app.R

# Load required libraries
library(shiny)
library(DT)
library(baseballr)
library(dplyr)
library(xgboost)
library(httr)

Sys.setenv(TZ='EST')
download_private_csv <- function(repo_id, filename) {
  url <- paste0("https://huggingface.co/datasets/", repo_id, "/resolve/main/", filename)
  response <- GET(url, add_headers(Authorization = paste("Bearer", Sys.getenv("GETCSV"))))
  
  if (status_code(response) == 200) {
    content <- content(response, "text")
    con <- textConnection(content)
    
    # Try different read options
    data <- read.csv(con, 
                     header = TRUE,
                     check.names = FALSE,  # This prevents R from modifying column names
                     fileEncoding = "UTF-8",
                     stringsAsFactors = FALSE)
    close(con)
    return(data)
  } else {
    stop("Failed to download dataset")
  }
}
download_private_parquet <- function(repo_id, filename) {
  library(httr)
  library(arrow)
  
  # Create the direct download URL based on your example
  url <- paste0("https://huggingface.co/datasets/", repo_id, "/resolve/main/", filename, "?download=true")
  
  # Create a temporary file
  temp_file <- tempfile(fileext = ".parquet")
  
  # Download directly to file
  response <- GET(
    url,
    add_headers(Authorization = paste("Bearer", Sys.getenv("GETCSV"))),
    write_disk(temp_file, overwrite = TRUE)
  )
  
  # Check if download was successful
  if (status_code(response) == 200) {
    tryCatch({
      # Read the parquet file
      data <- read_parquet(temp_file)
      file.remove(temp_file)
      return(data)
    }, error = function(e) {
      file.remove(temp_file)
      stop(paste("Error reading parquet file:", e$message))
    })
  } else {
    file.remove(temp_file)
    stop(paste("Failed to download file. Status code:", status_code(response)))
  }
}


MLB <- download_private_parquet("TimStats/StatcastDataAll", "MLB25.parquet")
AAA <- download_private_parquet("TimStats/StatcastDataAll", "AAA25.parquet")
FSL <- download_private_parquet("TimStats/StatcastDataAll", "FSL25.parquet")


MLB <- MLB %>% 
  select(
    `Pitcher Name`, `Pitcher ID`, pitch_name, start_speed, spin_rate, extension,
    IVB, HB, x0, z0
  )

# Same selection for AAA before rbind
AAA <- AAA %>%
  select(
    `Pitcher Name`, `Pitcher ID`, pitch_name, start_speed, spin_rate, extension,
    IVB, HB, x0, z0
  )

FSL <- FSL %>%
  select(
    `Pitcher Name`, `Pitcher ID`, pitch_name, start_speed, spin_rate, extension,
    IVB, HB, x0, z0
  )
# Helper functions
calculate_EAA <- function(extension) {
  extension / 6.3
}

calculate_SADiff <- function(pfxX, pfxZ, spinDirection) {
  inSA <- atan2(pfxZ, pfxX) * 180/pi + 90
  inSA <- ifelse(inSA < 0, inSA + 360, inSA)
  SADiff <- spinDirection - inSA
  SADiff <- ifelse(SADiff > 180, SADiff - 360, SADiff)
  SADiff <- ifelse(SADiff < -180, SADiff + 360, SADiff)
  return(SADiff)
}

calculate_VAA <- function(vz0, ay, az, vy0, y0) {
  -atan((vz0+(az*(-sqrt((vy0*vy0)-(2*ay*(y0-(17/12))))-vy0)/
                ay))/(-sqrt((vy0*vy0)-(2*ay*(y0-(17/12))))))*(180/pi)
}

pitcher_summary <- function(game_pk, date) {
  gdate <- as.Date.character(date)
  gdate <- as.Date(gdate)
  tmilb <- mlb_pbp(game_pk)
  tmilb <- tmilb %>%
    filter(type == "pitch") %>%
    select(matchup.batter.fullName, matchup.batter.id, matchup.pitcher.fullName,
           matchup.pitcher.id, matchup.pitchHand.code, details.type.description,
           pitchData.startSpeed, pitchData.breaks.spinRate, pitchData.extension,
           pitchData.coordinates.x0, pitchData.coordinates.y0, pitchData.coordinates.z0,
           pitchData.coordinates.aX, pitchData.coordinates.aY, pitchData.coordinates.aZ,
           pitchData.coordinates.vX0, pitchData.coordinates.vZ0, pitchData.coordinates.vY0,
           pitchData.coordinates.pfxX, pitchData.coordinates.pfxZ,
           pitchData.breaks.breakVerticalInduced, pitchData.breaks.breakHorizontal,
           pitchData.breaks.spinDirection)
  
  colnames(tmilb) <- c("Batter Name", "Batter ID", "Pitcher Name", "Pitcher ID",
                       "phand", "pitch_name", "start_speed", "spin_rate", "extension",
                       "x0", "y0", "z0", "ax", "ay", "az", "vx0", "vz0", "vy0",
                       "pfxX", "pfxZ", "IVB", "HB", "spinDirection")
  
  tmilb <- tmilb %>%
    mutate(date = gdate)
  
  return(tmilb)
}

calculate_timstuff <- function(game) {
  game <- game %>%
    mutate(VAA = calculate_VAA(vz0, ay, az, vy0, y0),
           EAA = calculate_EAA(extension),
           SADiff = calculate_SADiff(pfxX, pfxZ, spinDirection),
           ishandL = ifelse(phand == "L", 1, 0))
  
  feature_vars <- c("ishandL", "start_speed", "IVB", "HB", "EAA", "x0", "z0", "spin_rate", "SADiff")
  complete_rows <- complete.cases(game[, feature_vars])
  game_complete <- game[complete_rows, ]
  game_na <- game[!complete_rows,]
  game_na$TimStuff <- NA
  
  game_complete$TimStuff <- scale_TimStuff(
    predict(model, as.matrix(cbind(game_complete$ishandL, game_complete$start_speed,
                                   game_complete$IVB, game_complete$HB, game_complete$EAA,
                                   game_complete$x0, game_complete$z0, game_complete$spin_rate,
                                   game_complete$SADiff))),
    -0.002620635, 0.006021368)
  
  game_complete <- rbind(game_complete, game_na)
  return(game_complete)
}

scale_TimStuff <- function(raw_score, model_mean, model_sd) {
  scaled_score <- (raw_score - model_mean) / model_sd
  result <- 100 - (scaled_score * 10)
  return(result)
}

summary_table <- function(data) {
  # Current year summary
  current_summary <- data %>%
    group_by(`Pitcher Name`, `Pitcher ID`, pitch_name) %>%
    summarize(
      Pitches = n(),
      'Velo' = round(mean(start_speed, na.rm = TRUE), 1),
      'Spin' = round(mean(spin_rate, na.rm = TRUE), 0),
      'Ext' = round(mean(extension, na.rm = TRUE), 1),
      'IVB' = round(mean(IVB, na.rm = TRUE), 1),
      'HB' = round(mean(HB, na.rm = TRUE), 1),
      'RelX' = round(mean(x0, na.rm = TRUE), 1),
      'RelZ' = round(mean(z0, na.rm = TRUE), 1),
      'TimStuff' = round(mean(TimStuff, na.rm = TRUE), 0),
      .groups = "drop"
    )
  
  data_2024 <- rbind(MLB,AAA,FSL) %>%
    group_by(`Pitcher Name`, `Pitcher ID`, pitch_name) %>%
    summarize(
      'Velo25' = round(mean(start_speed, na.rm = TRUE), 1),
      'Spin25' = round(mean(spin_rate, na.rm = TRUE), 0),
      'Ext25' = round(mean(extension, na.rm = TRUE), 1),
      'IVB25' = round(mean(IVB, na.rm = TRUE), 1),
      'HB25' = round(mean(HB, na.rm = TRUE), 1),
      'RelX25' = round(mean(x0, na.rm = TRUE), 1),
      'RelZ25' = round(mean(z0, na.rm = TRUE), 1),
      .groups = "drop"
    )
  
  # Join and calculate differences
  combined_data <- current_summary %>%
    left_join(data_2024, by = c("Pitcher Name", "Pitcher ID", "pitch_name")) %>%
    mutate(
      'Velo_Diff' = round(Velo - Velo25, 1),
      'Spin_Diff' = round(Spin - Spin25, 1),
      'Ext_Diff' = round(Ext - Ext25, 1),
      # IVB calculation modified to handle sign changes correctly
      'IVB_Diff' = round(ifelse(sign(IVB) != sign(IVB25), 
                               sign(IVB) * (abs(IVB) + abs(IVB25)),
                               ifelse(IVB < 0,
                                    -1 * (abs(IVB) - abs(IVB25)),
                                    abs(IVB) - abs(IVB25))), 1),
      # HB shows increased movement in either direction as positive
      'HB_Diff' = round(abs(HB) - abs(HB25), 1),
      'RelX_Diff' = round(RelX - RelX25, 1),
      'RelZ_Diff' = round(RelZ - RelZ25, 1)
    ) %>%
    arrange(-Pitches)
  
  return(combined_data)
}
# Load TimStuff model
model <- xgb.load('TimStuff2.ubj')

# UI Definition
ui <- fluidPage(
  titlePanel("Spring Training Pitch Comparison Dashboard (Not Mobile Compatible)"),
  sidebarLayout(
    sidebarPanel(
      width = 2,
      dateInput("date", "Date:"),
      selectizeInput("level", "Level:", 
                     c("MLB")),
      actionButton("submit", "Get Dashboard"),
      downloadButton("download_summary", "Download Summary")
    ),
    mainPanel(
      dataTableOutput("schedule")
    )
  )
)

# Server Definition
server <- function(input, output, session) {
  data <- reactiveVal()
  games <- reactiveVal()
  summary <- reactiveVal()
  
  observeEvent(input$submit, {
    season <- format(input$date, "%Y")
    
    # Get schedule based on selected level
    schedule_data <- switch(input$level,
                            "MLB" = mlb_schedule(season = season, level_ids = "1"),
                            "AAA" = mlb_schedule(season = season, level_ids = "11"),
                            "FSL" = mlb_schedule(season = season, level_ids = "14"),
                            "College (Statcast Parks Only)" = mlb_schedule(season = season, level_ids = "22"),
                            "Futures Game" = mlb_schedule(season = season, level_ids = "21"),
                            "AFL" = {
                              sbid <- mlb_schedule(2026, 17) %>%
                                filter(teams_away_team_name %in% c("Glendale Desert Dogs", "Mesa Solar Sox",
                                                                   "Peoria Javelinas", "Salt River Rafters",
                                                                   "Scottsdale Scorpions", "Surprise Saguaros")) %>%
                                filter(gameday_type == "E")
                              sbid
                            }
    )
    
    data(schedule_data)
    schedule <- data() %>% filter(date == input$date)
    
    # Initialize empty games dataframe
    games_data <- data.frame()
    
    # Process each game
    for(n in 1:nrow(schedule)) {
      tryCatch({
        game1 <- pitcher_summary(schedule[n,6], schedule[n,1])
        games_data <- rbind(game1, games_data)
      }, error = function(e) {
        message(paste("Error occurred for game:", schedule[n,6], "on", schedule[n,1]))
      })
    }
    
    # Calculate TimStuff and create summary
    games_data <- calculate_timstuff(games_data)
    games(games_data)
    summary_data <- summary_table(games_data)
    summary(summary_data)
    
    # Render comparison table
    output$schedule <- renderDT({
      datatable(summary_data,
                options = list(
                  pageLength = 10,
                  lengthMenu = c(10, 25, 50, 100),
                  scrollX = TRUE,  # Enable horizontal scrolling
                  fixedColumns = list(left = 4),  # Freeze first 3 columns (Name, ID, Pitch)
                  columnDefs = list(
                    list(className = 'dt-center', targets = "_all"),
                    # Names and identifiers
                    list(width = '150px', targets = c(0, 1)),  # Pitcher Name, Pitcher ID
                    list(width = '100px', targets = 2),        # pitch_name
                    list(width = '70px', targets = 3),         # Pitches
                    # Current year stats
                    list(width = '50px', targets = c(4:11)),   # Velo, Spin, Ext, IVB, HB, RelX, RelZ, TimStuff
                    # 2024 stats
                    list(width = '10px', targets = c(12:18)),  # Velo_2024, Spin_2024, Ext_2024, IVB_2024, HB_2024, RelX_2024, RelZ_2024
                    # Difference columns
                    list(width = '50px', targets = c(19:25))   # All _Diff columns
                  )
                ),
    extensions = 'FixedColumns'
      ) %>%
        formatStyle(
          c('Velo_Diff', 'Spin_Diff', 'Ext_Diff', 'IVB_Diff', 
            'HB_Diff', 'RelX_Diff', 'RelZ_Diff'),
          backgroundColor = styleInterval(
            cuts = 0,
            values = c('#ffcdd2', '#c8e6c9')
          )
        )
    })
    
  })
  
  # Download handler for summary CSV
  output$download_summary <- downloadHandler(
    filename = function() {
      paste("comparison_data_", Sys.Date(), ".csv", sep = "")
    },
    content = function(file) {
      write.csv(summary(), file, row.names = FALSE)
    }
  )
}

shinyApp(ui, server)