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# functions to compute and graph component estimates of a batting average
# Jim Albert
# JQAS paper "Improved Component Predictions of Batting and Pitching Measures"

fit_comp_half <- function(d){
  # input d - a list with components playerID, AB, H, HR, SO
  # output - a list with components
  #     S - a data frame with all of the component and shrinkage estimates
  #     component -- values of K and eta for all the component fits
  #     shrinkage -- values of K and eta for the shrinkage fit

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###############################
# getting 2015 retrosheet data
# and computing runs expectancies
# assuming chadwick files are installed

library(devtools)
setwd("~/Desktop/retrosheet")
source_gist("https://gist.github.com/bayesball/8892981")
source_gist("https://gist.github.com/bayesball/8892999")
parse.retrosheet2.pbp(2015)

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# Note:  the openWAR and ggplot2 packages need to be installed

library(openWAR)
library(ggplot2)

ds <- getData(start = '2016-04-29', end = '2016-04-29')

ds$event <- as.character(ds$event)
ds$stand <- ifelse(ds$stand=="R", "Right-Handed Batter",
                   "Left-Handed Batter")

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# The inputs to these functions are
# data - Retrosheet play-by-play data frame with variable RUNS.VALUE that indicates the runs value for each play, and
# variables c01, c10, etc that indicate if the PA went through the specific pitch counts
# p - name of the player
# type - by default, type = "p" (pitcher); use another value of type for a batter

count_plot <- function(data, p, type="p"){
require(ggplot2)
require(Lahman)
require(tidyr)

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# read in Retrosheet play-by-play data for 2015 season
load("~/OneDriveBusiness/Retrosheet/pbp.2015.Rdata")

# limit to batting plays
d2015 <- subset(d2015, BAT_EVENT_FL==TRUE)

# removes all non-pitches from PITCH_SEQ_TX
d2015$pseq <- gsub("[.>123N+*]", "", d2015$PITCH_SEQ_TX)

# create a b and s sequence

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# to simulate the number of runs in one half-inning
# st <- runs_setup() 
# simulate_half_inning(st)

runs_setup <- function(){
  # based on 2015 season data
  Prob_Single <- matrix(0, 8, 8)
  dimnames(Prob_Single)[[1]] <- c("000", "100", "010", "001",
                       "110", "101", "011", "111")
  dimnames(Prob_Single)[[2]] <- c("000", "100", "010", "001",

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# read in Lahman batting and master files
# can also use Lahman package -- data is only through 2014 season

Batting <- read.csv("~/OneDriveBusiness/lahman-csv_2015-01-24/Batting.csv")
Master <- read.csv("~/OneDriveBusiness/lahman-csv_2015-01-24/Master.csv")

# find players with at least 500 career homes (through 2015)

library(dplyr)

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compare_batting_trajectories <- function(Names, 
                                         table="batting_value",
                                         stat="oWAR",
                                         NCOL=1, 
                                      playerIDs=FALSE){
  # table value - one of "batting_standard", "batting_value"
  require(Lahman)
  require(XML)
  require(ggplot2)
  require(ggthemes)

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heat_plot <- function(player, d, HR=FALSE){
 # inputs
 # player - name of player
 # d - pitchRX data frame with variables Batter, Event, and X, Z (location of pitch)
 # will output a ggplot2 object
 # need to use print function to display the pot
  require(dplyr)
  require(ggplot2)
  require(mgcv)
  # define the strike zone

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library(pitchRx)
library(dplyr)
library(ggplot2)

# scrape data

ws3 <- scrape("2016-10-28", "2016-10-28")

# choose variables of interest