This is a quick tutorial on how to use cfbplotR to quickly and easily include college football team logos in your ggplot.

Load and Process Data

First we need to load the necessary libraries. cfbfastR will get us the data we want to use for analysis. cfbplotR will help us easily plot the results. tidyverse will help us do the necessary data manipulation and of course includes ggplot2 that we will use for plotting. You can use the commented out code to install these packages if you don’t already have them.

#remotes::install_github(repo = "saiemgilani/cfbfastR")
#remotes::install_github(repo = "Kazink36/cfbplotR")
#install.packages(tidyverse)

library(cfbfastR)
library(cfbplotR)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.1.6     ✓ dplyr   1.0.7
## ✓ tidyr   1.1.4     ✓ stringr 1.4.0
## ✓ readr   2.0.2     ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

This first chunk of code will pull the play-by-play data from the first week of the 2021 season using the cfbfastR data repo and create the advanced metrics like EPA that we will be plotting (this will take a second). We’ll also pull in the general team info so we can filter down to just teams in a power 5 conference.

pbp <- cfbfastR::load_cfb_pbp(2021) %>% 
  filter(week == 1)

team_info <- cfbfastR::cfbd_team_info()
team_info <- team_info %>% 
  select(team = school,conference) %>% 
  filter(conference %in% c("Pac-12","ACC","SEC","Big Ten","Big 12"))

Now we quickly roll up the EPA data and find the EPA per rush and EPA per pass for every team in week 1 and take a look at our plotting data.

team_plot_data <- pbp %>% 
  group_by(team = offense_play) %>% 
  summarize(rush_epa = mean(if_else(rush == 1,EPA,NA_real_),na.rm = TRUE),
            n_rush = sum(rush),
            pass_epa = mean(if_else(pass == 1,EPA,NA_real_),na.rm = TRUE),
            n_pass = sum(pass)) %>% 
  filter(team %in% team_info$team) %>% 
  left_join(team_info,by = "team")

head(team_plot_data)
## # A tibble: 6 × 6
##   team          rush_epa n_rush pass_epa n_pass conference
##   <chr>            <dbl>  <dbl>    <dbl>  <dbl> <chr>     
## 1 Alabama        -0.0552     36    0.365     40 SEC       
## 2 Arizona         0.178      31   -0.140     53 Pac-12    
## 3 Arizona State   0.305      38    0.187     19 Pac-12    
## 4 Arkansas        0.308      40   -0.393     23 SEC       
## 5 Auburn          0.641      35    0.589     27 SEC       
## 6 Baylor          0.369      45   -0.137     24 Big 12

Plotting with cfbplotR

Now that the data is prepped, we can being to use cfbplotR. First we’ll plot all the teams with Passing EPA on the x-axis and Rushing EPA on the y-axis with lines showing the median value for each. It’s important to set width or height in geom_cfb_logos to small values. The default of 1 will create extremely large logos.

ggplot(team_plot_data, aes(x = pass_epa, y = rush_epa)) +
  geom_median_lines(aes(v_var = pass_epa, h_var = rush_epa)) +
  geom_cfb_logos(aes(team = team), width = 0.075) +
  labs(x = "EPA per Pass",y = "EPA per Rush") +
  theme_bw()

This is still pretty messy because of the large number of teams. Let’s try to focus in on the Pac-12 teams with a couple of handy tools. We’re going to add two columns to our data: one for the color and one for the alpha. Then we just add those two columns as aesthetics to geom_cfb_logos to turn the logos of non-Pac-12 teams black and white and lower the alpha.

team_plot_data %>% 
  mutate(color = if_else(conference == "Pac-12",NA_character_,"b/w"),
         alpha = if_else(conference == "Pac-12",1,.6)) %>% 
  ggplot(aes(x = pass_epa, y = rush_epa)) +
  geom_median_lines(aes(v_var = pass_epa, h_var = rush_epa)) +
  geom_cfb_logos(aes(team = team, alpha = alpha, color = color), width = 0.075) +
  scale_alpha_identity() +
  scale_color_identity() +
  labs(x = "EPA per Pass",y = "EPA per Rush") +
  theme_bw()

Finally let’s make a bar chart showing the Pac-12 EPA per pass for each team. Because cfbplotR creates a custom geom for ggplot, we can use annotate() to place a log anywhere we’d like. scale_color_cfb() and scale_fill_cfb() let us automatically use a teams primary color on a plot. The alt_colors argument lets us pass through a vector of team names that we want to use an alternate color for. sacle_x_cfb() and scale_y_cfb() change the axis labels that are team names into logos. Due to the way ggplot works, you have to add the corresponding theme function theme_x_cfb() or theme_y_cfb().

team_plot_data %>% 
  filter(conference == "Pac-12") %>% 
  mutate(team = fct_reorder(team,pass_epa)) %>% 
  ggplot(aes(x = team, y = pass_epa)) +
  # 
  geom_col(aes(fill = team, color = team),size = 1.5) +
  annotate(cfbplotR::GeomCFBlogo,x = "California",y = 0.2,team = "Pac-12",height = .35,alpha = .3) +
  scale_fill_cfb(alpha = .8) +
  scale_color_cfb(alt_colors = team_plot_data$team) +
  scale_x_cfb(size = 18) +
  labs(x = "", y = "EPA per Pass") +
  theme_bw() +
  theme_x_cfb()
## Warning in png::readPNG(get_file(path), native = TRUE): libpng warning: iCCP:
## known incorrect sRGB profile

cfbplotR also allows you to plot player headshots. Let’s look at the top 10 rushing EPA players with more than 10 rushes for week 1.

player_plot_data <- pbp %>% 
  filter(!is.na(rush_player_id)) %>% 
  group_by(rush_player_id) %>% 
  summarize(epa = mean(EPA, na.rm = TRUE),
            player_name = first(rusher_player_name),
            team = first(pos_team),
            n = n()) %>% 
  filter(n >= 10) %>% 
  arrange(desc(epa)) %>% 
  slice(1:10)

player_plot_data %>% 
  mutate(team_ordered = fct_reorder(team,epa)) %>% 
  ggplot(aes(y = team_ordered, x = epa)) +
  geom_col(aes(color = team, fill = team)) +
  geom_label(aes(label = player_name, x = epa / 2), alpha = .6) +
  geom_cfb_headshots(aes(player_id = rush_player_id, x = epa + .1), height = .1) +
  scale_color_cfb(alt_colors = valid_team_names()) +
  scale_fill_cfb() +
  labs(y = "", x = "EPA per Rush") +
  scale_y_cfb(size = 18) +
  theme_minimal() +
  theme(legend.position = "none",
        panel.grid.major.y = element_blank()) +
  theme_y_cfb()
## Warning in png::readPNG(get_file(path), native = TRUE): libpng warning: iCCP:
## known incorrect sRGB profile

Tables with cfbplotR

The gt package offers an easy way to create nice tables of data and the gtExtras package from Tom Mock provides a number of convenient functions for styling those tables. The gt_fmt_cfb_logo() function is a slightly modified version of gtExtras::gt_image_rows() to easily add team and conference logos based on names from valid_team_names(). We can quickly make a table showing the top teams from week 1 by EPA per pass.

library(gt)

team_plot_data %>% 
  transmute(conference, team,logo = team, 
            pass_epa = round(pass_epa,2),n_pass, 
            rush_epa = round(rush_epa,2),n_rush) %>% 
  arrange(desc(pass_epa)) %>% 
  head(8) %>% 
  gt() %>% 
  gt_fmt_cfb_logo(columns = c(conference,logo)) 
conference team logo pass_epa n_pass rush_epa n_rush
Texas 0.72 29 0.01 39
Michigan 0.70 17 0.31 41
Boston College 0.65 30 0.07 35
Auburn 0.59 27 0.64 35
Ohio State 0.50 22 0.36 26
Ole Miss 0.50 33 0.14 39
Minnesota 0.49 26 -0.05 48
Virginia 0.49 32 -0.08 31

We can also use the gt_fmt_cfb_headshot() function to add headshots to a gt using the player_id or headshot_url available through cfbfastR.

player_plot_data %>% 
  select(team,rush_player_id,player_name,n,epa) %>% 
  gt() %>% 
  gt_fmt_cfb_logo(team) %>% 
  gt_fmt_cfb_headshot(rush_player_id)
team rush_player_id player_name n epa
Adrian Martinez 15 1.6017047
Zach Charbonnet 17 0.8643612
Kenneth Walker III 23 0.8290600
Jashaun Corbin 15 0.7706696
Tank Bigsby 16 0.7607350
Malik Cunningham 16 0.7015135
DeAndre Torrey 24 0.7010476
Matt Corral 10 0.6226238
Tahj Brooks 13 0.5926412
Abram Smith 19 0.5819387