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soccerPassNetEventing.R
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soccerPassNetEventing.R
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soccerPassNetEventing <- function(gameID = 7545, TeamName = "Argentina", poss = F, pass_dir = T, convex = T,
minPass = 3, node_pos = "origin", nodeFill = "blue", edgeAlpha = 0.8, edgeCol = "black",
label = TRUE, shortNames = TRUE, maxNodeSize = 18, maxEdgeSize = 6,
labelSize = 4, title = NULL, Flipx = F, field = 1) {
##read background image
img <- jpeg::readJPEG(paste0("fields/football-pitch_cut", field, ".jpg"))
g <- rasterGrob(img, width=unit(1,"npc"), height=unit(1,"npc"), interpolate = F)
## read data
df <- readRDS("data/SB_WC_events_clean.RDS") %>%
filter(match_id == gameID)
## set variable names, filter team and transform coordinates
df_events <- df %>%
soccermatics::soccerTransform(method = "statsbomb") %>%
rename("x" = "location.x",
"y" = "location.y",
"endx" = "pass.end_location.x",
"endy" = "pass.end_location.y",
"From" = "player.name",
"team" = "team.name") %>%
mutate(y = 68 - y,
endy = 68 - endy,
x = ifelse(Flipx & !is.na(x), 105 - x, x),
endx = ifelse(Flipx & !is.na(x), 105 - endx, endx))
# # Proteger si Equipo no está en el match ID ingresado
data <- df_events %>%
filter(team == TeamName)
#Passes until time of 1st substitution or red card
first_red_card_minute <- data %>%
filter(foul_committed.card.name %in% c("Second Yellow", "Red Card")) %>%
summarise_at("minute", min, na.rm = T) %>%
as.numeric()
first_substitution_minute <- data %>%
filter(type.name == "Substitution") %>%
summarise_at("minute", min, na.rm = T) %>%
as.numeric()
max_minute <- max(data$minute, na.rm = T)
num_minutes <- min(first_red_card_minute, first_substitution_minute, max_minute, na.rm = T)
## passes data to use
passes <- data %>%
filter(type.name == "Pass" & team == TeamName & minute < num_minutes)
c_passes <- passes %>%
filter(is.na(pass.outcome.name))
### get nodes and edges for plotting
# node position (tracking mean position) and size (based on number of passes)
if(node_pos == "both"){
nodes <- c_passes %>%
group_by(From) %>%
dplyr::summarise(xini = median(x, na.rm=T), yini = median(y, na.rm=T),
xend = median(endx, na.rm=T), yend = median(endy, na.rm=T),
num_pass = n()) %>%
mutate(x = (xini + xend)/2, y = (yini + yend)/2) %>%
na.omit() %>%
mutate(size = scales::rescale(num_pass, c(2, maxNodeSize), c(min(num_pass), max(num_pass))))
location_text <- "Location: origin & end of passes"
}
if(node_pos == "origin"){
nodes <- c_passes %>%
group_by(From) %>%
dplyr::summarise(x = median(x, na.rm=T), y = median(y, na.rm=T), num_pass = n()) %>%
na.omit() %>%
mutate(size = scales::rescale(num_pass, c(2, maxNodeSize), c(min(num_pass), max(num_pass))))
location_text <- "Location: origin of passes"
}
## edges based only on completed passes [pass_dir = T means splitting by direction and ploting arrows]
if(pass_dir){
segmentsDf <- function(data, shorten.start, shorten.end, offset){
data$dx = data$xend - data$x
data$dy = data$yend - data$y
data$dist = sqrt( data$dx^2 + data$dy^2 )
data$px = data$dx/data$dist
data$py = data$dy/data$dist
data$x = data$x + data$px * shorten.start
data$y = data$y + data$py * shorten.start
data$xend = data$xend - data$px * shorten.end
data$yend = data$yend - data$py * shorten.end
data$x = data$x - data$py * offset
data$xend = data$xend - data$py * offset
data$y = data$y + data$px * offset
data$yend = data$yend + data$px * offset
return(data)
}
edgelist <- c_passes %>%
select(from = From, to = pass.recipient.name) %>%
group_by(from, to) %>%
dplyr::summarise(pass_value = n()) %>%
na.omit()
edges <- edgelist %>%
left_join(nodes %>% select(From, x, y), by = c("from" = "From")) %>%
left_join(nodes %>% select(From, xend = x, yend = y), by = c("to" = "From")) %>%
segmentsDf(3, 3, 0.6)
arrow <- arrow(type = "closed", angle = 30, length = unit(0.1, "inches"))
}else{
edgelist <- c_passes %>%
select(from = From, to = pass.recipient.name) %>%
mutate(pairs = paste(pmin(from, to), pmax(from, to), sep = "-")) %>%
group_by(pairs) %>%
summarise(pass_value = n()) %>%
mutate(name1 = sub("-.*", "", pairs),
name2 = sub(".*-", "", pairs))
edges <- edgelist %>%
left_join(nodes %>% select(From, x, y), by = c("name1" = "From")) %>%
left_join(nodes %>% select(From, xend = x, yend = y), by = c("name2" = "From"))
arrow <- NULL
}
edges <- edges %>%
filter(pass_value >= minPass) %>%
ungroup() %>%
mutate(size = scales::rescale(pass_value, c(0.1, maxEdgeSize), c(min(pass_value), max(pass_value))))
### Stats
## Passing stats
pass_n <- nrow(passes)
pass_pc <- nrow(c_passes)/nrow(passes) * 100
pass_length_m <- median(c_passes$pass.length, na.rm = T)
## Posession stats
poss_data <- df %>%
filter(minute < num_minutes) %>%
group_by(possession_team.name, possession) %>%
summarise(num_pass = sum(type.name == "Pass"),
pT = max(TimeInPoss))
teams_poss <- poss_data %>%
group_by(possession_team.name) %>%
summarise(num_poss = n(),
total_pass = sum(num_pass),
total_pT_min = sum(pT)/60)
effective_poss_time <- sum(teams_poss$total_pT_min)
##Subtitle
var_minute <- ifelse(first_red_card_minute == num_minutes, "until first red card",
ifelse(first_substitution_minute == num_minutes, "until first substitution", "until game finished"))
subtitle <- paste0("Data ", var_minute ,": 1' - ", round(num_minutes, 0), "'. ")
subtitle <- paste0(subtitle, "Effective played time: ", round(effective_poss_time, 0), " mins. ", minPass, "+ passes shown.")
poss_data <- teams_poss %>%
filter(possession_team.name == TeamName)
possession <- poss_data$num_poss
pass_per_poss <- poss_data$total_pass / possession
poss_minutes <- poss_data$total_pT_min
poss_percent <- poss_data$total_pT_min / effective_poss_time *100
pass_per_1M_poss <- poss_data$total_pass / poss_minutes
## shorten player name
if(shortNames) {
nodes$From <- sub(":", " ", sub(".* ", "", sub(" (di|De|de|El|el|Da|da|Dos|dos|Van|van|Von|von|Le|le|La|la|N') ", " \\1:", nodes$From)))
}
# if no title given
if(is.null(title)) {
title <- paste0(TeamName, "'s passing network against ", setdiff(unique(df_events$team), TeamName), " (Statsbomb eventing data)")
}
if(convex){
# convex hull
if(Flipx == T){
xown = 105
xopp = 0
xGK = max(nodes$x)
}else{
xown = 0
xopp = 105
xGK = min(nodes$x)
}
hull_data <- nodes %>%
filter(x != xGK) %>% # removing GK
dplyr::select(x, y) %>%
slice(chull(x, y))
hull_center <- hull_data %>%
summarise(xmean = mean(x), ymean = mean(y))
dist_own_goal <- sqrt( (hull_center$xmean - xown)^2 + (hull_center$ymean - 34)^2 )
dist_opp_goal <- sqrt( (hull_center$xmean - xopp)^2 + (hull_center$ymean - 34)^2 )
amplitude <- max(hull_data$y) - min(hull_data$y)
depth <- max(hull_data$x) - min(hull_data$x)
occupied_area <- areapl(cbind(hull_data$x, hull_data$y))
xlabel <- 104
ylabel <- 64
hjust <- 1
}else{
xlabel <- 1
ylabel <- 1
hjust <- 0
}
if(field == 3){
colText = "black"
colConvex = "darkblue"
} else{
colText = "white"
colConvex = "gold2"
}
# plot network
p <- ggplot() +
annotation_custom(g, xmin = -0.5, xmax = 105.5, ymin = -0.5, ymax = 68.5) +
scale_x_continuous(limits = c(-0.5, 105.5), expand = c(0,0)) +
scale_y_continuous(limits = c(-0.5, 68.5), expand = c(0,0)) +
labs(x = "", y = "", title = title, subtitle = subtitle, caption = "@DatoFutbol_cl") +
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
plot.title = element_text(vjust = -0.2, size = 14),
plot.subtitle = element_text(size = 10, face = "italic"),
plot.caption = element_text(size = 10, vjust = 7),
plot.margin = unit(c(0.6, 0.8, 0.2, 0.4), "cm"))
# Convex hull
if (convex == T) {
p <- p + geom_polygon(data = hull_data, aes(x=x, y=y), col = colConvex, fill = "transparent", alpha = 0.2, size = 0.7) +
geom_point(data = hull_center, aes(x=xmean, y=ymean), pch = 21, col = colConvex,
fill = "black", stroke = 2, size = 6) +
geom_text_repel(data = hull_center, aes(x=xmean, y=ymean), label = "CENTROID", point.padding = 0.5,
size = 2, col=colConvex, alpha=0.8, nudge_y = 1) +
annotate("text", 1, 1, label = paste0("Distance to own goal: ", round(dist_own_goal, 1), " [m]",
"\nDistance to opponent goal: ", round(dist_opp_goal, 1), " [m]",
"\nAmplitude: ", round(amplitude, 1), " [m]",
"\nDepth: ", round(depth, 1), " [m]",
"\nOccupied area: ", round(occupied_area, 1), " [m2]"),
hjust = 0, vjust = 0, size = 4 * 7/8, col = colConvex)
}
p <- p +
geom_segment(data = edges, aes(x = x, y = y, xend = xend, yend = yend, size = size),
arrow = arrow, alpha = edgeAlpha) +
geom_point(data = nodes, aes(x = x, y = y, size = size),
pch = 21, fill = nodeFill, col = "white", alpha = 0.8, stroke = 1.5) +
scale_size_identity() +
scale_alpha_identity() +
guides(size = F) +
(if (poss){
annotate("text", 104, 1, label = paste0("Total passes: ", pass_n,
"\nCompleted: ", sprintf("%.1f", pass_pc),
"%\nMedian pass length:", round(pass_length_m,1), " [m]",
"\nPossession time: ", round(poss_minutes, 1), " min. (", round(poss_percent, 0), "%)",
"\nPasses per min. possession: ", round(pass_per_1M_poss, 1),
"\nPasses per possesion: ", round(pass_per_poss, 1)),
hjust = 1, vjust = 0, size = labelSize * 7/8, col = colText)
} else{
annotate("text", 104, 1, label = paste0("Total Passes: ", pass_n,
"\nCompleted: ", sprintf("%.1f", pass_pc), "%\n"),
hjust = 1, vjust = 0.3, size = labelSize * 6.5/8, col = colText)
}) +
annotate("text", xlabel, ylabel, label = paste0(location_text,
"\nSize: number of passes"),
hjust = hjust, vjust = 0, size = labelSize * 7/8, col = "grey")
# add labels
if(label) {
p <- p +
geom_label_repel(data = nodes, aes(x, y, label=From), point.padding = 0.1,
size = labelSize, col="black", fill="white", alpha=0.8,
min.segment.length = 1)
}
return(p)
}