Update app.R

app.R

@@ -1,58 +1,1332 @@
+# ============================================================================
+# COASTAL CAROLINA BASEBALL - DEFENSIVE ANALYTICS APPLICATION
+# ============================================================================
 library(shiny)
-library(bslib)
-library(dplyr)
+library(DT)
 library(ggplot2)
+library(dplyr)
+library(plotly)
+library(gt)
+library(gtExtras)
+library(shinyWidgets)
+library(tidyr)
+library(purrr)
+library(stringr)
+library(fmsb)
+# ============================================================================
+# HELPER FUNCTIONS
+# ============================================================================
 
-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")
-)
+make_curve_segments <- function(x_start, y_start, x_end, y_end, curvature = 0.3, n = 40) {
+  if (n < 2) stop("n must be >= 2")
+  t <- seq(0, 1, length.out = n)
+  
+  cx <- (x_start + x_end) / 2 + curvature * (y_end - y_start)
+  cy <- (y_start + y_end) / 2 - curvature * (x_end - x_start)
+  
+  x <- (1 - t)^2 * x_start + 2 * (1 - t) * t * cx + t^2 * x_end
+  y <- (1 - t)^2 * y_start + 2 * (1 - t) * t * cy + t^2 * y_end
+  
+  idx_from <- seq_len(n - 1)
+  idx_to   <- seq_len(n - 1) + 1
+  
+  tibble::tibble(
+    x    = x[idx_from],
+    y    = y[idx_from],
+    xend = x[idx_to],
+    yend = y[idx_to]
+  )
+}
 
-server <- function(input, output, session) {
-  subsetted <- reactive({
-    req(input$species)
-    df |> filter(Species %in% input$species)
-  })
-
-  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()
-        )
+curve1 <- make_curve_segments(89.095, 89.095, -1, 160, curvature = 0.36, n = 60)
+curve2 <- make_curve_segments(-89.095, 89.095, 1, 160, curvature = -0.36, n = 60)
+curve3 <- make_curve_segments(233.35, 233.35, -10, 410, curvature = 0.36, n = 60)
+curve4 <- make_curve_segments(-233.35, 233.35, 10, 410, curvature = -0.36, n = 60)
 
-      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
-        )
-      }
+# ============================================================================
+# SYNTHETIC DATA GENERATION
+# ============================================================================
+
+set.seed(42)
+
+# Team rosters
+cc_players <- c("Smith, John", "Johnson, Mike", "Williams, Chris", "Brown, David", 
+                "Jones, Tyler", "Garcia, Alex", "Martinez, Ryan", "Davis, Kyle",
+                "Rodriguez, Sam", "Wilson, Jake")
+
+opponent_teams <- c("Georgia Southern", "Appalachian State", "Troy", "Texas State",
+                    "South Alabama", "Arkansas State", "Louisiana", "ULM")
 
-      p
-    },
-    res = 100
+opponent_players <- c("Thompson, Mark", "Anderson, Jake", "Taylor, Ben", "Thomas, Cole",
+                      "Jackson, Drew", "White, Nick", "Harris, Luke", "Martin, Josh",
+                      "Moore, Zach", "Lee, Grant")
+
+positions <- c("RF", "CF", "LF")
+infield_positions <- c("SS", "1B", "2B", "3B")
+
+# Generate Outfield OAA data
+generate_of_oaa_data <- function(n = 500, team = "CCU") {
+  players <- if(team == "CCU") cc_players else opponent_players
+  
+  tibble(
+    PitchUID = paste0("P", 1:n),
+    Date = sample(seq(as.Date("2025-02-14"), as.Date("2025-05-30"), by = "day"), n, replace = TRUE),
+    obs_player = sample(players[1:4], n, replace = TRUE),
+    hit_location = sample(positions, n, replace = TRUE),
+    Batter = sample(opponent_players, n, replace = TRUE),
+    Pitcher = sample(cc_players[5:8], n, replace = TRUE),
+    Inning = sample(1:9, n, replace = TRUE),
+    BatterTeam = if(team == "CCU") sample(opponent_teams, n, replace = TRUE) else "CCU",
+    PitcherTeam = if(team == "CCU") "CCU" else sample(opponent_teams, n, replace = TRUE),
+    closest_pos_dist = runif(n, 5, 120),
+    HangTime = runif(n, 2.5, 5.5),
+    x_pos = runif(n, 100, 350),
+    z_pos = runif(n, -200, 200),
+    CF_PositionAtReleaseX = runif(n, 280, 320),
+    CF_PositionAtReleaseZ = runif(n, -20, 20),
+    RF_PositionAtReleaseX = runif(n, 250, 290),
+    RF_PositionAtReleaseZ = runif(n, 80, 140),
+    LF_PositionAtReleaseX = runif(n, 250, 290),
+    LF_PositionAtReleaseZ = runif(n, -140, -80),
+    angle_from_home = runif(n, -180, 180),
+    ExitSpeed = runif(n, 70, 110),
+    Angle = runif(n, 15, 45)
+  ) %>%
+    mutate(
+      # Calculate catch probability based on distance and hang time
+      catch_prob = pmax(0, pmin(1, 1 - (closest_pos_dist / 150) + (HangTime - 3) * 0.2 + rnorm(n, 0, 0.1))),
+      # Determine success based on probability with some randomness
+      success_ind = rbinom(n, 1, pmin(0.95, pmax(0.05, catch_prob + rnorm(n, 0, 0.15)))),
+      # Calculate OAA
+      OAA = success_ind - catch_prob,
+      star_group = case_when(
+        catch_prob >= 0.90 ~ "1 Star",
+        catch_prob >= 0.70 ~ "2 Star",
+        catch_prob >= 0.50 ~ "3 Star",
+        catch_prob >= 0.25 ~ "4 Star",
+        TRUE ~ "5 Star"
+      )
+    )
+}
+
+# Generate Infield OAA data
+generate_if_oaa_data <- function(n = 400, team = "CCU") {
+  players <- if(team == "CCU") cc_players else opponent_players
+  
+  tibble(
+    PitchUID = paste0("IF", 1:n),
+    Date = sample(seq(as.Date("2025-02-14"), as.Date("2025-05-30"), by = "day"), n, replace = TRUE),
+    obs_player_name = sample(players[5:8], n, replace = TRUE),
+    obs_player = sample(infield_positions, n, replace = TRUE),
+    Batter = sample(opponent_players, n, replace = TRUE),
+    Pitcher = sample(cc_players[1:4], n, replace = TRUE),
+    Inning = sample(1:9, n, replace = TRUE),
+    BatterTeam = if(team == "CCU") sample(opponent_teams, n, replace = TRUE) else "CCU",
+    Distance = runif(n, 50, 150),
+    Bearing = runif(n, -45, 45),
+    ExitSpeed = runif(n, 60, 110),
+    Angle = runif(n, -10, 30),
+    HangTime = runif(n, 1.5, 4.0),
+    obs_player_bearing = runif(n, -45, 45),
+    obs_player_x = runif(n, 80, 130),
+    obs_player_z = runif(n, -60, 60),
+    Direction = sample(c("Pull", "Center", "Oppo"), n, replace = TRUE)
+  ) %>%
+    mutate(
+      obs_player_bearing_diff = Bearing - obs_player_bearing,
+      dist_from_lead_base = sqrt((Distance * cos(Bearing * pi/180) - 90)^2 + (Distance * sin(Bearing * pi/180))^2),
+      player_angle_rad = atan2(obs_player_z, obs_player_x),
+      # Calculate play probability
+      play_prob = pmax(0, pmin(1, 0.8 - abs(obs_player_bearing_diff) * 0.02 - Distance * 0.003 + rnorm(n, 0, 0.1))),
+      success_ind = rbinom(n, 1, pmin(0.95, pmax(0.05, play_prob + rnorm(n, 0, 0.12)))),
+      OAA = success_ind - play_prob,
+      star_group = case_when(
+        play_prob >= 0.90 ~ "1 Star",
+        play_prob >= 0.70 ~ "2 Star",
+        play_prob >= 0.50 ~ "3 Star",
+        play_prob >= 0.25 ~ "4 Star",
+        TRUE ~ "5 Star"
+      )
+    )
+}
+
+# Generate Positioning data
+generate_positioning_data <- function(n = 800) {
+  tibble(
+    PitchUID = paste0("POS", 1:n),
+    Date = sample(seq(as.Date("2025-02-14"), as.Date("2025-05-30"), by = "day"), n, replace = TRUE),
+    PitcherTeam = sample(c("CCU", opponent_teams), n, replace = TRUE),
+    BatterTeam = sample(c("CCU", opponent_teams), n, replace = TRUE),
+    Pitcher = sample(c(cc_players, opponent_players), n, replace = TRUE),
+    Batter = sample(c(cc_players, opponent_players), n, replace = TRUE),
+    BatterSide = sample(c("Right", "Left"), n, replace = TRUE, prob = c(0.6, 0.4)),
+    PitcherThrows = sample(c("Right", "Left"), n, replace = TRUE, prob = c(0.7, 0.3)),
+    Outs = sample(0:2, n, replace = TRUE),
+    pitch_count = sample(c("0-0", "0-1", "0-2", "1-0", "1-1", "1-2", "2-0", "2-1", "2-2", "3-0", "3-1", "3-2"), n, replace = TRUE),
+    man_on_firstbase = sample(c(0, 1), n, replace = TRUE, prob = c(0.7, 0.3)),
+    man_on_secondbase = sample(c(0, 1), n, replace = TRUE, prob = c(0.8, 0.2)),
+    man_on_thirdbase = sample(c(0, 1), n, replace = TRUE, prob = c(0.9, 0.1)),
+    away_team = sample(opponent_teams, n, replace = TRUE),
+    home_score_before = sample(0:8, n, replace = TRUE),
+    away_score_before = sample(0:8, n, replace = TRUE),
+    `1B_PositionAtReleaseX` = rnorm(n, 85, 8),
+    `1B_PositionAtReleaseZ` = rnorm(n, 45, 10),
+    `2B_PositionAtReleaseX` = rnorm(n, 115, 10),
+    `2B_PositionAtReleaseZ` = rnorm(n, 35, 12),
+    `3B_PositionAtReleaseX` = rnorm(n, 85, 8),
+    `3B_PositionAtReleaseZ` = rnorm(n, -45, 10),
+    `SS_PositionAtReleaseX` = rnorm(n, 115, 10),
+    `SS_PositionAtReleaseZ` = rnorm(n, -35, 12),
+    `LF_PositionAtReleaseX` = rnorm(n, 270, 15),
+    `LF_PositionAtReleaseZ` = rnorm(n, -110, 20),
+    `CF_PositionAtReleaseX` = rnorm(n, 300, 15),
+    `CF_PositionAtReleaseZ` = rnorm(n, 0, 25),
+    `RF_PositionAtReleaseX` = rnorm(n, 270, 15),
+    `RF_PositionAtReleaseZ` = rnorm(n, 110, 20)
   )
 }
 
-shinyApp(ui, server)
+# Generate Catcher data
+generate_catcher_data <- function(n = 1000) {
+  catchers <- c("Martinez, Ryan", "Wilson, Jake")
+  
+  tibble(
+    PitchUID = paste0("C", 1:n),
+    Date = sample(seq(as.Date("2025-02-14"), as.Date("2025-05-30"), by = "day"), n, replace = TRUE),
+    Catcher = sample(catchers, n, replace = TRUE),
+    Pitcher = sample(cc_players[1:6], n, replace = TRUE),
+    Batter = sample(opponent_players, n, replace = TRUE),
+    BatterTeam = sample(opponent_teams, n, replace = TRUE),
+    TaggedPitchType = sample(c("Fastball", "Sinker", "Slider", "Curveball", "ChangeUp", "Cutter"), n, replace = TRUE, prob = c(0.35, 0.15, 0.2, 0.12, 0.12, 0.06)),
+    PitchCall = sample(c("BallCalled", "StrikeCalled", "StrikeSwinging", "InPlay", "FoulBall"), n, replace = TRUE, prob = c(0.3, 0.25, 0.15, 0.15, 0.15)),
+    PlateLocSide = rnorm(n, 0, 0.8),
+    PlateLocHeight = rnorm(n, 2.5, 0.6),
+    RelSpeed = rnorm(n, 88, 6),
+    InducedVertBreak = rnorm(n, 12, 5),
+    HorzBreak = rnorm(n, 5, 8),
+    Balls = sample(0:3, n, replace = TRUE),
+    Strikes = sample(0:2, n, replace = TRUE),
+    PopTime = ifelse(runif(n) > 0.85, runif(n, 1.85, 2.15), NA),
+    ThrowSpeed = ifelse(!is.na(PopTime), rnorm(sum(!is.na(PopTime)), 78, 4), NA),
+    ExchangeTime = ifelse(!is.na(PopTime), rnorm(sum(!is.na(PopTime)), 0.75, 0.08), NA),
+    TimeToBase = ifelse(!is.na(PopTime), PopTime - ExchangeTime, NA),
+    BasePositionZ = ifelse(!is.na(PopTime), rnorm(sum(!is.na(PopTime)), 0, 2), NA),
+    BasePositionY = ifelse(!is.na(PopTime), rnorm(sum(!is.na(PopTime)), 5, 1.5), NA),
+    Notes = ifelse(!is.na(PopTime), sample(c("2b out", "2b safe", "3b out", "3b safe"), sum(!is.na(PopTime)), replace = TRUE, prob = c(0.4, 0.3, 0.2, 0.1)), NA)
+  ) %>%
+    mutate(
+      in_zone = as.integer(PlateLocSide >= -0.83 & PlateLocSide <= 0.83 & PlateLocHeight >= 1.5 & PlateLocHeight <= 3.38),
+      is_swing = as.integer(PitchCall %in% c("StrikeSwinging", "InPlay", "FoulBall")),
+      StolenStrike = as.integer(in_zone == 0 & PitchCall == "StrikeCalled"),
+      StrikeLost = as.integer(in_zone == 1 & PitchCall == "BallCalled"),
+      frame = case_when(
+        in_zone == 1 & PitchCall == "BallCalled" ~ "Strike Lost",
+        in_zone == 0 & PitchCall == "StrikeCalled" ~ "Strike Added",
+        TRUE ~ NA_character_
+      ),
+      frame_numeric = case_when(
+        in_zone == 1 & PitchCall == "BallCalled" ~ -1,
+        in_zone == 0 & PitchCall == "StrikeCalled" ~ 1,
+        TRUE ~ NA_real_
+      ),
+      Count = paste0(Balls, "-", Strikes)
+    )
+}
+
+
+
+# Initialize datasets
+OAA_DF <- generate_of_oaa_data(500, "CCU")
+IF_OAA <- generate_if_oaa_data(400, "CCU")
+OPP_OAA_DF <- generate_of_oaa_data(500, "OPP")
+OPP_IF_OAA <- generate_if_oaa_data(400, "OPP")
+def_pos_data <- generate_positioning_data(800)
+catcher_data <- generate_catcher_data(1000)
+
+# ============================================================================
+# VISUALIZATION FUNCTIONS
+# ============================================================================
+
+# Star graph for outfielders
+star_graph <- function(player, position, data = OAA_DF) {
+  if (position %in% c("RF", "CF", "LF")) {
+    player_data <- data %>% 
+      filter(obs_player == player) %>% 
+      filter(hit_location == position)
+  } else {
+    player_data <- data %>% 
+      filter(obs_player == player) %>% 
+      mutate(hit_location = "All")
+  }
+  
+  if (nrow(player_data) == 0) {
+    return(ggplotly(ggplot() + theme_void() + ggtitle("No data available")))
+  }
+  
+  OAA <- round(sum(player_data$OAA, na.rm = TRUE), 1)
+  n_plays <- nrow(player_data)
+  expected_success <- round(sum(player_data$catch_prob, na.rm = TRUE), 1)
+  actual_success <- sum(player_data$success_ind, na.rm = TRUE)
+  
+  p <- ggplot(data, aes(closest_pos_dist, HangTime)) + 
+    stat_summary_2d(aes(z = catch_prob), fun = mean, bins = 51) +
+    scale_fill_gradientn(
+      colors = c("white", "#ffffb2", "#feb24c", "#fc4e2a", "#800026"),
+      limits = c(0, 1),
+      breaks = c(0.01, 0.25, 0.5, 0.7, 0.9, 1),
+      labels = c("5 Star", "4 Star", "3 Star", "2 Star", "1 Star", " "),
+      name = "Catch Rating",
+      guide = guide_colorbar(barheight = unit(5, "in"))
+    ) +
+    scale_x_continuous("Distance to Ball (ft)", limits = c(0, 140), breaks = seq(0, 140, by = 20)) +
+    scale_y_continuous("Hang Time (sec)", limits = c(2, 6)) +
+    theme_classic() +
+    ggtitle(paste0(player, " - ", position, " (", OAA, " OAA)")) +
+    theme(
+      legend.position = "right",
+      plot.title = element_text(hjust = .5, size = 20),
+      legend.title = element_blank(),
+      panel.grid.minor = element_blank(),
+      axis.title = element_text(size = 12),
+      axis.text = element_text(size = 10)
+    )
+  
+  point_colors <- ifelse(player_data$success_ind == 1, "green4", "white")
+  
+  p_interactive <- suppressWarnings({
+    ggplotly(p, tooltip = NULL) %>%
+      add_trace(
+        data = player_data,
+        x = ~closest_pos_dist,
+        y = ~HangTime,
+        type = "scatter",
+        mode = "markers",
+        color = I(point_colors),
+        marker = list(size = 10, line = list(color = "black", width = 1)),
+        text = ~paste0(
+          "Date: ", Date, "<br>",
+          "Batter: ", Batter, "<br>", 
+          "Pitcher: ", Pitcher, "<br>",
+          "Inning: ", Inning, "<br>",
+          "Opportunity Time: ", round(HangTime, 2), " sec<br>",
+          "Distance Needed: ", round(closest_pos_dist, 1), " ft<br>",
+          "Catch Probability: ", round(100 * catch_prob, 1), "%<br>",
+          star_group
+        ),
+        hoverinfo = "text"
+      ) %>%
+      layout(
+        hovermode = "closest",
+        title = list(
+          text = paste0(player, " - ", position,
+                        "<br><sup>OAA: ", OAA, " | Plays: ", n_plays,
+                        " | Expected Catches: ", expected_success, " | Actual Catches: ", actual_success, "</sup>"),
+          font = list(size = 18)
+        ),
+        margin = list(t = 80)
+      )
+  })
+  
+  return(p_interactive)
+}
+
+# Field graph for outfielders
+field_graph <- function(player, position, data = OAA_DF) {
+  if (position %in% c("RF", "CF", "LF")) {
+    player_data <- data %>% 
+      filter(obs_player == player) %>% 
+      filter(hit_location == position) 
+  } else {
+    player_data <- data %>% 
+      filter(obs_player == player) %>% 
+      mutate(hit_location = "All")
+  }
+  
+  if (nrow(player_data) == 0) {
+    return(list(
+      ggplotly(ggplot() + theme_void() + ggtitle("No data available")),
+      gt(data.frame(Message = "No data")) %>% tab_header(title = "Play Success by Difficulty")
+    ))
+  }
+  
+  df_filtered <- player_data %>% 
+    mutate(
+      avg_y = mean(CF_PositionAtReleaseX, na.rm = TRUE),
+      avg_x = mean(CF_PositionAtReleaseZ, na.rm = TRUE)
+    )
+  
+  df_table <- df_filtered %>% 
+    group_by(star_group) %>% 
+    summarize(
+      OAA = round(sum(OAA, na.rm = TRUE), 1),
+      Plays = n(),
+      Successes = sum(success_ind, na.rm = TRUE),
+      .groups = "drop"
+    ) %>% 
+    mutate(`Success Rate` = paste0(Successes, "/", Plays, " (", round((Successes/Plays)*100, 1), "%)")) %>% 
+    dplyr::select(`Star Group` = star_group, `Success Rate`) %>% 
+    t() %>% as.data.frame() %>%
+    `colnames<-`(.[1,]) %>% tibble::as_tibble() %>% dplyr::slice(-1) %>%
+    mutate(OAA = round(sum(df_filtered$OAA, na.rm = TRUE), 2)) %>% 
+    dplyr::select(OAA, everything()) %>% 
+    gt::gt() %>%
+    gtExtras::gt_theme_guardian() %>%
+    gt::tab_header(title = "Play Success by Difficulty") %>%
+    gt::cols_align(align = "center", columns = gt::everything()) %>%
+    gt::sub_missing(columns = gt::everything(), missing_text = "-") %>%
+    gt::tab_options(
+      heading.background.color = "darkcyan",
+      column_labels.background.color = "darkcyan",
+      table.border.top.color = "peru",
+      table.border.bottom.color = "peru"
+    ) %>%
+    gt::tab_style(style = gt::cell_text(color = "white"), locations = gt::cells_title(groups = "title"))
+  
+  p <- ggplot() +
+    geom_segment(aes(x = 0, y = 0, xend = 318.1981, yend = 318.1981), color = "black") +
+    geom_segment(aes(x = 0, y = 0, xend = -318.1981, yend = 318.1981), color = "black") +
+    geom_segment(aes(x = 63.6396, y = 63.6396, xend = 0, yend = 127.279), color = "black") +
+    geom_segment(aes(x = -63.6396, y = 63.6396, xend = 0, yend = 127.279), color = "black") +
+    geom_segment(data = curve1, aes(x = x, y = y, xend = xend, yend = yend), size = 0.5, color = "black", lineend = "round") +
+    geom_segment(data = curve2, aes(x = x, y = y, xend = xend, yend = yend), size = 0.5, color = "black", lineend = "round") +
+    annotate("text", x = c(-155, 155), y = 135, label = "200", size = 3) +
+    annotate("text", x = c(-190, 190), y = 170, label = "250", size = 3) +
+    annotate("text", x = c(-227, 227), y = 205, label = "300", size = 3) +
+    annotate("text", x = c(-262, 262), y = 242, label = "350", size = 3) +
+    theme_void() +
+    geom_point(
+      data = df_filtered %>% mutate(is_catch = ifelse(success_ind == 1, 'Catch', "Hit")),
+      aes(x = z_pos, y = x_pos, fill = is_catch,
+          text = paste0("Date: ", Date, "<br>",
+                        "Catch Probability: ", round(100 * catch_prob, 1), "%<br>", star_group)),
+      color = "black", shape = 21, size = 2, alpha = .6
+    ) +
+    labs(title = paste0(player, " Possible Catches - ", position)) +
+    scale_fill_manual(values = c("Hit" = "white", "Catch" = "green4"), name = " ") +
+    geom_point(data = df_filtered, aes(x = avg_x, y = avg_y), fill = "red", color = "black", size = 3, shape = 21) +
+    coord_cartesian(xlim = c(-330, 330), ylim = c(0, 400)) + 
+    coord_equal()
+  
+  p <- ggplotly(p, tooltip = "text") %>% plotly::layout(hovermode = "closest")
+  
+  return(list(p, df_table))
+}
+
+# Infield star graph
+infield_star_graph <- function(player, position, data = IF_OAA) {
+  if (position %in% c("SS", "1B", "2B", "3B")) {
+    player_data <- data %>% 
+      filter(obs_player_name == player) %>% 
+      filter(obs_player == position)
+  } else {
+    player_data <- data %>% 
+      filter(obs_player_name == player) %>% 
+      mutate(obs_player = "All")
+  }
+  
+  if (nrow(player_data) == 0) {
+    return(ggplotly(ggplot() + theme_void() + ggtitle("No data available")))
+  }
+  
+  OAA <- round(sum(player_data$OAA, na.rm = TRUE), 1)
+  n_plays <- nrow(player_data)
+  expected_success <- round(sum(player_data$play_prob, na.rm = TRUE), 1)
+  actual_success <- sum(player_data$success_ind, na.rm = TRUE)
+  
+  points_data <- player_data %>%
+    filter(play_prob > 0) %>%
+    filter(play_prob <= 0.5 | between(obs_player_bearing_diff, -12.5, 12.5))
+  
+  p <- ggplot(data, aes(obs_player_bearing_diff, play_prob)) + 
+    stat_summary_2d(aes(z = play_prob), fun = mean, bins = 15) +
+    scale_fill_gradientn(
+      colors = c("white", "#ffffb2", "#feb24c", "#fc4e2a", "#800026"),
+      limits = c(0, 1),
+      breaks = c(0.01, 0.25, 0.5, 0.7, 0.9, 1),
+      labels = c("5 Star", "4 Star", "3 Star", "2 Star", "1 Star", " "),
+      name = "Play Rating"
+    ) +
+    scale_x_continuous("Bearing Difference (degrees)", limits = c(-25, 25), breaks = seq(-25, 25, by = 12.5)) +
+    scale_y_continuous("Play Probability", limits = c(0, 1), labels = scales::percent) +
+    theme_minimal() +
+    theme(
+      legend.position = "right",
+      plot.title = element_text(hjust = 0.5, size = 18, face = "bold"),
+      panel.grid.minor = element_blank(),
+      axis.title = element_text(size = 12),
+      axis.text = element_text(size = 10)
+    )
+  
+  point_colors <- ifelse(points_data$success_ind == 1, "green4", "white")
+  
+  p_interactive <- suppressWarnings({
+    ggplotly(p, tooltip = NULL) %>%
+      add_trace(
+        data = points_data,
+        x = ~obs_player_bearing_diff,
+        y = ~play_prob,
+        type = "scatter",
+        mode = "markers",
+        color = I(point_colors),
+        marker = list(size = 10, line = list(color = "black", width = 1)),
+        text = ~paste0(
+          "Date: ", Date, "<br>",
+          "Batter: ", Batter, "<br>", 
+          "Play Probability: ", round(100 * play_prob, 1), "%<br>",
+          "Angular Distance Away: ", round(obs_player_bearing_diff, 1), "°<br>",
+          "Exit Velocity: ", round(ExitSpeed, 1), " mph<br>",
+          star_group
+        ),
+        hoverinfo = "text"
+      ) %>%
+      layout(
+        hovermode = "closest",
+        title = list(
+          text = paste0(player, " - ", position,
+                        "<br><sup>OAA: ", OAA, " | Plays: ", n_plays,
+                        " | Expected: ", expected_success, " | Actual: ", actual_success, "</sup>"),
+          font = list(size = 18)
+        ),
+        margin = list(t = 80)
+      )
+  })
+  
+  suppressWarnings(p_interactive)
+}
+
+# Infield field graph
+infield_field_graph <- function(player, position, data = IF_OAA) {
+  if (position %in% c("SS", "1B", "2B", "3B")) {
+    player_data <- data %>% 
+      filter(obs_player_name == player) %>% 
+      filter(obs_player == position)
+  } else {
+    player_data <- data %>% 
+      filter(obs_player_name == player)
+  }
+  
+  if (nrow(player_data) == 0) {
+    return(list(
+      ggplotly(ggplot() + theme_void() + ggtitle("No data available")),
+      gt(data.frame(Message = "No data")) %>% tab_header(title = "Play Success by Difficulty")
+    ))
+  }
+  
+  df_filtered <- player_data %>% 
+    mutate(
+      rad = Bearing * pi/180,
+      x_pos = Distance * cos(rad),
+      z_pos = Distance * sin(rad),
+      avg_y = mean(obs_player_x, na.rm = TRUE),
+      avg_x = mean(obs_player_z, na.rm = TRUE)
+    )
+  
+  df_table <- df_filtered %>% 
+    group_by(star_group) %>% 
+    summarize(
+      OAA = round(sum(OAA, na.rm = TRUE), 1),
+      Plays = n(),
+      Successes = sum(success_ind, na.rm = TRUE),
+      .groups = "drop"
+    ) %>% 
+    mutate(`Success Rate` = paste0(Successes, "/", Plays, " (", round((Successes/Plays)*100, 1), "%)")) %>% 
+    dplyr::select(`Star Group` = star_group, `Success Rate`) %>% 
+    t() %>% as.data.frame() %>%
+    `colnames<-`(.[1,]) %>% dplyr::slice(-1) %>%
+    mutate(OAA = round(sum(df_filtered$OAA, na.rm = TRUE), 1)) %>% 
+    dplyr::select(OAA, everything()) %>% 
+    gt::gt() %>%
+    gtExtras::gt_theme_guardian() %>%
+    gt::tab_header(title = "Play Success by Difficulty") %>%
+    gt::cols_align(align = "center", columns = gt::everything()) %>%
+    gt::sub_missing(columns = gt::everything(), missing_text = "-") %>%
+    gt::tab_options(
+      heading.background.color = "darkcyan",
+      column_labels.background.color = "darkcyan"
+    ) %>%
+    gt::tab_style(style = gt::cell_text(color = "white"), locations = gt::cells_title(groups = "title"))
+  
+  p <- ggplot() +
+    geom_segment(aes(x = 0, y = 0, xend = 100, yend = 100), color = "black") +
+    geom_segment(aes(x = 0, y = 0, xend = -100, yend = 100), color = "black") +
+    geom_segment(aes(x = 63.6396, y = 63.6396, xend = 0, yend = 127.279), color = "black") +
+    geom_segment(aes(x = -63.6396, y = 63.6396, xend = 0, yend = 127.279), color = "black") +
+    geom_segment(data = curve1, aes(x = x, y = y, xend = xend, yend = yend), size = 0.5, color = "black") +
+    geom_segment(data = curve2, aes(x = x, y = y, xend = xend, yend = yend), size = 0.5, color = "black") +
+    theme_void() +
+    geom_point(
+      data = df_filtered %>% mutate(is_play = ifelse(success_ind == 1, 'Play Made', "Not Made")),
+      aes(x = z_pos, y = x_pos, fill = is_play,
+          text = paste0("Date: ", Date, "<br>", "Play Probability: ", round(100 * play_prob, 1), "%<br>", star_group)),
+      color = "black", shape = 21, size = 2, alpha = .6
+    ) +
+    labs(title = paste0(player, " Possible Plays - ", position)) +
+    scale_fill_manual(values = c("Not Made" = "white", "Play Made" = "green4"), name = " ") +
+    geom_point(data = df_filtered, aes(x = avg_x, y = avg_y), fill = "red", color = "black", size = 3, shape = 21) +
+    ylim(0, 160) + xlim(-120, 120) + coord_equal()
+  
+  p <- ggplotly(p, tooltip = "text") %>% plotly::layout(hovermode = "closest")
+  
+  return(list(p, df_table))
+}
+
+# Positioning heatmap
+infield_positioning_heatmap <- function(team, batter_hand = "No Filter", pitcher_hand = "No Filter",
+                                        man_on_first = "No Filter", man_on_second = "No Filter", man_on_third = "No Filter") {
+  
+  team_data <- def_pos_data %>% filter(PitcherTeam == team)
+  
+  if (batter_hand != "No Filter") team_data <- team_data %>% filter(BatterSide == batter_hand)
+  if (pitcher_hand != "No Filter") team_data <- team_data %>% filter(PitcherThrows == pitcher_hand)
+  if (man_on_first != "No Filter") team_data <- team_data %>% filter(man_on_firstbase == as.numeric(man_on_first == "Yes"))
+  if (man_on_second != "No Filter") team_data <- team_data %>% filter(man_on_secondbase == as.numeric(man_on_second == "Yes"))
+  if (man_on_third != "No Filter") team_data <- team_data %>% filter(man_on_thirdbase == as.numeric(man_on_third == "Yes"))
+  
+  n_observations <- nrow(team_data)
+  
+  if (n_observations < 10) {
+    return(ggplot() + theme_void() + ggtitle("Insufficient data for selected filters"))
+  }
+  
+  positions_long <- team_data %>%
+    pivot_longer(
+      cols = matches("^(1B|2B|3B|SS|LF|CF|RF)_PositionAtRelease[XZ]$"),
+      names_to = c("position", ".value"),
+      names_pattern = "^(.+)_PositionAtRelease([XZ])$"
+    )
+  
+  p <- ggplot() +
+    geom_hline(yintercept = seq(0, 400, by = 20), linetype = "dotted", color = "gray80", size = 0.3) +
+    geom_vline(xintercept = seq(-240, 240, by = 20), linetype = "dotted", color = "gray80", size = 0.3) +
+    geom_density_2d_filled(data = positions_long, aes(x = Z, y = X), bins = 35, alpha = 0.5) +
+    scale_fill_manual(values = colorRampPalette(c("#FFFFFF", "#FFF5F0", "#FEE0D2", "#FCBBA1",
+                                                  "#FC9272", "#FB6A4A", "#EF3B2C", "#CB181D", "#99000D"))(35)) +
+    geom_segment(aes(x = 0, y = 0, xend = 233.35, yend = 233.35), color = "black") +
+    geom_segment(aes(x = 0, y = 0, xend = -233.35, yend = 233.35), color = "black") +
+    geom_segment(aes(x = 63.6396, y = 63.6396, xend = 0, yend = 127.279), color = "black") +
+    geom_segment(aes(x = -63.6396, y = 63.6396, xend = 0, yend = 127.279), color = "black") +
+    geom_segment(data = curve1, aes(x = x, y = y, xend = xend, yend = yend), size = 0.5, color = "black") +
+    geom_segment(data = curve2, aes(x = x, y = y, xend = xend, yend = yend), size = 0.5, color = "black") +
+    geom_segment(data = curve3, aes(x = x, y = y, xend = xend, yend = yend), size = 0.5, color = "black") +
+    geom_segment(data = curve4, aes(x = x, y = y, xend = xend, yend = yend), size = 0.5, color = "black") +
+    labs(
+      title = paste0("Starting Fielder Position — ", team),
+      subtitle = paste0("Based on ", n_observations, " observations"),
+      x = "", y = ""
+    ) + 
+    ylim(0, 411) + xlim(-250, 250) + coord_equal() + 
+    theme_void() +
+    theme(
+      legend.position = "none",
+      plot.title = element_text(hjust = .5, size = 20, face = "bold"),
+      plot.subtitle = element_text(hjust = 0.5)
+    )
+  
+  return(p)
+}
+
+# ============================================================================
+# UI
+# ============================================================================
+
+ui <- fluidPage(
+  tags$head(
+    tags$style(HTML("
+      @import url('https://fonts.googleapis.com/css2?family=Outfit:wght@300;400;500;600;700;800&display=swap');
+      
+      body { 
+        font-family: 'Outfit', sans-serif; 
+        background: linear-gradient(135deg, #f5f7fa 0%, #e4e9f2 100%);
+        color: #2c3e50;
+      }
+      
+      .app-header {
+        display: flex;
+        justify-content: center;
+        align-items: center;
+        padding: 25px 40px;
+        background: linear-gradient(135deg, #006F71 0%, #008B8B 50%, #20B2AA 100%);
+        border-bottom: 4px solid #A27752;
+        margin-bottom: 25px;
+        box-shadow: 0 8px 32px rgba(0, 111, 113, 0.3);
+      }
+      
+      .header-title {
+        color: white;
+        font-size: 2.2rem;
+        font-weight: 800;
+        text-shadow: 2px 2px 4px rgba(0,0,0,0.2);
+        letter-spacing: 1px;
+      }
+      
+      .header-subtitle {
+        color: rgba(255,255,255,0.9);
+        font-size: 1rem;
+        font-weight: 400;
+        margin-top: 5px;
+      }
+      
+      .nav-tabs {
+        border: none !important;
+        border-radius: 50px;
+        padding: 8px 16px;
+        margin: 20px auto;
+        max-width: 95%;
+        background: linear-gradient(135deg, #d4edeb 0%, #e8ddd0 50%, #d4edeb 100%);
+        box-shadow: 0 4px 20px rgba(0,139,139,.15), inset 0 2px 4px rgba(255,255,255,.7);
+        display: flex;
+        justify-content: center;
+        flex-wrap: wrap;
+        gap: 8px;
+      }
+      
+      .nav-tabs > li > a {
+        color: #006F71 !important;
+        border: none !important;
+        border-radius: 50px !important;
+        background: transparent !important;
+        font-weight: 600;
+        font-size: 15px;
+        padding: 12px 28px;
+        transition: all 0.3s ease;
+        letter-spacing: 0.3px;
+      }
+      
+      .nav-tabs > li > a:hover {
+        color: #004d4e !important;
+        background: rgba(255,255,255,0.6) !important;
+        transform: translateY(-2px);
+      }
+      
+      .nav-tabs > li.active > a,
+      .nav-tabs > li.active > a:focus,
+      .nav-tabs > li.active > a:hover {
+        background: linear-gradient(135deg, #006F71 0%, #008B8B 50%, #20B2AA 100%) !important;
+        color: #fff !important;
+        text-shadow: 0 1px 2px rgba(0,0,0,0.2);
+        box-shadow: 0 6px 20px rgba(0,139,139,0.4);
+        border: none !important;
+      }
+      
+      .tab-content {
+        background: white;
+        border-radius: 20px;
+        padding: 30px;
+        margin-top: 15px;
+        box-shadow: 0 10px 40px rgba(0,0,0,0.08);
+        border: 1px solid rgba(0,139,139,0.1);
+      }
+      
+      .well {
+        background: linear-gradient(135deg, #f8fffe 0%, #f5f5f0 100%);
+        border-radius: 15px;
+        border: 1px solid rgba(0,139,139,0.15);
+        box-shadow: 0 4px 15px rgba(0,0,0,0.05);
+        padding: 20px;
+      }
+      
+      .control-label {
+        font-weight: 600;
+        color: #006F71;
+        font-size: 13px;
+        text-transform: uppercase;
+        letter-spacing: 0.5px;
+        margin-bottom: 8px;
+      }
+      
+      .selectize-input, .form-control {
+        border-radius: 12px !important;
+        border: 2px solid rgba(0,139,139,0.25) !important;
+        padding: 10px 15px !important;
+        font-size: 14px !important;
+        transition: all 0.3s ease;
+      }
+      
+      .selectize-input:focus, .form-control:focus {
+        border-color: #006F71 !important;
+        box-shadow: 0 0 0 3px rgba(0,139,139,0.15) !important;
+      }
+      
+      .section-header {
+        font-size: 1.4rem;
+        font-weight: 700;
+        color: #006F71;
+        margin-bottom: 20px;
+        padding-bottom: 10px;
+        border-bottom: 3px solid #A27752;
+      }
+      
+      .metric-card {
+        background: linear-gradient(135deg, #006F71 0%, #008B8B 100%);
+        border-radius: 15px;
+        padding: 20px;
+        color: white;
+        text-align: center;
+        box-shadow: 0 6px 20px rgba(0,139,139,0.3);
+        margin-bottom: 15px;
+      }
+      
+      .metric-value {
+        font-size: 2.5rem;
+        font-weight: 800;
+      }
+      
+      .metric-label {
+        font-size: 0.9rem;
+        opacity: 0.9;
+        text-transform: uppercase;
+        letter-spacing: 1px;
+      }
+      
+      .btn-primary {
+        background: linear-gradient(135deg, #006F71 0%, #008B8B 100%) !important;
+        border: none !important;
+        border-radius: 12px !important;
+        font-weight: 600 !important;
+        padding: 12px 25px !important;
+        transition: all 0.3s ease !important;
+      }
+      
+      .btn-primary:hover {
+        transform: translateY(-2px);
+        box-shadow: 0 6px 20px rgba(0,139,139,0.4) !important;
+      }
+      
+      .gt_table {
+        border-radius: 12px !important;
+        overflow: hidden;
+      }
+      
+      hr {
+        border-top: 2px solid rgba(162, 119, 82, 0.3);
+        margin: 30px 0;
+      }
+    "))
+  ),
+  
+  # Header
+  div(class = "app-header",
+      div(style = "text-align: center;",
+          div(class = "header-title", "Defensive Analytics Dashboard"),
+          div(class = "header-subtitle", "Coastal Carolina Baseball • Spring 2025")
+      )
+  ),
+  
+  # Main tabs
+  tabsetPanel(
+    id = "main_tabs",
+    
+    # ==================== TAB 1: TEAM OAA ====================
+    tabPanel(
+      "Team OAA",
+      br(),
+      fluidRow(
+        column(12, h3(class = "section-header", "Outfielder Outs Above Average"))
+      ),
+      fluidRow(
+        column(3, wellPanel(
+          selectInput("of_player", "Select Player:", choices = unique(OAA_DF$obs_player)),
+          selectInput("of_position", "Position:", choices = c("All", "RF", "CF", "LF")),
+          hr(),
+          h5("Quick Stats"),
+          uiOutput("of_quick_stats")
+        )),
+        column(9,
+               plotlyOutput("of_star_graph", height = "500px"),
+               br(),
+               fluidRow(
+                 column(6, plotlyOutput("of_field_graph", height = "450px")),
+                 column(6, gt_output("of_success_table"))
+               )
+        )
+      ),
+      hr(),
+      fluidRow(
+        column(12, h3(class = "section-header", "Infielder Outs Above Average"))
+      ),
+      fluidRow(
+        column(3, wellPanel(
+          selectInput("if_player", "Select Player:", choices = unique(IF_OAA$obs_player_name)),
+          selectInput("if_position", "Position:", choices = c("All", "SS", "2B", "3B", "1B"))
+        )),
+        column(9,
+               plotlyOutput("if_star_graph", height = "500px"),
+               br(),
+               fluidRow(
+                 column(6, plotlyOutput("if_field_graph", height = "400px")),
+                 column(6, gt_output("if_success_table"))
+               )
+        )
+      ),
+      hr(),
+      fluidRow(
+        column(12, h3(class = "section-header", "Team OAA Leaderboard")),
+        column(12, DT::dataTableOutput("team_oaa_leaderboard"))
+      )
+    ),
+    
+    # ==================== TAB 2: OPPONENT OAA ====================
+    tabPanel(
+      "Opponent OAA",
+      br(),
+      fluidRow(
+        column(12, h3(class = "section-header", "Opponent Outfield Defense Analysis"))
+      ),
+      fluidRow(
+        column(3, wellPanel(
+          selectInput("opp_team_filter", "Opponent Team:", 
+                      choices = c("All", unique(OPP_OAA_DF$PitcherTeam))),
+          selectInput("opp_of_player", "Select Player:", choices = unique(OPP_OAA_DF$obs_player)),
+          selectInput("opp_of_position", "Position:", choices = c("All", "RF", "CF", "LF"))
+        )),
+        column(9,
+               plotlyOutput("opp_of_star_graph", height = "500px"),
+               br(),
+               fluidRow(
+                 column(6, plotlyOutput("opp_of_field_graph", height = "450px")),
+                 column(6, gt_output("opp_of_success_table"))
+               )
+        )
+      ),
+      hr(),
+      fluidRow(
+        column(12, h3(class = "section-header", "Opponent Infield Defense Analysis"))
+      ),
+      fluidRow(
+        column(3, wellPanel(
+          selectInput("opp_if_player", "Select Player:", choices = unique(OPP_IF_OAA$obs_player_name)),
+          selectInput("opp_if_position", "Position:", choices = c("All", "SS", "2B", "3B", "1B"))
+        )),
+        column(9,
+               plotlyOutput("opp_if_star_graph", height = "500px")
+        )
+      ),
+      hr(),
+      fluidRow(
+        column(12, h3(class = "section-header", "Opponent OAA Summary")),
+        column(12, DT::dataTableOutput("opp_oaa_leaderboard"))
+      )
+    ),
+    
+    # ==================== TAB 3: OPPONENT POSITIONING ====================
+    tabPanel(
+      "Opponent Positioning",
+      br(),
+      fluidRow(
+        column(12, h3(class = "section-header", "Defensive Positioning Analysis"))
+      ),
+      fluidRow(
+        column(3, wellPanel(
+          selectInput("pos_team", "Select Team:", 
+                      choices = c("CCU", unique(def_pos_data$PitcherTeam[def_pos_data$PitcherTeam != "CCU"]))),
+          hr(),
+          h5("Situational Filters", style = "font-weight: 600; color: #006F71;"),
+          selectInput("pos_batter_hand", "Batter Hand:", choices = c("No Filter", "Right", "Left")),
+          selectInput("pos_pitcher_hand", "Pitcher Hand:", choices = c("No Filter", "Right", "Left")),
+          hr(),
+          h5("Baserunners", style = "font-weight: 600; color: #006F71;"),
+          selectInput("pos_first", "Runner on 1st:", choices = c("No Filter", "Yes", "No")),
+          selectInput("pos_second", "Runner on 2nd:", choices = c("No Filter", "Yes", "No")),
+          selectInput("pos_third", "Runner on 3rd:", choices = c("No Filter", "Yes", "No"))
+        )),
+        column(9,
+               plotOutput("positioning_heatmap", height = "650px")
+        )
+      ),
+      hr(),
+      fluidRow(
+        column(12, h3(class = "section-header", "Average Positioning by Situation")),
+        column(12, gt_output("positioning_summary"))
+      )
+    ),
+    
+    # ==================== TAB 4: CATCHING ANALYSIS ====================
+    tabPanel(
+      "Catching Analysis",
+      br(),
+      fluidRow(
+        column(12, h3(class = "section-header", "Catcher Framing & Throwing"))
+      ),
+      fluidRow(
+        column(3, wellPanel(
+          selectInput("catcher_select", "Select Catcher:", choices = unique(catcher_data$Catcher)),
+          hr(),
+          h5("Framing Filters", style = "font-weight: 600; color: #006F71;"),
+          pickerInput("pitch_type_filter", "Pitch Types:",
+                      choices = unique(catcher_data$TaggedPitchType),
+                      selected = unique(catcher_data$TaggedPitchType),
+                      multiple = TRUE, options = list(`actions-box` = TRUE)),
+          numericInput("min_pitches_catcher", "Min Pitches:", value = 50, min = 1, max = 500)
+        )),
+        column(9,
+               fluidRow(
+                 column(4, div(class = "metric-card",
+                               div(class = "metric-value", textOutput("catcher_net_strikes")),
+                               div(class = "metric-label", "Net Strikes")
+                 )),
+                 column(4, div(class = "metric-card",
+                               div(class = "metric-value", textOutput("catcher_frame_rate")),
+                               div(class = "metric-label", "Frame Rate")
+                 )),
+                 column(4, div(class = "metric-card",
+                               div(class = "metric-value", textOutput("catcher_avg_pop")),
+                               div(class = "metric-label", "Avg Pop Time")
+                 ))
+               ),
+               br(),
+               fluidRow(
+                 column(6, 
+                        h4("Framing Heatmap"),
+                        plotOutput("catcher_framing_heatmap", height = "500px")),
+                 column(6, 
+                        h4("Framing Points"),
+                        plotlyOutput("catcher_framing_points", height = "500px"))
+               )
+        )
+      ),
+      hr(),
+      fluidRow(
+        column(6,
+               h4("Throwing Accuracy Map"),
+               plotOutput("catcher_throw_map", height = "500px")),
+        column(6,
+               h4("Pop Time Distribution"),
+               plotlyOutput("catcher_pop_dist", height = "250px"),
+               h4("Throw Velocity Trend"),
+               plotlyOutput("catcher_velo_trend", height = "250px"))
+      ),
+      hr(),
+      fluidRow(
+        column(12, h3(class = "section-header", "Catcher Leaderboard")),
+        column(12, DT::dataTableOutput("catcher_leaderboard"))
+      )
+    )
+    )
+  )
+
+# ============================================================================
+# SERVER
+# ============================================================================
+
+server <- function(input, output, session) {
+  
+  # ==================== TAB 1: TEAM OAA ====================
+  
+  output$of_quick_stats <- renderUI({
+    player_data <- OAA_DF %>% filter(obs_player == input$of_player)
+    if (input$of_position != "All") {
+      player_data <- player_data %>% filter(hit_location == input$of_position)
+    }
+    
+    total_oaa <- round(sum(player_data$OAA, na.rm = TRUE), 1)
+    plays <- nrow(player_data)
+    success_rate <- round(mean(player_data$success_ind) * 100, 1)
+    
+    tagList(
+      div(class = "metric-card", style = "padding: 12px;",
+          div(class = "metric-value", style = "font-size: 1.8rem;", total_oaa),
+          div(class = "metric-label", "Total OAA")
+      ),
+      div(class = "metric-card", style = "padding: 12px; background: linear-gradient(135deg, #A27752 0%, #c49a6c 100%);",
+          div(class = "metric-value", style = "font-size: 1.8rem;", plays),
+          div(class = "metric-label", "Total Plays")
+      ),
+      div(class = "metric-card", style = "padding: 12px; background: linear-gradient(135deg, #2c3e50 0%, #4a6278 100%);",
+          div(class = "metric-value", style = "font-size: 1.8rem;", paste0(success_rate, "%")),
+          div(class = "metric-label", "Success Rate")
+      )
+    )
+  })
+  
+  output$of_star_graph <- renderPlotly({
+    star_graph(input$of_player, input$of_position, OAA_DF)
+  })
+  
+  of_field_results <- reactive({
+    field_graph(input$of_player, input$of_position, OAA_DF)
+  })
+  
+  output$of_field_graph <- renderPlotly({
+    of_field_results()[[1]]
+  })
+  
+  output$of_success_table <- render_gt({
+    of_field_results()[[2]]
+  })
+  
+  output$if_star_graph <- renderPlotly({
+    infield_star_graph(input$if_player, input$if_position, IF_OAA)
+  })
+  
+  if_field_results <- reactive({
+    infield_field_graph(input$if_player, input$if_position, IF_OAA)
+  })
+  
+  output$if_field_graph <- renderPlotly({
+    if_field_results()[[1]]
+  })
+  
+  output$if_success_table <- render_gt({
+    if_field_results()[[2]]
+  })
+  
+  output$team_oaa_leaderboard <- DT::renderDataTable({
+    of_summary <- OAA_DF %>%
+      group_by(Player = obs_player) %>%
+      summarise(
+        `OF OAA` = round(sum(OAA, na.rm = TRUE), 1),
+        `OF Plays` = n(),
+        `OF Success %` = round(mean(success_ind) * 100, 1),
+        .groups = "drop"
+      )
+    
+    if_summary <- IF_OAA %>%
+      group_by(Player = obs_player_name) %>%
+      summarise(
+        `IF OAA` = round(sum(OAA, na.rm = TRUE), 1),
+        `IF Plays` = n(),
+        `IF Success %` = round(mean(success_ind) * 100, 1),
+        .groups = "drop"
+      )
+    
+    full_join(of_summary, if_summary, by = "Player") %>%
+      mutate(
+        `Total OAA` = coalesce(`OF OAA`, 0) + coalesce(`IF OAA`, 0)
+      ) %>%
+      arrange(desc(`Total OAA`)) %>%
+      DT::datatable(
+        options = list(pageLength = 15, scrollX = TRUE),
+        rownames = FALSE
+      )
+  })
+  
+  # ==================== TAB 2: OPPONENT OAA ====================
+  
+  output$opp_of_star_graph <- renderPlotly({
+    star_graph(input$opp_of_player, input$opp_of_position, OPP_OAA_DF)
+  })
+  
+  opp_of_field_results <- reactive({
+    field_graph(input$opp_of_player, input$opp_of_position, OPP_OAA_DF)
+  })
+  
+  output$opp_of_field_graph <- renderPlotly({
+    opp_of_field_results()[[1]]
+  })
+  
+  output$opp_of_success_table <- render_gt({
+    opp_of_field_results()[[2]]
+  })
+  
+  output$opp_if_star_graph <- renderPlotly({
+    infield_star_graph(input$opp_if_player, input$opp_if_position, OPP_IF_OAA)
+  })
+  
+  output$opp_oaa_leaderboard <- DT::renderDataTable({
+    opp_summary <- OPP_OAA_DF %>%
+      group_by(Player = obs_player, Team = PitcherTeam) %>%
+      summarise(
+        OAA = round(sum(OAA, na.rm = TRUE), 1),
+        Plays = n(),
+        `Success %` = round(mean(success_ind) * 100, 1),
+        .groups = "drop"
+      ) %>%
+      arrange(desc(OAA))
+    
+    DT::datatable(opp_summary, options = list(pageLength = 15), rownames = FALSE)
+  })
+  
+  # ==================== TAB 3: POSITIONING ====================
+  
+  output$positioning_heatmap <- renderPlot({
+    infield_positioning_heatmap(
+      team = input$pos_team,
+      batter_hand = input$pos_batter_hand,
+      pitcher_hand = input$pos_pitcher_hand,
+      man_on_first = input$pos_first,
+      man_on_second = input$pos_second,
+      man_on_third = input$pos_third
+    )
+  })
+  
+  output$positioning_summary <- render_gt({
+    team_data <- def_pos_data %>% filter(PitcherTeam == input$pos_team)
+    
+    team_data %>%
+      summarise(
+        `Avg 1B X` = round(mean(`1B_PositionAtReleaseX`, na.rm = TRUE), 1),
+        `Avg 1B Z` = round(mean(`1B_PositionAtReleaseZ`, na.rm = TRUE), 1),
+        `Avg SS X` = round(mean(`SS_PositionAtReleaseX`, na.rm = TRUE), 1),
+        `Avg SS Z` = round(mean(`SS_PositionAtReleaseZ`, na.rm = TRUE), 1),
+        `Avg CF X` = round(mean(`CF_PositionAtReleaseX`, na.rm = TRUE), 1),
+        `Avg CF Z` = round(mean(`CF_PositionAtReleaseZ`, na.rm = TRUE), 1),
+        Observations = n()
+      ) %>%
+      gt() %>%
+      tab_header(title = paste("Average Positioning -", input$pos_team)) %>%
+      cols_align(align = "center") %>%
+      tab_options(heading.background.color = "darkcyan") %>%
+      tab_style(style = cell_text(color = "white"), locations = cells_title())
+  })
+  
+  # ==================== TAB 4: CATCHING ====================
+  
+  catcher_filtered <- reactive({
+    catcher_data %>%
+      filter(Catcher == input$catcher_select) %>%
+      filter(TaggedPitchType %in% input$pitch_type_filter)
+  })
+  
+  output$catcher_net_strikes <- renderText({
+    df <- catcher_filtered()
+    net <- sum(df$frame_numeric, na.rm = TRUE)
+    sprintf("%+d", net)
+  })
+  
+  output$catcher_frame_rate <- renderText({
+    df <- catcher_filtered() %>% filter(!is.na(frame_numeric))
+    if (nrow(df) == 0) return("N/A")
+    added <- sum(df$frame_numeric == 1, na.rm = TRUE)
+    total <- sum(df$frame_numeric != 0, na.rm = TRUE)
+    if (total == 0) return("N/A")
+    paste0(round(added / total * 100, 1), "%")
+  })
+  
+  output$catcher_avg_pop <- renderText({
+    df <- catcher_filtered() %>% filter(!is.na(PopTime))
+    if (nrow(df) == 0) return("N/A")
+    paste0(round(mean(df$PopTime, na.rm = TRUE), 2), "s")
+  })
+  
+  output$catcher_framing_heatmap <- renderPlot({
+    df <- catcher_filtered() %>%
+      filter(!is.na(frame), !is.na(PlateLocSide), !is.na(PlateLocHeight)) %>%
+      filter(frame %in% c("Strike Added", "Strike Lost"))
+    
+    if (nrow(df) < 10) {
+      return(ggplot() + theme_void() + ggtitle("Insufficient data"))
+    }
+    
+    ggplot(df, aes(PlateLocSide, PlateLocHeight)) +
+      stat_density_2d(aes(fill = after_stat(density)), geom = "raster", contour = FALSE, alpha = 0.8) +
+      scale_fill_gradientn(colours = c("white", "lightblue", "#FF9999", "red", "darkred")) +
+      annotate("rect", xmin = -0.83, xmax = 0.83, ymin = 1.5, ymax = 3.38, 
+               fill = NA, color = "black", linewidth = 1.5) +
+      coord_fixed() + xlim(-2, 2) + ylim(0, 4.5) +
+      facet_wrap(~frame, nrow = 1) +
+      theme_void() +
+      theme(strip.text = element_text(size = 16, face = "bold"), legend.position = "none")
+  })
+  
+  output$catcher_framing_points <- renderPlotly({
+    df <- catcher_filtered() %>%
+      filter(!is.na(frame), !is.na(PlateLocSide), !is.na(PlateLocHeight),
+             frame %in% c("Strike Added", "Strike Lost"))
+    
+    if (nrow(df) < 10) {
+      return(ggplotly(ggplot() + theme_void()))
+    }
+    
+    pitch_colors <- c("Fastball" = "#FA8072", "Sinker" = "#fdae61", "Slider" = "#A020F0",
+                      "Curveball" = "#2c7bb6", "ChangeUp" = "#90EE90", "Cutter" = "red")
+    
+    p <- ggplot(df, aes(PlateLocSide, PlateLocHeight, color = TaggedPitchType,
+                        text = paste0("Pitch: ", TaggedPitchType, "<br>Frame: ", frame))) +
+      geom_point(alpha = 0.7, size = 2) +
+      annotate("rect", xmin = -0.83, xmax = 0.83, ymin = 1.5, ymax = 3.38, 
+               fill = NA, color = "black", linewidth = 1) +
+      coord_fixed(xlim = c(-2, 2), ylim = c(0, 4.5)) +
+      facet_wrap(~frame, nrow = 1) +
+      scale_color_manual(values = pitch_colors, na.value = "grey60") +
+      theme_minimal() +
+      theme(strip.text = element_text(face = "bold"))
+    
+    ggplotly(p, tooltip = "text")
+  })
+  
+  output$catcher_throw_map <- renderPlot({
+    df <- catcher_filtered() %>%
+      filter(!is.na(Notes), Notes %in% c("2b out", "2b safe", "3b out", "3b safe"))
+    
+    if (nrow(df) == 0) {
+      return(ggplot() + theme_void() + ggtitle("No throwing data available"))
+    }
+    
+    ggplot(df) +
+      geom_polygon(data = data.frame(x = c(-10,10,10,-10), y = c(0.25,0.25,8,8)),
+                   aes(x = x, y = y), fill = '#14a6a8', color = '#14a6a8') +
+      geom_polygon(data = data.frame(x = c(-10,10,10,-10), y = c(8,8,9,9)),
+                   aes(x = x, y = y), fill = 'yellow', color = 'yellow') +
+      geom_polygon(data = data.frame(x = c(-10,10,10,-10), y = c(-2,-2,0.25,0.25)),
+                   aes(x = x, y = y), fill = 'brown', color = 'brown') +
+      geom_polygon(data = data.frame(x = c(-10,10,10,-10), y = c(-5,-5,-2,-2)),
+                   aes(x = x, y = y), fill = 'darkgreen', color = 'darkgreen') +
+      geom_polygon(data = data.frame(x = c(-1,1,1,-1), y = c(0,0,0.45,0.45)),
+                   aes(x = x, y = y), fill = 'white', color = 'black') +
+      geom_point(aes(x = BasePositionZ, y = BasePositionY, fill = Notes),
+                 color = 'white', pch = 21, size = 4) +
+      scale_fill_manual(values = c('2b safe' = 'red', '2b out' = '#339a1d',
+                                   '3b safe' = '#ff6b6b', '3b out' = '#1a5d1a')) +
+      scale_x_continuous(limits = c(-10, 10)) + 
+      scale_y_continuous(limits = c(-5, 9)) +
+      coord_fixed() +
+      theme_void() +
+      theme(legend.position = "bottom") +
+      ggtitle(paste(input$catcher_select, "- Throwing Report"))
+  })
+  
+  output$catcher_pop_dist <- renderPlotly({
+    df <- catcher_filtered() %>% filter(!is.na(PopTime))
+    
+    if (nrow(df) == 0) return(ggplotly(ggplot() + theme_void()))
+    
+    p <- ggplot(df, aes(PopTime)) +
+      geom_histogram(bins = 15, fill = "darkcyan", alpha = 0.7, color = "white") +
+      geom_vline(xintercept = mean(df$PopTime), color = "peru", linetype = "dashed", size = 1) +
+      labs(x = "Pop Time (s)", y = "Count") +
+      theme_minimal()
+    
+    ggplotly(p)
+  })
+  
+  output$catcher_velo_trend <- renderPlotly({
+    df <- catcher_filtered() %>% 
+      filter(!is.na(ThrowSpeed)) %>%
+      arrange(Date) %>%
+      mutate(throw_num = row_number())
+    
+    if (nrow(df) == 0) return(ggplotly(ggplot() + theme_void()))
+    
+    p <- ggplot(df, aes(throw_num, ThrowSpeed)) +
+      geom_point(color = "darkcyan", size = 2) +
+      geom_smooth(method = "loess", color = "peru", se = TRUE, fill = "peru", alpha = 0.3) +
+      labs(x = "Throw #", y = "Velocity (MPH)") +
+      theme_minimal()
+    
+    ggplotly(p)
+  })
+  
+  output$catcher_leaderboard <- DT::renderDataTable({
+    catcher_data %>%
+      group_by(Catcher) %>%
+      summarise(
+        `Pitches Caught` = n(),
+        `Strikes Added` = sum(frame_numeric == 1, na.rm = TRUE),
+        `Strikes Lost` = sum(frame_numeric == -1, na.rm = TRUE),
+        `Net Strikes` = sum(frame_numeric, na.rm = TRUE),
+        `Frame Rate` = round(sum(frame_numeric == 1, na.rm = TRUE) / 
+                               max(1, sum(!is.na(frame_numeric) & frame_numeric != 0)) * 100, 1),
+        `Avg Pop Time` = round(mean(PopTime, na.rm = TRUE), 2),
+        `Avg Throw Velo` = round(mean(ThrowSpeed, na.rm = TRUE), 1),
+        .groups = "drop"
+      ) %>%
+      arrange(desc(`Net Strikes`)) %>%
+      DT::datatable(options = list(pageLength = 10), rownames = FALSE)
+  })
+  
+
+# ============================================================================
+# RUN APP
+# ============================================================================
+
+shinyApp(ui = ui, server = server)