Update app.R

app.R

@@ -1,58 +1,560 @@
+# app.R
 library(shiny)
-library(bslib)
-library(dplyr)
+library(tidyverse)
 library(ggplot2)
+library(dplyr)
+library(patchwork)
+library(showtext)
+library(magick)
+library(grid)
+library(gridExtra)
+library(gtable)
 
-df <- readr::read_csv("penguins.csv")
-# Find subset of columns that are suitable for scatter plot
-df_num <- df |> select(where(is.numeric), -Year)
-
-ui <- page_sidebar(
-  theme = bs_theme(bootswatch = "minty"),
-  title = "Penguins explorer",
-  sidebar = sidebar(
-    varSelectInput("xvar", "X variable", df_num, selected = "Bill Length (mm)"),
-    varSelectInput("yvar", "Y variable", df_num, selected = "Bill Depth (mm)"),
-    checkboxGroupInput("species", "Filter by species",
-      choices = unique(df$Species), selected = unique(df$Species)
-    ),
-    hr(), # Add a horizontal rule
-    checkboxInput("by_species", "Show species", TRUE),
-    checkboxInput("show_margins", "Show marginal plots", TRUE),
-    checkboxInput("smooth", "Add smoother"),
-  ),
-  plotOutput("scatter")
-)
+is_barrel <- function(df) {
+  df$hit_speedr <- round(df$hit_speed)
+  df <- df |>
+    mutate(barrel = ifelse((hit_speedr >= 124) &
+                             (hit_angle >= 0 & hit_angle <= 50),1,0)) |>
+    mutate(barrel = ifelse((hit_speedr == 123) &
+                             (hit_angle >= 1 & hit_angle <= 50),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 122) &
+                             (hit_angle >= 2 & hit_angle <= 50),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 121) &
+                             (hit_angle >= 3 & hit_angle <= 50),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 120) &
+                             (hit_angle >= 4 & hit_angle <= 50),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 119) &
+                             (hit_angle >= 5 & hit_angle <= 50),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 118) &
+                             (hit_angle >= 6 & hit_angle <= 50),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 117) &
+                             (hit_angle >= 7 & hit_angle <= 50),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 116) &
+                             (hit_angle >= 8 & hit_angle <= 50),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 115) &
+                             (hit_angle >= 9 & hit_angle <= 50),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 114) &
+                             (hit_angle >= 10 & hit_angle <= 50),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 113) &
+                             (hit_angle >= 11 & hit_angle <= 50),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 112) &
+                             (hit_angle >= 12 & hit_angle <= 50),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 111) &
+                             (hit_angle >= 13 & hit_angle <= 50),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 110) &
+                             (hit_angle >= 14 & hit_angle <= 48),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 109) &
+                             (hit_angle >= 15 & hit_angle <= 46),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 108) &
+                             (hit_angle >= 16 & hit_angle <= 45),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 107) &
+                             (hit_angle >= 17 & hit_angle <= 43),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 106) &
+                             (hit_angle >= 18 & hit_angle <= 42),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 105) &
+                             (hit_angle >= 19 & hit_angle <= 40),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 104) &
+                             (hit_angle >= 20 & hit_angle <= 39),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 103) &
+                             (hit_angle >= 21 & hit_angle <= 37),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 102) &
+                             (hit_angle >= 22 & hit_angle <= 36),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 101) &
+                             (hit_angle >= 23 & hit_angle <= 34),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 100) &
+                             (hit_angle >= 24 & hit_angle <= 33),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 99) &
+                             (hit_angle >= 25 & hit_angle <= 31),1,barrel)) |>
+    mutate(barrel = ifelse((hit_speedr == 98) &
+                             (hit_angle >= 26 & hit_angle <= 30),1,barrel)) |>
+    select(-hit_speedr)
+  return(df)
+}
 
-server <- function(input, output, session) {
-  subsetted <- reactive({
-    req(input$species)
-    df |> filter(Species %in% input$species)
-  })
+apply_percentile_calcs <- function(data) {
+  # List of columns to apply percent_rank
+  percent_rank_cols <- c("Z-Con%", "Z-Swing%", "O-Con%", "Avg EV", "Max EV", "EV90", "Barrel%", "Swing%", "wOBA",
+                         "wOBACON","xwOBA","xwOBACON")
+  
+  # List of columns to apply inverse percent_rank
+  inverse_percent_rank_cols <- c("Chase%", "Whiff%", "stdev(LA)", "SwStr%")
+  
+  # Create an empty list to store results
+  percentile_list <- list()
+  
+  # Calculate regular percentiles
+  for(col in percent_rank_cols) {
+    percentile_list[[col]] <- data.frame(
+      `Batter Name` = data[["Batter Name"]],  # Using [[ ]] to preserve exact column name
+      `Batter ID` = data[["Batter ID"]],      # Using [[ ]] to preserve exact column name
+      metric = col,
+      percentile = round(percent_rank(data[[col]]) * 100),
+      value = data[[col]],
+      stringsAsFactors = FALSE
+    )
+  }
+  
+  # Calculate inverse percentiles
+  for(col in inverse_percent_rank_cols) {
+    percentile_list[[col]] <- data.frame(
+      `Batter Name` = data[["Batter Name"]],  # Using [[ ]] to preserve exact column name
+      `Batter ID` = data[["Batter ID"]],      # Using [[ ]] to preserve exact column name
+      metric = col,
+      percentile = round((1 - percent_rank(data[[col]])) * 100),
+      value = data[[col]],
+      stringsAsFactors = FALSE
+    )
+  }
+  
+  # Combine all results into one data frame
+  result <- do.call(rbind, percentile_list)
+  
+  # Reset row names
+  rownames(result) <- NULL
+  
+  return(result)
+}
 
-  output$scatter <- renderPlot(
-    {
-      p <- ggplot(subsetted(), aes(!!input$xvar, !!input$yvar)) +
-        theme_light() +
-        list(
-          theme(legend.position = "bottom"),
-          if (input$by_species) aes(color = Species),
-          geom_point(),
-          if (input$smooth) geom_smooth()
-        )
+get_player_image <- function(player_id) {
+  # Try MLB silo image first
+  silo_url <- sprintf("https://img.mlbstatic.com/mlb-photos/image/upload/w_200,q_auto:best/v1/people/%s/headshot/silo/current", player_id)
+  
+  # Check if silo works
+  silo_result <- tryCatch({
+    response <- httr::HEAD(silo_url)
+    httr::status_code(response) == 200
+  }, error = function(e) FALSE)
+  
+  # If silo fails, use MiLB with correct formatting
+  if (!silo_result) {
+    return(sprintf("https://img.mlbstatic.com/mlb-photos/image/upload/c_fill,g_auto,b_white,ar_1:1/w_180/v1/people/%s/headshot/milb/current", player_id))
+  }
+  
+  # Return silo if it worked
+  return(silo_url)
+}
+
+get_player_info <- function(player_id, season, level = "MLB") {
+  # Initialize return values
+  team <- "MLB"
+  position <- NA
+  
+  # If MLB level, use original endpoint
+  if(level == "MLB") {
+    url <- paste0("https://statsapi.mlb.com/api/v1/people/", player_id, 
+                  "/stats?stats=season&season=", season, "&group=hitting")
+    
+    response <- httr::GET(url)
+    data <- httr::content(response, "parsed")
+    
+    if(length(data$stats) > 0 && length(data$stats[[1]]$splits) > 0) {
+      team <- data$stats[[1]]$splits[[length(data$stats[[1]]$splits)]]$team$name
+    }
+  } else {
+    # For minor leagues, get player info from sports endpoint
+    sport_code <- if(level == "AAA") "11" else "14"  # 11 for AAA, 14 for FSL
+    url <- paste0("https://statsapi.mlb.com/api/v1/sports/", sport_code, "/players?season=", season)
+    
+    response <- httr::GET(url)
+    # Convert response to data frame
+    players_df <- jsonlite::fromJSON(rawToChar(response$content), flatten = TRUE)$people
+    
+    # Find player directly
+    found_player <- players_df[players_df$id == player_id, ]
+    
+    if(nrow(found_player) > 0) {
+      team_id <- found_player$currentTeam.id
+      
+      # Get parent org using team id
+      team_url <- paste0("https://statsapi.mlb.com/api/v1/teams/", team_id, "?season=", season)
+      team_response <- httr::GET(team_url)
+      team_data <- jsonlite::fromJSON(rawToChar(team_response$content))
+      
+      team <- team_data$teams$parentOrgName
+    }
+  }
+  
+  # Get position info (same for all levels)
+  url2 <- paste0("https://statsapi.mlb.com/api/v1/people/", player_id)
+  response2 <- httr::GET(url2)
+  data2 <- httr::content(response2, "parsed")
+  
+  if(length(data2$people) > 0) {
+    full_position <- data2$people[[1]]$primaryPosition$name
+    position <- case_when(
+      full_position == "First Base" ~ "1B",
+      full_position == "Second Base" ~ "2B",
+      full_position == "Third Base" ~ "3B",
+      full_position == "Shortstop" ~ "SS",
+      full_position == "Catcher" ~ "C",
+      full_position == "Left Field" ~ "LF",
+      full_position == "Center Field" ~ "CF",
+      full_position == "Right Field" ~ "RF",
+      full_position == "Designated Hitter" ~ "DH",
+      full_position == "Pitcher" ~ "P",
+      full_position == "Two-Way Player" ~ "TWP",
+      TRUE ~ as.character(full_position)
+    )
+  }
+  
+  return(list(
+    team = team,
+    position = position
+  ))
+}
+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")
+  }
+}
+MLB <- download_private_csv("TimStats/StatcastDataAll", "MLB.csv")
+AAA <- download_private_csv("TimStats/StatcastDataAll", "AAA.csv")
+FSLAll <- download_private_csv("TimStats/StatcastDataAll", "FSL.csv")
+
+temp_players <- MLB %>% filter(season == 2024)
+MLBC <- rbind(MLB,AAA,FSLAll)
+data <- is_barrel(MLBC) %>%
+  mutate(
+    BBE = case_when(description %in% c('In play, run(s)','In play, out(s)','In play, no out') ~ TRUE, TRUE ~ FALSE),
+    Swing = case_when(description %in% c('Foul','Foul Bunt','Foul Pitchout','Foul Tip',
+                                         'In play, run(s)','In play, out(s)','In play, no out',
+                                         'Swinging Strike','Swinging Strike (Blocked)',
+                                         'Missed Bunt') ~ TRUE, TRUE ~ FALSE),
+    Contact = case_when(description %in% c('In play, run(s)','In play, out(s)','In play, no out',
+                                           'Foul','Foul Bunt','Foul Pitchout') ~ TRUE, TRUE ~ FALSE),
+    Whiff = case_when(description %in% c('Swinging Strike','Swinging Strike (Blocked)',
+                                         'Missed Bunt','Foul Tip') ~ TRUE, TRUE ~ FALSE),
+    IZ = ifelse(zone <= 9, TRUE, FALSE),
+    Single = case_when(result == "Single" & BBE == TRUE ~ TRUE, TRUE ~ FALSE),
+    Double = case_when(result == "Double" & BBE == TRUE ~ TRUE, TRUE ~ FALSE),
+    Triple = case_when(result == "Triple" & BBE == TRUE ~ TRUE, TRUE ~ FALSE),
+    `Home Run` = case_when(result == "Home Run" & BBE == TRUE ~ TRUE, TRUE ~ FALSE),
+    Walk = case_when(balls >= 4 & result == "Walk" ~ TRUE, TRUE ~ FALSE),
+    HBP = case_when(description == "Hit By Pitch" & result == "Hit By Pitch" ~ TRUE, TRUE ~ FALSE),
+    Strikeout = case_when(strikes >= 3 & result %in% c("Strikeout",'Stikeout Double Play') ~ TRUE, TRUE ~ FALSE),
+    Sac = case_when(BBE == TRUE & result %in% c('Sac Fly','Sac Bunt',
+                                                'Sac Fly Double Play','Sac Bunt Double Play') ~ TRUE, TRUE ~ FALSE),
+    IBB = case_when(pitchNum == 1 & result == "Intent Walk" ~ TRUE, TRUE ~ FALSE),
+    AB = Strikeout + BBE - Sac,
+    PA = AB + Walk + HBP + IBB
+  ) %>%
+  group_by(`Batter Name`,`Batter ID`,season,level) %>%
+  summarise(
+    BIP = sum(BBE,na.rm = TRUE),
+    wOBA = round((sum(Single,na.rm = TRUE) * .882 + sum(Double, na.rm = TRUE) * 1.254 + 
+                    sum(Triple,na.rm = TRUE) * 1.59 + sum(`Home Run`,na.rm = TRUE) * 2.05 +
+                    sum(Walk,na.rm = TRUE) * .689 + sum(HBP,na.rm = TRUE) * .720) / 
+                   (sum(PA,na.rm = TRUE) - sum(IBB,na.rm = TRUE)), 3),
+    wOBACON = round((sum(Single,na.rm = TRUE) * .882 + sum(Double, na.rm = TRUE) * 1.254 + 
+                       sum(Triple,na.rm = TRUE) * 1.59 + sum(`Home Run`,na.rm = TRUE) * 2.05 )/ 
+                      sum(BBE,na.rm = TRUE), 3),
+    xwOBA = round(mean(expected_woba,na.rm = TRUE), 3),
+    xwOBACON = round(mean(expected_woba[BBE == TRUE],na.rm = TRUE), 3),
+    `Avg EV` = round(mean(hit_speed,na.rm = TRUE), 1),
+    EV90 = round(quantile(hit_speed,0.9,na.rm = TRUE), 1),
+    `Max EV` = round(max(hit_speed,na.rm = TRUE), 1),
+    'stdev(LA)' = round(sd(hit_angle,na.rm = TRUE), 1),
+    'Barrel%' = round(100 * mean(barrel[Swing == TRUE],na.rm = TRUE), 1),
+    "Z-Con%" = round(100 * mean(Contact[IZ == TRUE & Swing == TRUE],na.rm = TRUE), 1),
+    "Z-Swing%" = round(100 * mean(Swing[IZ == TRUE],na.rm = TRUE), 1),
+    "O-Con%" = round(100 * mean(Contact[IZ == FALSE & Swing == TRUE],na.rm = TRUE), 1),
+    "Chase%" = round(100 * mean(Swing[IZ == FALSE],na.rm = TRUE), 1),
+    "Whiff%" = round(100 * mean(Whiff[Swing == TRUE],na.rm = TRUE), 1),
+    "Swing%" = round(100 * mean(Swing,na.rm = TRUE), 1),
+    "SwStr%" = round(100 * mean(Whiff,na.rm = TRUE), 1)
+  )
 
-      if (input$show_margins) {
-        margin_type <- if (input$by_species) "density" else "histogram"
-        p <- p |> ggExtra::ggMarginal(
-          type = margin_type, margins = "both",
-          size = 8, groupColour = input$by_species, groupFill = input$by_species
+# UI definition
+ui <- fluidPage(
+  titlePanel(NULL, windowTitle = "Baseball Stats Visualization"),
+  
+  sidebarLayout(
+    sidebarPanel(
+      selectInput("szn", "Season:", c(2024, 2023, 2022, 2021, 2021)),
+      selectInput("level", "Level:", c("MLB", "AAA", "FSL")),
+      selectInput("type", "Player Type:", c("Batter", "Pitcher")),
+      selectInput("player", "Player:", choices = unique(temp_players$`Batter Name`)),
+      # Add toggle for custom team
+      checkboxInput("use_custom_team", "Use Custom Team", FALSE),
+      # Conditional panel for team selection
+      conditionalPanel(
+        condition = "input.use_custom_team == true",
+        selectInput(
+          inputId = "team",
+          label = "Select Team",
+          choices = c(
+            # Regular teams (sorted alphabetically)
+            "Angels" = "LAA",
+            "Astros" = "HOU", 
+            "Athletics" = "OAK",
+            "Blue Jays" = "TOR",
+            "Braves" = "ATL",
+            "Brewers" = "MIL",
+            "Cardinals" = "STL",
+            "Cubs" = "CHC",
+            "D-backs" = "ARI",
+            "Dodgers" = "LAD",
+            "Giants" = "SF",
+            "Guardians" = "CLE",
+            "Mariners" = "SEA",
+            "Marlins" = "MIA",
+            "Mets" = "NYM",
+            "Nationals" = "WSH",
+            "Orioles" = "BAL",
+            "Padres" = "SD",
+            "Phillies" = "PHI",
+            "Pirates" = "PIT",
+            "Rangers" = "TEX",
+            "Rays" = "TB",
+            "Red Sox" = "BOS",
+            "Reds" = "CIN",
+            "Rockies" = "COL",
+            "Royals" = "KC",
+            "Tigers" = "DET",
+            "Twins" = "MIN",
+            "White Sox" = "CHW",
+            "Yankees" = "NYY",
+            # MLB option at the top
+            "MLB" = "MLB"
+          ),
+          selected = "MLB"
         )
+      ),
+    ),
+    
+    mainPanel(
+      plotOutput("statsPlot", height = "1000px", width = "1000px")
+    )
+  )
+)
+
+# Server logic
+server <- function(input, output,session) {
+  
+  observeEvent(c(input$szn,input$level), {
+    # Filter data based on selected season
+    filtered_data <- MLBC[MLBC$season == input$szn & MLBC$level == input$level,]
+    
+    updateSelectInput(session,
+                      inputId = "player",
+                      choices = unique(filtered_data$`Batter Name`))
+  })
+  # Create reactive value to store team
+  team_value <- reactiveVal("MLB")
+  position_value <- reactiveVal("")
+  # Watch for player or season changes to update team
+  observeEvent(c(input$player, input$szn), {
+    if (!input$use_custom_team && !is.null(input$player)) {
+      
+      player_id <- MLBC %>%
+        filter(`Batter Name` == input$player) %>%
+        pull(`Batter ID`) %>%
+        unique() %>%
+        first()
+      
+      if (!is.null(player_id)) {
+        player_info <- get_player_info(player_id, input$szn, input$level)
+        
+        team_abb <- switch(player_info$team,
+                           "Los Angeles Angels" = "LAA",
+                           "Houston Astros" = "HOU",
+                           "Oakland Athletics" = "OAK",
+                           "Toronto Blue Jays" = "TOR",
+                           "Atlanta Braves" = "ATL",
+                           "Milwaukee Brewers" = "MIL",
+                           "St. Louis Cardinals" = "STL",
+                           "Chicago Cubs" = "CHC",
+                           "Arizona Diamondbacks" = "ARI",
+                           "Los Angeles Dodgers" = "LAD",
+                           "San Francisco Giants" = "SF",
+                           "Cleveland Guardians" = "CLE",
+                           "Seattle Mariners" = "SEA",
+                           "Miami Marlins" = "MIA",
+                           "New York Mets" = "NYM",
+                           "Washington Nationals" = "WSH",
+                           "Baltimore Orioles" = "BAL",
+                           "San Diego Padres" = "SD",
+                           "Philadelphia Phillies" = "PHI",
+                           "Pittsburgh Pirates" = "PIT",
+                           "Texas Rangers" = "TEX",
+                           "Tampa Bay Rays" = "TB",
+                           "Boston Red Sox" = "BOS",
+                           "Cincinnati Reds" = "CIN",
+                           "Colorado Rockies" = "COL",
+                           "Kansas City Royals" = "KC",
+                           "Detroit Tigers" = "DET",
+                           "Minnesota Twins" = "MIN",
+                           "Chicago White Sox" = "CHW",
+                           "New York Yankees" = "NYY",
+                           "MLB") 
+        if(is.na(team_abb)){
+          team_abb <- "MLB"
+        }
+        team_value(team_abb)
+        position_value(player_info$position)
       }
+    }
+  })
+  
+  current_team <- reactive({
+    if (input$use_custom_team) {
+      return(input$team)
+    } else {
+      return(team_value())
+    }
+  })
+  
+  output$statsPlot <- renderPlot({
+    req(position_value())
+    data <- data %>% filter(season == input$szn,level == input$level)
+    
+    BBE <- MLBC %>%
+      filter(season == input$szn) %>%
+      filter(`Batter Name` == input$player) %>%
+      mutate(
+        BBE = case_when(description %in% c('In play, run(s)','In play, out(s)','In play, no out') ~ TRUE, TRUE ~ FALSE)
+      )
+    
+  
+    indv <- data %>% filter(`Batter Name` == input$player,level == input$level)
+    #if(indv[1,5] >= 149){
+    qual <- data %>% filter(BIP > 249)
+    data <- rbind(indv,qual)
+    data <- unique(data)
+    #}
+    current_data <- apply_percentile_calcs(data %>% select(-BIP)) %>% 
+      filter(`Batter.Name` == input$player) %>%
+      mutate(metric = factor(metric, levels = c(
+        "wOBA", "wOBACON", "xwOBA", "xwOBACON",
+        "Avg EV", "EV90", "Max EV", 
+        "stdev(LA)", "Barrel%",
+        "Z-Con%", "Z-Swing%", "O-Con%", 
+        "Chase%", "Whiff%", "Swing%", "SwStr%"
+      ))) %>%
+      arrange(metric)
+    #current_data <- data
+    pos <- position_value()
 
-      p
-    },
-    res = 100
-  )
+    BBE <- sum(BBE$BBE,na.rm = TRUE)
+    
+    # Add Roboto Condensed font
+    font_add_google("Roboto Condensed", "roboto")
+    showtext_auto()
+    
+    # Color function
+    current_data$color <- scales::gradient_n_pal(c("#325aa1","#90A4AE", "#D82129"))(current_data$percentile/100)
+    
+    # Labels plot
+    labels_plot <- ggplot() +
+      annotate("text", x = c(10, 50, 90), y = 1,
+               label = c("Poor", "Average", "Great"),
+               color = c("#3661ad", "#90A4AE", "#DC3545"),
+               family = "roboto", size = 6) +
+      annotate("text", x = c(10, 50, 90), y = 0.5,
+               label = "▲",
+               color = c("#3661ad", "#90A4AE", "#DC3545"), size = 12) +
+      scale_x_continuous(limits = c(-16, 113), expand = c(0, 0)) +
+      scale_y_continuous(limits = c(0.5, 1.5)) +
+      theme_void()
+    
+    # Main plot
+    main_plot <- ggplot(current_data, aes(y = factor(metric, levels = rev(metric)))) +
+      geom_tile(aes(x = 50, width = 100),
+                fill = "#c7dcdc", alpha = 0.3, height = 0.25) +
+      geom_tile(aes(x = percentile/2, width = percentile, fill = color),
+                height = 0.7) +
+      annotate("segment", x = c(10, 50, 90), xend = c(10, 50, 90),
+               y = 0, yend = 15.35,
+               color = c("white"),
+               linewidth = 1.5, alpha = 0.5) +
+      geom_segment(aes(x = -2, xend = -16,
+                       y = as.numeric(factor(metric, levels = rev(metric))) - 0.3,
+                       yend = as.numeric(factor(metric, levels = rev(metric))) - 0.3),
+                   linetype = "longdash", color = "#399098", size = 1) +
+      geom_segment(aes(x = 102, xend = 113,
+                       y = as.numeric(factor(metric, levels = rev(metric))) - 0.3,
+                       yend = as.numeric(factor(metric, levels = rev(metric))) - 0.3),
+                   linetype = "longdash", color = "#399098", size = 1) +
+      geom_text(aes(x = -3, label = metric),
+                hjust = 1, size = 6, family = "roboto") +
+      geom_text(aes(x = 103, label = value),
+                hjust = 0, size = 6, family = "roboto") +
+      geom_point(aes(x = percentile, color = "white", fill = color),
+                 size = 16, shape = 21, stroke = 3) +
+      geom_text(aes(x = percentile, label = percentile),
+                size = 6, color = "white", fontface = "bold", family = "roboto") +
+      scale_x_continuous(limits = c(-16, 113), expand = c(0, 0)) +
+      scale_fill_identity() +
+      scale_color_identity() +
+      theme_minimal() +
+      theme(
+        axis.text = element_blank(),
+        axis.title = element_blank(),
+        panel.grid = element_blank(),
+        plot.margin = margin(t = 0, r = 0, b = -20, l = 0),  # Reduced bottom margin
+        text = element_text(family = "roboto")
+      )
+    
+    # Load and process team logo
+    if(current_team() == "MLB"){
+      logo_url <- "https://a.espncdn.com/combiner/i?img=/i/teamlogos/leagues/500/mlb.png?w=400&h=400&transparent=true"
+    } else {
+      logo_url <- sprintf("https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/%s.png&h=200&w=200",
+                          current_team())
+    }
+    logo_img <- image_read(logo_url)
+    logo_raster <- as.raster(logo_img)
+    
+    
+    #player_url <- sprintf(paste0("https://img.mlbstatic.com/mlb-photos/image/upload/d_headshot_silo_generic.png,ar_1:1,b_auto:border,c_pad,q_auto:best/w_60/v1/people/427012/headshot/milb/current"))
+    player_url <- get_player_image(current_data[1,2])
+    player_img <- image_read(player_url)
+    player_raster <- as.raster(player_img)
+    # Create title with logo
+    title_grob <- textGrob(paste0(input$player, " - ", pos,
+                                  "\n BBE - ", BBE, "\n",
+                                  "Percentile Rankings - ",input$szn),
+                           gp = gpar(fontsize = 25, fontface = "bold",
+                                     fontfamily = "roboto"))
+    logo_grob <- rasterGrob(logo_raster, x = 0, width = unit(.5, "npc"),hjust = 0)
+    player_grob <- rasterGrob(player_raster, x = 0.5, width = unit(.5, "npc"),hjust = 0)
+    title_with_logo <- arrangeGrob(logo_grob, title_grob,player_grob, ncol = 3,
+                                   widths = c(.25,.5,.25))
+    caption_grob <- textGrob("Viz by: @TimStats | tim-stats.com | Data: MLB",gp = gpar(fontsize = 15, fontface = "bold",
+                                                                                       fontfamily = "roboto"))
+    
+    # Final arrangement with logo in title
+    final_plot <- grid.arrange(
+      title_with_logo,
+      labels_plot,
+      main_plot,
+      caption_grob,
+      heights = c(0.15, 0.05, 0.75, 0.05)
+    )
+    
+    grid.arrange(
+      gtable_add_padding(
+        final_plot,
+        padding = unit(c(20, 20, 20, 20), "points")  # top, right, bottom, left margins
+      )
+    )
+  }, height = 1000, width = 1000)
 }
 
-shinyApp(ui, server)
+# Run the app
+shinyApp(ui = ui, server = server)