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compute_expected_runs <- function(events){
  # function computes expected runs for event table
  # obtained via statsapi.mlb.com
  
  require(dplyr)
  # find the total runs scored and number of outs each half-inning
  events |> 
    group_by(game_id, inning, half_inning) |> 
    summarize(max_runs = sum(runs_on_event),
              total_outs = sum(post_outs - pre_outs),

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get_standings <- function(s_date, e_date){
  require(sabRmetrics)
  require(dplyr)
  
  Teams <- c("Chicago White Sox",     "Cleveland Guardians",  
             "Detroit Tigers",        "Kansas City Royals",   
             "Minnesota Twins",       "Baltimore Orioles",    
              "Boston Red Sox",        "New York Yankees",     
              "Tampa Bay Rays",        "Toronto Blue Jays",    
              "Houston Astros",        "Los Angeles Angels",   

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fit_norm_mix_model <- function(pname, statcast){
  require(dplyr)
  require(LearnBayes)
  library(magrittr)
  logpost <- function(theta, y){
    mu1 <- theta[1]
    sigma1 <- exp(theta[2])
    mu2 <- theta[3]
    sigma2 <- exp(theta[4])
    p <- exp(theta[5]) / (1 + exp(theta[5]))

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prediction_normal <- function(S, games, f_games){
  # data frame S contains batter, BIP, HR for
  # first part of season
  # games - average number of games already played
  # f_games - number of future games played
  
  # need to have the JAGS software installed
  # https://mcmc-jags.sourceforge.io/
  
  library(readr)

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---
title: "Count Effects Model"
format: html
editor: visual
---

Illustration of Bayesian fitting of a multilevel model for situational data using JAGS software. 
Read in Statcast data from the first half of the 2023 season.

Reference:  Albert, Jim (2024).  Bayesian Workflow of a Situational Random Effects Model

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# load some packages

library(dplyr)
library(ggplot2)
library(mgcv)
library(janitor)
library(metR)

# have Statcast data through games of April 5, 2024

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---
title: "Retrosheet Package - Comparing Count Rates for Two Seasons"
format: html
editor: visual
---

Load packages for this particular run.
```{r}
#| message: FALSE
library(abdwr3edata)

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# live version of Shiny app
# https://bayesball.shinyapps.io/fg2023_discipline/

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

# data work

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---
title: "Zach Wheeler 2023 NCS"
format: html
editor: visual
---

Load in necessary packages:

```{r}
library(baseballr)

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player_function_lb <- function(player_id){

  # uses LearnBayes package to simulate from
  # multilevel model

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