relation between pitching rhythm and assist score

gistfile1.r

library(pitchRx)
library(chron)
library(doBy)
library(plyr)
library(ggplot2)

#data2013 <- scrapeFX(start="2013-02-01", end="2013-10-01")
data2013 <- read.csv("2013.csv")
#data2013 <- subset(data2013, sv_id != "NA")

# add game_id
data2013$game_id <- substr(data2013$url, 70, 95)

# add pitching time
make_time <- function(game_data = game_data){
  pitch_time <- substr(game_data$sv_id, 8,13) 
  hour <- substr(pitch_time, 1,2)
  min  <- substr(pitch_time, 3,4)
  sec  <- substr(pitch_time, 5,6)
  time <- paste(hour,min,sec,sep=":")
  game_data$time <- time
  return(game_data)
}

# get the diff of pitching time
pitching_rhythm <- function(time){
  pitches <- length(time)
  time <- times(time)
  time_diff <- diff(time)
  time_diff <- as.numeric(time_diff)
  time_sec <- time_diff * 24 * 60 * 60 
  time_sec <- time_sec[abs(time_sec)<120]
  return(c(mean(time_sec, na.rm=TRUE), var(time_sec, na.rm=TRUE), pitches))
}

# make pitching_tempo data
tempo_data <- summaryBy(time~pitcher_name + game_id + top_inning, 
                        data = make_time(data2013), 
                        FUN=pitching_rhythm,
                        keep.names = TRUE)


get_runs <- function(x){
  return(max(x, na.rm =TRUE))
}

# make the game score
game_score <- summaryBy(home_team_runs + away_team_runs ~ game_id,
                        data = data2013,
                        FUN = get_runs,
                        keep.names =TRUE)
# remove the NAs and INF
game_score <- subset(game_score, home_team_runs != -Inf)

# join the dataframe 
tempo_score_data <- join(tempo_data, game_score, by = "game_id")

# add the assist score 
tempo_score_data$assist <- with(tempo_score_data, 
                                ifelse(top_inning=="Y", 
                                       home_team_runs, away_team_runs))
# over 50 pitches
tempo_score_data_over50 <- subset(tempo_score_data, time.FUN3 > 50)


# personal data
japs <- c("Yu Darvish", "Hisashi Iwakuma", "Hiroki Kuroda")

darvish_data <- subset(tempo_score_data_over50, pitcher_name == "Yu Darvish")
kuroda_data <- subset(tempo_score_data_over50, pitcher_name == "Hiroki Kuroda")
iwakuma_data <- subset(tempo_score_data_over50, pitcher_name == "Hisashi Iwakuma")
japs_data <- subset(tempo_score_data_over50, pitcher_name %in% japs)


# histogram (mean) devided by assist score 
tempo_score_data_over50_under8score <- subset(tempo_score_data_over50, assist<=8)
gghist <- ggplot(data = tempo_score_data_over50_under8score, aes(x=time.FUN1)) 
gghist <- gghist + geom_histogram(fill="white", colour="black")
gghist <- gghist + facet_wrap(~assist, nrow = 10) 
gghist <- gghist + xlab("mean tempo")
gghist <- gghist + theme(axis.title.x = element_text(size = 16, face = "bold"))
gghist <- gghist + theme(axis.title.y = element_text(size = 16, face = "bold"))
gghist <- gghist + theme(strip.text.x = element_text(size = 16, face = "bold"))
gghist <- gghist + ggtitle("mean tempo histogram")
gghist <- gghist + theme(plot.title = element_text(size = 20, face = "bold"))
gghist

# mean vs assist score (Kuroda)
p <- ggplot(data = kuroda_data, aes(x = time.FUN1, y = assist)) 
p <- p + geom_point(size = 3)
p <- p + xlab("mean (sec)")
p <- p + ggtitle("mean tempo (sec) vs assist score (Kuroda)")
p <- p + theme(axis.title.x = element_text(size = 16, face = "bold"))
p <- p + theme(axis.title.y = element_text(size = 16, face = "bold"))
p <- p + theme(plot.title = element_text(size = 16, face = "bold"))
p