Team Tables with the gt Toolkit

On this page

sdvplotR has two kinds of gt helpers, and this article uses both on one table.

The team layer turns team keys into logos, headshots and team colors (gt_sdv_logos(), gt_sdv_headshots(), gt_merge_stack_team_color()). It resolves every abbreviation, alias and relocated franchise through sdv_resolve().
The table toolkit, ported from Andrew Weatherman’s gtUtils, handles the editorial side: cut lines, colored ranks, captions, legends, grids of tables, themes, and saving images sized for posting.

Every one of these functions takes a gt table and returns one, so they chain in any order. Having both in one package means one install, and one set of conventions for how a sports table is built.

library(sdvplotR)
library(gt)

The data

The final 2025 standings. nflseedR::nfl_standings() computes them from nflverse’s schedule, read with nflreadr, NFL tiebreakers included; its conf_rank is each team’s place in its conference, so ranks 1-7 are the playoff seeds. The seeds come first in seed order, followed by the rest.

standings <- nflseedR::nfl_standings(nflreadr::load_schedules(2025), verbosity = "NONE")

conference_standings <- function(conference) {
  s <- standings[standings$conf == conference, ]
  s <- s[order(s$conf_rank), ]
  data.frame(
    team = s$team,
    seed = ifelse(s$conf_rank <= 7, s$conf_rank, NA),
    w = s$true_wins,
    l = s$losses,
    pct = s$win_pct
  )
}

afc <- conference_standings("AFC")

Step by step

Start with logos, labels and a box-score win percentage (.765, not 0.765).

base <- function(df, conference) {
  gt(df) |>
    gt_sdv_logos(columns = "team", league = "nfl", height = 24) |>
    cols_label(team = "", seed = "Seed", w = "W", l = "L", pct = "Win pct") |>
    sub_missing(columns = "seed", missing_text = "") |>
    fmt(columns = "pct", fns = function(x) sub("^0", "", sprintf("%.3f", x))) |>
    tab_header(paste(conference, "playoff picture"), "Final 2025 standings")
}
base(afc, "AFC")

	Seed	W	L	Win pct
AFC playoff picture
Final 2025 standings
	1	14	3	.824
	2	14	3	.824
	3	13	4	.765
	4	10	7	.588
	5	12	5	.706
	6	12	5	.706
	7	11	6	.647
		8	9	.471
		8	9	.471
		7	10	.412
		6	11	.353
		6	11	.353
		5	12	.294
		3	14	.176
		3	14	.176
		3	14	.176

A standings table is read around one question: who is in? gt_cutline() draws the answer as a line after the seventh row, with a label, so readers don’t have to count seeds.

base(afc, "AFC") |>
  gt_cutline(after = 7, label = "Playoff line")

	Seed	W	L	Win pct
AFC playoff picture
Final 2025 standings
	1	14	3	.824
	2	14	3	.824
	3	13	4	.765
	4	10	7	.588
	5	12	5	.706
	6	12	5	.706
	7	11	6	.647
		8	9	.471
		8	9	.471
		7	10	.412
		6	11	.353
		6	11	.353
		5	12	.294
		3	14	.176
		3	14	.176
		3	14	.176

gt_color_ranks() colors a column by its values, which shows where the wins cluster faster than reading the numbers; reverse = TRUE puts the most wins at the green end. The cut line’s label sits in the row below the line and clears that row’s cell colors, so with colored cells, keep the line unlabeled and say what it means in the caption. gt_538_caption() separates that note from the source line. gt_theme_sdv() goes last so it styles everything added before it, and density is passed through for the saved version below.

playoff_table <- function(df, conference, density = "comfortable") {
  base(df, conference) |>
    gt_cutline(after = 7) |>
    gt_color_ranks(columns = "w", reverse = TRUE) |>
    gt_538_caption(
      top_caption = "Above the line: playoff teams.",
      bottom_caption = "Data: nflverse via nflreadr and nflseedR"
    ) |>
    gt_theme_sdv(density = density)
}
playoff_table(afc, "AFC")

¹	Seed¹	W¹	L¹	Win pct¹
AFC playoff picture
Final 2025 standings
	1	14	3	.824
	2	14	3	.824
	3	13	4	.765
	4	10	7	.588
	5	12	5	.706
	6	12	5	.706
	7	11	6	.647
		8	9	.471
		8	9	.471
		7	10	.412
		6	11	.353
		6	11	.353
		5	12	.294
		3	14	.176
		3	14	.176
		3	14	.176
¹ Above the line: playoff teams.
Data: nflverse via nflreadr and nflseedR

Both conferences at once

gt_grid() lays several finished tables out under one shared title and source line. Each table keeps its own theme and cut line; a note that applies to both goes in the grid’s caption, so each table’s own caption stays short and the tables stay narrow enough to sit side by side.

nfc <- conference_standings("NFC")

gt_grid(
  list(playoff_table(afc, "AFC"), playoff_table(nfc, "NFC")),
  ncol = 2,
  title = "The 2025 NFL playoff field",
  subtitle = "Final regular-season standings",
  caption = "Seeds 1-4 won their division; 5-7 are wild cards.",
  title_style = list(font = "Chivo", weight = 800)
)

The 2025 NFL playoff field

Final regular-season standings

¹	Seed¹	W¹	L¹	Win pct¹
AFC playoff picture
Final 2025 standings
	1	14	3	.824
	2	14	3	.824
	3	13	4	.765
	4	10	7	.588
	5	12	5	.706
	6	12	5	.706
	7	11	6	.647
		8	9	.471
		8	9	.471
		7	10	.412
		6	11	.353
		6	11	.353
		5	12	.294
		3	14	.176
		3	14	.176
		3	14	.176
¹ Above the line: playoff teams.
Data: nflverse via nflreadr and nflseedR

¹	Seed¹	W¹	L¹	Win pct¹
NFC playoff picture
Final 2025 standings
	1	14	3	.824
	2	11	6	.647
	3	11	6	.647
	4	8	9	.471
	5	12	5	.706
	6	12	5	.706
	7	9	7	.559
		9	8	.529
		9	8	.529
		8	9	.471
		8	9	.471
		7	9	.441
		6	11	.353
		5	12	.294
		4	13	.235
		3	14	.176
¹ Above the line: playoff teams.
Data: nflverse via nflreadr and nflseedR

Seeds 1-4 won their division; 5-7 are wild cards.

On a narrow screen the grid scrolls sideways inside its own box, so the page around it stays in place.

Posting it

gt_save_crop() saves a single table, trimmed and padded; gt_social_crop() pads it to a platform’s aspect ratio. Use the theme’s background from theme_bg so the padding blends with the table. gt_grid() saves the whole grid when you give it a file. Saving drives a headless Chrome through webshot2, so these lines are not run here.

bg <- theme_bg$bg[theme_bg$theme == "gt_theme_sdv" & theme_bg$has_style == "light"]

playoff_table(afc, "AFC", density = "social") |>
  gt_social_crop("afc-playoffs.png", aspect_ratio = "4:5", bg = bg)

gt_grid(
  list(playoff_table(afc, "AFC"), playoff_table(nfc, "NFC")),
  title = "The 2025 NFL playoff field",
  file = "playoff-field.png"
)

Where to go next

SportsDataverse Table Themes covers the house theme, the dark style and team colors.
The rest of the Tables cookbook goes deeper on single tools: styling headers, legends and captions, percentile bars and cut lines, faceted tables, tier lists, border bars, schedule matrices, rolling windows and saving and posting.
Smaller helpers each have an example on their reference page: gt_bold_rows(), gt_row_accent(), gt_group_stripes(), gt_highlight_na(), gt_color_results(), gt_fmt_rank(), gt_indicator_boxes(), gt_outliers(), gt_column_subheaders(), gt_marginalia(), gt_scale_note(), gt_set_font() and gt_wrap_labels(); gt_sdv_cols_label() puts logos in the column labels. gt_theme_preview() shows every theme that needs only the table on your own data.

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