bayesball

# load relevant packages

library(dplyr)
library(ggplot2)

# function will download retrosheet game log data for a particular
# season

load.gamelog <- function(season, headers){
  download.file(

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# loads in the Retrosheet data
load("~/OneDriveBusiness/Retrosheet/pbp.2014.Rdata")

# removes all non-pitches from PITCH_SEQ_TX
pbp.14$pseq <- gsub("[.>123N+*]", "", pbp.14$PITCH_SEQ_TX)

# create a b and s sequence
pbp.14$pseq <-gsub("[BIPV]", "b", pbp.14$pseq)
pbp.14$pseq <-gsub("[CFKLMOQRST]", "s", pbp.14$pseq)

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groundball.plot <- function(pbp, season){
   require(dplyr)
   require(ggplot2)
   require(car)
   inplay <- filter(pbp, BATTEDBALL_CD == "F" |
                   BATTEDBALL_CD == "G" |
                   BATTEDBALL_CD == "L" |
                   BATTEDBALL_CD == "P")

    inplay <- mutate(inplay,

bayesball

# Script to Learn About Ryan Howard's Batting Ability from a "0 for 35" Slump

# Uses a function from the BayesTestStreak package
# install_github("bayesball/BayesTestStreak")

library(MASS)
library(BayesTestStreak)


# Simulate 500 at-bats with a constant hitting probability p = 0.250 

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# Daniel Murphy Exercise

# Part I -- learning about Murphy's home run ability
# and updating this knowledge after the NLDS and NLCS

library(ggplot2)
library(LearnBayes)

# career home run data for Murphy 

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# requires packages
# dplyr, Lahman, ggplot2

# some preliminary work

library(dplyr)
library(Lahman)

get.birthyear <- function(player.id){

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library(shiny)

shinyServer(function(input, output) {

  output$distPlot <- renderPlot({
   
    library(LearnBayes)
    quantile1 = list(p=.5, x=input$p50)
    quantile2 = list(p=.9, x=input$p90)
    ab = beta.select(quantile1, quantile2)

bayesball

# R Script for Jan 6, 2014 blog

# function shrink will compute improved estimates at true batting rates
# for all players with at least Lower.Bound opportunities

# be sure that packages Lahman, LearnBayes, and plyr have been installed
# before running this function

shrink <- function(year, n.var, d.var, Lower.Bound=100){
  # load required packages (each should be installed first)

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library(shiny)
library(Lahman)
library(ggplot2)

# Define server logic required to plot various variables against year
shinyServer(function(input, output) {
  
  # Compute the forumla text in a reactive function since it is 
  # shared by the output$caption and output$mpgPlot functions
  formulaText <- reactive({

bayesball

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

dat <- scrape(start = "2015-09-05", end = "2015-09-11")
pitches <- inner_join(select(dat$atbat, 
                   batter_name, pitcher_name, inning, 
                   gameday_link, num, url),
                select(dat$pitch,
                   start_speed, pitch_type, sv_id, num, url),