Men's College Basketball Visualizations with hoopR and sdvplotR

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Division I men’s basketball has more than 360 teams in 31 conferences, and the conferences change from one season to the next. These ten examples chart the 2025-26 season: matching ESPN team ids and names to sdvplotR, keeping a 360-team chart legible, ranking the top 25 with logos on the axis, the power conferences in small multiples, a season-by-season realignment table with conference logos, a conference standings table, March as a tier list, a headshot leaderboard, a shot chart on a college court and the AP poll as a bump chart, then the 360-team chart again as an interactive one. The data are hoopR’s ESPN release files on GitHub (load_mbb_*()): box scores, standings, schedules, shots and the adjusted team ratings. Nothing here calls stats.ncaa.org.

Setup

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

season <- 2026 # the 2025-26 season: hoopR names a season by the year it ends
label <- "2025-26"
source_note <- "Data: hoopR (ESPN) | Viz: sdvplotR"

theme_set(
  theme_minimal(base_size = 12) +
    theme(
      plot.title = element_text(face = "bold"),
      plot.title.position = "plot",
      plot.caption = element_text(color = "grey40", size = 8),
      plot.caption.position = "plot",
      panel.grid.minor = element_blank()
    )
)

1. Team ids, names and what doesn’t resolve

sdv_teams("mbb") lists every Division I program sdvplotR knows, one row per team, with its ESPN team id (team_id). Full names, short names, ESPN ids and the names other providers use (NCAA.com, KenPom, Torvik) all resolve to that id:

mbb_teams <- sdv_teams("mbb") |>
  mutate(team_id = as.integer(team_id))
nrow(mbb_teams)
#> [1] 366

sdv_resolve(
  c("Michigan", "Michigan Wolverines", "Connecticut", "Saint Mary's", "130", "American"),
  league = "mbb"
)
#> [1] "130"  "130"  "41"   "2608" "130"  "44"

Two things in that output matter for real data. "130" is Michigan’s ESPN id, and "Michigan" and "Michigan Wolverines" give the same team. And "American" is American University, a Patriot League team: the American Conference is "American Conference", a conference row, listed with sdv_teams("mbb", include_conferences = TRUE), not a team. A short form no provider uses does not resolve; sdv_suggest() proposes candidates, and never picks one for you:

sdv_suggest("St. Marys", "mbb", n = 3)
#> [1] team_id name   
#> <0 rows> (or 0-length row.names)

hoopR’s adjusted ratings store the ESPN id as text while the box scores store it as an integer; mbb_teams above holds it as an integer, so cast the ratings before the join. The ratings also rate every non-Division I opponent a Division I team played; the inner join keeps the Division I teams. Each team’s 2025-26 conference comes from that season’s standings, not from sdv_teams(), whose conference is today’s (the College Basketball Crown is a postseason event filed as a group, so it is dropped).

standings <- hoopR::load_mbb_standings(seasons = season)
conferences <- standings |>
  filter(group_abbreviation != "CBC") |>
  distinct(team_id, conference = group_short_name)
stopifnot(!anyDuplicated(conferences$team_id))

ratings <- hoopR::load_mbb_ratings(seasons = season) |>
  mutate(team_id = as.integer(team_id))
nrow(ratings)
#> [1] 727

ratings <- ratings |>
  inner_join(select(mbb_teams, team_id, team = abbr, team_name = name), by = "team_id") |>
  left_join(conferences, by = "team_id") |>
  arrange(desc(adj_em), team)
nrow(ratings)
#> [1] 365

2. Tempo against efficiency: 360 teams, 25 logos

With 360 teams, a logo for everyone is a smear. Draw every team as a grey point and give logos only to the top 25 by adjusted efficiency margin (points scored minus allowed per 100 possessions, adjusted for the opponents).

top25 <- head(ratings, 25)

ggplot(ratings, aes(adj_tempo, adj_em)) +
  geom_hline(yintercept = 0, color = "grey60", linewidth = 0.4) +
  geom_point(color = "grey75", size = 1.6) +
  geom_sdv_logos(aes(team = team), data = top25, league = "mbb", width = 0.035) +
  labs(
    title = paste("Tempo vs efficiency, Division I men's basketball,", label),
    subtitle = paste(nrow(ratings), "teams; logos for the top 25 by adjusted efficiency margin"),
    x = "Adjusted tempo (possessions per 40 minutes)",
    y = "Adjusted efficiency margin (per 100 possessions)",
    caption = source_note
  )

Scatter plot of every Division I men's basketball team in 2025-26 by adjusted tempo (horizontal) and adjusted efficiency margin (vertical): grey points for all teams and logos for the top 25.

3. A ranked top 25 with logos on the axis

scale_y_sdv() swaps a discrete axis of abbreviations for logos, so the bars keep their order and the labels stay short. A thin black edge keeps pale team colors visible.

top25 |>
  mutate(team = factor(team, levels = rev(team))) |>
  ggplot(aes(adj_em, team, fill = team)) +
  geom_col(width = 0.75, color = "black", linewidth = 0.2) +
  geom_text(aes(label = sprintf("%.1f", adj_em)), hjust = -0.15, size = 3.3) +
  scale_fill_sdv(league = "mbb") +
  scale_y_sdv(league = "mbb", size = 16) +
  scale_x_continuous(expand = expansion(mult = c(0, 0.08))) +
  labs(
    title = paste("The top 25 by adjusted efficiency margin,", label),
    x = "Adjusted efficiency margin (per 100 possessions)",
    y = NULL,
    caption = source_note
  ) +
  theme_y_sdv() +
  theme(legend.position = "none", panel.grid.major.y = element_blank())

Horizontal bar chart of the top 25 Division I men's basketball teams of 2025-26 by adjusted efficiency margin, each bar in the team's color with its logo on the vertical axis and its margin at the end of the bar.

4. Offense vs defense in the power conferences

A conference is the natural small multiple: its 11 to 18 logos fit in one panel. geom_sdv_logos() maps the team as an aesthetic, so it follows facet_wrap(), and geom_mean_lines() draws each conference’s own averages. Defense is points allowed, so its axis is reversed to put good defenses on top.

power <- ratings |>
  filter(conference %in% c("ACC", "Big 12", "Big East", "Big Ten", "SEC"))

ggplot(power, aes(adj_o, adj_d)) +
  geom_mean_lines(aes(x0 = adj_o, y0 = adj_d), color = "grey60", linewidth = 0.4) +
  geom_sdv_logos(aes(team = team), league = "mbb", width = 0.085) +
  facet_wrap(~conference, ncol = 3) +
  scale_y_reverse() +
  labs(
    title = paste("Offense vs defense in the power conferences,", label),
    x = "Adjusted offensive efficiency (points per 100 possessions)",
    y = "Adjusted defensive efficiency (allowed per 100)",
    caption = source_note
  ) +
  theme(strip.text = element_text(face = "bold", size = 11))

Five panels, one per power conference (ACC, Big 12, Big East, Big Ten, SEC), placing each team's logo by adjusted offensive efficiency (horizontal) and adjusted defensive efficiency (vertical, reversed), with each conference's average lines.

5. Conference realignment, season by season

A past season’s conference comes from that season’s data. Load three seasons of standings and follow the twelve teams of the 2023-24 Pac-12: ten left for the ACC, Big 12 and Big Ten in 2024, and Oregon State and Washington State spent two seasons in the West Coast Conference. The last column is sdv_teams()’s conference, today’s, where those two anchor the rebuilt Pac-12. Conference names give conference logos, so gt_sdv_logos() draws every column; include_name = TRUE keeps the name beside the mark.

conf_rows <- sdv_teams("mbb", include_conferences = TRUE) |>
  filter(program == "conference")

history <- hoopR::load_mbb_standings(seasons = (season - 2):season) |>
  filter(group_abbreviation != "CBC") |>
  distinct(season, team_id, conference = group_short_name)

pac12 <- history |>
  filter(season == !!season - 2, conference == "Pac-12") |>
  pull(team_id)

history |>
  filter(team_id %in% pac12) |>
  pivot_wider(names_from = season, values_from = conference, names_prefix = "s") |>
  inner_join(
    select(mbb_teams, team_id, team = abbr, team_name = name, today = conference),
    by = "team_id"
  ) |>
  arrange(s2026, team_name) |>
  mutate(today = conf_rows$team_id[match(today, conf_rows$name)]) |>
  select(team, team_name, s2024, s2025, s2026, today) |>
  gt() |>
  tab_header(
    title = "Where the 2023-24 Pac-12 went",
    subtitle = "Each season's conference, from that season's ESPN standings"
  ) |>
  cols_label(
    team = "", team_name = "Team", s2024 = "2023-24", s2025 = "2024-25",
    s2026 = "2025-26", today = "Today"
  ) |>
  tab_source_note(source_note) |>
  gt_sdv_logos(columns = team, league = "mbb", height = 28) |>
  gt_sdv_logos(columns = c(s2024, s2025, s2026, today), league = "mbb", height = 22, include_name = TRUE) |>
  gt_theme_sdv()

	Team	2023-24	2024-25	2025-26	Today
Where the 2023-24 Pac-12 went
Each season's conference, from that season's ESPN standings
	California Golden Bears	Pac-12	ACC	ACC	ACC
	Stanford Cardinal	Pac-12	ACC	ACC	ACC
	Arizona State Sun Devils	Pac-12	Big 12	Big 12	Big 12
	Arizona Wildcats	Pac-12	Big 12	Big 12	Big 12
	Colorado Buffaloes	Pac-12	Big 12	Big 12	Big 12
	Utah Utes	Pac-12	Big 12	Big 12	Big 12
	Oregon Ducks	Pac-12	Big Ten	Big Ten	Big Ten
	UCLA Bruins	Pac-12	Big Ten	Big Ten	Big Ten
	USC Trojans	Pac-12	Big Ten	Big Ten	Big Ten
	Washington Huskies	Pac-12	Big Ten	Big Ten	Big Ten
	Oregon State Beavers	Pac-12	WCC	WCC	Pac-12
	Washington State Cougars	Pac-12	WCC	WCC	Pac-12
Data: hoopR (ESPN) \| Viz: sdvplotR

6. A conference standings table

The Big Ten’s 2025-26 standings: the conference and overall records, home and road, the scoring margin and the adjusted efficiency margin, in ESPN’s standings order (playoffseed). The standings frame is long, one row per team and stat, and college standings repeat each stat for every split (home, road, conference, against ranked teams), so the unique key is stat_type, not stat_name. gt_merge_stack_team_color() puts each overall record under the team name in the team’s color, and gt_color_pills() colors the rating.

standings |>
  filter(group_short_name == "Big Ten") |>
  select(team_id, stat_type, display_value) |>
  pivot_wider(names_from = stat_type, values_from = display_value) |>
  inner_join(select(ratings, team_id, team, team_name, adj_em), by = "team_id") |>
  mutate(
    seed = as.integer(playoffseed),
    logo = team,
    margin = as.numeric(avgpointsfor) - as.numeric(avgpointsagainst)
  ) |>
  arrange(seed, team) |>
  select(seed, logo, team_name, total, team, vsconf, home, road, margin, adj_em) |>
  gt() |>
  tab_header(
    title = paste("Big Ten standings,", label),
    subtitle = "Overall record under each team; conference, home and road records beside it"
  ) |>
  cols_label(
    seed = "", logo = "", team_name = "Team", vsconf = "Conf", home = "Home",
    road = "Road", margin = "Margin", adj_em = "Adj. EM"
  ) |>
  fmt_number(margin, decimals = 1, force_sign = TRUE) |>
  tab_source_note(source_note) |>
  gt_merge_stack_team_color(team_name, total, team, league = "mbb") |>
  gt_sdv_logos(columns = logo, league = "mbb", height = 28) |>
  gt_color_pills(adj_em, digits = 1, domain = c(-10, 50)) |>
  gt_theme_athletic() |>
  cols_hide(team)

	Team	Conf	Home	Road	Margin	Adj. EM
Big Ten standings, 2025-26
Overall record under each team; conference, home and road records beside it
1	Michigan Wolverines 37-3	19-1	14-1	11-0	+17.8	47.4
2	Nebraska Cornhuskers 28-7	15-5	16-2	7-3	+10.9	32.3
3	Michigan State Spartans 27-8	15-5	15-2	6-4	+10.5	35.2
4	Illinois Fighting Illini 28-9	15-5	14-3	8-2	+14.4	40.8
5	Wisconsin Badgers 24-11	14-6	15-2	6-5	+6.9	28.5
6	Purdue Boilermakers 30-9	13-7	12-5	8-3	+11.1	37.8
7	UCLA Bruins 24-12	13-7	17-1	4-6	+6.0	26.5
8	Ohio State Buckeyes 21-13	12-8	14-3	5-6	+6.7	28.0
9	Iowa Hawkeyes 24-13	10-10	15-3	3-8	+8.2	29.7
10	Indiana Hoosiers 18-14	9-11	14-4	3-8	+6.0	24.2
11	Minnesota Golden Gophers 15-18	8-12	13-4	2-9	+0.9	15.0
12	Washington Huskies 16-17	7-13	10-6	4-9	+3.5	21.0
13	USC Trojans 18-14	7-13	9-7	5-6	+0.7	15.5
14	Rutgers Scarlet Knights 14-20	6-14	10-7	2-9	−4.8	6.9
15	Northwestern Wildcats 15-19	5-15	9-8	3-8	+1.1	18.5
16	Oregon Ducks 12-20	5-15	10-7	2-8	−3.1	12.3
17	Maryland Terrapins 12-21	4-16	7-8	2-10	−6.9	7.2
18	Penn State Nittany Lions 12-20	3-17	9-8	2-9	−5.6	6.4
Data: hoopR (ESPN) \| Viz: sdvplotR

7. March as a tier list

The schedule’s notes_headline names each NCAA tournament game’s round, so each team’s last round is how far it went, and the title game’s winner is the champion. sdv_team_tiers() turns that into a tier list of the Sweet 16, best adjusted efficiency margin first within each tier. On its dark background each team draws its dark-background logo, so Iowa’s black hawkeye is gold.

rounds <- c(
  "First Four" = 0, "1st Round" = 1, "2nd Round" = 2, "Sweet 16" = 3,
  "Elite 8" = 4, "Final Four" = 5, "National Championship" = 6
)

ncaa <- hoopR::load_mbb_schedule(seasons = season) |>
  filter(grepl("^NCAA Men's Basketball Championship", notes_headline)) |>
  mutate(round = rounds[sub(".* - ", "", notes_headline)])
stopifnot(!anyNA(ncaa$round))

runs <- bind_rows(
  ncaa |> transmute(team_id = home_id, round, won = home_winner),
  ncaa |> transmute(team_id = away_id, round, won = away_winner)
) |>
  group_by(team_id) |>
  summarise(last_round = max(round), champion = any(round == 6 & won)) |>
  inner_join(select(ratings, team_id, team, adj_em), by = "team_id")

tiers <- runs |>
  filter(last_round >= 3) |>
  mutate(tier_no = if_else(champion, 1L, as.integer(8 - last_round))) |>
  arrange(tier_no, desc(adj_em)) |>
  select(tier_no, team)

sdv_team_tiers(
  tiers,
  league = "mbb",
  title = paste("The", season, "NCAA tournament, by how far each team went"),
  subtitle = "The Sweet 16; best adjusted efficiency margin first within each tier",
  caption = source_note,
  tier_desc = c(
    "1" = "Champion", "2" = "Runner-up", "3" = "Final Four",
    "4" = "Elite Eight", "5" = "Sweet 16"
  ),
  alpha = 1
)

The 16 teams of the 2026 men's NCAA tournament Sweet 16 as logos in five tiers by how far they went: Champion, Runner-up, Final Four, Elite Eight and Sweet 16.

8. Scoring leaders with headshots

Player box scores carry ESPN athlete ids, which geom_sdv_headshots() draws for college players too. Only Division I teams count, and a player’s team is the one he played for last. Leaders need at least 20 games.

players <- hoopR::load_mbb_player_box(seasons = season) |>
  filter(season_type == 2, !did_not_play, team_id %in% mbb_teams$team_id)

scorers <- players |>
  arrange(game_date) |>
  group_by(athlete_id, athlete_display_name) |>
  summarise(games = n(), team_id = last(team_id), ppg = mean(points, na.rm = TRUE), .groups = "drop") |>
  filter(games >= 20) |>
  arrange(desc(ppg), athlete_id) |>
  head(10) |>
  inner_join(select(ratings, team_id, team), by = "team_id") |>
  mutate(athlete_display_name = factor(athlete_display_name, levels = rev(athlete_display_name)))

ggplot(scorers, aes(ppg, athlete_display_name)) +
  geom_col(aes(fill = team), width = 0.7, color = "black", linewidth = 0.2) +
  geom_sdv_logos(aes(x = ppg + 1.3, team = team), league = "mbb", height = 0.075) +
  geom_sdv_headshots(aes(x = ppg + 3.8, player_id = athlete_id), league = "mbb", height = 0.09) +
  geom_text(aes(x = ppg + 6.2, label = sprintf("%.1f", ppg)), fontface = "bold", hjust = 0) +
  scale_fill_sdv(league = "mbb") +
  scale_x_continuous(limits = c(0, max(scorers$ppg) + 9), expand = expansion(0)) +
  labs(
    title = paste("Division I scoring leaders,", label),
    subtitle = "Points per game, regular season, 20 or more games",
    x = "Points per game",
    y = NULL,
    caption = source_note
  ) +
  theme(legend.position = "none", panel.grid.major.y = element_blank())

Horizontal bar chart of the ten Division I men's basketball scoring leaders of 2025-26 (20 or more games), each bar in the team's color, followed by the team logo, the player's headshot and his points per game.

9. A shot chart on a college court

load_mbb_shots() holds ESPN’s shot locations, which hoopR already converts to feet on a center-court frame, the frame sdv_surface()’s sportyR court uses, so they need no conversion (sdv_court_coords() is only for the stats.nba.com legacy frame). Two cleanups first: free throws are placed at the rim, so drop them, and a shot with no location carries a placeholder of about ±215 million, so keep only points on the floor. Then fold the right-basket shots onto the left one. The player is the national champion’s leading scorer.

champion_id <- filter(runs, champion)$team_id
star <- players |>
  filter(team_id == champion_id) |>
  group_by(athlete_id, athlete_display_name) |>
  summarise(games = n(), points = sum(points, na.rm = TRUE), .groups = "drop") |>
  slice_max(points, n = 1, with_ties = FALSE)
star_team <- filter(ratings, team_id == champion_id)$team

shots <- hoopR::load_mbb_shots(seasons = season) |>
  filter(
    athlete_id_1 == star$athlete_id,
    !grepl("FreeThrow", type_text),
    abs(coordinate_x) <= 47, abs(coordinate_y) <= 25
  ) |>
  mutate(
    right = coordinate_x > 0,
    x = if_else(right, -coordinate_x, coordinate_x),
    y = if_else(right, -coordinate_y, coordinate_y)
  )

sdv_surface("mbb", star_team, display_range = "defense") +
  geom_point(
    aes(x, y),
    data = filter(shots, !scoring_play),
    shape = 4, color = "grey25", size = 1.6, alpha = 0.65
  ) +
  geom_point(
    aes(x, y),
    data = filter(shots, scoring_play),
    shape = 21, fill = sdv_team_colors(star_team, league = "mbb", which = "secondary"),
    color = "black", stroke = 0.3, size = 2
  ) +
  labs(
    title = paste0(star$athlete_display_name, ": every field goal attempt, ", label),
    subtitle = sprintf(
      "%d made (filled), %d missed (crosses): %.1f%% from the field, all games",
      sum(shots$scoring_play), sum(!shots$scoring_play), 100 * mean(shots$scoring_play)
    ),
    caption = source_note
  ) +
  theme(plot.title = element_text(face = "bold"))

Half-court shot chart of the 2026 national champion's leading scorer on a college court painted in his team's colors: made shots as filled circles, missed shots as grey crosses.

10. The AP poll, week by week

Each schedule row carries both teams’ AP rank that week (99 means unranked), so the schedule holds the whole poll. Take each ranked team’s rank per week, keep the top 10 of the last regular-season poll, and draw a bump chart in team colors with the logos at the finish. A week a team spent unranked leaves a gap in its line.

games <- hoopR::load_mbb_schedule(seasons = season) |>
  filter(season_type == 2) |>
  mutate(week = as.Date(cut(game_date, "week", start.on.monday = TRUE)))
weekly_rank <- bind_rows(
  games |> transmute(week, team_id = home_id, rank = home_current_rank),
  games |> transmute(week, team_id = away_id, rank = away_current_rank)
) |>
  group_by(team_id, week) |>
  summarise(rank = min(rank), .groups = "drop")

A team that didn’t play in a week (most of them over Christmas) has no row for it, so carry its last rank forward; a team that played unranked keeps its 99 and drops out of the chart until it is ranked again. The final week of conference tournaments has too few ranked teams playing, so the chart ends with the last week in which at least 20 of the 25 played.

full_weeks <- weekly_rank |>
  filter(rank <= 25) |>
  count(week) |>
  filter(n >= 20) |>
  pull(week)
final_week <- max(full_weeks)

poll <- weekly_rank |>
  complete(team_id, week) |>
  arrange(team_id, week) |>
  group_by(team_id) |>
  fill(rank) |>
  ungroup() |>
  filter(week <= final_week) |>
  mutate(rank = if_else(rank <= 25, rank, NA)) |>
  inner_join(select(ratings, team_id, team), by = "team_id")

top_final <- poll |>
  filter(week == final_week, !is.na(rank)) |>
  arrange(rank, team) |>
  head(8)
poll_top <- filter(poll, team_id %in% top_final$team_id)

ggplot(poll_top, aes(week, rank)) +
  geom_line(aes(group = team), color = "black", linewidth = 1.5, na.rm = TRUE) +
  geom_line(aes(color = team), linewidth = 0.9, na.rm = TRUE) +
  geom_sdv_logos(aes(x = week + 5, team = team), data = top_final, league = "mbb", width = 0.04) +
  scale_color_sdv(league = "mbb") +
  scale_y_reverse(breaks = c(1, 5, 10, 15, 20, 25)) +
  scale_x_date(date_labels = "%b %d", expand = expansion(add = c(2, 9))) +
  labs(
    title = paste("The AP poll,", label),
    subtitle = "Weekly rank of the last full poll's top 8; a gap is a week unranked",
    x = NULL,
    y = "AP rank",
    caption = source_note
  ) +
  theme(legend.position = "none")

Bump chart of the AP poll rank by week through the 2025-26 regular season for the eight teams ranked highest in the last full poll, one line per team in its color with its logo at its final rank.

11. Make it interactive

Section 2 gave logos to 25 teams and left the rest as anonymous grey points. In an interactive chart every point can name itself: this native plotly version keeps the grey points as a marker trace with hover text, and add_sdv_logos() places the top 25’s logos as layout images. Hover any point or logo for the team, its conference, its tempo and its adjusted margin, with the offense and defense behind it; drag a box to zoom into a crowd and double-click to zoom back out.

ratings_hover <- ratings |>
  mutate(tip = sprintf(
    "<b>%s</b> (%s)<br>Tempo %.1f<br>Margin %+.1f (offense %.1f, defense %.1f)",
    team_name, coalesce(conference, "conference unknown"), adj_tempo, adj_em, adj_o, adj_d
  ))

plotly::plot_ly(
  ratings_hover, x = ~adj_tempo, y = ~adj_em, type = "scatter", mode = "markers",
  marker = list(color = "grey", size = 7, opacity = 0.6), text = ~tip, hoverinfo = "text"
) |>
  plotly::layout(
    title = list(text = paste("Tempo vs efficiency, Division I men's basketball,", label), x = 0.02),
    xaxis = list(title = "Adjusted tempo (possessions per 40 minutes)"),
    yaxis = list(title = "Adjusted efficiency margin (per 100 possessions)")
  ) |>
  add_sdv_logos(top25$adj_tempo, top25$adj_em, top25$team, "mbb", size = 0.05)

Only the top 25 get logos, but size is a share of the whole y-axis, which every team’s margin spans, so the logos stay small over the crowd of points. plotly loads each logo from its URL, so the page stays light. Interactive charts covers plotly, ggiraph and leaflet.

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