bayesball

player_function_lb <- function(player_id){

  # uses LearnBayes package to simulate from
  # multilevel model

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

bayesball

# read in two functions

source("generate_runs.R")
source("retro_game_work.R")

library(dplyr)

# Phillies game plays on April 16, 2023

plays <- c("HR",  "1B",  "BB",  "BB",  "1B",  "OUT", "1B",

bayesball

library(readr)
library(dplyr)
library(brms)

# read in data from Github site

all_data <- read_csv("https://raw.githubusercontent.com/bayesball/HomeRuns2021/main/ten_seasons_retro_hr.csv")

# remove all parks that are either dome or retractable

bayesball

plot_bivar_norm <- function(mx, my, sx, sy, r){
  
  require(ggplot2)
  
  logbinorm <- function (x, y, par) {
    m <- par$m
    v <- par$v
    zx <- (x - m[1]) / sqrt(v[1, 1])
    zy <- (y - m[2]) / sqrt(v[2, 2])
    r <- v[1, 2] / sqrt(v[1, 1] * v[2, 2])

bayesball

---
title: "ChatGPT Examples"
format: html
editor: visual
---

## Collecting Cards

_Write R function to illustrate baseball card collection problem._

bayesball

# reference
# https://billpetti.github.io/baseballr/articles/baseballr.html#follow-the-sportsdataverse-on-twitter-and-star-this-repo

library(baseballr)

# -----------------------------------------------------
# Retrosheet data
# -----------------------------------------------------

# acquire retro play-by-play data for seasons 2020:2022

bayesball

# load in required packages

library(dplyr)
library(ggplot2)
library(CalledStrike)
library(janitor)
library(ShinyBaseball)
library(readr)

# collect mlb ids from two pitchers

bayesball

# load in required packages

library(purrr)
library(ggplot2)
library(tidyr)
library(dplyr)

# read in modeling functions

source("fit_model.R")

bayesball

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

# datasets are read from a Github respository
fg_batting <- read_csv("https://raw.githubusercontent.com/bayesball/HomeRuns2021/main/fgbatting_complete.csv")
hof <- read_csv("https://raw.githubusercontent.com/bayesball/HomeRuns2021/main/hofdata.csv")
hof_candidates <- read_csv("https://raw.githubusercontent.com/bayesball/HomeRuns2021/main/hofdata_candidates.csv")
hof_cand_batting <- filter(hof_candidates,

bayesball

estimate_batting <- function(retro_final_PA_1990_2020d,
                             season,
                             s_woba = 0.5){
  require(dplyr)
  require(LearnBayes)
  retro_final_PA_1990_2020d %>% 
    filter(YEAR == season) -> retroseason
  retroseason %>% 
    group_by(BAT_ID) %>% 
    summarize(PA = n(),