library(tidyverse)
# install.packages("devtools")
# devtools::install_github("nwslR/nwslR")
library(nwslR)
library(ggimage)
library(showtext)
library(sysfonts)
library(scales)
library(ggpattern)
library(forcats)
library(gtExtras)
# Load custom font
font_legend <- "Source Sans Pro"
font_add_google(font_legend)
showtext_opts(dpi = 300, regular.wt = 200, bold.wt = 700)
showtext_auto(enable = TRUE)About
nwslR is an R ecosystem that contains datasets and analysis functionality for the National Women’s Soccer League (NWSL). The package was developed by Arielle Dror and Lydia Jackson, with more information here. As a women’s soccer fan, I felt it was only right to provide some open-source code to visualize some soccer data!
Load packages
Explore 2024 data
teams_2024_names <- c("CHI","HOU","NJY","RGN","ORL","POR","WAS",
"NC", "UTA","KCC","LOU","LA" ,"SD")
matches_2024 <- load_matches() |>
filter(season == 2024)
teams_2024 <- load_teams() |>
filter(team_abbreviation %in% teams_2024_names) |>
select(team_name, team_abbreviation, external_team_id) |>
rename(team_id = external_team_id)
team_stats_2024 <- team_stats <- purrr::map_df(teams_2024_names,
~load_team_season_stats(
team_id = .x,
season ="2024"),
.progress = TRUE)
team_stats_w_names_2024 <- team_stats_2024 |>
left_join(teams_2024, by = "team_id") |>
select(team_name, team_abbreviation, yellow_cards,
offsides,shot_accuracy, goals, corners_taken,
crosses_corners_successful, passes_total, penalties_saved)
team_stats_w_names_2024# A tibble: 13 × 10
team_name team_abbreviation yellow_cards offsides shot_accuracy goals
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Chicago Red Stars CHI 32 49 48.9 32
2 Houston Dash HOU 47 42 44.1 20
3 NJ/NY Gotham FC NJY 50 51 52.0 44
4 OL Reign RGN 27 40 46.6 27
5 Orlando Pride ORL 35 44 53.4 54
6 Portland Thorns … POR 30 48 54.7 38
7 Washington Spirit WAS 39 49 54.0 54
8 North Carolina C… NC 29 56 45.6 34
9 Utah Royals FC UTA 40 42 49.6 22
10 Kansas City Curr… KCC 31 56 49.6 60
11 Racing Louisvill… LOU 40 54 47.0 33
12 Angel City FC LA 45 51 55.2 29
13 San Diego Wave FC SD 33 46 44.3 24
# ℹ 4 more variables: corners_taken <dbl>, crosses_corners_successful <dbl>,
# passes_total <dbl>, penalties_saved <dbl>Add team with their associated logos
team_stats_with_logos <- team_stats_w_names_2024 |>
mutate(
# fix names
team_name = case_when(
str_detect(team_name, "Chicago") ~ "Chicago Red Stars",
str_detect(team_name, "Houston") ~ "Houston Dash",
str_detect(team_name, "Kansas City") ~ "Kansas City Current",
str_detect(team_name, "Gotham") ~ "NJ/NY Gotham FC",
str_detect(team_name, "North Caroli") ~ "North Carolina Courage",
str_detect(team_name, "OL Reign") ~ "OL Reign",
str_detect(team_name, "Orlando") ~ "Orlando Pride",
str_detect(team_name, "Portland") ~ "Portland Thorns",
str_detect(team_name, "Racing") ~ "Racing Louisville",
str_detect(team_name, "Angel") ~ "Angel City FC",
str_detect(team_name, "San Diego") ~ "San Diego Wave",
str_detect(team_name, "Utah") ~ "Utah Royals FC",
str_detect(team_name, "Washington") ~ "Washington Spirit",
TRUE ~ team_name
),
# add image path automatically
logo_path = paste0(
"team-logos/",
str_replace_all(team_name, c(
"/" = "_", " " = "_"
)),
".png"
)
)Apply logos to compare goals vs. shot accuracy across teams
ggplot(team_stats_with_logos, aes(x = goals, y = shot_accuracy)) +
geom_image(aes(image = logo_path), size = 0.06, by = "width") +
theme_minimal() +
labs(title = "Goals vs. Shot Accuracy, 2024 NWSL Regular Season",
x = "Goals",
y = "Shot Accuracy") +
scale_y_continuous(labels = label_percent(scale = 1)) +
theme(
text = element_text(size = 16, color = "black", family = font_legend,
margin = margin(r = 5, t = 5, b = 10, l = 20)),
plot.margin = margin(t = 10, r = 10, b = 10, l = 10),
plot.title = element_text(size = 25, face = "bold", family = font_legend,
margin = margin(t = 10, b = 10)),
axis.title.x = element_text(margin(t = 10), family = font_legend),
axis.title.y = element_text(margin(r = 10), family = font_legend)
)
Ranking of total passes
passes_total_order <- team_stats_with_logos |>
mutate(
team_name = fct_reorder(team_name, passes_total),
passes_label = passes_total
)
ggplot(passes_total_order, aes(x = passes_total, y = team_name)) +
geom_segment(aes(x = 0, xend = passes_total, yend = team_name),
linewidth = 1.2, color = "black") +
geom_point(shape = 21, size = 14, fill = "white",
stroke = 1.2, color = "black") +
geom_text(aes(label = scales::comma(passes_label)),
size = 3, fontface = "bold") +
geom_image(aes(x = -1000, image = logo_path), size = 0.04, asp = 1.4) +
scale_y_discrete(NULL) +
scale_x_continuous(expand = expansion(mult = c(0.2, 0.05))) +
labs(
title = "Total Passes, 2024 NWSL Regular Season",
x = NULL,
y = NULL
) +
theme_minimal() +
theme(
text = element_text(size = 16, color = "black", family = font_legend,
margin = margin(r = 5, t = 5, b = 10, l = 20)),
plot.margin = margin(t = 10, r = 10, b = 10, l = 10),
plot.title = element_text(size = 25, face = "bold", family = font_legend,
margin = margin(t = 10, b = 10)),
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.grid = element_blank()
)
Table of 2024 NWSL Regular season data
table_data <- team_stats_with_logos |>
select(logo_path, team_name, goals, shot_accuracy, passes_total, offsides, yellow_cards,
corners_taken, crosses_corners_successful, penalties_saved) |>
mutate(
shot_accuracy = paste0(round(shot_accuracy, 1), "%")
) |>
arrange(desc(goals))
table_data |>
gt() |>
gt_img_rows(columns = logo_path, height = 35) |>
tab_header(
title = md("**Team Performance Summary**"),
subtitle = md("2024 NWSL Regular Season")
) |>
cols_label(
logo_path = "",
team_name = "Team",
goals = "Goals",
shot_accuracy = "Shot accuracy",
passes_total = "Total passes",
offsides = "Offsides",
yellow_cards = "Yellow cards",
penalties_saved = "Penalties saved",
corners_taken = "Corner kicks taken",
crosses_corners_successful = "Successful corner kick crosses"
) |>
tab_options(
table.font.size = 14,
column_labels.font.weight = "bold",
heading.title.font.size = 20,
heading.subtitle.font.size = 14
)| Team Performance Summary | |||||||||
| 2024 NWSL Regular Season | |||||||||
| Team | Goals | Shot accuracy | Total passes | Offsides | Yellow cards | Corner kicks taken | Successful corner kick crosses | Penalties saved | |
|---|---|---|---|---|---|---|---|---|---|
 |
Kansas City Current | 60 | 49.6% | 11308 | 56 | 31 | 137 | 122 | NA |
 |
Orlando Pride | 54 | 53.4% | 11948 | 44 | 35 | 166 | 118 | NA |
 |
Washington Spirit | 54 | 54% | 12618 | 49 | 39 | 158 | 100 | 2 |
 |
NJ/NY Gotham FC | 44 | 52% | 12452 | 51 | 50 | 139 | 137 | NA |
 |
Portland Thorns | 38 | 54.7% | 11818 | 48 | 30 | 96 | 86 | 1 |
 |
North Carolina Courage | 34 | 45.6% | 14243 | 56 | 29 | 106 | 94 | 1 |
 |
Racing Louisville | 33 | 47% | 10625 | 54 | 40 | 84 | 119 | NA |
 |
Chicago Red Stars | 32 | 48.9% | 9291 | 49 | 32 | 104 | 75 | NA |
 |
Angel City FC | 29 | 55.2% | 11550 | 51 | 45 | 139 | 105 | NA |
 |
OL Reign | 27 | 46.6% | 11638 | 40 | 27 | 109 | 115 | NA |
 |
San Diego Wave | 24 | 44.3% | 11808 | 46 | 33 | 135 | 128 | NA |
 |
Utah Royals FC | 22 | 49.6% | 10512 | 42 | 40 | 110 | 88 | 1 |
 |
Houston Dash | 20 | 44.1% | 9822 | 42 | 47 | 115 | 98 | 1 |