(
#    played_win_pct = sum(conf_win) / (sum(conf_win) + sum(conf_loss)),
#    games_played = dplyr::n(),
#    diff_list = list(differential)
#  )

# remaining_win_pct <- sched_for_teams |> 
#  dplyr::left_join(standings_with_win_pct, by = c("opponent" = "team")) |>
#  dplyr::filter(status == "remaining") |>
#  dplyr::group_by(team) |>
#  dplyr::summarise(
#    remaining_win_pct = sum(conf_win) / (sum(conf_win) + sum(conf_loss)),
#    games_left = dplyr::n()
#  )

# combined_win_pct <-  sched_for_teams |> 
#  dplyr::left_join(standings_with_win_pct, by = c("opponent" = "team")) |>
#  dplyr::group_by(team) |>
#  dplyr::summarise(
#    combined_win_pct = sum(conf_win) / (sum(conf_win) + sum(conf_loss))
#  )


# final_standings <- standings_with_win_pct |> 
#  dplyr::left_join(played_win_pct, by = "team") |> 
#  dplyr::left_join(remaining_win_pct, by ="team") |> 
#  dplyr::left_join(combined_win_pct, by ="team") |> 
#  dplyr::select(
#    conf, 
#    team,
#    conf_win,
#    conf_loss,
#    conf_win_pct,
#    games_played,
#    played_win_pct,
#    games_left,
#    remaining_win_pct,
#    combined_win_pct,
#    full_diff,
#    diff_list
#  )

# saveRDS(final_standings, "conf_standings.RDS")

GT table one

Code 
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=/i/teamlogos/ncaa_conf/500/acc.png'
       style='height: 105px; 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;'>ACC standings through October 26 games</span>
       <span style='font-size: 14px; font-weight: normal; display: block; text-align: center;'>Sorted by overall conference win percentage.<br> Table includes opponent win percentages and point differentials in league play.</span>
     </div>
     <div>
       <img src='https://a.espncdn.com/combiner/i?img=/redesign/assets/img/icons/ESPN-icon-football-college.png'
       style='height: 45px; width: auto; vertical-align: middle;'>
     </div>
   </div>
  <br>"
)

acc_standings <- final_standings |>
  dplyr::filter(conf == "ACC") |>
  dplyr::mutate(win_loss = paste0(conf_win, "-", conf_loss)) |>
  dplyr::relocate(win_loss, .before = conf_win) |>
  dplyr::add_row(
    diff_list = list(c(7, 42, 18)),
    full_diff = 67,
    team = "Notre Dame",
    win_loss = "3-0",
    conf_win_pct = 1.00,
    games_played = 3,
    played_win_pct = 0.4375,
    games_left = 2,
    remaining_win_pct = .250,
    combined_win_pct = .357,
    conf_win = 3,
    conf_loss = 0
  ) |>
  dplyr::arrange(-conf_win_pct, -games_played, -combined_win_pct, -full_diff) |> 
  dplyr::mutate(
    rank_order = dplyr::case_when(
      team == "Notre Dame" ~ max(dplyr::row_number()) + 1,  # Assign the highest rank
      TRUE ~ dplyr::row_number()  # Keep original row numbers for other rows
    )
  ) |> 
  dplyr::arrange(rank_order) |> 
  cbbplotR::gt_cbb_teams(team, team, include_name = FALSE) |>
  gt::gt() |>
  gt::fmt_markdown(team) |>
  gt::cols_hide(columns = c(conf, conf_win, conf_loss, rank_order)) |>
  gtUtils::gt_theme_gtutils() |>
  gt::cols_nanoplot(
    columns = diff_list,
    autohide = TRUE,
    plot_type = "bar",
    after = "full_diff",
    new_col_label = "Trend",
    options = gt::nanoplot_options(
      data_bar_fill_color = "lightgreen",
      data_bar_negative_fill_color = "lightpink",
      data_bar_stroke_color = "#333333",
      show_data_area = FALSE,
    )
  ) |>
  gt::fmt(
    columns = c(full_diff),
    fns = function(x) {
      ifelse(x > 0, paste0("+", x), x)
    }
  ) |>
  gt::fmt_number(columns = ends_with("win_pct"), decimals = 3) |>
  gt::cols_align(columns = c(team), align = "left") |>
  gtUtils::gt_color_pills(
    conf_win_pct,
    domain = c(0, 1),
    format_type = "number",
    digits = 3,
    palette = c("#8B0000", "#FF0000", "#FF4500", "#ADFF2F", "#008000")
  ) |>
  gtUtils::gt_color_pills(
    combined_win_pct,
    domain = c(0, 1),
    format_type = "number",
    digits = 3,
    palette = c("#8B0000", "#FF0000", "#FF4500", "#ADFF2F", "#008000")
  ) |>
  gtUtils::gt_column_subheaders(
    conf_win_pct = list(heading = "ACC", subtitle = "Win %"),
    games_played = list(heading = "Games", subtitle = "Played"),
    played_win_pct = list(heading = "Win %", subtitle = "Played"),
    games_left = list(heading = "Games", subtitle = "Left"),
    remaining_win_pct = list(heading = "Win %", subtitle = "Remaining"),
    combined_win_pct = list(heading = "Win %", subtitle = "Combined"),
    heading_color = "black",
    subtitle_color = "gray"
  ) |>
  gt::cols_label(
    full_diff = "+/-",
    team = "",
    win_loss = "W-L",
    nanoplots = ""
  ) |>
  gtUtils::gt_color_pills(
    full_diff,
    domain = c(-90, 120),
    format_type = "number",
    digits = 0,
    palette = c("lightpink", "lightgreen")
  ) |>
  gt::tab_spanner(
    columns = c(full_diff, nanoplots),
    label = gt::html("Point Differentials")
  )  |>
  gt::tab_spanner(
    columns = c(
      games_played,
      played_win_pct,
      games_left,
      remaining_win_pct,
      combined_win_pct
    ),
    label = gt::html("Opponent Records")
  ) |>
  gt::tab_spanner(
    columns = c(team, win_loss, conf_win_pct),
    label = gt::html("Conference Play")
  ) |>
  gtExtras::gt_add_divider(columns = c(conf_win_pct,
                                       combined_win_pct),
                           sides = "right",
                           color = "black") |>
  gt::tab_header(title = gt::html(custom_header)) |>
  gt::tab_source_note(
    source_note =
      gt::html(
        "<hr>Data from {cfbfastR} and theme for {gtUtils}<br>
                ACC features 17 teams, plus Notre Dame plays five ACC opponents as part of its part-time membership. Irish not eligible for conference championship game.<br>
                The fifth tie breaker for the conference championship game is the winning percentage of conference opponents in conference games only.<br>
                <hr><b>Table by Chris at Bless your chart | October 28, 2024</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(10),
      weight = 250
    )
  ) |>
  gtExtras::gt_highlight_rows(rows = c(18),
                              columns = c(1:10),
                              fill = "#FFFFC5") |>
  gt::tab_options(table.width = gt::px(750))


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

acc_standings
ACC standings through October 26 games Sorted by overall conference win percentage.
Table includes opponent win percentages and point differentials in league play.

Conference Play Opponent Records Point Differentials
W-L
ACC
Win %
Games
Played
Win %
Played
Games
Left
Win %
Remaining
Win %
Combined
+/-

5-0 1.000 5 0.280 3 0.750 0.432 118
35 0 24 26 16 35 17

4-0 1.000 4 0.350 4 0.500 0.421 34
22 0 4 1 7 22

4-0 1.000 4 0.333 4 0.375 0.351 64
30 0 26 7 30 1

3-0 1.000 3 0.250 5 0.652 0.514 40
28 0 10 2 28

3-1 0.750 4 0.474 4 0.611 0.541 56
24 −4 −4 24 21 15

3-2 0.600 5 0.609 3 0.692 0.639 6
12 −7 12 −7 4 −7 4

3-3 0.500 6 0.429 2 0.625 0.472 -10
10 −15 3 −3 −12 10 7 −15

2-2 0.500 4 0.429 4 0.625 0.514 -3
7 −10 1 −10 7 −1

2-2 0.500 4 0.444 4 0.500 0.471 -20
7 −28 3 −2 7 −28

2-2 0.500 4 0.474 4 0.438 0.457 -29
4 −35 −1 4 −35 3

2-3 0.400 5 0.545 3 0.909 0.667 -37
10 −27 1 10 −4 −17 −27

1-3 0.250 4 0.429 4 0.667 0.528 -20
15 −21 15 −10 −21 −4

1-3 0.250 4 0.529 4 0.368 0.444 -34
1 −24 −24 −4 −7 1

1-3 0.250 4 0.556 4 0.263 0.405 9
27 −10 −1 −10 −7 27

1-4 0.200 5 0.762 3 0.308 0.588 -81
2 −30 2 −26 −24 −30 −3

1-6 0.143 7 0.613 1 0.250 0.571 -84
5 −26 −15 −3 5 −26 −16 −7 −22

0-4 0.000 4 0.500 4 0.529 0.514 -9
0 −5 −5 −1 −2 −1

3-0 1.000 3 0.438 2 0.250 0.357 67
42 0 7 42 18
Data from {cfbfastR} and theme for {gtUtils}
ACC features 17 teams, plus Notre Dame plays five ACC opponents as part of its part-time membership. Irish not eligible for conference championship game.
The fifth tie breaker for the conference championship game is the winning percentage of conference opponents in conference games only.
Table by Chris at Bless your chart | October 28, 2024

GT table two: FEI progression

Code 
# load as RDS
fei_ratings <- readRDS("fei-progress.RDS")


  fei_change_plot <- fei_ratings |> 
                    dplyr::select(category, 
                pre_fei_ptile, range_list, current_fei_ptile, team) |> 
  dplyr::group_by(category) |>
  gt::gt() |>
  gt::fmt_markdown(team) |>
  gtUtils::gt_theme_gtutils() |> 
  gt::cols_nanoplot(
    columns = range_list,
    autohide = TRUE,
    plot_type = "line",
    before = "current_fei_ptile",
    new_col_label = "Progression",
    reference_line = .50,
    options = gt::nanoplot_options(
      data_area_fill_color = "lightgreen",
      data_line_stroke_color = "#9E9ECC",
      data_point_stroke_color = "floralwhite",
      data_point_fill_color = "#333333",
      data_line_type = "curved",
      reference_line_color = "#333333",
      show_data_area = TRUE,
      show_data_points = FALSE
    )
  ) |> 
  gt::cols_align(columns = c(-team), "center") |>
  gt::cols_align(columns = c(team), "left") |> 
  gtUtils::gt_column_subheaders(
    pre_fei_ptile = list(heading = "Preseason", subtitle = "Percentile"),
    current_fei_ptile = list(heading = "Current", subtitle = "Percentile"),
    heading_color = "black",
    subtitle_color = "gray"
  ) |>
  gt::cols_label(team = "",
                 nanoplots = "Progression") |> 
  gtUtils::gt_color_pills(
    pre_fei_ptile,
    domain = c(0, 1),
    format_type = "percent",
    digits = 1,
    palette = c("#8B0000", "#FF0000", "#FF4500", "#ADFF2F", "#008000")
  ) |> 
  gtUtils::gt_color_pills(
    current_fei_ptile,
    domain = c(0, 1),
    format_type = "percent",
    digits = 1,
    palette = c("#8B0000", "#FF0000", "#FF4500", "#ADFF2F", "#008000")
  ) |> 
  gtUtils::gt_border_grid(color = "black",
                          weight = 0.5,
                          include_labels = FALSE) |> 
  gt::tab_style(
    locations = gt::cells_column_labels(),
    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(10),
      weight = 250
    )
  ) |> 
  gt::tab_style(
    locations = gt::cells_row_groups(),
    style = list(
      gt::cell_text(
        font = gt::google_font("Signika Negative"),
        weight = 850,
        size = gt::px(16),
        color = "black",
        align = "right"
      ),
      gt::cell_fill(
        color = "#C5C5FF"
      )
    )
  ) |> 
  gt::tab_style(
    locations = gt::cells_row_groups(),
    style = list(
      gt::cell_text(
        font = gt::google_font("Signika Negative"),
        weight = 850,
        size = gt::px(16),
        color = "black",
        align = "right"
      ),
      gt::cell_fill(
        color = "#C5C5FF"
      )
    )
  ) |> 
  gt::tab_style(
    locations = gt::cells_column_labels(columns = gt::everything()),
    style = list(
      gt::cell_text(
        font = gt::google_font("Signika Negative"),
        weight = 650,
        size = gt::px(12),
        color = "#333333"
      )
    )
  ) |> 
  gt::tab_header(
    title = "FEI ratings progression from preseason to week 9",
    subtitle = gt::md("Uses the percentile from overall FEI ratings to show progression since
                      the start of the season. The dotted line is the 50th percentile.")
  ) |> 
  gtUtils::gt_border_bars_bottom("#C5C5FF",
                        bar_height = 45,
                        img = "https://a.espncdn.com/combiner/i?img=/redesign/assets/img/icons/ESPN-icon-football-college.png",
                        text = gt::html("Data from bcftoys.com + theme from {gtUtils} <br> 
                                        Table by Chris at Bless your chart"),
                        img_width = 30, img_height = 30, text_size = 10, text_weight = "normal",
                        text_color = "black"
  ) |> 
  gt::tab_options(table.width = gt::px(450))
  
  gtUtils::gt_save_crop(
  fei_change_plot,
  file = "fei_change_plot.png",
  whitespace = 40,
  bg = "#FFFDF5"
)
  
  fei_change_plot
FEI ratings progression from preseason to week 9

Uses the percentile from overall FEI ratings to show progression since the start of the season. The dotted line is the 50th percentile.
Preseason
Percentile
Progression
Current
Percentile
Better
30.1%
0.50 0.80 0.30 0.30 0.32 0.38 0.36 0.47 0.50 0.65 0.77 0.80 0.79
78.9%

42.9%
0.50 0.91 0.43 0.43 0.43 0.44 0.59 0.68 0.76 0.82 0.81 0.90 0.91
91.0%

24.8%
0.50 0.74 0.24 0.25 0.24 0.35 0.34 0.48 0.56 0.68 0.66 0.74 0.68
67.7%

31.6%
0.50 0.71 0.32 0.32 0.42 0.42 0.50 0.45 0.64 0.60 0.65 0.69 0.71
71.4%

28.6%
0.50 0.65 0.29 0.29 0.34 0.33 0.31 0.38 0.39 0.51 0.56 0.56 0.65
65.4%

44.4%
0.50 0.78 0.37 0.44 0.43 0.37 0.38 0.44 0.56 0.56 0.59 0.74 0.78
78.2%

54.1%
0.50 0.87 0.50 0.54 0.53 0.57 0.65 0.79 0.84 0.83 0.86 0.86 0.87
87.2%

18.8%
0.50 0.50 0.19 0.19 0.29 0.26 0.23 0.24 0.26 0.26 0.26 0.34 0.49
48.9%

26.3%
0.50 0.56 0.26 0.26 0.26 0.40 0.44 0.42 0.41 0.49 0.56 0.50 0.53
53.4%

3.0%
0.50 0.50 0.030 0.030 0.030 0.038 0.060 0.17 0.29 0.27 0.28 0.30 0.29
28.6%

Worse
66.9%
0.50 0.68 0.13 0.67 0.68 0.58 0.47 0.48 0.37 0.27 0.15 0.14 0.13
12.8%

60.9%
0.50 0.61 0.17 0.61 0.48 0.39 0.34 0.35 0.31 0.26 0.29 0.23 0.17
17.3%

85.0%
0.50 0.85 0.44 0.85 0.76 0.76 0.69 0.69 0.55 0.53 0.53 0.47 0.44
43.6%

85.7%
0.50 0.86 0.45 0.86 0.86 0.86 0.81 0.79 0.80 0.71 0.61 0.56 0.45
45.1%

58.6%
0.50 0.59 0.17 0.59 0.58 0.42 0.38 0.25 0.25 0.18 0.17 0.18 0.20
19.5%

88.7%
0.50 0.89 0.50 0.89 0.89 0.87 0.85 0.83 0.75 0.72 0.68 0.59 0.54
54.1%

66.9%
0.50 0.68 0.34 0.67 0.68 0.60 0.53 0.50 0.47 0.45 0.43 0.42 0.34
33.8%

44.4%
0.50 0.50 0.11 0.44 0.29 0.28 0.20 0.11 0.19 0.20 0.18 0.14 0.11
11.3%

69.2%
0.50 0.70 0.37 0.69 0.70 0.56 0.56 0.41 0.41 0.38 0.37 0.38 0.38
37.6%

57.1%
0.50 0.57 0.24 0.57 0.56 0.56 0.39 0.36 0.34 0.24 0.25 0.26 0.26
26.3%

Data from bcftoys.com + theme from {gtUtils}
Table by Chris at Bless your chart