Border Bars

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gt_border_bars_top() and gt_border_bars_bottom() place colored bars above or below a table. Stack as many bars as you like, or use a single bar to carry an image and a line of text: a team’s logo over its season recap, or a publisher’s mark under a leaderboard. This article builds three tables with them:

A team report card: the 2026 men’s college basketball champion’s four-factor percentiles at home and on the road, under a bar in the team’s color with its logo.
A branded footer: the WNBA’s 2026 scoring leaders with a footer bar that carries the sdvplotR mark and the data source.
Team-color stripes: the 2025 Super Bowl champion’s game log, topped with its wordmark and finished with stripes in its colors.

The basketball comes from hoopR and wehoop’s ESPN box scores, the football from nflverse via nflreadr. The border bars, like the other gt_* table functions, come from Andrew Weatherman’s gtUtils, whose border-bars article this one follows.

library(sdvplotR)
library(gt)
library(dplyr)
library(tidyr)

1. A team report card

The team is whoever won the season’s last game, the national championship, so nothing about it is typed in. We plot national percentiles on a home/away split for a set of efficiency, four-factor and shooting numbers. The efficiency numbers are raw points per 100 possessions, not adjusted for opponents.

higher_better <- c(
  "off_eff", "tempo", "efg", "ftr", "oreb_rate", "dreb_rate", "def_tov_rate",
  "two_pt_pct", "three_pt_pct", "ft_pct"
)

The box scores hold one row per team per game, so joining each row to its opponent’s row puts both ends of the floor side by side. Possessions are estimated from the box score.

box <- hoopR::load_mbb_team_box(seasons = 2026)

The tables below use snapshots of this article’s three data calls, taken on November 09, 2026, because a GitHub release read that fails returns an empty frame and would stop the site build; data-raw/article_fixtures.R takes them with the same code.

box <- box |>
  mutate(poss = field_goals_attempted - offensive_rebounds + total_turnovers + 0.475 * free_throws_attempted)

champion <- box |>
  filter(season_type == 3) |>
  slice_max(game_date_time, n = 1, with_ties = TRUE) |>
  filter(team_winner) |>
  pull(team_id)

games <- box |>
  # Division I teams only; the box scores also hold their games against other divisions
  filter(team_id %in% sdv_teams("mbb")$team_id) |>
  inner_join(
    select(box, game_id,
      opponent_team_id = team_id, opp_score = team_score, opp_poss = poss,
      opp_fgm = field_goals_made, opp_fga = field_goals_attempted,
      opp_3pm = three_point_field_goals_made, opp_3pa = three_point_field_goals_attempted,
      opp_ftm = free_throws_made, opp_fta = free_throws_attempted,
      opp_oreb = offensive_rebounds, opp_dreb = defensive_rebounds, opp_tov = total_turnovers
    ),
    by = c("game_id", "opponent_team_id")
  )

team_factors <- function(data) {
  data |>
    summarise(
      off_eff = 100 * sum(team_score) / sum(poss),
      def_eff = 100 * sum(opp_score) / sum(opp_poss),
      tempo = mean((poss + opp_poss) / 2),
      efg = (sum(field_goals_made) + 0.5 * sum(three_point_field_goals_made)) / sum(field_goals_attempted),
      def_efg = (sum(opp_fgm) + 0.5 * sum(opp_3pm)) / sum(opp_fga),
      ftr = sum(free_throws_attempted) / sum(field_goals_attempted),
      def_ftr = sum(opp_fta) / sum(opp_fga),
      oreb_rate = sum(offensive_rebounds) / sum(offensive_rebounds + opp_dreb),
      dreb_rate = sum(defensive_rebounds) / sum(defensive_rebounds + opp_oreb),
      tov_rate = sum(total_turnovers) / sum(poss),
      def_tov_rate = sum(opp_tov) / sum(opp_poss),
      two_pt_pct = sum(field_goals_made - three_point_field_goals_made) /
        sum(field_goals_attempted - three_point_field_goals_attempted),
      three_pt_pct = sum(three_point_field_goals_made) / sum(three_point_field_goals_attempted),
      ft_pct = sum(free_throws_made) / sum(free_throws_attempted),
      def_two_pt_pct = sum(opp_fgm - opp_3pm) / sum(opp_fga - opp_3pa),
      def_three_pt_pct = sum(opp_3pm) / sum(opp_3pa),
      def_ft_pct = sum(opp_ftm) / sum(opp_fta),
      .by = team_id
    )
}

Percentiles come from percent_rank() across every Division I team on each split, with the sign flipped where a lower number is better, so 100% is always the best mark in the country.

percentiles <- function(data, location) {
  data |>
    mutate(
      across(all_of(higher_better), percent_rank),
      across(-c(team_id, all_of(higher_better)), ~ percent_rank(-.x))
    ) |>
    pivot_longer(-team_id, names_to = "stat", values_to = "percentile") |>
    mutate(location = location)
}

# ESPN labels every game home or away, neutral sites included
splits <- bind_rows(
  percentiles(team_factors(filter(games, team_home_away == "home")), "home"),
  percentiles(team_factors(filter(games, team_home_away == "away")), "away")
) |>
  pivot_wider(names_from = location, values_from = percentile)

The last step names each stat and groups them, so the table reads in sections.

labels <- c(
  off_eff = "Off. efficiency", def_eff = "Def. efficiency", tempo = "Tempo",
  efg = "Eff. FG%", ftr = "FTA per FGA", oreb_rate = "Off. rebound rate", tov_rate = "Turnover rate",
  def_efg = "Eff. FG%", def_ftr = "FTA per FGA", dreb_rate = "Def. rebound rate",
  def_tov_rate = "Turnover rate", two_pt_pct = "2FG%", three_pt_pct = "3FG%", ft_pct = "FT%",
  def_two_pt_pct = "2FG%", def_three_pt_pct = "3FG%", def_ft_pct = "FT%"
)

report <- splits |>
  filter(team_id == champion) |>
  mutate(
    group = case_when(
      stat %in% c("off_eff", "def_eff", "tempo") ~ "Efficiency",
      stat %in% c("efg", "ftr", "oreb_rate", "tov_rate") ~ "Offensive four factors",
      stat %in% c("def_efg", "def_ftr", "dreb_rate", "def_tov_rate") ~ "Defensive four factors",
      stat %in% c("two_pt_pct", "three_pt_pct", "ft_pct") ~ "Offensive shooting",
      TRUE ~ "Defensive shooting"
    ),
    stat = labels[stat]
  ) |>
  select(group, stat, home, away)

sdv_teams() holds the champion’s name; sdv_logo_url() its dark-mode logo (made for a colored ground) and sdv_team_colors() its primary color for the bar.

team <- filter(sdv_teams("mbb"), team_id == champion)
team_color <- unname(sdv_team_colors("mbb", team$team_id, "primary"))
team_logo <- sdv_logo_url(team$team_id, league = "mbb", variant = "dark")

Now the table. gt_theme_savant() gives a clean base, gt_color_pills() fills the two percentile columns over a fixed [0, 1] domain, and gt_border_bars_top() adds the header bar. With an image in the bar, bar_height has to be tall enough to hold it; the text picks up the font the theme uses for its headers unless you set it, and text_padding, text_align and img_align move the two around.

report |>
  group_by(group) |>
  gt() |>
  gt_theme_savant() |>
  gt_color_pills(home,
    domain = c(0, 1), format_type = "percent", digits = 0,
    palette = "ggsci::green_material"
  ) |>
  gt_color_pills(away,
    domain = c(0, 1), format_type = "percent", digits = 0,
    palette = "ggsci::green_material"
  ) |>
  cols_width(home:away ~ px(90)) |>
  cols_label(stat = "", home = "At home", away = "On the road") |>
  cols_align(columns = -stat, "center") |>
  tab_options(column_labels.border.top.style = "none") |>
  gt_538_caption(
    "National percentile among Division I teams on each split; 100% is the best mark.",
    "Data: ESPN box scores via hoopR"
  ) |>
  gt_border_bars_top(
    colors = team_color,
    img = team_logo,
    text = paste("2025-26 recap:", team$location),
    text_padding = 10,
    text_size = 20,
    img_width = 44,
    img_height = 44,
    bar_height = 62
  )

2025-26 recap: Michigan
¹	At home¹	On the road¹
Efficiency
Off. efficiency	93%	96%
Def. efficiency	90%	95%
Tempo	89%	62%
Offensive four factors
Eff. FG%	97%	95%
FTA per FGA	72%	61%
Off. rebound rate	61%	96%
Turnover rate	56%	43%
Defensive four factors
Eff. FG%	95%	100%
FTA per FGA	87%	98%
Def. rebound rate	84%	28%
Turnover rate	16%	33%
Offensive shooting
2FG%	99%	84%
3FG%	72%	95%
FT%	55%	95%
Defensive shooting
2FG%	97%	99%
3FG%	79%	94%
FT%	49%	1%
¹ National percentile among Division I teams on each split; 100% is the best mark.
Data: ESPN box scores via hoopR

A footer bar is the place for a publisher’s mark. The table is the WNBA’s top ten scorers of the 2026 regular season among players who appeared in at least 30 games, each with a headshot and team logo.

player_box <- wehoop::load_wnba_player_box(2026)

scorers <- player_box |>
  filter(season_type == 2, !did_not_play) |>
  summarise(
    player = last(athlete_display_name, order_by = game_date),
    team = last(team_abbreviation, order_by = game_date),
    gp = n(),
    ppg = mean(points),
    fg_pct = sum(field_goals_made) / sum(field_goals_attempted),
    .by = athlete_id
  ) |>
  filter(gp >= 30) |>
  arrange(desc(ppg), player) |>
  slice_head(n = 10) |>
  mutate(rank = row_number(), photo = athlete_id) |>
  select(rank, photo, player, team, gp, ppg, fg_pct)

gt_sdv_headshots() turns ESPN athlete ids into photos and gt_sdv_logos() turns the team abbreviations into logos. The footer bar holds the sdvplotR hex mark, drawn 32px wide and 37px tall to keep its shape, and the source line, on the SportsDataverse blue, where the dark hex stands out; a thin bar of the same color on top frames the table.

scorers |>
  gt() |>
  gt_theme_sofa() |>
  gt_sdv_headshots(photo, league = "wnba", height = 36) |>
  gt_sdv_logos(team, league = "wnba", height = 26) |>
  fmt_number(ppg, decimals = 1) |>
  fmt_percent(fg_pct, decimals = 1) |>
  cols_label(rank = "", photo = "", player = "Player", team = "", gp = "GP", ppg = "PPG", fg_pct = "FG%") |>
  cols_align(columns = -player, "center") |>
  opt_row_striping() |>
  tab_header(
    title = "The WNBA's top scorers, 2026",
    subtitle = "Points per game, regular season, 30 or more games"
  ) |>
  gt_border_bars_bottom("#2680E4",
    bar_height = 46,
    img = "https://raw.githubusercontent.com/sportsdataverse/sdvplotR/main/man/figures/logo.png",
    img_width = 32, img_height = 37,
    text = "Data: ESPN box scores via wehoop", text_size = 12, text_weight = "normal"
  ) |>
  gt_border_bars_top("#2680E4", bar_height = 6)


	Player	GP	PPG	FG%
The WNBA's top scorers, 2026
Points per game, regular season, 30 or more games
1	A'ja Wilson	42	25.8	52.6%
2	Kelsey Mitchell	45	24.8	51.1%
3	Caitlin Clark	41	22.1	44.3%
4	Kahleah Copper	41	21.0	44.8%
5	Marina Mabrey	33	20.9	44.1%
6	Paige Bueckers	43	20.7	51.1%
7	Breanna Stewart	44	20.6	47.6%
8	Olivia Miles	41	19.5	49.7%
9	Allisha Gray	45	19.0	46.3%
10	Jackie Young	45	18.8	48.8%
Data: ESPN box scores via wehoop

3. Team-color stripes

With no text or image, the bars are pure decoration, and you can have as many as you like at equal heights, drawn in order from the first color down. Here the 2025 Super Bowl champion’s game log gets its wordmark in a white top bar and a stripe in each of its two colors at the bottom.

The champion is the winner of the season’s Super Bowl, and its games are one filter on the schedule after clean_homeaway() turns it into one row per team.

schedule <- nflreadr::load_schedules(2025)


sb <- filter(schedule, game_type == "SB")
champ <- if_else(sb$home_score > sb$away_score, sb$home_team, sb$away_team)

game_log <- schedule |>
  select(week, game_type, home_team, away_team, home_score, away_score) |>
  nflreadr::clean_homeaway() |>
  filter(team == champ, !is.na(team_score)) |>
  arrange(week) |>
  transmute(
    week = if_else(game_type == "REG", as.character(week), game_type),
    where = if_else(location == "home", "vs", "@"),
    opponent,
    result = if_else(team_score > opponent_score, "W", if_else(team_score < opponent_score, "L", "T")),
    score = paste0(team_score, "-", opponent_score)
  )

sdv_team_colors(which = "all") returns a team’s colors as one comma-separated string, which splits into the stripe colors; sdv_logo_url(mark_type = "wordmark") returns the wordmark.

stripes <- strsplit(unname(sdv_team_colors("nfl", champ, "all")), ", ")[[1]]
wordmark <- sdv_logo_url(champ, league = "nfl", mark_type = "wordmark")

game_log |>
  gt() |>
  gt_theme_sdv() |>
  gt_sdv_logos(opponent, league = "nfl", height = 24) |>
  gt_highlight_cells(result, condition = ~ .x == "W", fill = "#E3F1E7", bold = TRUE) |>
  cols_label(week = "Week", where = "", opponent = "Opponent", result = "", score = "Score") |>
  cols_align(columns = everything(), "center") |>
  tab_source_note("Data: nflverse schedules via nflreadr") |>
  gt_border_bars_top("#FFFFFF", bar_height = 56, img = wordmark, img_width = 168, img_height = 46) |>
  gt_border_bars_bottom(stripes, bar_height = 8)


Week			Score
1	vs	L	13-17
2	@	W	31-17
3	vs	W	44-13
4	@	W	23-20
5	vs	L	35-38
6	@	W	20-12
7	vs	W	27-19
9	@	W	38-14
10	vs	W	44-22
11	@	L	19-21
12	@	W	30-24
13	vs	W	26-0
14	@	W	37-9
15	vs	W	18-16
16	vs	W	38-37
17	@	W	27-10
18	@	W	13-3
DIV	vs	W	41-6
CON	vs	W	31-27
SB	@	W	29-13
Data: nflverse schedules via nflreadr

The wordmark bar is white because a team’s wordmark is drawn in the team’s own colors, which would disappear on a bar of the same color. Put a logo on a colored bar (the report card above) and a wordmark on a light one.

Our Authors

Citation

To cite the sdvplotR R package in publications, use:

BibTeX Citation

@misc{gilani_carl_lee_kay_weatherman_sdvplotR,
  author = {Gilani, Saiem and Carl, Sebastian and Lee, Jared and Kay, Camden and Weatherman, Andrew},
  title = {sdvplotR: The SportsDataverse's R Package for Multi-League Sports Plotting.},
  url = {https://sdvplotR.sportsdataverse.org},
  year = {2026}
}

sdvplot - the Python plotting package
sdvplot-js - the JavaScript / TypeScript plotting packages
cfbfastR - college football
hoopR - men’s basketball
wehoop - women’s basketball
baseballr - baseball
fastRhockey - hockey
oddsapiR - betting odds
sportyR - playing surfaces
sportsdataverse-py - the Python package
sportsdataverse-R - the R meta-package

Saiem Gilani

1. A team report card

2. A branded footer

3. Team-color stripes

Our Authors

Citation

Border Bars

Saiem Gilani

1. A team report card

2. A branded footer

3. Team-color stripes

Our Authors

Citation

Related SportsDataverse packages