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Extract_Three_Point_Plays.R
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Extract_Three_Point_Plays.R
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## Get Three POint Shots
library(dplyr)
# setwd("/media/ladmin/Data1/Code/basketball/Sportvu")
library(foreach)
library(doParallel)
library(parallel)
source("_functions.R")
library(iterators)
library(stringr)
library(lubridate)
numCores <- detectCores()
cl <- makeCluster(numCores)
registerDoParallel(cl)
pathfl = "data/"
pathflpbp = "data/pbp/" # Path to where you have play by play data stored (pbp)
allFiles =
list.files(path = pathfl, pattern = "*.gz") # Assuming files are in
# data.frame format, but
# compressed as *.gz files
# Simple Function needed during script
insertRow <- function(existingDF, newrow, r) {
existingDF[seq(r + 1, nrow(existingDF) + 1), ] <- existingDF[
seq(r, nrow(existingDF)), ]
existingDF[r, ] <- newrow
existingDF
}
##Select which files to run . . . with 16GB of memory, I was not able to do all 631 games at once
allFiles <- allFiles[1:200]
#allFiles <- allFiles[201:400]
#allFiles <- allFiles[401:631]
#allFiles <- allFiles[1:1] ##This is for testing
rowCounts_chull <- foreach(
filename = allFiles, .packages = c(
'dplyr', 'readr', 'lubridate',
'stringr')) %dopar%
{
# filename = "0021500001" #This is for testing
#Load game data
gameid <- gsub(".csv.gz", "", filename)
filen <- paste0(pathfl, gameid, ".csv.gz", sep = "")
filep <- paste0(pathflpbp, gameid, "_pbp.txt", sep = "")
all.movements <- read.csv(filen)
pbp <- read.csv(filep)
df_total <- NULL
##Filter down data to ball in the air
df_game <- all.movements %>%
select(-X) %>% filter(player_id == "-1") %>% filter(radius > 8) %>%
#XY distance to the basket
mutate(threedist = ifelse(
x_loc < 47,
{
sqrt((x_loc - 5.25) ^ 2 + (y_loc - 25) ^ 2)
}, {
sqrt((x_loc - 88.75) ^ 2 + (y_loc - 25) ^ 2)
})) %>%
#XYZ distance to the basket
mutate(threedistz = ifelse(
x_loc < 47,
{
sqrt((x_loc - 5.25) ^ 2 + (y_loc - 25) ^ 2 +
(radius - 10) ^ 2)
}, {
sqrt((x_loc - 88.75) ^ 2 +
(y_loc - 25) ^ 2 + (radius - 10) ^ 2)
})) %>% arrange(quarter, desc(game_clock)) %>%
distinct(game_clock, .keep_all = TRUE)
## Find the start and end of plays when ball is in the air
shot_break_end <- df_game %>%
mutate(lead_game_clock = lead(game_clock, n = 1)) %>%
filter(game_clock - lead_game_clock > 1) %>%
distinct(game_clock, quarter) %>%
select(game_clock_end = game_clock, quarter)
shot_break_start <- df_game %>%
mutate(lag_game_clock = lag(game_clock, n = 1)) %>%
filter(lag_game_clock - game_clock > 1) %>%
distinct(game_clock, quarter) %>%
select(game_clock_start = game_clock, quarter)
##Creates dataframe with start and end times of ball in the air
r <- 1
newrow <- c(df_game$game_clock[1], df_game$quarter[1]) # Start with first
# time
length <- nrow(shot_break_start)
shot_row <- shot_break_start[length, ]
shot_break_start <- insertRow(shot_break_start, newrow, r)
shot_break_end <- bind_rows(shot_break_end, shot_row) # Add the last time
shot_break <- cbind(shot_break_start, shot_break_end)
##Now that we have the start/end times, lets start by filtering out our dataset to these times
##Also, lets get rid of any plays that are less than 22 feet
##Assign a new id to these plays - shot_id
sumtotal <- NULL
for (i in 1:nrow(shot_break)) {
df_event <- df_game %>%
filter(quarter == shot_break$quarter[i] &
game_clock <= shot_break$game_clock_start[i] &
game_clock > shot_break$game_clock_end[i]) %>%
filter(max(threedist) - min(threedist) > 22) %>% mutate(shot_id = i)
sumtotal <- bind_rows(df_event, sumtotal)
}
##This gives us a dataframe of the ball in air, on plays, where it goes greater than 22 feet
##The next step is matching this data to the play by play data:
##This brings in all the 3 points shots in the play by play data
##This is one way to bring in additional informaton in
pbp_shot <- pbp %>%
select(EVENTNUM, EVENTMSGTYPE, EVENTMSGACTIONTYPE, HOMEDESCRIPTION,
VISITORDESCRIPTION, PCTIMESTRING, PERIOD, PLAYER1_ID)
pbp_shot$HOMEDESCRIPTION <- as.character(pbp_shot$HOMEDESCRIPTION)
pbp_shot$VISITORDESCRIPTION <- as.character(pbp_shot$VISITORDESCRIPTION)
pbp_shot$threepoint <- ifelse(
grepl("3PT", pbp_shot$VISITORDESCRIPTION) |
grepl("3PT", pbp_shot$HOMEDESCRIPTION), 1, 0)
pbp_shot <- pbp_shot %>% filter(threepoint == 1)
pbp_shot$game_clock <- period_to_seconds(
ms(as.character(pbp_shot$PCTIMESTRING)))
#unique(sumtotal2$shot_id)
sumtotal3 <- NULL
for (q in 1:4) {
df_merge <- sumtotal %>% filter(quarter == q)
if (nrow(df_merge) > 0) {
events <- unique(df_merge$shot_id)
pbp_q <- pbp_shot %>% filter(PERIOD == q)
for (i in 1:length(events)) {
df_merge2 <- df_merge %>% filter(shot_id == events[i])
merge_time <- min(df_merge2$game_clock)
timeb <- ifelse(abs(pbp_q$game_clock - merge_time) < 5, 1,
0) # merges if the pbp time is within 5 seconds
indexc <- match(1, timeb)
if (Reduce("+", timeb) > 0) {
df_merge2$EVENTNUM <- pbp_q$EVENTNUM[indexc]
df_merge2$EVENTMSGTYPE <- pbp_q$EVENTMSGTYPE[indexc]
df_merge2$PLAYER1_ID <- pbp_q$PLAYER1_ID[indexc]
} else {
df_merge2$EVENTNUM <- 999 # 999 indicates no match
df_merge2$EVENTMSGTYPE <- 999
df_merge2$PLAYER1_ID <- 999
}
sumtotal3 <- bind_rows(df_merge2, sumtotal3)
}
}
}
sumtotal3 <- sumtotal3 %>% filter(EVENTMSGTYPE != '999') # Remove any no
# match plays
##Now we have a dataframe of 3 point plays from when the ball leaves the shooters hand to when it reaches the basket
##Finds the point where the ball leaves the shooters hand
df_startshot <- sumtotal3 %>%
group_by(shot_id) %>% filter(row_number() == 1) %>% ungroup() %>%
select(shot_id, EVENTMSGTYPE, game_clock, quarter, PLAYER1_ID,
shot_clock) %>% arrange(quarter, desc(game_clock))
##loops through each three point play
for (i in 1:nrow(df_startshot)) {
##Get start of the play
df_startplay <- all.movements %>%
filter(quarter == df_startshot$quarter[i] &
game_clock >= df_startshot$game_clock[i]) %>%
filter(player_id == "-1") %>%
distinct(quarter, game_clock, .keep_all = TRUE) %>%
arrange(quarter, game_clock) %>% filter(!is.na(shot_clock)) %>%
mutate(lead_shot_clock = lead(shot_clock, n = 1)) %>%
filter(shot_clock - lead_shot_clock > 1) %>% head(1)
##Get the ball/player data now that we have the start/end time
if (nrow(df_startplay) > 0) {
##Subset down to just data for this play based on length of play
df_play <- all.movements %>%
filter(quarter == df_startshot$quarter[i] &
game_clock <= (df_startplay$game_clock) &
game_clock >= df_startshot$game_clock[i]) %>%
# df_play <- all.movements %>% filter (quarter==df_startshot$quarter[i] & game_clock <= (df_startshot$game_clock[i]+length_of_play) & game_clock >= df_startshot$game_clock[i]) %>%
mutate(playid = i) %>%
distinct(player_id, quarter, game_clock, .keep_all = TRUE) %>%
arrange(desc(game_clock), player_id)
#Rotate plays depending upon location of the shot
if (tail(df_play$x_loc, 1) > 47) {
df_play <- df_play %>%
mutate(x_loc = 94 - x_loc) %>% mutate(y_loc = 50 - y_loc)
}
df_play$gameid <- gameid
df_play$EVENTMSGTYPE <- df_startshot$EVENTMSGTYPE[i] # Adding in some
# of the pbp
# data
df_play$PLAYER1_ID <- df_startshot$PLAYER1_ID[i]
df_total <- bind_rows(df_total, df_play)
}
}
df_total
}
final <- bind_rows(rowCounts_chull) %>%
arrange(gameid, playid, desc(game_clock)) %>%
select(-X, --a_score, -h_score) %>%
arrange(gameid, playid, desc(game_clock))
write.csv(final, "threepointers_201_400.csv", row.names = FALSE)
##Validate findings
test <- final %>% group_by(gameid, playid) %>% summarize(count = n())
summary(test$count)
##Get specific plays
testplay <- final %>% filter(gameid == '0021500418' & playid == '15')
testplay <- final %>% filter(playid == '21')
testplayball <- testplay %>% filter(player_id == '-1')