258 lines
8.8 KiB
R
258 lines
8.8 KiB
R
##################### The first dataset ##############################
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## Loading R packages and source the "getshots" customized own function
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library(jsonlite)
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library(tidyverse)
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library(ggsoccer)
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library(dplyr)
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# code and data from https://github.com/Dato-Futbol/xg-model
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get_shots <- function(file_path, name_detail, save_files = F){
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players <- fromJSON("data/players.json")
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shots <- fromJSON(file_path) %>%
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filter(subEventName == "Shot")
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tags <- tibble(tags = shots$tags) %>%
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hoist(tags,
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tags_id = "id") %>%
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unnest_wider(tags_id, names_sep = "")
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tags2 <- tags %>%
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mutate(is_goal = ifelse(rowSums(. == "101", na.rm = T) > 0, 1, 0),
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is_blocked = ifelse(rowSums(. == "2101", na.rm = T) > 0, 1, 0),
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is_CA = ifelse(rowSums(. == "1901", na.rm = T) > 0, 1, 0), # is countre attack
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body_part = ifelse(rowSums(. == "401", na.rm = T) > 0, "left",
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ifelse(rowSums(. == "402", na.rm = T) > 0, "right",
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ifelse(rowSums(. == "403", na.rm = T) > 0, "head/body", "NA"))))
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pos <- tibble(positions = shots$positions) %>%
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hoist(positions,
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y = "y",
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x = "x") %>%
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unnest_wider(y, names_sep = "") %>%
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unnest_wider(x, names_sep = "") %>%
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dplyr::select(-c(x2, y2))
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shots_ok <- shots %>%
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dplyr::select(matchId, teamId, playerId, eventSec, matchPeriod) %>%
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bind_cols(pos, tags2) %>%
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filter(is_blocked == 0) %>%
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dplyr::select(-c(8:13)) %>%
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left_join(players %>%
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dplyr::select(c("wyId", "foot")), by = c("playerId" = "wyId")) %>%
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mutate(league = name_detail)
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if(save_files){
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write_rds(shots, paste0("shots", name_detail, ".rds"))
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write_rds(tags2, paste0("tags2", name_detail, ".rds"))
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write_rds(pos, paste0("pos", name_detail, ".rds"))
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write_rds(shots_ok, paste0("unblocked_shots", name_detail, ".rds"))
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}
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shots_ok
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}
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# shotsEN <- get_shots("data/events/events_England.json", "EN")
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# shotsSP <- get_shots("data/events/events_Spain.json", "SP")
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# shotsWC <- get_shots("data/events/events_World_Cup.json", "WC")
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# shotsIT <- get_shots("data/events/events_Italy.json", "IT")
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# shotsGE <- get_shots("data/events/events_Germany.json", "GE")
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# shotsFR <- get_shots("data/events/events_France.json", "FR")
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# shotsEC <- get_shots("data/events/events_European_Championship.json", "EC")
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#
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# shots <- shotsEN %>%
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# bind_rows(shotsFR, shotsGE, shotsIT, shotsSP, shotsWC, shotsEC)
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get_final_data <- function(data) {
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data <- data %>% select(eventSec, y1, x1, is_goal, is_blocked, is_CA, body_part, foot)
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data$x1 <- (100 - data$x1) * 105/100
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data$y1 <- data$y1 * data$y1/100
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data <- data %>% mutate(angle = atan(7.32 * x1 / (x1^2 + y1^2 - (7.32/2)^2)))
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data$angle <- ifelse(data$angle<0, base::pi + data$angle, data$angle)
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data <- data %>% mutate(distance = sqrt( (100 - x1)^2 + (34 - y1)^2),
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minute = round(eventSec / 60),
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eventSec = round(eventSec))
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data
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}
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# data1 <- get_final_data(shots)
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# write.csv(data1, file = "data/data1.csv")
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##################### The second dataset ##############################
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get_data <- function(event_path, info_path) {
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events <- read.csv(event_path)
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info <- read.csv(info_path)
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events <- merge(events, info[, c('id_odsp', 'country', 'date')], by = 'id_odsp', all.x = TRUE)
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data <- subset(events, event_type == 1)
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data_final <- data %>% select(sort_order, time, shot_place, shot_outcome, is_goal, location, bodypart, assist_method, situation,
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fast_break)
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data_final
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}
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# data2 <- get_data(event_path = "data/events.csv", info_path = "data/ginf.csv")
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# write.csv(data2, file = "data/data2.csv")
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# TODO dodać kolumne z x y coordinates bazując na location
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# TODO dodać kolumnę z kątem do bramki
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##################### The third dataset ##############################
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# TODO not all json data have these same keys/fields, customize function to cover all cases
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get_shots2 <- function(json_file) {
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data <- fromJSON(json_file) %>% filter(type$name == "Shot") %>% dplyr::select(c(minute, position, location, shot))
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df_temp <- do.call(rbind, data$location)
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colnames(df_temp) <- c("x1", "y1")
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data$x1 <- df_temp[,1]
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data$y1 <- df_temp[,2]
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tryCatch({ # TODO reduce error cases
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data$number_of_players_opponents <- mapply(function(sublist, x1_threshold) {
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# Extracting the first location value and converting it to numeric
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first_location_values <- sapply(sublist$location, function(loc) as.numeric(loc[1]))
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if ("teammate" %in% names(sublist)) {
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# Filtering and counting
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res <- sum(!sublist$teammate & first_location_values > x1_threshold) # error here
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} else {
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res <- 0
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}
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res
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}, data$shot$freeze_frame, data$x1)
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},
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error = function(e) {
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# handle the error
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print(paste("An error occurred:", e$message))
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})
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tryCatch({ # TODO reduce error cases
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data$number_of_players_teammates <- mapply(function(sublist, x1_threshold) {
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# Extracting the first location value and converting it to numeric
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first_location_values <- sapply(sublist$location, function(loc) as.numeric(loc[1]))
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if ("teammate" %in% names(sublist)) {
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# Filtering and counting
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res <- sum(sublist$teammate & first_location_values > x1_threshold) # error here
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} else {
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res <- 0
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}
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res
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}, data$shot$freeze_frame, data$x1)
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},
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error = function(e) {
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# handle the error
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print(paste("An error occurred:", e$message))
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})
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data$shot <- data$shot %>% select(-freeze_frame, -statsbomb_xg, -key_pass_id)
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data$shot$body_part <- data$shot$body_part %>% select(-id)
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data$shot$technique <- data$shot$technique %>% select(-id)
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data$shot$type <- data$shot$type %>% select(-id)
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data$position <- data$position %>% select(-id)
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data$shot <- data$shot %>% select(-end_location)
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tryCatch({ # TODO reduce error cases
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if ("one_on_one" %in% colnames(data$shot)) {
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data[is.na(data$shot$one_on_one), ]$shot$one_on_one <- FALSE
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} else {
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data$shot$one_on_one <- FALSE
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}
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if ("first_time" %in% colnames(data$shot)) {
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data[is.na(data$shot$first_time), ]$shot$first_time <- FALSE
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} else {
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data$shot$first_time <- FALSE
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}
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if ("aerial_won" %in% colnames(data$shot)) {
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data[is.na(data$shot$aerial_won), ]$shot$aerial_won <- FALSE
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} else {
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data$shot$aerial_won <- FALSE
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}
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if ("saved_to_post" %in% colnames(data$shot)) {
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data[is.na(data$shot$saved_to_post), ]$shot$saved_to_post <- FALSE
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} else {
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data$shot$saved_to_post <- FALSE
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}
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if ("deflected" %in% colnames(data$shot)) {
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data[is.na(data$shot$deflected), ]$shot$deflected <- FALSE
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} else {
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data$shot$deflected <- FALSE
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}
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if ("saved_off_target" %in% colnames(data$shot)) {
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data[is.na(data$shot$saved_off_target), ]$shot$saved_off_target <- FALSE
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} else {
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data$shot$saved_off_target <- FALSE
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}
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if ("open_goal" %in% colnames(data$shot)) {
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data[is.na(data$shot$open_goal), ]$shot$open_goal <- FALSE
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} else {
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data$shot$open_goal <- FALSE
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}
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if ("follows_dribble" %in% colnames(data$shot)) {
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data[is.na(data$shot$follows_dribble), ]$shot$follows_dribble <- FALSE
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} else {
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data$shot$follows_dribble <- FALSE
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}
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if ("redirect" %in% colnames(data$shot)) {
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data[is.na(data$shot$redirect), ]$shot$redirect <- FALSE
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} else {
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data$shot$redirect <- FALSE
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}
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if ("kick_off" %in% colnames(data$kick_off)) {
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data[is.na(data$shot$kick_off), ]$shotf$kick_off <- FALSE
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} else {
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data$kick_off <- FALSE
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}
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},
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error = function(e) {
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# handle the error
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print(paste("An error occurred:", e$message))
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})
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data <- data %>% mutate(angle = atan(7.32 * x1 / (x1^2 + y1^2 - (7.32/2)^2)),
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is_goal = ifelse(shot$outcome$id == 97, 1, 0)) %>%
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select(-location)
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data$angle <- ifelse(data$angle<0, base::pi + data$angle, data$angle)
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data$shot$outcome <- data$shot$outcome %>% select(-id)
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data <- data %>% unnest(shot, names_sep = "_") %>%
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unnest(position, names_sep = "_") %>%
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unnest(shot_type, names_sep = "_") %>%
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unnest(shot_outcome, names_sep = "_") %>%
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unnest(shot_technique, names_sep = "_") %>%
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unnest(shot_body_part, names_sep = "_")
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data
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}
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# file_names <- list.files(path = "data/la_liga_events/", pattern = "*.json")
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# data_list <- lapply(paste("data/la_liga_events/", file_names, sep = ""), get_shots2)
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# combined_data <- do.call(rbind, data_list)
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# ss <- lapply(data_list, nrow)
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# sss <- unlist(ss)
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#
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# # sample data
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# data_test <- get_shots2("data/la_liga_events/303377.json")
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# write.csv(data_test, file = "data/data3_test.csv")
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##################### The fourth dataset ##############################
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