MBB Visualizations with hoopR and sdvplotR

Introduction

This vignette demonstrates how to create rich MBB (Men’s College Basketball) visualizations by combining hoopR for player and team data and sdvplotR for team logos, headshots, colors, and gt tables.

Setup

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

# Get valid MBB team abbreviations
mbb_teams <- valid_team_names("mbb")
length(mbb_teams)  # ~350+ D1 teams
head(mbb_teams)

Loading MBB Data

Use hoopR to load this season’s ESPN team and player box scores:

# The last completed regular season (hoopR names a season for the year it
# ends; the regular season ends in mid-March)
season <- as.integer(format(Sys.Date(), "%Y")) -
  (format(Sys.Date(), "%m-%d") < "03-20")

# One row per team per game (ESPN box scores), regular season only; Division I
# teams only (the box scores also hold their games against other divisions)
team_stats <- hoopR::load_mbb_team_box(seasons = season) |>
  filter(season_type == 2, team_abbreviation %in% team_reference("mbb")$team_abbr)

# One row per player per game, with ESPN athlete IDs; players who did not
# play are dropped so games played counts real games
player_stats <- hoopR::load_mbb_player_box(seasons = season) |>
  filter(season_type == 2, !did_not_play)

MBB Team Performance

Visualize team performance with team logos:

# Calculate team metrics
team_perf <- team_stats |>
  filter(!is.na(team_abbreviation)) |>
  group_by(team_id, team_abbreviation) |>
  summarise(
    avg_points = mean(team_score, na.rm = TRUE),
    avg_rebounds = mean(total_rebounds, na.rm = TRUE),
    games = n(),
    .groups = "drop"
  ) |>
  filter(games >= 10)

ggplot(team_perf, aes(x = avg_points, y = avg_rebounds)) +
  geom_sdv_logos(
    aes(team = team_abbreviation),
    sport = "mbb",
    width = 0.075
  ) +
  labs(
    title = "MBB Team Performance",
    subtitle = paste("Season", season),
    x = "Average Points per Game",
    y = "Average Rebounds per Game",
    caption = "Data: hoopR | Viz: sdvplotR"
  ) +
  theme_minimal()

MBB Team Colors

Use team colors to visualize win percentages:

# Calculate win percentage
team_wins <- team_stats |>
  filter(!is.na(team_abbreviation)) |>
  group_by(team_id, team_abbreviation) |>
  summarise(
    wins = sum(team_winner, na.rm = TRUE),
    games = n(),
    .groups = "drop"
  ) |>
  filter(games >= 10) |>
  mutate(win_pct = wins / games) |>
  arrange(desc(win_pct)) |>
  head(25)

ggplot(team_wins, aes(x = reorder(team_abbreviation, win_pct), y = win_pct)) +
  geom_col(aes(fill = team_abbreviation), width = 0.7) +
  scale_fill_sdv(sport = "mbb", alpha = 0.8) +
  scale_y_continuous(labels = scales::percent) +
  labs(
    title = "Top 25 MBB Teams by Win Percentage",
    x = NULL,
    y = "Win Percentage"
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    legend.position = "none"
  )

Player Headshots

Visualize top performers with player headshots:

# Top scorers
top_scorers <- player_stats |>
  filter(!is.na(athlete_id)) |>
  group_by(athlete_id, athlete_display_name) |>
  summarise(
    avg_points = mean(points, na.rm = TRUE),
    games = n(),
    .groups = "drop"
  ) |>
  filter(games >= 15) |>
  arrange(desc(avg_points)) |>
  head(8)

ggplot(top_scorers, aes(x = games, y = avg_points)) +
  geom_sdv_headshots(
    aes(player_id = athlete_id),
    sport = "mbb",
    height = 0.15
  ) +
  geom_label(
    aes(label = athlete_display_name),
    nudge_y = -1.5,
    size = 3,
    alpha = 0.7
  ) +
  labs(
    title = "Top 8 MBB Scorers",
    subtitle = paste("Season", season),
    x = "Games Played",
    y = "Average Points per Game"
  ) +
  theme_minimal()

Tournament Seeds with Logos

Plot a set of tournament seeds with team logos. These seeds are an example; swap in the real bracket once it is announced:

# For demonstration, create sample bracket data
bracket_data <- data.frame(
  seed = 1:16,
  team = c("HOU", "UCLA", "KANSAS", "PURDUE", "GONZAGA", "BAYLOR",
           "ARIZONA", "DUKE", "CREIGHTON", "MARQUETTE", "TEXAS",
           "AUBURN", "MSU", "TENN", "SDSU", "TCU")
)

ggplot(bracket_data, aes(x = seed, y = 1)) +
  geom_sdv_logos(
    aes(team = team),
    sport = "mbb",
    width = 0.075
  ) +
  scale_x_continuous(breaks = 1:16) +
  labs(
    title = "NCAA Tournament Seeds",
    subtitle = paste("Season", season),
    x = "Seed",
    y = NULL
  ) +
  theme_minimal() +
  theme(
    axis.text.y = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank()
  )

MBB Team Tiers

Create a tier plot ranking MBB teams:

# Top 25 teams by win percentage
top_25 <- team_wins |>
  mutate(
    tier_no = case_when(
      win_pct > 0.85 ~ 1,
      win_pct > 0.75 ~ 2,
      win_pct > 0.65 ~ 3,
      win_pct > 0.55 ~ 4,
      TRUE ~ 5
    )
  ) |>
  select(tier_no, team = team_abbreviation)

sdv_team_tiers(
  top_25,
  sport = "mbb",
  title = "MBB Power Rankings",
  subtitle = paste("Example tiers,", season, "season"),
  tier_desc = c(
    "1" = "Elite",
    "2" = "Championship Contenders",
    "3" = "Top 25",
    "4" = "Bubble Teams",
    "5" = "Rebuilding"
  ),
  presort = TRUE
)

MBB Conference Map

Show each major conference’s teams in a column:

# The five major conferences, from the conferences sdvplotR keeps for every team
conference_map <- team_reference("mbb") |>
  filter(conference %in% c("ACC", "Big 12", "Big East", "Big Ten", "SEC")) |>
  arrange(conference, team_location) |>
  group_by(conference) |>
  mutate(team_rank = row_number()) |>
  ungroup() |>
  mutate(conference_num = as.numeric(factor(conference)))

ggplot(conference_map, aes(x = conference_num, y = team_rank)) +
  geom_sdv_logos(
    aes(team = team_abbr),
    sport = "mbb",
    width = 0.05
  ) +
  scale_x_continuous(
    breaks = seq_along(levels(factor(conference_map$conference))),
    labels = levels(factor(conference_map$conference))
  ) +
  scale_y_reverse() +
  labs(
    title = "MBB Teams in the Major Conferences",
    x = NULL,
    y = NULL
  ) +
  theme_minimal() +
  theme(
    axis.text.y = element_blank(),
    panel.grid = element_blank()
  )

MBB Standings Table with Logos

Create a gt table with team logos:

standings_table <- team_wins |>
  head(20) |>
  mutate(
    logo = team_abbreviation,
    rank = row_number()
  ) |>
  select(rank, logo, team_abbreviation, wins, games, win_pct)

standings_table |>
  gt() |>
  gt_sdv_logos(columns = "logo", sport = "mbb", height = 30) |>
  fmt_number(columns = "win_pct", decimals = 3) |>
  cols_label(
    rank = "#",
    logo = "Team",
    team_abbreviation = "Abbrev",
    wins = "Wins",
    games = "Games",
    win_pct = "Win %"
  ) |>
  tab_header(
    title = "MBB Top 20",
    subtitle = paste("Season", season)
  )

Axis Labels with Logos

Replace axis labels with team logos:

top_8 <- team_wins |>
  head(8) |>
  mutate(team_abbreviation = factor(team_abbreviation, levels = team_abbreviation))

ggplot(top_8, aes(x = team_abbreviation, y = win_pct)) +
  geom_col(aes(fill = team_abbreviation), width = 0.6) +
  scale_fill_sdv(sport = "mbb", alpha = 0.7) +
  scale_x_sdv(sport = "mbb") +
  theme_minimal() +
  theme_x_sdv() +
  labs(
    title = "Top 8 MBB Teams by Win %",
    x = NULL,
    y = "Win Percentage"
  ) +
  theme(legend.position = "none")

Next Steps

• Explore hoopR documentation
• Combine with oddsapiR for betting lines

Related Vignettes

• Getting Started
• Social Posting Patterns
• Leaderboard Dashboards