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NFL Visualizations with nflfastR and sdvplotR

Source: vignettes/nfl-viz.Rmd
nfl-viz.Rmd

Introduction

This vignette demonstrates how to create rich NFL visualizations by combining nflfastR for data ingestion with sdvplotR for team logos, headshots, colors, and gt tables.

Setup 

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

# Get valid NFL team abbreviations
nfl_teams <- valid_team_names("nfl")
head(nfl_teams)

Loading NFL Data

Use nflfastR to load a season of play-by-play data:

# The last completed regular season (NFL seasons are named for the year they
# start, and the regular season ends in early January)
season <- as.integer(format(Sys.Date(), "%Y")) - 1 -
  (format(Sys.Date(), "%m-%d") < "01-15")

pbp <- nflfastR::load_pbp(seasons = season, file_type = "rds")

# Filter to pass plays only for EPA analysis
pass_plays <- pbp |>
  filter(
    !is.na(epa),
    pass == 1,
    !is.na(posteam)
  )

EPA by Team with Logos

Create a scatter plot showing team EPA (Expected Points Added) per play, with team logos replacing the data points:

team_epa <- pass_plays |>
  group_by(posteam) |>
  summarise(
    mean_epa = mean(epa, na.rm = TRUE),
    n_plays = n(),
    .groups = "drop"
  ) |>
  filter(n_plays >= 100)

ggplot(team_epa, aes(x = n_plays, y = mean_epa)) +
  geom_sdv_logos(
    aes(team = posteam),
    sport = "nfl",
    width = 0.075
  ) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "grey50") +
  scale_x_continuous(labels = scales::comma) +
  labs(
    title = "NFL Team Pass EPA per Play",
    subtitle = paste("Season", season),
    x = "Number of Pass Plays",
    y = "Mean EPA per Play",
    caption = "Data: nflfastR | Viz: sdvplotR"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    plot.subtitle = element_text(color = "grey40")
  )

Team Standings with Colors

Use team colors to visualize win-loss records:

# Regular-season records from the schedule, one row per team per game
standings <- nflreadr::load_schedules(season) |>
  filter(game_type == "REG", !is.na(result)) |>
  nflreadr::clean_homeaway() |>
  group_by(team) |>
  summarise(
    wins = sum(team_score > opponent_score) + 0.5 * sum(team_score == opponent_score),
    games = n(),
    .groups = "drop"
  ) |>
  mutate(win_pct = wins / games) |>
  arrange(desc(win_pct))

ggplot(head(standings, 16), aes(x = reorder(team, win_pct), y = win_pct)) +
  geom_col(aes(fill = team), width = 0.7) +
  scale_fill_sdv(sport = "nfl", alpha = 0.8) +
  labs(
    title = "Top 16 NFL Teams by Win Percentage",
    subtitle = paste("Season", season),
    x = NULL,
    y = "Win Percentage"
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    legend.position = "none"
  )

Quarterback Headshots

Visualize quarterback performance with player headshots:

# Top QBs by EPA
qb_epa <- pass_plays |>
  filter(pass == 1, !is.na(passer_player_id)) |>
  group_by(passer_player_id, passer) |>
  summarise(
    mean_epa = mean(epa, na.rm = TRUE),
    n_passes = n(),
    .groups = "drop"
  ) |>
  filter(n_passes >= 200) |>
  arrange(desc(mean_epa)) |>
  head(8)

ggplot(qb_epa, aes(x = n_passes, y = mean_epa)) +
  geom_sdv_headshots(
    aes(player_id = passer_player_id),
    sport = "nfl",
    height = 0.15
  ) +
  geom_label(
    aes(label = passer),
    nudge_y = -0.02,
    size = 3,
    alpha = 0.7
  ) +
  labs(
    title = "Top 8 Quarterbacks by EPA per Pass",
    x = "Number of Passes",
    y = "Mean EPA per Pass"
  ) +
  theme_minimal()

NFL Team Tiers

Create a tier plot ranking NFL teams:

# Sample tier assignments (replace with your own rankings)
tier_data <- data.frame(
  tier_no = c(1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4),
  team = c("KC", "BUF", "SF", "PHI", "DAL", "MIA", "CIN",
           "BAL", "DET", "JAX", "CLE", "GB",
           "LAR", "MIN", "PIT", "TEN")
)

sdv_team_tiers(
  tier_data,
  sport = "nfl",
  title = "NFL Power Rankings",
  subtitle = paste("Example tiers,", season, "season"),
  tier_desc = c(
    "1" = "Elite",
    "2" = "Contenders",
    "3" = "Playoff Bubble",
    "4" = "Rebuilding"
  )
)

NFL Standings Table with Logos

Create a gt table with team logos:

standings_table <- standings |>
  head(10) |>
  mutate(
    logo = team,
    team_name = nflfastR::teams_colors_logos$team_nick[
      match(team, nflfastR::teams_colors_logos$team_abbr)
    ]
  ) |>
  select(logo, team_name, wins, games, win_pct)

standings_table |>
  gt() |>
  gt_sdv_logos(columns = "logo", sport = "nfl", height = 35) |>
  fmt_number(columns = "win_pct", decimals = 3) |>
  cols_label(
    logo = "Team",
    team_name = "Name",
    wins = "Wins",
    games = "Games",
    win_pct = "Win %"
  ) |>
  tab_header(
    title = "NFL Standings",
    subtitle = paste("Season", season)
  )

NFL Division Map with Logos

Visualize teams grouped by division:

# Each team's division, from the reference data sdvplotR keeps for every team
divisions <- team_reference("nfl") |>
  arrange(division, team_name) |>
  group_by(division) |>
  mutate(y = row_number()) |>
  ungroup() |>
  mutate(x = as.numeric(factor(division)))

ggplot(divisions, aes(x = x, y = y)) +
  geom_sdv_logos(
    aes(team = team_abbr),
    sport = "nfl",
    width = 0.075
  ) +
  scale_x_continuous(
    breaks = seq_along(levels(factor(divisions$division))),
    labels = levels(factor(divisions$division))
  ) +
  scale_y_reverse() +
  labs(
    title = "NFL Teams by Division",
    x = NULL,
    y = NULL
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    axis.text.y = element_blank(),
    panel.grid = element_blank()
  )

Axis Labels with Logos

Replace axis labels with team logos using scale_x_sdv():

top_8 <- standings |>
  head(8) |>
  mutate(team = factor(team, levels = team))

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

Next Steps