134: ACC vs SEC

ggraph
as_gtable
grob
cbbplotR
Published

March 7, 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

# acc_sec <- games_with_ratings |> 
#  dplyr::filter(
#    conf == "SEC" & opp_conf == "ACC"
#  ) |> 
#  dplyr::mutate(delta = pts_scored - pts_allowed) |> 
#  dplyr::select(date, team, opp, conf, opp_conf, result, delta, location,  pts_scored , # pts_allowed)

acc_sec <- readr::read_csv("acc-sec.csv")

# this code is pulled from the evidence app as well 
games_with_ratings <- readr::read_csv("games_ratings.csv")

game_scores_by_team <- games_with_ratings |> 
  dplyr::filter(conf %in% c("ACC", "SEC")) |> 
  dplyr::group_by(team, conf) |> 
  dplyr::arrange(team, date) |>  #
  dplyr::mutate(
    game_index = dplyr::row_number(), 
    total_games = dplyr::n(),  
    first_12_avg = ifelse(game_index <= 12, game_score, NA_real_),
    next_12_avg = ifelse(game_index > 12 & game_index <= 24, game_score, NA_real_),
    last_6_avg = ifelse(game_index > (total_games - 6), game_score, NA_real_) # unc played 31 games!
  ) |> 
  dplyr::summarise(
    season_avg = mean(game_score),
    first_12_avg = mean(first_12_avg, na.rm = TRUE),
    next_12_avg = mean(next_12_avg, na.rm = TRUE),
    last_6_avg = mean(last_6_avg, na.rm = TRUE),
    season_perf = list(game_score)
  ) |> 
  dplyr::select(team, conf, season_avg, first_12_avg, next_12_avg, last_6_avg, season_perf) |> 
  dplyr::ungroup()

max_games <- max(purrr::map_int(game_scores_by_team$season_perf, length))

# this is a poorly written function to try to match game lengths
pad_list <- function(x, length_out) {
  if (length(x) < length_out) {
    c(x, rep(NA_real_, length_out - length(x)))
  } else {
    x
  }
}

# UNC played 31 games, everyone else at 30 - so trying to pad the length
game_scores_by_team <- game_scores_by_team |> 
  dplyr::mutate(season_perf = lapply(season_perf, pad_list, length_out = max_games))

Network plot with table

Code 
results_table <- acc_sec |> 
  dplyr::mutate(score_result = dplyr::if_else(
    pts_scored > pts_allowed, 
    paste0(team, " ", pts_scored, ", ", opp, " ", pts_allowed),
    paste0(opp, " ", pts_allowed, ", ", team, " ", pts_scored)
  )) |> 
  dplyr::select(delta, score_result) |> 
  dplyr::arrange(-delta) |> 
  gt::gt() |> 
  gt::cols_hide(delta) |> 
  gt::cols_label(
    score_result = ""
  ) |> 
  gtExtras::gt_theme_dot_matrix() |> 
  gt::tab_header(title = "Results") |> 
  gt::tab_options (
    source_notes.font.size = gt::px(10),
    row.striping.background_color = 'white',
    table.font.size = gt::px(10),
    column_labels.text_transform = 'uppercase',
    column_labels.font.weight = 'bold'
  ) |> 
  gt::tab_style(
    style = gt::cell_text(weight = "bold"),
    locations = gt::cells_title(groups = "title")
  ) |> 
  gt::tab_style(
    style = gt::cell_borders(
      sides = c("top"),
      color = '#efefef',
      weight = gt::px(0.3),
      style = 'solid'
    ),
    locations = gt::cells_body(rows = gt::everything())
  ) |>
  gtExtras::gt_highlight_rows(
    rows = c(31:34),
    fill = "#99b1db",
    alpha = 0.4,
    font_weight = "normal",
  ) |> 
  gtExtras::gt_highlight_rows(
    rows = c(1:30),
    fill = "#fdeba9",
    alpha = 0.4,
    font_weight = "normal",
  ) 
  
# table to inset in the plot 
results_table_gtable <- gt::as_gtable(results_table)

# network plot 
edges <- acc_sec |>
  dplyr::select(from = team, to = opp) |>
  dplyr::distinct()

nodes <- tibble::tibble(
  name = unique(c(edges$from, edges$to)),
  type = dplyr::if_else(name %in% unique(acc_sec$team[acc_sec$conf == 'SEC']), 'SEC', 'ACC')
)

graph <- tidygraph::tbl_graph(
  nodes = nodes,    
  edges = edges,
  directed = FALSE
)

node_positions <- graph |> 
  ggraph::create_layout(layout = 'stress') |> 
  dplyr::select(x, y, name)

nodes_with_positions <- dplyr::left_join(nodes, node_positions, by = "name")

net_plot <- graph |>
  ggraph::ggraph(layout = 'stress') +
  ggraph::geom_edge_link(
    ggplot2::aes(alpha = 0.3),
    color = 'gray50',
    show.legend = FALSE
  ) +
  ggraph::geom_node_point(
    ggplot2::aes(
      color = type, 
      size = 5
    )
  ) +
  cbbplotR::geom_cbb_teams(
    data = nodes_with_positions,  
    mapping = ggplot2::aes(
      x = x,
      y = y,
      team = name 
    ),
    logo_type = "normal",  
    show.legend = FALSE
  ) +
  ggplot2::scale_color_manual(
    values = c('SEC' = '#fdeba9', 'ACC' = '#99b1db'),
    name = 'Conference'
  ) +
  ggplot2::theme_minimal() +
  ggplot2::labs(
    title = "SEC vs ACC  \nNetwork of Opponents",
    subtitle = "SEC teams won 30 of 34 games against ACC teams in non-conference play in 2024-25 season.  \nAll games played before Christmas 2024.",
    caption = "Data via barttorvik.com + logos from cbbplotR  \n  \nViz by Chris at Bless your Chart",
    x = NULL,
    y = NULL
  ) +
  ggplot2::theme(
    plot.title = ggplot2::element_text(hjust = 0.5, size = 14, face = 'bold'),
    plot.subtitle = ggplot2::element_text(hjust = 0.5, size = 10)
  ) +
    ggplot2::annotation_custom(
      grob = results_table_gtable, 
      xmin = -12, xmax = 0, ymin = Inf, ymax = 0
    ) +
  ggplot2::theme(
    plot.title = ggplot2::element_text(
      hjust = 0.5,
      size = 16,
      family = "Courier"
    ),
    plot.subtitle = ggtext::element_markdown(
      hjust = 0.5,
      size = 8,
      lineheight = 1.5,
      family = "Courier"
    ),
    legend.position = "none",
    panel.grid.major = ggplot2::element_blank(),
    panel.grid.minor = ggplot2::element_blank(),
    plot.caption = ggtext::element_markdown( size = 6, "Courier"),
    axis.text.x = ggplot2::element_blank(),
    axis.text.y = ggplot2::element_blank()
  ) 

ggplot2::ggsave(
  "net_plot.png",
  net_plot,
  w = 10.5,
  h = 7.5,
  bg = "white",
  dpi = 600,
  type = 'cairo'
)

net_plot

Game Score table

Code 
acc_sec_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/sec.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;'>SEC and ACC Men's Basketball</span>
       <span style='font-size: 14px; font-weight: normal; display: block; text-align: center;'>Shows the Torvik Game Scores for each team over the course of the 2024-25 season.</span>
     </div>
     <div>
       <img src='https://a.espncdn.com/combiner/i?img=/i/teamlogos/ncaa_conf/500/acc.png'
       style='height: 60px; width: auto; vertical-align: middle;'>
     </div>
   </div>
  <br>"
)

acc_sec_tbl <- game_scores_by_team |> 
  dplyr::arrange(-last_6_avg) |> 
  cbbplotR::gt_cbb_teams(team, team) |>  
  gt::gt() |> 
  gtUtils::gt_theme_gtutils() |> 
  gt::fmt_markdown(team) |> 
  gt::cols_hide(conf) |> 
  gt::cols_align(team, align = "left") |> 
  gt::tab_style(
    style = gt::cell_fill(color = "#fdeba9", alpha = 0.6),  # SEC color
    locations = gt::cells_body(columns = team, rows = conf == "SEC")
  ) |> 
  gt::tab_style(
    style = gt::cell_fill(color = "#99b1db", alpha = 0.6),  # ACC color
    locations = gt::cells_body(columns = team, rows = conf == "ACC")
  ) |> 
  gtUtils::gt_column_subheaders(
    season_avg = list(heading = "Season", subtitle = "Avg"),
    first_12_avg = list(heading = "Beginning", subtitle = "1st 12 Games"),
    next_12_avg = list(heading = "Middle", subtitle = "2nd 12 Games"),
    last_6_avg = list(heading = "End", subtitle = "Last 6 Games"),
    season_perf = list(heading = "Season", subtitle = "Performance"),
    heading_color = "black",
    subtitle_color = "gray"
  ) |> 
        gt::cols_nanoplot(
    columns = season_perf,
    reference_line = "mean",
    autohide = TRUE,
    new_col_label = "Season Performance",
    missing_vals = "gap",
    options = gt::nanoplot_options(
      data_area_fill_color = "#eabfff",
      reference_line_color = "#333333",
      show_data_area = TRUE,
      show_data_points = FALSE
    )
  ) |> 
  gt::cols_label(team = "") |> 
  gtExtras::gt_add_divider(
    columns = c(team, last_6_avg),
    sides = "right",
    color = "black"
  ) |> 
  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_column_labels(),
    style = gt::cell_text(
      font = gt::google_font("Signika Negative"),
      weight = "bold",
    )
  ) |>
  gtUtils::gt_color_pills(season_avg,
                          domain = c(40, 100), format_type = "number", digits = 1,
                          palette = c("#d7191c", "#fdae61", 
                                      "#ffffbf", "#a6d96a", 
                                      "#1a9641")
  ) |> 
  gtUtils::gt_color_pills(first_12_avg,
                          domain = c(40, 100), format_type = "number", digits = 1,
                          palette = c("#d7191c", "#fdae61", 
                                      "#ffffbf", "#a6d96a", 
                                      "#1a9641")
  ) |> 
  gtUtils::gt_color_pills(next_12_avg,
                          domain = c(40, 100), format_type = "number", digits = 1,
                          palette = c("#d7191c", "#fdae61", 
                                      "#ffffbf", "#a6d96a", 
                                      "#1a9641")
  ) |> 
  gtUtils::gt_color_pills(last_6_avg,
                          domain = c(35, 100), format_type = "number", digits = 1,
                          palette = c("#d7191c", "#fdae61", 
                                      "#ffffbf", "#a6d96a", 
                                      "#1a9641")
  ) |> 
  gt::tab_header(title = gt::html(acc_sec_header)) |> 
  gt::tab_source_note(
    source_note =
      gt::html(
        "<hr>Data via barttorvik.com | theme via {gtUtils} <br>
        Bart Torvik's Game Score calculates a Barthag for each team for each individual game given adjusted offensive and defensive efficiencies are calculated game-by-game.<br>
        It can be used to show how a team performs over time.<br>
        <hr><b>Table by Chris at Bless your chart | data through March 5 games</b>"
      )
  ) |>
  gtUtils::gt_border_bars_bottom(c("#636363", "#969696", "#cccccc")) |> 
  gt::tab_options(table.width = gt::px(705)) |>
  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_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(14)
      )
    ),
    locations = gt::cells_body(
      rows = gt::everything(),
      columns = gt::everything()
    )
  )

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

acc_sec_tbl
SEC and ACC Men's Basketball Shows the Torvik Game Scores for each team over the course of the 2024-25 season.

Season
Avg
Beginning
1st 12 Games
Middle
2nd 12 Games
End
Last 6 Games
Season Performance

Duke

 95.8 95.9 94.1 99.0
95.8 100 70.5 95.3 96.7 92.0 100 99.4 82.7 97.5 98.3 96.7 94.5 98.9 99.1 92.7 99.5 99.1 91.9 98.9 94.1 94.0 70.5 99.4 99.1 92.5 97.9 99.1 98.7 99.9 99.0 98.9 98.6 NA

Clemson

 87.3 82.1 88.8 94.4
87.3 99.7 59.8 87.2 89.7 77.4 71.4 97.9 95.0 89.0 77.6 94.1 77.8 61.7 65.9 93.8 89.3 85.0 79.9 96.3 96.7 89.7 93.1 95.3 89.2 59.8 98.1 98.7 99.7 97.9 93.2 89.8 87.4 NA

Auburn

 95.7 97.8 94.8 93.5
95.7 99.9 73.7 99.9 98.5 97.8 97.2 91.7 98.3 98.3 97.5 99.2 99.9 95.9 99.6 91.2 99.5 96.8 82.7 99.3 97.2 96.1 95.2 97.0 98.5 85.3 98.3 99.1 92.1 97.0 99.4 99.5 73.7 NA

North Carolina

 81.7 82.5 78.0 93.0
81.7 98.6 48.3 74.6 86.7 96.4 90.6 66.3 81.7 84.8 69.1 69.3 92.3 89.4 89.2 81.1 79.3 83.2 98.6 80.6 97.4 70.2 78.6 51.6 71.7 65.2 78.7 48.3 85.2 98.2 96.0 91.2 88.5 98.6

Florida

 91.2 91.6 90.6 91.6
91.2 100 62.8 63.6 94.4 80.3 95.3 90.3 95.3 94.8 99.4 92.2 98.4 95.0 99.9 97.0 91.3 100 95.9 77.9 98.0 72.3 99.8 64.7 92.6 99.5 98.3 95.8 99.2 94.9 62.8 98.6 98.2 NA

Alabama

 91.9 90.2 93.9 91.6
91.9 99.7 50.2 99.4 88.0 93.9 89.4 98.9 98.6 84.8 85.3 98.4 96.8 50.2 98.5 95.6 98.9 98.4 97.9 71.0 96.5 97.5 83.5 93.7 98.8 96.2 98.9 89.2 76.6 96.4 99.7 96.2 91.2 NA

Louisville

 84.0 75.6 88.6 91.5
84.0 99.7 23.5 99.3 26.5 82.7 83.2 99.3 92.2 70.1 23.5 95.7 57.4 83.8 93.6 56.8 95.1 97.8 97.3 84.5 98.4 95.7 99.7 98.4 69.5 97.1 73.2 98.8 95.4 90.3 86.3 87.2 91.2 NA

Tennessee

 90.7 94.6 87.1 90.3
90.7 99.9 15.1 82.9 99.9 97.4 94.8 98.3 99.8 99.4 95.5 87.6 95.0 99.1 85.9 92.9 99.4 15.1 90.3 98.2 81.5 99.0 97.2 78.1 99.7 93.7 99.7 82.4 81.9 96.2 94.3 94.8 92.3 NA

Kentucky

 87.3 87.6 85.9 89.7
87.3 99.5 40.9 98.7 94.2 98.4 98.6 98.8 91.1 83.9 90.7 91.9 66.7 96.8 40.9 97.2 97.0 67.4 94.6 97.1 91.4 81.1 98.7 59.9 50.4 97.5 98.3 84.9 98.2 91.7 90.2 73.8 99.5 NA

Georgia

 81.8 82.9 76.8 89.7
81.8 99.6 27.8 35.2 89.2 70.5 90.8 97.3 66.4 95.5 99.3 97.2 89.4 99.6 64.5 42.0 66.4 98.2 91.9 76.3 90.9 91.1 27.8 96.0 69.0 95.5 77.0 81.3 71.2 91.5 99.2 99.4 95.5 NA

Missouri

 85.7 79.8 91.0 86.7
85.7 99.6 51.1 81.3 70.6 51.1 98.2 95.1 89.3 68.9 55.9 98.9 92.6 67.9 88.2 86.2 75.3 97.5 96.8 99.1 98.9 72.3 95.3 99.6 95.4 76.7 99.2 98.1 99.0 82.5 98.6 89.2 52.7 NA

Texas A&M

 88.7 89.3 91.2 82.6
88.7 99.4 58.5 83.4 97.4 95.8 98.3 59.5 79.5 88.7 83.1 92.8 97.9 97.2 97.6 98.8 96.6 83.2 84.5 81.6 91.7 88.6 93.0 89.8 92.6 98.1 95.7 92.0 58.5 90.4 74.8 80.7 99.4 NA

Vanderbilt

 82.0 83.3 80.4 82.4
82.0 97.9 38.7 78.1 59.1 93.3 75.4 81.1 90.2 58.5 88.9 92.4 87.1 97.7 97.9 95.0 92.5 38.7 76.3 79.0 96.8 79.2 95.2 49.5 93.2 86.0 82.9 96.8 61.6 95.9 96.2 92.2 51.9 NA

Oklahoma

 75.0 78.5 68.8 80.6
75.0 98.5 24.2 92.0 61.6 61.0 75.2 71.6 98.1 96.3 83.4 54.1 97.8 88.2 62.4 48.2 56.1 92.1 78.9 41.3 93.8 93.1 86.8 97.1 75.7 24.2 38.6 70.0 50.1 91.9 87.8 85.4 98.5 NA

Mississippi

 83.3 83.4 85.3 79.1
83.3 99.7 12.5 90.8 12.5 87.8 99.1 83.5 96.5 87.4 99.7 82.2 97.0 81.8 82.2 37.8 97.3 93.6 91.5 98.7 77.4 92.7 86.3 80.2 91.6 99.1 76.9 92.4 60.8 70.4 69.5 88.1 93.6 NA

Arkansas

 79.1 80.4 78.2 78.3
79.1 100 5.80 92.7 69.7 89.1 71.7 84.4 100 58.9 47.9 77.0 91.5 91.2 90.7 90.1 42.2 76.7 86.1 76.1 48.1 82.1 67.7 98.0 98.1 83.2 89.6 84.8 95.2 94.6 92.4 5.80 97.1 NA

Mississippi St.

 79.5 80.9 79.5 76.4
79.5 99.8 10.0 84.0 94.4 88.0 81.9 89.7 98.8 48.9 99.6 10.0 81.6 95.0 99.2 60.8 99.8 98.7 84.3 68.3 94.8 60.2 94.6 91.6 30.2 94.8 75.9 97.6 98.3 76.4 34.5 84.3 67.5 NA

Georgia Tech

 63.3 57.4 64.2 73.4
63.3 97.6 9.10 84.6 9.50 89.6 65.9 11.1 83.4 56.0 69.4 86.1 28.9 53.5 51.3 95.5 91.7 92.6 38.5 9.10 48.0 29.8 73.2 76.1 92.3 94.8 28.2 97.6 59.3 10.8 93.0 94.4 85.1 NA

LSU

 69.5 68.3 69.3 72.5
69.5 98.5 10.2 91.4 38.2 98.0 30.2 33.3 75.4 83.6 81.5 89.1 70.0 98.5 30.3 97.3 60.2 52.5 69.3 83.2 74.6 92.6 85.7 10.2 52.9 88.0 64.8 86.0 96.1 79.8 78.0 79.2 15.7 NA

Virginia

 63.1 65.8 56.4 70.8
63.1 99.1 8.50 74.0 62.6 97.2 45.5 20.3 71.4 95.6 83.4 66.2 58.7 76.7 38.6 57.4 38.8 23.2 24.2 66.2 55.6 96.9 8.50 82.2 31.6 99.1 92.9 84.5 62.0 37.0 93.3 75.5 72.5 NA

Stanford

 69.7 69.9 69.7 69.2
69.7 98.9 12.2 85.0 52.0 88.7 83.0 70.5 86.9 60.6 29.9 91.4 92.1 73.1 25.3 81.8 38.7 66.7 95.7 62.9 89.1 98.9 87.4 92.9 12.2 53.7 56.5 15.0 60.3 79.5 95.6 90.4 74.7 NA

Syracuse

 56.9 52.6 55.4 68.5
56.9 95.9 11.1 11.1 36.2 40.1 52.4 88.8 70.2 60.3 57.1 67.9 55.3 17.1 75.1 35.3 37.2 84.9 72.6 23.9 63.4 58.0 61.1 27.0 94.8 43.1 63.6 30.1 62.6 70.3 95.9 62.1 90.1 NA

SMU

 75.7 79.9 76.1 66.7
75.7 99.3 17.4 97.2 82.4 81.5 50.6 65.5 82.3 70.4 86.9 78.6 86.7 87.2 89.9 70.8 60.6 33.5 99.2 86.7 99.3 17.4 88.0 84.8 99.2 75.1 98.4 39.5 99.3 58.5 88.9 64.9 48.8 NA

South Carolina

 66.2 73.6 59.3 65.2
66.2 99.6 3.70 22.8 92.3 97.5 41.2 47.9 76.6 88.1 97.9 57.0 79.8 94.7 87.4 37.8 3.70 50.5 96.2 83.4 49.3 96.6 54.4 50.1 72.8 49.7 67.1 76.6 25.2 98.2 38.4 99.6 53.5 NA

California

 57.4 56.3 55.8 62.9
57.4 94.5 11.0 72.5 75.5 40.5 90.9 50.6 24.4 84.8 94.5 28.0 19.8 75.0 19.4 64.8 75.3 91.6 13.4 19.2 79.0 83.8 50.5 59.3 11.0 82.9 39.3 62.4 69.3 53.9 50.3 78.4 63.0 NA

Wake Forest

 69.7 63.2 80.3 61.3
69.7 96.1 18.5 69.7 55.5 88.9 18.5 48.1 44.9 60.4 80.4 84.5 71.8 55.1 80.2 69.2 89.1 94.5 79.8 84.1 86.1 82.9 90.7 35.5 77.1 88.6 86.6 69.8 96.1 39.7 31.9 62.2 68.2 NA

Notre Dame

 66.0 69.7 65.5 59.8
66.0 98.4 8.40 88.0 26.4 98.4 81.3 25.2 78.0 88.2 69.0 35.5 72.5 88.8 85.2 31.7 73.7 71.1 91.4 77.4 67.0 98.2 61.6 46.7 55.9 54.6 56.3 56.9 8.40 77.1 66.2 79.8 70.6 NA

Texas

 76.6 78.5 83.4 59.4
76.6 99.2 21.2 58.1 93.4 97.4 41.6 87.8 89.6 86.4 80.3 24.7 98.6 98.7 85.3 96.3 67.2 87.6 91.9 97.3 72.5 96.7 81.4 91.5 99.2 34.9 84.5 56.9 92.2 21.2 69.9 21.5 94.7 NA

Pittsburgh

 70.5 83.5 63.8 57.7
70.5 99.6 5.50 98.5 76.7 89.8 99.6 97.6 94.2 93.1 87.7 8.10 67.4 95.1 94.7 69.4 93.5 58.3 86.5 36.8 79.8 74.1 87.5 71.1 5.50 82.8 20.6 60.6 65.4 65.9 32.8 83.6 37.8 NA

Boston College

 47.7 48.2 45.2 51.9
47.7 98.8 5.50 28.1 16.9 56.3 74.5 94.7 49.8 86.2 31.3 5.50 69.3 27.6 37.8 54.7 41.3 14.9 36.2 38.8 76.2 6.50 84.8 54.2 25.8 46.2 62.9 15.6 98.8 94.7 12.3 27.9 62.3 NA

North Carolina St.

 60.6 68.1 57.5 51.8
60.6 97.4 10.0 82.5 65.6 50.7 95.5 93.8 73.6 47.6 67.6 81.0 14.3 51.4 94.1 73.9 21.9 60.5 71.3 65.9 39.2 56.0 97.4 70.7 33.3 80.1 20.1 89.7 16.0 87.9 10.0 18.4 88.8 NA

Florida St.

 68.2 79.3 65.3 51.7
68.2 96.8 6.70 78.9 78.0 84.2 73.2 83.3 76.2 96.8 92.5 58.1 45.7 94.5 90.1 64.2 85.0 90.0 49.6 91.4 92.0 37.7 49.4 8.10 63.7 79.3 72.9 6.70 52.8 73.7 56.4 55.4 65.2 NA

Virginia Tech

 53.8 47.7 65.7 42.3
53.8 96.9 1.50 84.3 36.4 58.3 13.5 23.8 76.6 33.5 40.8 64.1 83.4 35.2 22.7 84.3 35.7 66.7 92.4 52.6 37.3 51.6 43.2 96.9 86.4 70.4 70.7 34.9 1.50 80.7 71.2 54.9 10.4 NA

Miami FL

 47.8 59.2 41.4 37.9
47.8 93.6 2.50 93.6 84.4 71.6 38.6 22.5 63.0 10.1 82.1 72.0 68.9 82.8 20.8 50.9 63.7 15.4 35.4 30.0 2.80 2.50 51.9 27.3 66.1 77.0 73.2 58.6 59.3 18.8 29.5 37.9 23.1 NA
Data via barttorvik.com | theme via {gtUtils}
Bart Torvik's Game Score calculates a Barthag for each team for each individual game given adjusted offensive and defensive efficiencies are calculated game-by-game.
It can be used to show how a team performs over time.
Table by Chris at Bless your chart | data through March 5 games