MLB Visualizations with baseballr and sdvplotR

On this page

Ten charts and tables from the 2026 MLB regular season: run differential in team colors, division standings, Pythagorean luck, team and player Statcast numbers, home run leaders with their headshots, a spray chart on the field, the division races week by week, the franchises that moved, and the postseason field as a tier list, then the Pythagorean chart as an interactive one. The data comes from two public sources through baseballr: the MLB Stats API and Baseball Savant. Neither needs a key.

library(sdvplotR)
library(ggplot2)
library(dplyr)
library(gt)

Load the season

Standings, every regular-season result, the home run leaders and the name and abbreviation of nine relocated franchises in every season since 1901, from the MLB Stats API:

season <- 2026 # the regular season ended on September 27

teams <- baseballr::mlb_teams(season = season, sport_ids = 1) |>
  select(team_id, abbreviation = team_abbreviation, team_name = team_full_name, division = division_name)

standings <- baseballr::mlb_standings(season = season, league_id = "103,104") |>
  transmute(
    team_id = team_records_team_id,
    rank = as.integer(team_records_division_rank),
    w = team_records_wins,
    l = team_records_losses,
    gb = team_records_games_back,
    wc_gb = team_records_wild_card_games_back,
    rs = team_records_runs_scored,
    ra = team_records_runs_allowed,
    diff = team_records_run_differential,
    strk = team_records_streak_streak_code,
    clinch = team_records_clinch_indicator
  ) |>
  inner_join(teams, by = "team_id")

games <- baseballr::mlb_schedule(season = season, level_ids = "1") |>
  filter(game_type == "R", status_detailed_state %in% c("Final", "Completed Early")) |>
  transmute(
    game_pk,
    date = as.Date(official_date),
    home_id = teams_home_team_id,
    away_id = teams_away_team_id,
    home_score = teams_home_score,
    away_score = teams_away_score
  )

hitters <- baseballr::mlb_stats(
  stat_type = "season", stat_group = "hitting", season = season, player_pool = "All",
  sort_stat = "homeRuns", order = "desc", limit = 40
) |>
  select(player_id, player = player_full_name, team_id, games_played, home_runs)

# Athletics, Braves, Dodgers, Giants, Orioles, Twins, Rangers, Nationals, Brewers
moved <- c(133, 144, 119, 137, 110, 142, 140, 120, 158)
identities <- lapply(1901:season, \(s) baseballr::mlb_teams(season = s, sport_ids = 1)) |>
  bind_rows() |>
  filter(team_id %in% moved) |>
  select(team_id, season, name = team_full_name, abbreviation = team_abbreviation)

Team expected statistics, every qualified hitter’s quality of contact and the home run leaders’ batted balls, from Baseball Savant:

team_xstats <- baseballr::statcast_leaderboards(
  leaderboard = "expected_statistics", year = season, player_type = "batter-team"
) |>
  select(team_id, woba, est_woba)

contact <- baseballr::statcast_leaderboards(
  leaderboard = "exit_velocity_barrels", year = season, player_type = "batter"
) |>
  select(player_id, name = `last_name, first_name`, attempts, avg_hit_speed, brl_percent)

# every home run the season's leader(s) hit, with Statcast's hit coordinates
leaders <- hitters$player_id[hitters$home_runs == max(hitters$home_runs)]
homers <- lapply(leaders, \(id) {
  baseballr::statcast_search_batters(paste0(season, "-03-01"), paste0(season, "-10-01"), batterid = id)
}) |>
  bind_rows() |>
  filter(events == "home_run", game_type == "R") |>
  select(batter, game_date, hc_x, hc_y, hit_distance_sc, launch_speed)

The output below uses a snapshot of these calls taken on October 05, 2026 (baseballr 2.0.0), because neither API is called when this site is built; data-raw/article_fixtures.R takes it with the same code.

The Stats API and Savant write a few team abbreviations differently from sdvplotR’s team index. sdv_resolve() converts them once, at the start, to the index’s team ids, so every later join and color scale agrees:

standings <- standings |>
  mutate(team = sdv_resolve(abbreviation, league = "mlb"), pct = w / (w + l))

standings |>
  mutate(sdv_abbr = sdv_teams("mlb")$abbr[match(team, sdv_teams("mlb")$team_id)]) |>
  filter(abbreviation != sdv_abbr) |>
  select(team_name, abbreviation, sdv_abbr)
#>              team_name abbreviation sdv_abbr
#> 1    Chicago White Sox          CWS      CHW
#> 2 Arizona Diamondbacks           AZ      ARI

stats_api <- "Data: MLB Stats API via baseballr | Viz: sdvplotR"
savant <- "Data: Baseball Savant via baseballr | Viz: sdvplotR"
# baseball writes rates without the leading zero: .412
rate <- function(x) sub("^(-?)0\\.", "\\1.", sprintf("%.3f", x))

1. Run differential in team colors

scale_fill_sdv() fills each bar with its team’s color, and scale_x_sdv() with theme_x_sdv() puts the logos on the axis in place of the abbreviations.

run_diff <- standings |>
  arrange(desc(diff), team) |>
  mutate(team = factor(team, levels = team))

ggplot(run_diff, aes(team, diff, fill = team)) +
  geom_col(width = 0.75) +
  geom_hline(yintercept = 0, colour = "grey20", linewidth = 0.4) +
  scale_fill_sdv(league = "mlb") +
  scale_x_sdv(league = "mlb", size = 20) +
  labs(
    title = paste(season, "MLB run differential"),
    subtitle = "Regular season, runs scored minus runs allowed",
    x = NULL,
    y = "Run differential",
    caption = stats_api
  ) +
  theme_minimal(base_size = 12) +
  theme_x_sdv() +
  theme(
    legend.position = "none",
    panel.grid.major.x = element_blank(),
    plot.title = element_text(face = "bold")
  )

Bar chart of the 30 MLB teams' 2026 regular-season run differential, sorted from best to worst, each bar in its team's color with the team logo under it on the horizontal axis.

2. Division standings table

A gt table grouped by division: gt_sdv_logos() turns the abbreviation column into logos and gt_theme_savant() gives it the Baseball Savant look. The run differential fill goes on after the theme, so the theme’s styling cannot replace it.

divisions <- paste(
  rep(c("American League", "National League"), each = 3),
  c("East", "Central", "West")
)
lim <- max(abs(standings$diff))

standings |>
  mutate(division = factor(division, levels = divisions), logo = team) |>
  arrange(division, rank) |>
  select(division, logo, team_name, w, l, pct, gb, rs, ra, diff, strk) |>
  gt(groupname_col = "division") |>
  gt_sdv_logos(columns = "logo", league = "mlb", height = 24) |>
  fmt(columns = pct, fns = rate) |>
  cols_label(
    logo = "", team_name = "Team", w = "W", l = "L", pct = "Pct", gb = "GB",
    rs = "RS", ra = "RA", diff = "Diff", strk = "Strk"
  ) |>
  tab_header(
    title = paste(season, "MLB standings"),
    subtitle = "Final regular-season standings by division"
  ) |>
  tab_source_note(stats_api) |>
  gt_theme_savant() |>
  data_color(columns = diff, palette = c("#c84630", "white", "#2a7ab9"), domain = c(-lim, lim))

	Team	W	L	Pct	GB	RS	RA	Diff	Strk
2026 MLB standings
Final regular-season standings by division
American League East
	Tampa Bay Rays	98	64	.605	-	736	650	86	L1
	New York Yankees	93	68	.578	4.5	739	601	138	W1
	Boston Red Sox	87	75	.537	11.0	689	611	78	L1
	Baltimore Orioles	79	82	.491	18.5	718	739	-21	L1
	Toronto Blue Jays	79	83	.488	19.0	648	694	-46	W1
American League Central
	Cleveland Guardians	85	77	.525	-	678	667	11	L1
	Chicago White Sox	84	78	.519	1.0	776	720	56	W1
	Minnesota Twins	77	85	.475	8.0	739	797	-58	W1
	Detroit Tigers	76	86	.469	9.0	723	652	71	L1
	Kansas City Royals	69	93	.426	16.0	690	810	-120	W1
American League West
	Houston Astros	81	81	.500	-	734	766	-32	W2
	Texas Rangers	80	82	.494	1.0	673	719	-46	L1
	Seattle Mariners	76	86	.469	5.0	664	722	-58	W2
	Athletics	64	98	.395	17.0	699	937	-238	L2
	Los Angeles Angels	62	100	.383	19.0	655	752	-97	L2
National League East
	Atlanta Braves	94	68	.580	-	742	626	116	L1
	Philadelphia Phillies	88	74	.543	6.0	713	698	15	W1
	Miami Marlins	80	82	.494	14.0	715	712	3	W1
	Washington Nationals	77	85	.475	17.0	821	815	6	W1
	New York Mets	74	88	.457	20.0	699	731	-32	L1
National League Central
	Milwaukee Brewers	103	59	.636	-	832	618	214	W5
	Chicago Cubs	89	73	.549	14.0	850	703	147	W1
	Pittsburgh Pirates	82	80	.506	21.0	767	739	28	W1
	St. Louis Cardinals	77	85	.475	26.0	720	749	-29	L4
	Cincinnati Reds	75	87	.463	28.0	666	826	-160	L1
National League West
	Los Angeles Dodgers	100	62	.617	-	801	600	201	W3
	San Diego Padres	91	71	.562	9.0	722	681	41	W2
	Arizona Diamondbacks	86	76	.531	14.0	739	730	9	L2
	San Francisco Giants	65	97	.401	35.0	669	760	-91	L5
	Colorado Rockies	58	104	.358	42.0	752	944	-192	L1
Data: MLB Stats API via baseballr \| Viz: sdvplotR

3. Pythagorean wins: who beat their run differential

Expected wins from runs scored and allowed (Bill James’s formula with the 1.83 exponent) against actual wins. Teams above the dashed line won more games than their runs suggest.

pyth <- standings |>
  mutate(xw = rs^1.83 / (rs^1.83 + ra^1.83) * (w + l), luck = w - xw)
lims <- range(pyth$xw, pyth$w) + c(-3, 3)

ggplot(pyth, aes(xw, w)) +
  geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = "grey60") +
  annotate(
    "text", x = lims[1] + 1, y = lims[2] - 1, hjust = 0, vjust = 1, size = 3.6,
    colour = "grey35", label = "Won more than\ntheir runs suggest"
  ) +
  annotate(
    "text", x = lims[2] - 1, y = lims[1] + 1, hjust = 1, vjust = 0, size = 3.6,
    colour = "grey35", label = "Won fewer"
  ) +
  geom_sdv_logos(aes(team = team), league = "mlb", width = 0.065) +
  coord_equal(xlim = lims, ylim = lims) +
  labs(
    title = paste(season, "MLB: actual against expected wins"),
    subtitle = "Expected wins from runs scored and allowed (Pythagorean, exponent 1.83)",
    x = "Expected wins",
    y = "Actual wins",
    caption = stats_api
  ) +
  theme_minimal(base_size = 12) +
  theme(plot.title = element_text(face = "bold"))

Scatter plot of the 30 MLB teams in 2026, each drawn as its logo, placed by Pythagorean expected wins (horizontal) and actual wins (vertical), with a dashed line where the two are equal.

The Cincinnati Reds won 9.8 games more than their run differential predicts; the Detroit Tigers won 12.6 fewer.

4. Statcast: team wOBA against expected wOBA

Baseball Savant’s expected statistics at the team level. Expected wOBA (xwOBA) grades every batted ball by its exit velocity and launch angle, so a team above the line got more from its contact than the contact’s quality predicts. Savant’s team_id column holds abbreviations, which sdv_resolve() converts like any other.

xstats <- team_xstats |>
  mutate(team = sdv_resolve(team_id, league = "mlb"))
lims <- range(xstats$woba, xstats$est_woba) + c(-0.004, 0.004)

ggplot(xstats, aes(est_woba, woba)) +
  geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = "grey60") +
  geom_sdv_logos(aes(team = team), league = "mlb", width = 0.055) +
  scale_x_continuous(labels = rate) +
  scale_y_continuous(labels = rate) +
  coord_equal(xlim = lims, ylim = lims) +
  labs(
    title = paste(season, "team offense: wOBA against expected wOBA"),
    subtitle = "Above the line: more production than the quality of contact predicts",
    x = "Expected wOBA (xwOBA)",
    y = "wOBA",
    caption = savant
  ) +
  theme_minimal(base_size = 12) +
  theme(plot.title = element_text(face = "bold"))

Scatter plot of the 30 MLB team offenses in 2026, each drawn as its logo, placed by expected wOBA (horizontal) and actual wOBA (vertical), with a dashed line where the two are equal.

5. Home run leaders with headshots

The Stats API and Savant identify players by their MLBAM ID, which is why the headshots pass id_system = "league": it draws MLBAM IDs from MLB’s own image CDN. Leave id_system unset for ESPN athlete IDs, such as those in baseballr’s espn_mlb_*() data. Both are plain numbers, so the wrong setting draws someone else or nothing. Everyone tied for tenth makes the list.

hr_leaders <- hitters |>
  filter(min_rank(desc(home_runs)) <= 10) |>
  inner_join(select(standings, team_id, team), by = "team_id") |>
  arrange(home_runs, desc(player)) |>
  mutate(player = factor(player, levels = player))

ggplot(hr_leaders, aes(home_runs, player)) +
  geom_col(aes(fill = team), width = 0.72) +
  geom_sdv_logos(aes(x = -3, team = team), league = "mlb", height = 0.065) +
  geom_sdv_headshots(
    aes(x = home_runs + 3.2, player_id = player_id),
    league = "mlb", id_system = "league", height = 0.085
  ) +
  geom_text(aes(x = home_runs + 6.6, label = home_runs), hjust = 0, fontface = "bold", size = 4) +
  scale_fill_sdv(league = "mlb") +
  scale_x_continuous(limits = c(-6, max(hr_leaders$home_runs) + 10), expand = expansion(0)) +
  labs(
    title = paste(season, "MLB home run leaders"),
    subtitle = "Regular season",
    x = "Home runs",
    y = NULL,
    caption = stats_api
  ) +
  theme_minimal(base_size = 12) +
  theme(
    legend.position = "none",
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),
    plot.title = element_text(face = "bold")
  )

Horizontal bar chart of the 2026 MLB home run leaders, each bar in the player's team color, with the team logo at the left, the player's headshot at the end of the bar and the home run total beside it.

6. A spray chart on the field

sdv_surface("mlb") draws a regulation infield with ‘sportyR’, and the data goes on top as ordinary layers. Statcast’s hit coordinates become feet from home plate with the usual transform (baseballr’s mlbam_xy_transformation() uses the same one); xlims and ylims pass through to ‘sportyR’ to show the outfield. The two co-leaders get a panel each.

spray <- homers |>
  mutate(x = 2.5 * (hc_x - 125.42), y = 2.5 * (198.27 - hc_y)) |>
  filter(!is.na(x)) |>
  inner_join(select(hitters, batter = player_id, player, team_id, home_runs), by = "batter") |>
  inner_join(select(standings, team_id, team), by = "team_id") |>
  mutate(player = paste0(player, " (", home_runs, " HR)"))
arc <- data.frame(t = seq(-pi / 4, pi / 4, length.out = 50)) |>
  mutate(x = 400 * sin(t), y = 400 * cos(t))

sdv_surface("mlb", xlims = c(-330, 330), ylims = c(-20, 480)) +
  geom_path(aes(x, y), data = arc, linetype = "dashed", colour = "white", alpha = 0.7) +
  annotate("text", x = 0, y = 390, label = "400 ft", colour = "white", size = 3, vjust = 1) +
  geom_point(
    aes(x, y, fill = hit_distance_sc),
    data = spray, shape = 21, size = 2.6, colour = "grey15", stroke = 0.3
  ) +
  geom_sdv_logos(
    aes(x = -265, y = 430, team = team),
    data = distinct(spray, player, team), league = "mlb", width = 0.13
  ) +
  scale_fill_distiller(palette = "YlOrRd", direction = 1, name = "Distance (ft)") +
  facet_wrap(~player) +
  labs(
    title = paste("Every home run by the", season, "MLB co-leaders"),
    subtitle = "Regular season, placed where Statcast tracked each one landing",
    caption = "Data: Baseball Savant and MLB Stats API via baseballr | Viz: sdvplotR"
  ) +
  # sportyR paints the whole figure as the field, so the text goes white
  theme(
    plot.title = element_text(face = "bold", colour = "white"),
    plot.subtitle = element_text(colour = "white"),
    plot.caption = element_text(colour = "white"),
    strip.text = element_text(face = "bold", size = 12),
    legend.position = "bottom"
  )

Two baseball fields side by side, one per 2026 MLB home run co-leader, with a dot for every home run the player hit placed where it landed, colored by distance, and the player's team logo in the corner.

7. Quality of contact: exit velocity and barrels

Every qualified hitter from Savant’s exit velocity leaderboard as a grey point, and the five highest barrel rates as headshots (a tie for fifth makes it six hitters). A barrel is a batted ball with the exit velocity and launch angle that produce at least a .500 batting average and 1.500 slugging. The names sit beside the headshots, pushed apart vertically by ggrepel.

contact <- contact |>
  mutate(player = paste(sub(".*, ", "", name), sub(",.*", "", name))) # "Ohtani, Shohei"
barrels <- contact |>
  filter(min_rank(desc(brl_percent)) <= 5) |>
  # names to the left of the headshots, except at the right edge
  mutate(side = if_else(avg_hit_speed > 94.5, 1, -1))
name_labels <- function(side) {
  ggrepel::geom_text_repel(
    aes(label = player),
    data = filter(barrels, side == !!side), size = 3.3, point.size = 13, box.padding = 0.4,
    nudge_x = 0.9 * side, direction = "y", hjust = if (side > 0) 0 else 1,
    min.segment.length = 0, bg.colour = "white", bg.r = 0.15, seed = 1
  )
}

ggplot(contact, aes(avg_hit_speed, brl_percent)) +
  geom_point(colour = "grey70", size = 1.6) +
  geom_sdv_headshots(
    aes(player_id = player_id),
    data = barrels, league = "mlb", id_system = "league", height = 0.1
  ) +
  name_labels(-1) +
  name_labels(1) +
  scale_x_continuous(expand = expansion(add = c(0.3, 2))) +
  labs(
    title = paste("Who hit the ball hardest in", season),
    subtitle = paste(nrow(contact), "qualified hitters; the five highest barrel rates as headshots"),
    x = "Average exit velocity (mph)",
    y = "Barrels per batted ball (%)",
    caption = savant
  ) +
  theme_minimal(base_size = 12) +
  theme(plot.title = element_text(face = "bold"))

Scatter plot of qualified MLB hitters in 2026 by average exit velocity (horizontal) and barrels per batted ball (vertical); most are grey points, and the hitters with the five highest barrel rates are drawn as headshots with their names beside them.

8. The division races, week by week

A bump chart: each team’s place in its division at the end of every week, from every regular-season result. A suspended game is listed on the day it started and the day it ended; keeping the last date counts it once. The final week matches the official standings.

Division rivals often share a color (four of the five AL Central clubs are navy or black), so the lines take their colors from sdv_team_colors(): each team keeps its primary color unless that is too close to a color already used in its division, and then takes its secondary.

games <- slice_max(games, date, by = game_pk)
results <- bind_rows(
  transmute(games, date, team_id = home_id, win = home_score > away_score),
  transmute(games, date, team_id = away_id, win = away_score > home_score)
)
# every Sunday from the first full week to the last day of the season
weeks <- seq(as.Date(paste0(season, "-04-05")), max(games$date), by = "week")

race <- cross_join(tibble(week = weeks), distinct(results, team_id)) |>
  left_join(results, by = join_by(team_id, week >= date)) |>
  summarise(w = sum(win), l = sum(!win), .by = c(week, team_id)) |>
  inner_join(select(standings, team_id, team, division, official = rank), by = "team_id") |>
  arrange(week, desc(w), team) |>
  mutate(place = row_number(desc(w / (w + l))), .by = c(week, division))

final <- filter(race, week == max(weeks))
stopifnot(all(final$place == final$official))

# the primary, unless it is within 20 (CIE 2000 color difference) of the white
# background or of a color already taken, and the secondary is farther away
distinct_colours <- function(primary, secondary, min_gap = 20) {
  taken <- "#FFFFFF"
  gap <- function(x) {
    min(farver::compare_colour(
      farver::decode_colour(x), farver::decode_colour(taken), "rgb",
      method = "cie2000"
    ))
  }
  for (i in seq_along(primary)) {
    swap <- gap(primary[i]) < min_gap && gap(secondary[i]) > gap(primary[i])
    taken <- c(taken, if (swap) secondary[i] else primary[i])
  }
  taken[-1]
}
line_colours <- final |>
  arrange(division, place) |>
  inner_join(sdv_team_colors("mlb", which = "all"), by = c(team = "team_id")) |>
  mutate(colour = distinct_colours(primary, secondary), .by = division)

ggplot(race, aes(week, place, colour = team)) +
  geom_line(linewidth = 1) +
  geom_point(size = 1.3) +
  # inherit.aes = FALSE: a colour aesthetic would tint the logos
  geom_sdv_logos(
    aes(x = week + 10, y = place, team = team),
    data = final, league = "mlb", width = 0.07, inherit.aes = FALSE
  ) +
  scale_colour_manual(values = setNames(line_colours$colour, line_colours$team)) +
  scale_y_reverse(breaks = 1:5) +
  scale_x_date(date_labels = "%b", expand = expansion(add = c(3, 16))) +
  facet_wrap(~ factor(division, levels = divisions), ncol = 2) +
  labs(
    title = paste("The", season, "division races"),
    subtitle = "Place in the division at the end of each week",
    x = NULL,
    y = "Place",
    caption = stats_api
  ) +
  theme_minimal(base_size = 12) +
  theme(
    legend.position = "none",
    panel.grid.minor = element_blank(),
    strip.text = element_text(face = "bold"),
    plot.title = element_text(face = "bold")
  )

Six small line charts, one per MLB division, showing each team's place in its division at the end of every week of the 2026 season, lines in team colors, with the team logo at the end of each line.

9. Franchise moves since 1901

Nine franchises that changed cities, one bar per franchise, one shade per city. The abbreviations change with the eras (PHA, KCA, OAK, ATH for the Athletics), but the Stats API team id never does, so each franchise is keyed by its id and drawn with today’s logo on the axis (scale_y_sdv() with theme_y_sdv()). Old abbreviations are not a reliable key:

sdv_resolve(c("OAK", "KCA", "MON", "BRO"), league = "mlb")
#> [1] "11" "7"  "20" "19"

sdvplotR maps the Oakland and Montreal codes to the clubs they became, but KCA, the Kansas City Athletics, matches KC, today’s Royals. The Stats API lists the 1969 expansion Expos and Pilots from 1968, the year they were founded.

eras <- identities |>
  # the city is the name without its last word ("Athletics" alone has none)
  mutate(city = sub(" [^ ]+$", "", name)) |>
  arrange(team_id, season) |>
  mutate(era = consecutive_id(city), .by = team_id) |>
  summarise(
    from = min(season), to = max(season), city = first(city), name = last(name),
    .by = c(team_id, era)
  ) |>
  inner_join(select(standings, team_id, team), by = "team_id") |>
  mutate(
    last_move = max(from), current = era == max(era),
    # today's city in full color, earlier ones alternating lighter and darker
    dark = (max(era) - era) %% 2 == 0,
    width = to - from + 1, mid = from + width / 2,
    .by = team_id
  ) |>
  mutate(
    team = reorder(team, last_move),
    # earlier cities go inside their era where they fit
    label = if_else(!current & width >= 1.15 * nchar(city), city, NA)
  )

ggplot(eras, aes(y = team)) +
  geom_tile(aes(x = mid, width = width, fill = team, alpha = dark), height = 0.72, colour = "white") +
  geom_text(
    aes(x = mid, label = label, colour = if_else(dark, "white", "grey10")),
    size = 2.9, na.rm = TRUE
  ) +
  geom_text(
    aes(x = to + 3, label = name),
    data = filter(eras, current), hjust = 0, size = 3.3, fontface = "bold"
  ) +
  scale_fill_sdv(league = "mlb") +
  scale_alpha_manual(values = c(`TRUE` = 1, `FALSE` = 0.55)) +
  scale_colour_identity() +
  scale_x_continuous(
    breaks = c(1901, seq(1925, 2000, 25), season),
    limits = c(1901, season + 32), expand = expansion(0)
  ) +
  scale_y_sdv(league = "mlb", size = 22) +
  labs(
    title = "Nine MLB franchises that moved",
    subtitle = "One shade per city, from 1901 or the franchise's first season",
    x = NULL,
    y = NULL,
    caption = stats_api
  ) +
  theme_minimal(base_size = 12) +
  theme_y_sdv() +
  theme(
    legend.position = "none",
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),
    plot.title = element_text(face = "bold")
  )

Timeline from 1901 to 2026 with one bar per MLB franchise that relocated (Athletics, Nationals, Rangers, Brewers, Braves, Twins, Dodgers, Giants and Orioles), each in its team color with a lighter or darker shade for each city it played in, the earlier cities written inside the bars, today's name at the right and today's logo on the axis.

10. The postseason field as a tier list

sdv_team_tiers() draws a tier list from a tier and a team column. The tiers here come from the standings’ clinch codes (y and z won a division, w a wild card) and the games behind the last wild card; within a tier the teams go by wins. On the dark background each team draws its dark-background logo, so the navy and brown marks (the Yankees’, the Padres’, the Royals’) come out white or gold instead of disappearing.

field <- standings |>
  mutate(
    behind = suppressWarnings(as.numeric(wc_gb)), # "-" and "+2.0" belong to playoff teams
    tier_no = case_when(
      clinch %in% c("y", "z") ~ 1,
      clinch == "w" ~ 2,
      behind <= 5 ~ 3,
      .default = 4
    )
  ) |>
  arrange(tier_no, desc(w), team) |>
  mutate(tier_rank = row_number(), .by = tier_no)
stopifnot(sum(field$tier_no == 1) == 6, sum(field$tier_no == 2) == 6)

sdv_team_tiers(
  select(field, tier_no, tier_rank, team),
  league = "mlb",
  title = paste("How the", season, "postseason field was built"),
  subtitle = "Regular season; teams ordered by wins within each tier",
  caption = stats_api,
  tier_desc = c(
    "1" = "Division winners",
    "2" = "Wild cards",
    "3" = "Missed by 5 games or fewer",
    "4" = "Missed by more"
  ),
  width = 0.06
)

Tier list of the 30 MLB teams in 2026, drawn as logos in four rows: division winners, wild cards, teams that missed the postseason by five games or fewer, and teams that missed by more.

11. Make it interactive

Section 3’s Pythagorean chart as 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 team’s record, run differential, expected wins and how far it beat or missed them, the numbers the static chart can only place against the dashed line.

pyth_hover <- pyth |>
  mutate(tip = sprintf(
    "<b>%s</b><br>%.0f-%.0f, %+.0f run differential<br>%.1f expected wins (%+.1f)",
    team_name, w, l, diff, xw, luck
  ))
lims <- range(pyth$xw, pyth$w) + c(-3, 3) # section 4 reused the name

plotly::plot_ly(
  pyth_hover, x = ~xw, y = ~w, type = "scatter", mode = "markers",
  marker = list(size = 26, opacity = 0), text = ~tip, hoverinfo = "text"
) |>
  plotly::layout(
    title = list(text = paste(season, "MLB: actual against expected wins"), x = 0.02),
    xaxis = list(title = "Expected wins", range = lims),
    yaxis = list(title = "Actual wins", range = lims),
    shapes = list(list(
      type = "line", x0 = lims[1], y0 = lims[1], x1 = lims[2], y1 = lims[2],
      line = list(color = "grey", dash = "dash", width = 1)
    ))
  ) |>
  add_sdv_logos(pyth_hover$xw, pyth_hover$w, pyth_hover$team, "mlb", size = 0.07)

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 MLB Run Differential recipe does the same for section 1’s bars.

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