This is a parameterized report that showcases the benefits of reproducing analysis at the click of a button.
It makes it very easy to update any work and alter any input parameters within the report.
This report is adaptable to the 4 semifinalists of the Euro 2024: Spain, France, England, Netherlands.
# Parameters are set in yaml header and retrieved here
my_teams <-
tibble(
code = c(params$code_a, params$code_b),
fullname = c(params$fullname_a, params$fullname_b))
# # Same as
# my_teams <-
# tibble(
# code = c("NED", "ENG"),
# fullname = c("Netherlands", "England"))The clash between Netherlands and England in Euro 2024 semifinals is set to be a thrilling and highly anticipated match. Check out this quantitative comparison between the two teams.
Here are the teams’ venues and training camps across the Euro 2024.
# Write function for plotting
plot_team_journey <- function(team, show_legend = TRUE) {
match_data <- euro_2024_matches %>%
filter(home_team_code == team | away_team_code == team) %>%
# since we use geom_text_repel() a city would be plotted twice in different positions
distinct(stadium_city, .keep_all = TRUE)
basecamp_data <- filter(basecamps, team_code == team)
ggplot() +
# Plot German map with map package data
geom_polygon(data = germany,
aes(x = long, y = lat, group = group),
colour = "grey10", fill = "#fff7bc") +
geom_point(data = match_data,
aes(x = stadium_longitude, y = stadium_latitude, color = "Venues")) +
geom_point(data = basecamp_data,
aes(x = long, y = lat, color = "Basecamp"), shape = 15) +
geom_text_repel(data = basecamp_data,
aes(label = basecamp, x = long, y = lat, color = "Basecamp"),
show.legend = FALSE,
hjust = -0.05) +
geom_text_repel(data = match_data,
aes(label = stadium_city, x = stadium_longitude, y = stadium_latitude, color = "Venues"),
show.legend = FALSE) +
scale_color_manual(name = "",
values = c("Venues" = "black", "Basecamp" = "blue")) +
theme_void() +
# Use paste() function to enquote team variable
ggtitle(paste0(team)) +
theme(plot.title = element_text(hjust = 0.5),
legend.position = "top")
}
# Show both plots in the same pane
grid.arrange(plot_team_journey(my_teams$code[1]),
plot_team_journey(my_teams$code[2]),
ncol = 2)
euro_2024_matches_pivoted <- euro_2024_matches %>%
filter(date < params$match_day) %>%
select(id_match, starts_with("home"), starts_with("away")) %>%
pivot_longer(
# pivot all columns except id_match
cols = -id_match,
# split into multiple columns
names_to = c("Location", # receives the values "home" or "away"
".value"), # the remaining part of the column names should become the names of the new columns
names_pattern = "(home|away)_(.*)") # how to split into multiple columns (".*" matches the ".value" from before)
euro_2024_matches_pivoted_joined <- euro_2024_matches_pivoted %>%
left_join(euro_2024_matches_pivoted,
join_by(id_match),
suffix = c("", "_against"),
# set relationship to silence the warning
relationship = "many-to-many") %>%
filter(team != team_against)Scoring dynamics: comparing the number of goals scored and received by each team throughout the current tournament.
euro_2024_matches_pivoted_joined_goal_summary <- euro_2024_matches_pivoted_joined %>%
filter(!is.na(score)) %>%
group_by(Team = team) %>% # group_by() and rename
summarise(
`Matches played` = n(),
`Goals scored` = sum(score),
`Goals received` = sum(score_against),
`Goals scored per match` = mean(score),
`Goals received per match` = mean(score_against))
euro_2024_matches_pivoted_joined_goal_summary %>%
select(1:4) %>%
filter(Team %in% c(my_teams$fullname)) %>%
kable()| Team | Matches played | Goals scored | Goals received |
|---|---|---|---|
| England | 5 | 4 | 3 |
| Netherlands | 5 | 9 | 5 |
Let’s put this performance in visual relation to all other teams.
euro_2024_matches_pivoted_joined_goal_summary %>%
ggplot(aes(x = `Goals scored per match`,
y = `Goals received per match`)) +
geom_point(aes(colour = Team %in% c(my_teams$fullname),
size = Team %in% c(my_teams$fullname))) +
geom_text_repel(aes(label = Team,
colour = Team %in% c(my_teams$fullname)),
nudge_y = .05) +
scale_size_manual(values = c(2, 3)) +
scale_color_manual(values = c("black", "red")) +
theme(legend.position = "none")
Inside the teams: what are their strengths and weaknesses?
For each team, we look at the average per player of
euro_2024_players_summary <- euro_2024_players %>%
group_by(Country) %>%
summarise(avg_caps = round(mean(Caps)),
avg_goals = round(mean(Goals)),
avg_value = round(mean(MarketValue)),
avg_height = round(mean(Height)),
avg_age = round(mean(Age)))
euro_2024_players_summary %>%
filter(Country %in% my_teams$fullname) %>%
transmute(
Country,
`Caps` = avg_caps,
`Goals` = avg_goals,
`Market Value (M€)` = round((avg_value / 1000000)),
`Height (cm)` = avg_height,
`Age` = avg_age) %>%
kable()| Country | Caps | Goals | Market Value (M€) | Height (cm) | Age |
|---|---|---|---|---|---|
| England | 25 | 4 | 58 | 182 | 26 |
| Netherlands | 32 | 5 | 32 | 185 | 27 |
For a clearer picture, here’s a visual breakdown.
# Write function to bind_rows regardless of column names
# Thanks to https://exploratory.io/note/kanaugust/How-to-Force-Merging-Rows-Ignoring-Columns-Names-xpI8bqI4Tm
force_bind <- function(tbl1, tbl2) {
colnames(tbl2) = colnames(tbl1)
bind_rows(tbl1, tbl2)
}
euro_2024_players_summary_filtered <- euro_2024_players_summary %>%
filter(Country %in% my_teams$fullname)
radarchart_data <- euro_2024_players_summary_filtered %>%
force_bind(
euro_2024_players_summary %>%
summarise("0_max", # For sorting later
max(avg_caps),
max(avg_goals),
max(avg_value),
max(avg_height),
max(avg_age))) %>%
force_bind(
euro_2024_players_summary %>%
summarise("1_min", # For sorting later
min(avg_caps),
min(avg_goals),
min(avg_value),
min(avg_height),
min(avg_age))) %>%
# arrange() to get maximum values as row 1 and minimum values as row 2
arrange(Country) %>%
select(-Country)
# Set the plot dimensions (width, height)
par(pin = c(5, 5))
colours <- c("blue", "red")
radarchart_data %>%
radarchart(
# custom polygon
pcol = colours,
pfcol = adjustcolor(colours, alpha.f = 0.3),
plwd = 2,
plty = 1,
vlabels=c("Caps", "Goals",
"Market Value", "Height", "Age"),
# custom the grid
cglcol = "#000000",
cglty = 1,
axislabcol = "#000000",
cglwd = 1
)
mtext(paste0(my_teams$fullname, collapse = " vs "), side = 3, line = 0.5, cex = 2, at = 0, font = 1,
col = "#000000")
legend("topright",
bty = "n", # to avoid a box around the plot
legend = euro_2024_players_summary_filtered$Country, # get values like this to make sure the order corresponds to color values
col = colours,
lty = 1,
lwd = 5)
Spotlight on the stars: who are the key players?
euro_2024_players_max_goals <- euro_2024_players %>%
group_by(Country) %>%
filter(Goals == max(Goals)) %>%
ungroup()
euro_2024_players_max_caps <- euro_2024_players %>%
group_by(Country) %>%
filter(Caps == max(Caps)) %>%
ungroup()
euro_2024_players_max_value <- euro_2024_players %>%
group_by(Country) %>%
filter(MarketValue == max(MarketValue)) %>%
ungroup()
caps_fct <- function(team) {
euro_2024_players_max_caps %>%
filter(Country %in% team) %>%
glue_data("{Name} with {Caps} Caps")
}
goals_fct <- function(team) {
euro_2024_players_max_goals %>%
filter(Country %in% team) %>%
glue_data("{Name} with {Goals} Goals")
}
value_fct <- function(team) {
euro_2024_players_max_value %>%
filter(Country %in% team) %>%
mutate(MarketValue = MarketValue / 1000000) %>%
glue_data("{Name} with {MarketValue} M€ of market value")
}England appears to have the strongest team according to their players’ market value. The Netherlands, on the other side, have a very balanced squad.
We’ll know who moves to the final tonight.
euro_2024_matches.csv downloaded from Kaggle - dataset name: Football - Soccer - UEFA EURO, 1960 - 2024.euro_2024_players.csv (player statistics from before the European Championship) downloaded from Kaggle - dataset name: UEFA EURO 2024 - Players.