bayesball

prediction_work <- function(seasons,
                            mPA = 1000,
                            retrodata,
                            mPA_season = 200){

  # seasons is a vector of previous seasons
  # mPA is the minimum number of cumulative PA
  # retrodata - Retrosheet data for current season
  # mPA_season - minimum number of PA in both
  #   halves of current season

bayesball

sac_fly_work <- function(sc, season){

  # load required packages
  require(dplyr)
  require(ggplot2)
  require(metR)
  require(mgcv)
  require(CalledStrike)

  # define location, distance, and spray angle vars

bayesball

# load in Retrosheet pbp data frames
# for seasons 2011 through 2021

library(Lahman)
library(dplyr)
library(ggplot2)

# get Mike Trout's retro id from the People
# data frame in Lahman package

bayesball

data_work <- function(){
  require(readr)
  require(dplyr)
  require(lubridate)

  sc_2021 <- read_csv("https://raw.githubusercontent.com/bayesball/HomeRuns2021/main/statcast2021.csv")
  sc_2022 <- read_csv("https://raw.githubusercontent.com/bayesball/HomeRuns2021/main/statcast_2022.csv")
  sc_old <- read_csv("https://raw.githubusercontent.com/bayesball/HomeRuns2021/main/SC_BB_mini.csv")

  names(sc_old)[2] <- "Game_Date"

bayesball

# using Statcast data for the 2021 season

# two functions plot_xb_contour_work() and
# plot_woba_contour_work() implement the
# logistic and ordinal fitting algorithms

# ggplot2 code is used to show the barrel
# region over the (launch speed, launch angle)
# space

bayesball

# read in three packages

library(readr)
library(dplyr)
library(ggplot2)

# helper ggplot2 function 

helper_ggplot <- function(){
  theme(text=element_text(size=18)) +

bayesball

# load in packages

library(readr)
library(dplyr)
library(lme4)
library(ggplot2)
library(posterior)
library(gridExtra)
library(Lahman)

bayesball

# load in packages

library(readr)
library(dplyr)
library(brms)
library(ggplot2)
library(posterior)
library(gridExtra)
library(Lahman)

bayesball

# preparation work for FanGraphs leaderboard data

# collected FanGraphs Leaderboard batting data
# choose Multiple Seasons (all), Split Seasons
# minimum 100 PA in each season
# download data and saved as "fgbatting.csv"

library(dplyr)
library(readr)
library(Lahman)

bayesball

library(shiny)
library(ggplot2)
library(dplyr)
library(readr)

# read in Retrosheet data for two seasons
d <- read_csv("retro_2seasons_ip.csv")

# main drawing function