NFL Visualizations with nflverse and sdvplotR

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Ten charts and tables from the 2025 NFL season: offense against defense with team logos, a ranked bar chart with logos on the axis, division standings, small multiples in team colors, the Super Bowl’s win probability with a logo in the title, two quarterback leaderboards with headshots, three relocated franchises in the logos of their eras, a field in a team’s colors and a tier list. The last section makes the first chart interactive.

The play-by-play is nflfastR’s, read from the nflverse releases with nflreadr; nflfastR::load_pbp() reads the same file. sdvplotR takes nflverse’s team abbreviations (LA, LV, WAS) and GSIS player ids as they are, so nothing needs renaming on the way in.

library(sdvplotR)
library(ggplot2)
library(dplyr, warn.conflicts = FALSE)
library(gt)

season <- 2025
caption <- "Data: nflverse via nflreadr | 2025 regular season"

pbp <- nflreadr::load_pbp(season)

# Regular-season scrimmage plays with an EPA value
plays <- pbp |>
  filter(season_type == "REG", play_type %in% c("pass", "run"), !is.na(epa))
nrow(plays)
#> [1] 32941

A plain theme_minimal() with a bold, left-aligned title serves every chart below:

theme_set(
  theme_minimal(base_size = 12) +
    theme(
      plot.title = element_text(face = "bold"),
      plot.title.position = "plot",
      plot.caption = element_text(color = "grey45", size = 8)
    )
)

1. Offense vs defense EPA per play

The chart every NFL season ends with: each team’s offensive EPA per play against the EPA per play its defense allowed, with the team’s logo as the point. geom_mean_lines() draws the league averages, and the defense axis is reversed so the best teams sit top right.

offense <- plays |>
  group_by(team = posteam) |>
  summarise(off_epa = mean(epa), off_sr = mean(success), .groups = "drop")
defense <- plays |>
  group_by(team = defteam) |>
  summarise(def_epa = mean(epa), def_sr = mean(success), .groups = "drop")
teams <- inner_join(offense, defense, by = "team")

ggplot(teams, aes(x = off_epa, y = def_epa)) +
  geom_mean_lines(aes(x0 = off_epa, y0 = def_epa), color = "grey50", linewidth = 0.4) +
  geom_sdv_logos(aes(team = team), league = "nfl", width = 0.055, alpha = 0.9) +
  annotate(
    "text", x = max(teams$off_epa), y = min(teams$def_epa),
    label = "good offense, good defense", hjust = 1, vjust = 1, color = "grey45", size = 3.2
  ) +
  annotate(
    "text", x = min(teams$off_epa), y = max(teams$def_epa),
    label = "bad offense, bad defense", hjust = 0, vjust = 0, color = "grey45", size = 3.2
  ) +
  scale_y_reverse() +
  labs(
    title = "NFL offense vs defense, 2025",
    x = "Offense EPA per play",
    y = "Defense EPA per play allowed (better is up)",
    caption = caption
  )

Scatter plot of the 32 NFL teams in the 2025 regular season, each drawn as its logo, with offensive EPA per play on the horizontal axis and defensive EPA per play allowed on the vertical axis, reversed so better defenses sit higher. Dashed lines mark the league averages.

2. A ranked bar chart with logos on the axis

Offensive success rate against the league average, sorted, with each bar in its team’s primary color from scale_fill_sdv(). The x axis keeps the abbreviations as its labels, and element_sdv_logo() set on axis.text.x draws them as logos.

league_sr <- mean(plays$success)

ranked <- teams |>
  mutate(sr_diff = off_sr - league_sr) |>
  arrange(desc(sr_diff), team) |>
  mutate(team = factor(team, levels = team))

ggplot(ranked, aes(x = team, y = sr_diff)) +
  geom_col(aes(fill = team), width = 0.75) +
  geom_hline(yintercept = 0, color = "grey30", linewidth = 0.3) +
  scale_fill_sdv(league = "nfl") +
  scale_y_continuous(labels = function(v) sub("^\\+0$", "0", sprintf("%+.0f", 100 * v))) +
  labs(
    title = "Offensive success rate, 2025",
    subtitle = sprintf("Percentage points above or below the league's %.1f%%", 100 * league_sr),
    x = NULL,
    y = NULL,
    caption = caption
  ) +
  theme(
    legend.position = "none",
    panel.grid.major.x = element_blank(),
    axis.text.x = element_sdv_logo(league = "nfl", size = 0.5)
  )

Bar chart of each NFL team's offensive success rate in the 2025 regular season minus the league average, sorted from highest to lowest, with bars in team colors and each team's logo in place of its name on the horizontal axis.

element_sdv_logo() draws the axis labels with ggpath. The alternative, scale_x_sdv() with theme_x_sdv(), writes them as HTML <img> tags for ggtext; the college football tutorial uses it on the y axis.

3. Division standings table

Records come from the schedule (nflreadr::load_schedules()), divisions and nicknames from nflreadr::load_teams(). gt_sdv_logos() turns the abbreviation column into logos, data_color() shades the point differential and gt_theme_sdv() gives the table the SportsDataverse look.

schedule <- nflreadr::load_schedules(season)

# One row per team per regular-season game
games <- schedule |>
  filter(game_type == "REG", !is.na(result)) |>
  nflreadr::clean_homeaway() |>
  select(week, team, opponent, team_score, opponent_score)

nfl_teams <- nflreadr::load_teams() |>
  select(team = team_abbr, name = team_name, nick = team_nick, division = team_division)

standings <- games |>
  group_by(team) |>
  summarise(
    W = sum(team_score > opponent_score),
    L = sum(team_score < opponent_score),
    T = sum(team_score == opponent_score),
    PF = sum(team_score),
    PA = sum(opponent_score),
    .groups = "drop"
  ) |>
  mutate(Diff = PF - PA, pct = (W + T / 2) / (W + L + T)) |>
  inner_join(nfl_teams, by = "team") |>
  # win percentage, then point differential: not the NFL's tiebreakers
  arrange(division, desc(pct), desc(Diff), team) |>
  select(division, logo = team, nick, W, L, T, PF, PA, Diff)

limit <- max(abs(standings$Diff)) # a color scale centered on zero

standings |>
  gt(groupname_col = "division") |>
  gt_sdv_logos(columns = "logo", league = "nfl", height = 22) |>
  cols_label(logo = "", nick = "Team") |>
  data_color(
    columns = "Diff",
    palette = c("#b2182b", "#f7f7f7", "#1b7837"),
    domain = c(-limit, limit)
  ) |>
  tab_header(title = "2025 NFL standings", subtitle = "Regular season, by division") |>
  tab_source_note(caption) |>
  gt_theme_sdv(density = "compact")

	Team	W	L	T	PF	PA	Diff
2025 NFL standings
Regular season, by division
AFC East
	Patriots	14	3	0	490	320	170
	Bills	12	5	0	481	365	116
	Dolphins	7	10	0	347	424	-77
	Jets	3	14	0	300	503	-203
AFC North
	Steelers	10	7	0	397	387	10
	Ravens	8	9	0	424	398	26
	Bengals	6	11	0	414	492	-78
	Browns	5	12	0	279	379	-100
AFC South
	Jaguars	13	4	0	474	336	138
	Texans	12	5	0	404	295	109
	Colts	8	9	0	466	412	54
	Titans	3	14	0	284	478	-194
AFC West
	Broncos	14	3	0	401	311	90
	Chargers	11	6	0	368	340	28
	Chiefs	6	11	0	362	328	34
	Raiders	3	14	0	241	432	-191
NFC East
	Eagles	11	6	0	379	325	54
	Cowboys	7	9	1	471	511	-40
	Commanders	5	12	0	356	451	-95
	Giants	4	13	0	381	439	-58
NFC North
	Bears	11	6	0	441	415	26
	Packers	9	7	1	391	360	31
	Lions	9	8	0	481	413	68
	Vikings	9	8	0	344	333	11
NFC South
	Buccaneers	8	9	0	380	411	-31
	Falcons	8	9	0	353	401	-48
	Panthers	8	9	0	311	380	-69
	Saints	6	11	0	306	383	-77
NFC West
	Seahawks	14	3	0	483	292	191
	Rams	12	5	0	518	346	172
	49ers	12	5	0	437	371	66
	Cardinals	3	14	0	355	488	-133
Data: nflverse via nflreadr \| 2025 regular season

4. Small multiples by division

Each team’s running point differential through the season, one panel per division. scale_color_sdv() colors each line by its team, and a second geom_sdv_logos() layer, given only each team’s last week, puts the logo just past the end of its line. Because that layer’s data carries the division column too, each logo lands in its own panel.

running <- games |>
  arrange(team, week) |>
  group_by(team) |>
  mutate(diff = cumsum(team_score - opponent_score)) |>
  ungroup() |>
  inner_join(select(nfl_teams, team, division), by = "team")

ends <- running |>
  group_by(team) |>
  slice_max(week, n = 1) |>
  ungroup()

ggplot(running, aes(x = week, y = diff, color = team)) +
  geom_hline(yintercept = 0, color = "grey70", linewidth = 0.3) +
  geom_line(linewidth = 0.8) +
  geom_sdv_logos(
    aes(x = week + 1.4, y = diff, team = team),
    data = ends, league = "nfl", width = 0.12, inherit.aes = FALSE
  ) +
  scale_color_sdv(league = "nfl") +
  scale_x_continuous(breaks = c(1, 6, 12, 18), limits = c(1, 20)) +
  facet_wrap(~division, ncol = 4) +
  labs(
    title = "Running point differential by division, 2025",
    x = "Week",
    y = "Point differential",
    caption = caption
  ) +
  theme(legend.position = "none", panel.grid.minor = element_blank())

Eight small line charts, one per NFL division, each tracing its four teams' running point differential by week through the 2025 regular season in team colors, with each team's logo at the end of its line.

5. A win probability chart with a logo in the title

nflfastR’s home_wp traced through the Super Bowl. ggtitle_image() puts the winner’s logo beside the title (with theme_title_image(), which renders the title as markdown and centres the logo on the words). The fills are the teams’ colors from sdv_team_colors(); Seattle and New England share the same navy, so when the two primaries match the away team switches to its secondary color.

sb <- schedule |> filter(game_type == "SB")
away <- sb$away_team
home <- sb$home_team
winner <- if (sb$away_score > sb$home_score) away else home

wp <- pbp |>
  filter(game_id == sb$game_id, !is.na(home_wp)) |>
  transmute(minute = (3600 - game_seconds_remaining) / 60, away_wp = 1 - home_wp)

away_color <- sdv_team_colors("nfl", away)
home_color <- sdv_team_colors("nfl", home)
if (away_color == home_color) away_color <- sdv_team_colors("nfl", away, which = "secondary")

nick <- function(abbr) nfl_teams$nick[match(abbr, nfl_teams$team)]
title <- sprintf(
  "Super Bowl %s: %s %d, %s %d",
  utils::as.roman(season - 1965), nick(away), sb$away_score, nick(home), sb$home_score
)

ggplot(wp, aes(x = minute)) +
  geom_ribbon(aes(ymin = 0.5, ymax = pmax(away_wp, 0.5)), fill = away_color, alpha = 0.85) +
  geom_ribbon(aes(ymin = pmin(away_wp, 0.5), ymax = 0.5), fill = home_color, alpha = 0.85) +
  geom_line(aes(y = away_wp), linewidth = 0.4) +
  geom_hline(yintercept = 0.5, color = "grey40", linewidth = 0.3) +
  scale_x_continuous(breaks = c(0, 15, 30, 45, 60), limits = c(0, 60), expand = c(0, 0)) +
  scale_y_continuous(labels = scales::percent, limits = c(0, 1)) +
  ggtitle_image(
    title_image = winner,
    title = title,
    subtitle = "nflfastR win probability, by minutes played",
    image_height = 30,
    league = "nfl"
  ) +
  labs(
    x = "Minutes played",
    y = paste(nick(away), "win probability"),
    caption = "Data: nflverse via nflreadr"
  ) +
  theme_title_image(size = 15, face = "bold")

Area chart of Seattle's win probability through Super Bowl LX by minutes played, shaded in Seattle green above 50 percent and New England navy below, with the Seahawks logo beside the title.

6. A quarterback leaderboard with headshots

nflverse identifies players by GSIS id; the id column of the play-by-play is the quarterback on each dropback, scrambles included. geom_sdv_headshots() takes those ids and draws each player’s NFL.com headshot. The full names come from nflreadr::load_players(), and the bars take each quarterback’s team color.

players <- nflreadr::load_players() |>
  select(id = gsis_id, player = display_name)

qbs <- pbp |>
  filter(season_type == "REG", qb_dropback == 1, !is.na(epa), !is.na(id)) |>
  arrange(game_id, play_id) |>
  group_by(id) |>
  summarise(
    team = last(posteam), # the team he finished the season with
    dropbacks = n(),
    epa = mean(epa),
    success = mean(success),
    cpoe = mean(cpoe, na.rm = TRUE),
    .groups = "drop"
  ) |>
  filter(dropbacks >= 300) |>
  left_join(players, by = "id") |>
  arrange(desc(epa), id)

top_qbs <- head(qbs, 16)

ggplot(top_qbs, aes(x = epa, y = reorder(player, epa))) +
  geom_col(aes(fill = team), width = 0.7) +
  geom_sdv_headshots(aes(x = epa + 0.022, player_id = id), league = "nfl", id_system = "gsis", height = 0.058) +
  scale_fill_sdv(league = "nfl") +
  scale_x_continuous(expand = expansion(mult = c(0, 0.08))) +
  labs(
    title = "Top 16 quarterbacks by EPA per dropback, 2025",
    subtitle = "Regular season, 300+ dropbacks",
    x = "EPA per dropback",
    y = NULL,
    caption = caption
  ) +
  theme(legend.position = "none", panel.grid.major.y = element_blank())

Horizontal bar chart of the 16 NFL quarterbacks with the highest EPA per dropback in the 2025 regular season (300 or more dropbacks), each bar in the quarterback's team color with his headshot at the end of the bar.

7. The same leaderboard as a table

gt_sdv_headshots() is the table version: it turns a column of GSIS ids into headshots, next to gt_sdv_logos() for the team. This one is styled with gt_theme_athletic().

top_qbs |>
  head(12) |>
  mutate(rank = row_number(), headshot = id) |>
  select(rank, headshot, player, team, dropbacks, epa, success, cpoe) |>
  gt() |>
  gt_sdv_headshots(columns = "headshot", league = "nfl", id_system = "gsis", height = 36) |>
  gt_sdv_logos(columns = "team", league = "nfl", height = 24) |>
  fmt_number(columns = "epa", decimals = 3) |>
  fmt_percent(columns = "success", decimals = 1) |>
  fmt_number(columns = "cpoe", decimals = 1, force_sign = TRUE) |>
  data_color(columns = "epa", palette = c("#f7f7f7", "#1b7837")) |>
  cols_label(
    rank = "#", headshot = "", player = "Quarterback", team = "Team",
    dropbacks = "Dropbacks", epa = "EPA/db", success = "Success", cpoe = "CPOE"
  ) |>
  tab_header(
    title = "The 2025 quarterback leaderboard",
    subtitle = "EPA per dropback, regular season, 300+ dropbacks"
  ) |>
  tab_source_note(caption) |>
  gt_theme_athletic(density = "compact")

#	Quarterback	Dropbacks	EPA/db	Success	CPOE
The 2025 quarterback leaderboard
EPA per dropback, regular season, 300+ dropbacks
1	Drake Maye	602	0.318	55.3%	+10.8
2	Jordan Love	485	0.262	50.5%	+5.5
3	Matthew Stafford	624	0.223	52.7%	+1.5
4	Brock Purdy	316	0.202	53.2%	+7.2
5	Jared Goff	619	0.177	48.6%	+1.8
6	Dak Prescott	657	0.173	48.7%	+2.2
7	Patrick Mahomes	589	0.168	49.9%	+0.3
8	Josh Allen	554	0.161	48.4%	+3.5
9	Daniel Jones	431	0.151	50.3%	+2.3
10	Sam Darnold	516	0.134	52.5%	+5.2
11	Mac Jones	312	0.130	52.2%	+3.7
12	Bo Nix	674	0.118	45.1%	−1.2
Data: nflverse via nflreadr \| 2025 regular season

8. Relocated franchises and their eras

Schedules use the abbreviation a team had that season: OAK until 2019, SD until 2016, STL until 2015. sdv_resolve() maps each to today’s franchise, and every other spelling too, so other sources’ LAR and a full name become nflverse’s LA team as well:

nfl_index <- sdv_teams("nfl")

data.frame(abbr = c("OAK", "SD", "STL", "LAR", "Los Angeles Rams")) |>
  mutate(
    team_id = sdv_resolve(abbr, league = "nfl"),
    current = nfl_index$abbr[match(team_id, nfl_index$team_id)]
  )
#>               abbr team_id current
#> 1              OAK      13      LV
#> 2               SD      24     LAC
#> 3              STL      14      LA
#> 4              LAR      14      LA
#> 5 Los Angeles Rams      14      LA

Grouping on that franchise keeps each franchise together across its move. The logos take the season’s own abbreviation plus a season aesthetic. sdvplotR keeps the marks of relocated NFL identities, so the STL and SD seasons draw the St. Louis Rams’ and San Diego Chargers’ logos; every season of a current identity draws today’s logo, including the Rams’ and Chargers’ first years in Los Angeles before their 2020 redesigns. (The Raiders kept their shield.)

history <- nflreadr::load_schedules(2012:season) |>
  filter(game_type == "REG") |>
  nflreadr::clean_homeaway() |>
  filter(team %in% c("OAK", "LV", "SD", "LAC", "STL", "LA"))

wins <- history |>
  group_by(season, team) |>
  summarise(wins = sum(team_score > opponent_score), .groups = "drop") |>
  mutate(franchise = nfl_index$abbr[match(sdv_resolve(team, league = "nfl"), nfl_index$team_id)])

moves <- data.frame(
  franchise = c("LV", "LAC", "LA"),
  moved = c(2020, 2017, 2016) # the first season in the new city
) |>
  mutate(label = paste0(nick(franchise), " move"))

ggplot(wins, aes(x = season, y = wins)) +
  geom_vline(aes(xintercept = moved - 0.5), data = moves, linetype = "dashed", color = "grey50") +
  geom_line(aes(color = franchise), linewidth = 0.8) +
  geom_sdv_logos(aes(team = team, season = season), league = "nfl", height = 0.22) +
  geom_text(
    aes(x = moved - 0.6, y = 16.5, label = label),
    data = moves, hjust = 1, vjust = 1, size = 3, color = "grey40"
  ) +
  scale_color_sdv(league = "nfl") +
  scale_x_continuous(breaks = seq(2012, season, 2)) +
  scale_y_continuous(limits = c(-1, 17), breaks = c(0, 4, 8, 12, 16)) +
  facet_wrap(~franchise, ncol = 1, labeller = as_labeller(\(x) nick(x))) +
  labs(
    title = "Three relocated franchises, 2012-2025",
    subtitle = "Regular-season wins; St. Louis and San Diego seasons in those identities' logos",
    x = NULL,
    y = "Wins",
    caption = "Data: nflverse via nflreadr | regular season"
  ) +
  theme(legend.position = "none", panel.grid.minor = element_blank())

Three line charts, one each for the Rams, Chargers and Raiders, of regular-season wins from 2012 to 2025, each season drawn as a team logo, with the St. Louis Rams and San Diego Chargers logos before their moves and a dashed line at the season each franchise moved.

9. A field in team colors

sdv_surface("nfl", team) draws a regulation field with sportyR, its end zones in the team’s colors, and center_logo = TRUE puts the logo at midfield. sportyR’s field is centered on midfield in yards, so a line of scrimmage yardline_100 yards from the goal is at x = 50 - yardline_100. On top: every Seattle touchdown from scrimmage, from the line of scrimmage into the end zone, placed by the side of the field the play went to.

tds <- pbp |>
  filter(
    season_type == "REG", posteam == "SEA", td_team == "SEA",
    play_type %in% c("pass", "run")
  ) |>
  arrange(game_id, play_id) |>
  mutate(side = coalesce(pass_location, run_location))

set.seed(1) # the jitter that keeps arrows in one lane apart
lane <- c(left = 15, middle = 0, right = -15)
tds <- tds |>
  mutate(
    y = coalesce(lane[side], 0) + runif(n(), -6, 6),
    x = 50 - yardline_100
  )

sdv_surface("nfl", "SEA", center_logo = TRUE, display_range = "in_bounds_only") +
  geom_segment(
    aes(x = x, xend = 53, y = y, yend = y, color = play_type),
    data = tds,
    arrow = arrow(length = unit(0.12, "cm")), linewidth = 0.6
  ) +
  scale_color_manual(
    values = c(pass = unname(sdv_team_colors("nfl", "SEA", which = "secondary")), run = "white")
  ) +
  labs(
    title = sprintf("Seattle's %d touchdowns from scrimmage, 2025", nrow(tds)),
    subtitle = "From the line of scrimmage into the end zone: green arrows are passes, white arrows runs",
    caption = caption
  ) +
  theme_void() +
  theme(
    plot.title = element_text(face = "bold"),
    plot.caption = element_text(color = "grey45", size = 8),
    plot.margin = margin(8, 8, 8, 8),
    legend.position = "none"
  )

An NFL field with Seattle-colored end zones and the Seahawks logo at midfield, with one arrow per Seattle touchdown from scrimmage in the 2025 regular season running from the line of scrimmage into the right end zone, green for passes and white for runs.

10. Team tiers

A tier list from net EPA per play (offense EPA per play minus defense EPA per play allowed), drawn by sdv_team_tiers(). It needs only tier_no and team; the order within each tier is the order of the rows.

sizes <- c(5, 7, 7, 7, 6) # teams per tier, top to bottom

tiers <- teams |>
  mutate(net = off_epa - def_epa) |>
  arrange(desc(net), team) |>
  mutate(tier_no = rep(seq_along(sizes), sizes)) |>
  select(tier_no, team)

sdv_team_tiers(
  tiers,
  league = "nfl",
  title = "NFL tiers by net EPA per play, 2025",
  subtitle = "Offense EPA per play minus defense EPA per play allowed",
  caption = caption,
  tier_desc = c(
    "1" = "Contenders",
    "2" = "Good",
    "3" = "Middle",
    "4" = "Flawed",
    "5" = "Rebuilding"
  )
)

NFL team logos in five labeled tiers, from Contenders to Rebuilding, ranked by net EPA per play in the 2025 regular season, best teams first within each tier.

11. Make it interactive

The offense-vs-defense chart from section 1 again, built with native plotly. A transparent marker under each team carries the hover text, and add_sdv_logos() places the logos as layout images at the same coordinates. Hover a logo for the exact EPA per play on each side of the ball, the success rates behind it and the net, numbers the static chart can only place.

nfl_index <- sdv_teams("nfl")
epa_hover <- teams |>
  mutate(
    name = nfl_index$name[match(sdv_resolve(team, league = "nfl"), nfl_index$team_id)],
    tip = sprintf(
      "<b>%s</b><br>Offense: %+.3f EPA/play, %.1f%% success<br>Defense: %+.3f EPA/play allowed, %.1f%% success allowed<br>Net: %+.3f",
      name, off_epa, 100 * off_sr, def_epa, 100 * def_sr, off_epa - def_epa
    )
  )
pad <- 0.03
avg_line <- list(color = "grey", dash = "dash", width = 1)

plotly::plot_ly(
  epa_hover, x = ~off_epa, y = ~def_epa, type = "scatter", mode = "markers",
  marker = list(size = 30, opacity = 0), text = ~tip, hoverinfo = "text"
) |>
  plotly::layout(
    title = list(text = "NFL offense vs defense, 2025", x = 0.02),
    xaxis = list(title = "Offense EPA per play", range = range(epa_hover$off_epa) + c(-pad, pad), zeroline = FALSE),
    yaxis = list(
      title = "Defense EPA per play allowed (better is up)", zeroline = FALSE,
      range = rev(range(epa_hover$def_epa) + c(-pad, pad))
    ),
    shapes = list(
      list(type = "line", x0 = mean(epa_hover$off_epa), x1 = mean(epa_hover$off_epa), yref = "paper", y0 = 0, y1 = 1, line = avg_line),
      list(type = "line", y0 = mean(epa_hover$def_epa), y1 = mean(epa_hover$def_epa), xref = "paper", x0 = 0, x1 = 1, line = avg_line)
    )
  ) |>
  add_sdv_logos(epa_hover$off_epa, epa_hover$def_epa, epa_hover$team, "nfl", size = 0.08)

A range given high to low reverses the axis, so good defenses sit at the top as in section 1. plotly loads each logo from its URL rather than embedding it, so the page stays light. Interactive charts covers plotly, ggiraph and leaflet, and the Interactive NFL EPA Scatter recipe builds this chart up step by step.

Next steps

nflfastR documents every play-by-play column used here, and nflreadr the other loaders.
The college football tutorial covers 130+ teams, conference logos and logos on the y axis.
Social Posting sizes and saves charts like these for social media.

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