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Basic_EDA.R
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Basic_EDA.R
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###NBA_SportVU EDA
source("_functions.R")
library(plotly)
#########LOAD DATA
## Grab the data using: wget https://github.com/neilmj/BasketballData/blob/master/2016.NBA.Raw.SportVU.Game.Logs/12.18.2015.DET.at.CHI.7z?raw=true
## The munge for this file takes 7 minutes on my computer and results in a dataframe 3258100 obs. of 13 variables
# See NBA stats for this game at http://stats.nba.com/game/#!/0021500391/playbyplay/
all.movements <- sportvu_convert_json("data/0021500391.json")
write.csv(all.movements,"all.movements_0021500391.csv", row.names = FALSE)
########Extract Movement for One Player
## Code from http://tcbanalytics.com/blog/nba-movement-data-R.html#.VnX8d4RiOCQ
## Play 6 by Rose - http://on.nba.com/1OoYi7e
all.movements <- all.movements %>% dplyr::arrange(quarter,desc(game_clock),x_loc)
id6 <- all.movements[which(all.movements$event.id == 6),]
rose <- all.movements[which(all.movements$lastname == "Rose" & all.movements$event.id == 6),]
gasol <- all.movements[which(all.movements$lastname == "Gasol" & all.movements$event.id == 6),]
plot_ly(data = rose, x = x_loc, y = y_loc, mode = "markers", color=cut(rose$game_clock, breaks=3)) %>%
layout(xaxis = list(range = c(0, 100)),
yaxis = list(range = c(0, 50)))
plot_ly(data = gasol, x = x_loc, y = y_loc, mode = "markers") %>%
layout(xaxis = list(range = c(0, 100)),
yaxis = list(range = c(0, 50)))
########Extract Distance for One Player
## Play 6 by Rose - http://on.nba.com/1OoYi7e
travelDist(rose$x_loc, rose$y_loc)
# 76.39166
travelDist(gasol$x_loc, gasol$y_loc)
# 87.84398
########Extract Distance for Group of Players for one event
seconds = max(gasol$game_clock) - min(gasol$game_clock)
speed = travelDist(gasol$x_loc, gasol$y_loc)/seconds #in feet per second
########Extract Distance for Group of Players for one event
player.groups <- group_by(id6, lastname)
dist.traveled.players <- summarise(player.groups, totalDist=travelDist(x_loc, y_loc),playerid = max(player_id))
arrange(dist.traveled.players, desc(totalDist))
########Extract Distance for Group of Players for the entire game
#Need to dedup data because multiple events for different event.ids
deduped.data <- unique( all.movements[ , 1:12 ] )
player.groups <- group_by(deduped.data, lastname)
dist.traveled.players <- summarise(player.groups, totalDist=travelDist(x_loc, y_loc),playerid = max(player_id))
total <- arrange(dist.traveled.players, desc(totalDist))
########Extract Distance for one player for the entire game
gasol <- deduped.data[which(deduped.data$lastname == "Gasol"),]
travelDist(gasol$x_loc, gasol$y_loc)
# 18561.93 - Seems a little high, but this was a very long game
########Distance Matrix for one player with the ball for one event
rose <- all.movements[which((all.movements$lastname == "Rose"| all.movements$lastname == "ball") & all.movements$event.id == 6),]
testrose <- rose %>% filter (lastname=="Rose") %>% select (x_loc,y_loc)
testball <- rose %>% filter (lastname=="ball") %>% select (x_loc,y_loc)
testrosel <- 1:nrow(testrose)
distsdf <- unlist(lapply(testrosel,function(x) {dist(rbind(testrose[x,], testball[x,]))}))
ball_distance <- rose %>% filter (lastname=="ball") %>% select (game_clock) %>% mutate(distance=distsdf)
plot_ly(data = ball_distance, x=game.clock, y=distsdf,mode = "markers")
########Distance Matrix w/ function for one player with the ball for one event
player_dist <- function(lastnameA,lastnameB, eventID) {
df <- all.movements[which((all.movements$lastname == lastnameA | all.movements$lastname == lastnameB) & all.movements$event.id == eventID),]
dfA <- df %>% filter (lastname==lastnameA) %>% select (x_loc,y_loc)
dfB <- df %>% filter (lastname==lastnameB) %>% select (x_loc,y_loc)
df.l <- 1:nrow(dfA)
distsdf <- unlist(lapply(df.l,function(x) {dist(rbind(dfA[x,], dfB[x,]))}))
return(distsdf)
}
temp <- player_dist("Rose","ball",6)
plot_ly(data = ball_distance, x=game_clock, y=temp,mode = "markers")
########Distance Matrix for one player with all other players for one event
pickplayer <- "ball"
pickeventID <- 6
players <- all.movements %>% filter(event.id==pickeventID) %>% select(lastname) %>% distinct(lastname)
bigdistance <- lapply(list(players$lastname)[[1]],function (x){ player_dist(pickplayer,x,pickeventID)})
bigdistancedf <- as.data.frame(do.call('cbind',bigdistance))
colnames(bigdistancedf) <- list(players$lastname)[[1]]
#Get Clock Info
clockinfo <- get_game_clock("Ginobili",303)
bigdistancedf$game_clock <- clockinfo$game_clock
head(bigdistancedf)
##Plot with plotly - not elegant but works
for(i in 1:(ncol(bigdistancedf)-1)){
if(i==1){
pString<-"p <- plot_ly(data = bigdistancedf, x = game_clock, y = bigdistancedf[,1], name = colnames(bigdistancedf[1]),mode = 'markers')"
} else {
pString<-paste(pString, " %>% add_trace(y =", eval(paste("bigdistancedf[,",i,"]",sep="")),", name=", eval(paste("colnames(bigdistancedf[", i,"])",sep="")), ")", sep="")
}
}
eval(parse(text=pString))
print(p)
########Distance Matrix for between all players for one eventID
pickeventID <- 6
players_matrix <- player_dist_matrix(pickeventID)
########Lets try to get the game clock in minutes/seconds
library(lubridate)
#Simple example
seconds_to_period(361.27)
#Add to main data frame
all.movements$game_clock_minutes <- as.character(seconds_to_period(all.movements$game_clock))