app.R

# =========================================================================
# Hyannis Harbor Hawks — Lower Level Overview App
# Two tabs:  (1) Hitter Overview   (2) Pitcher Overview
# Data source: college_26_LowerLevels.parquet  (PastSeasonData private dataset)
# UI theme comes from the attached theme.R
# =========================================================================

library(shiny)
library(bslib)
library(glue)
library(httr)
library(arrow)
library(dplyr)
library(ggplot2)
library(plotly)
library(gt)
library(gtExtras)
library(tidyr)
library(stringr)
library(scales)
library(base64enc)
library(magick)

source("theme.R")

`%||%` <- function(a, b) if (!is.null(a)) a else b

# Hawks logo (absolute path so the working dir doesn't matter in Docker)
logo_b64 <- base64enc::base64encode("/app/HHLogo.png")
logo_uri <- paste0("data:image/png;base64,", logo_b64)

# Load + mirror pitcher silhouette for the inferred arm angle graphic
img          <- magick::image_read("https://i.imgur.com/RLlIVrc.png")
img_raster_R <- as.raster(img)
img_raster_L <- as.raster(magick::image_flop(img))

# ============================================================
# Data Loading
# ============================================================

# Union of the columns the two overview tabs need
needed_cols <- c(
  # identity
  "Batter", "Pitcher", "BatterSide", "BatterTeam",
  "PitcherThrows", "Date",
  # count
  "Balls", "Strikes", "PitchofPA",
  # pitch traits
  "TaggedPitchType", "RelSpeed", "SpinRate", "InducedVertBreak", "HorzBreak",
  "VertApprAngle", "Extension", "RelSide", "RelHeight", "stuff_plus",
  # location
  "PlateLocSide", "PlateLocHeight",
  # results
  "PitchCall", "PlayResult", "KorBB", "AutoHitType", "event_type",
  "ExitSpeed", "Angle", "Bearing", "Distance",
  # expected stats
  "prob_0", "prob_1", "prob_2", "prob_3", "prob_4", "xwOBA", "woba", "slg",
  # flags
  "is_hit", "is_at_bat", "is_plate_appearance", "is_whiff", "is_swing",
  "in_zone", "in_zone_whiff", "is_csw", "is_hard_hit", "is_k", "is_walk",
  "on_base", "chase"
)

# Download a parquet from the HF private dataset (HF_Token set as a Space secret)
download_private_parquet <- function(repo_id, filename, max_retries = 3) {
  url     <- paste0("https://huggingface.co/datasets/", repo_id, "/resolve/main/", filename)
  api_key <- Sys.getenv("HF_Token")
  if (api_key == "") stop("HF_Token environment variable is not set.")
  for (attempt in 1:max_retries) {
    tryCatch({
      response <- GET(url, add_headers(Authorization = paste("Bearer", api_key)), timeout(60))
      if (status_code(response) == 200) {
        tmp <- tempfile(fileext = ".parquet")
        writeBin(content(response, "raw"), tmp)
        return(read_parquet(tmp, col_select = any_of(needed_cols)))
      } else {
        warning(paste("Attempt", attempt, "failed:", status_code(response)))
      }
    }, error = function(e) {
      warning(paste("Attempt", attempt, "error:", e$message))
      if (attempt < max_retries) Sys.sleep(2)
    })
  }
  stop(paste("Failed after", max_retries, "attempts"))
}

# Single data source for this app
all_data <- download_private_parquet("HyannisHarborHawksCCBL/PastSeasonData",
                                     "college_26_LowerLevels.parquet") %>%
  mutate(Date = as.Date(Date))

# ============================================================
# Shared helper functions
# ============================================================

# Pitch colors
pitch_colors <- c(
  "Fastball" = "red", "Changeup" = "green4", "Curveball" = "blue",
  "Slider" = "gold", "Sweeper" = "maroon2", "Splitter" = "lightblue",
  "Cutter" = "brown", "Sinker" = "orange", "Knuckleball" = "#6A89A7"
)

# blue - white - red color gradient
make_color_fn <- function(min_val, max_val,
                          low_color = "#3661ad", mid_color = "white", high_color = "#d82029") {
  if (is.na(min_val) || is.na(max_val) || min_val == max_val) return(function(x) "white")
  gradient_fn <- col_numeric(
    palette  = c(low_color, mid_color, high_color),
    domain   = c(min_val, max_val),
    na.color = "white"
  )
  function(x) {
    x_clamped <- pmin(pmax(x, min_val, na.rm = TRUE), max_val, na.rm = TRUE)
    ifelse(is.na(x), "white", gradient_fn(x_clamped))
  }
}

# red - white - blue color gradient
make_color_fn_reverse <- function(min_val, max_val,
                                  low_color = "#d82029", mid_color = "white", high_color = "#3661ad") {
  make_color_fn(min_val, max_val, low_color, mid_color, high_color)
}

# Curves for spray-chart fence
make_curve_segments <- function(x_start, y_start, x_end, y_end, curvature = 0.3, n = 40) {
  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 <- seq_len(n - 1)
  tibble(x = x[idx], y = y[idx], xend = x[idx + 1], yend = y[idx + 1])
}

# ============================================================
# HITTER OVERVIEW FUNCTIONS
# ============================================================

# Reference data for conditional formatting
reference_stats <- function(df) {
  df %>% summarise(
    PA        = sum(is_plate_appearance, na.rm = TRUE),
    Avg       = mean(is_hit,    na.rm = TRUE),
    OBP       = mean(on_base,   na.rm = TRUE),
    SLG       = mean(slg,       na.rm = TRUE),
    `Chase%`  = 100 * mean(chase,         na.rm = TRUE),
    `Whiff%`  = 100 * mean(is_whiff,      na.rm = TRUE),
    `Z-Con%`  = 100 * (1 - mean(in_zone_whiff, na.rm = TRUE)),
    `Avg EV`  = mean(ExitSpeed, na.rm = TRUE),
    EV50      = quantile(ExitSpeed, .5, na.rm = TRUE),
    EV90      = quantile(ExitSpeed, .9, na.rm = TRUE),
    `HH%`     = 100 * mean(is_hard_hit, na.rm = TRUE),
    `GB%`     = 100 * (sum((AutoHitType == "GroundBall" & PitchCall == "InPlay"), na.rm = TRUE) /
                         sum(PitchCall == "InPlay", na.rm = TRUE)),
    xwOBAcon  = mean(xwOBA[PitchCall == "InPlay"], na.rm = TRUE),
    xwOBA     = mean(xwOBA, na.rm = TRUE)
  )
}

# overall hitter summary table
batter_summary <- function(data, batter_name) {
  data <- data %>%
    filter(Batter == batter_name) %>%
    mutate(PitchGroup = case_when(
      TaggedPitchType %in% c("Fastball","Cutter","Sinker","FastBall") ~ "Fastball",
      TaggedPitchType %in% c("Slider","Curveball","Sweeper")          ~ "Breaking",
      TaggedPitchType %in% c("Changeup","Splutter","Knuckleball")     ~ "Offspeed",
      TRUE ~ "Other"
    ))

  summary_stats <- function(df) {
    df %>% summarise(
      PA        = sum(is_plate_appearance, na.rm = TRUE),
      Avg       = mean(is_hit,    na.rm = TRUE),
      OBP       = mean(on_base,   na.rm = TRUE),
      SLG       = mean(slg,       na.rm = TRUE),
      `Chase%`  = 100 * mean(chase,         na.rm = TRUE),
      `Whiff%`  = 100 * mean(is_whiff,      na.rm = TRUE),
      `Z-Con%`  = 100 * (1 - mean(in_zone_whiff, na.rm = TRUE)),
      `Avg EV`  = mean(ExitSpeed, na.rm = TRUE),
      EV50      = quantile(ExitSpeed, .5, na.rm = TRUE),
      EV90      = quantile(ExitSpeed, .9, na.rm = TRUE),
      `HH%`     = 100 * mean(is_hard_hit, na.rm = TRUE),
      `GB%`     = 100 * (sum((AutoHitType == "GroundBall" & PitchCall == "InPlay"), na.rm = TRUE) /
                           sum(PitchCall == "InPlay", na.rm = TRUE)),
      xwOBAcon  = mean(xwOBA[PitchCall == "InPlay"], na.rm = TRUE),
      xwOBA     = mean(xwOBA, na.rm = TRUE)
    )
  }

  pitch_summary      <- data %>% group_by(PitchGroup)    %>% summary_stats() %>% rename(GROUP = PitchGroup)    %>% filter(GROUP != "Other")
  handedness_summary <- data %>% group_by(PitcherThrows) %>% summary_stats() %>% rename(GROUP = PitcherThrows)
  overall_summary    <- data %>% summary_stats() %>% mutate(GROUP = "Overall")

  full_summary <- bind_rows(pitch_summary, handedness_summary, overall_summary)

  full_summary %>%
    gt() %>% gt_theme_espn() %>%
    tab_header(title = paste0("Batter Summary — ", batter_name)) %>%
    tab_options(heading.align = "center") %>%
    sub_missing(missing_text = "—") %>%
    fmt_number(columns = c(Avg, OBP, SLG, xwOBA, xwOBAcon), decimals = 3) %>%
    fmt_number(columns = c(`Chase%`,`Whiff%`,`Z-Con%`,`Avg EV`,EV50,EV90,`HH%`,`GB%`), decimals = 1) %>%
    fmt_number(columns = PA, decimals = 0) %>%
    data_color(columns = `Z-Con%`,  fn = make_color_fn(lg_ref$`Z-Con%`  - 10, lg_ref$`Z-Con%`  + 10)) %>%
    data_color(columns = `Avg EV`,  fn = make_color_fn(lg_ref$`Avg EV`  - 6,  lg_ref$`Avg EV`  + 6))  %>%
    data_color(columns = EV50,      fn = make_color_fn(lg_ref$EV50      - 6,  lg_ref$EV50      + 6))  %>%
    data_color(columns = EV90,      fn = make_color_fn(lg_ref$EV90      - 6,  lg_ref$EV90      + 6))  %>%
    data_color(columns = `HH%`,     fn = make_color_fn(lg_ref$`HH%`     - 12, lg_ref$`HH%`     + 12)) %>%
    data_color(columns = xwOBA,     fn = make_color_fn(lg_ref$xwOBA     - .080, lg_ref$xwOBA   + .040)) %>%
    data_color(columns = xwOBAcon,  fn = make_color_fn(lg_ref$xwOBAcon  - .080, lg_ref$xwOBAcon + .040)) %>%
    data_color(columns = `Chase%`,  fn = make_color_fn_reverse(lg_ref$`Chase%` - 10, lg_ref$`Chase%` + 10)) %>%
    data_color(columns = `Whiff%`,  fn = make_color_fn_reverse(lg_ref$`Whiff%` - 10, lg_ref$`Whiff%` + 10)) %>%
    tab_style(style = cell_text(color = "#002855"),
              locations = cells_body(columns = everything(), rows = everything())) %>%
    tab_style(style = cell_text(color = "#002855"),
              locations = cells_column_labels(columns = everything())) %>%
    tab_style(style = cell_text(color = "#002855", size = px(22)),
              locations = cells_title(groups = "title")) %>%
    tab_style(style = cell_borders(sides = c("top","bottom","left","right"),
                                   color = "#002855", weight = px(.5)),
              locations = cells_body(columns = everything(), rows = everything())) %>%
    tab_style(style = cell_borders(sides = "top",    color = "#002855", weight = px(10)),
              locations = cells_body(rows = GROUP %in% c("Left"))) %>%
    tab_style(style = cell_borders(sides = "bottom", color = "#002855", weight = px(10)),
              locations = cells_body(rows = GROUP %in% c("Right"))) %>%
    tab_style(style = list(cell_text(weight = "bold"), cell_fill(color = "#f0f0f0")),
              locations = cells_body(rows = GROUP == "Overall")) %>%
    tab_style(style = list(cell_fill(color = "red"),     cell_text(color = "white", weight = "bold")),
              locations = cells_body(columns = GROUP, rows = GROUP == "Fastball")) %>%
    tab_style(style = list(cell_fill(color = "green4"),  cell_text(color = "white", weight = "bold")),
              locations = cells_body(columns = GROUP, rows = GROUP == "Offspeed")) %>%
    tab_style(style = list(cell_fill(color = "blue"),    cell_text(color = "white", weight = "bold")),
              locations = cells_body(columns = GROUP, rows = GROUP == "Breaking")) %>%
    tab_style(style = list(cell_fill(color = "#2962FF"), cell_text(color = "white", weight = "bold")),
              locations = cells_body(columns = GROUP, rows = GROUP == "Right")) %>%
    tab_style(style = list(cell_fill(color = "#C62828"), cell_text(color = "white", weight = "bold")),
              locations = cells_body(columns = GROUP, rows = GROUP == "Left")) %>%
    tab_style(style = list(cell_fill(color = "orange"),  cell_text(color = "white", weight = "bold")),
              locations = cells_body(columns = GROUP, rows = GROUP == "Overall"))
}

# percentile references for the percentile chart
get_percentile_refs_hitter <- function(data) {
  data %>%
    group_by(Batter) %>%
    summarise(
      xwOBA          = mean(xwOBA, na.rm = TRUE),
      xBA             = 1 - mean(prob_0, na.rm = TRUE),
      `Avg EV`        = mean(ExitSpeed, na.rm = TRUE),
      `HH%`     = 100 * mean(is_hard_hit, na.rm = TRUE),
      `SS%`   = 100 * sum(PitchCall == "InPlay" & Angle >= 8 & Angle <= 32, na.rm = TRUE) /
        sum(PitchCall == "InPlay", na.rm = TRUE),
      `P-Air%`     = 100 * sum(if_else(BatterSide == "Right", Bearing < -15, Bearing > 15) &
                                 AutoHitType %in% c("FlyBall","LineDrive","Popup") &
                                 PitchCall == "InPlay", na.rm = TRUE) /
        sum(PitchCall == "InPlay", na.rm = TRUE),
      `HR/FB%`        = 100 * sum(AutoHitType == "FlyBall" & PlayResult == "Homerun" &
                                    PitchCall == "InPlay", na.rm = TRUE) /
        sum(AutoHitType == "FlyBall" & PitchCall == "InPlay", na.rm = TRUE),
      `Chase%`        = 100 * mean(chase,         na.rm = TRUE),
      `Whiff%`        = 100 * mean(is_whiff,       na.rm = TRUE),
      `Z-Con%`        = 100 * (1 - mean(in_zone_whiff, na.rm = TRUE)),
      `K%`            = 100 * mean(is_k,           na.rm = TRUE),
      `BB%`           = 100 * mean(is_walk,        na.rm = TRUE),
      number          = sum(is_plate_appearance, na.rm = TRUE),
      .groups = "drop"
    ) %>%
    select(-Batter) %>%
    filter(number >= 10) %>%
    select(-number)
}

# savant-style percentile chart
batter_percentiles <- function(df, batter, summary_ref) {
  batter_split <- df %>%
    filter(!is.na(TaggedPitchType), Batter == batter) %>%
    summarise(
      xwOBA        = mean(xwOBA, na.rm = TRUE),
      xBA           = 1 - mean(prob_0, na.rm = TRUE),
      `Avg EV`      = mean(ExitSpeed, na.rm = TRUE),
      `HH%`   = 100 * mean(is_hard_hit, na.rm = TRUE),
      `SS%` = 100 * sum(PitchCall == "InPlay" & Angle >= 8 & Angle <= 32, na.rm = TRUE) /
        sum(PitchCall == "InPlay", na.rm = TRUE),
      `P-Air%`   = 100 * sum(if_else(BatterSide == "Right", Bearing < -15, Bearing > 15) &
                               AutoHitType %in% c("FlyBall","LineDrive","Popup") &
                               PitchCall == "InPlay", na.rm = TRUE) /
        sum(PitchCall == "InPlay", na.rm = TRUE),
      `HR/FB%`      = 100 * sum(AutoHitType == "FlyBall" & PlayResult == "Homerun" &
                                  PitchCall == "InPlay", na.rm = TRUE) /
        sum(AutoHitType == "FlyBall" & PitchCall == "InPlay", na.rm = TRUE),
      `Chase%`      = 100 * mean(chase,         na.rm = TRUE),
      `Whiff%`      = 100 * mean(is_whiff,       na.rm = TRUE),
      `Z-Con%`      = 100 * (1 - mean(in_zone_whiff, na.rm = TRUE)),
      `K%`          = 100 * mean(is_k,           na.rm = TRUE),
      `BB%`         = 100 * mean(is_walk,        na.rm = TRUE),
      .groups = "drop"
    )

  combined <- bind_cols(
    batter_split,
    batter_split %>%
      mutate(across(everything(), ~ {
        if (cur_column() %in% c("Chase%","Whiff%","K%")) {
          round(mean(summary_ref[[cur_column()]] >= .x, na.rm = TRUE) * 100)
        } else {
          round(mean(summary_ref[[cur_column()]] <= .x, na.rm = TRUE) * 100)
        }
      }, .names = "{.col}_percentile")) %>%
      select(matches("_percentile"))
  )

  custom_order <- c("xwOBA","xBA","Avg EV","HH%","SS%",
                    "P-Air%","HR/FB%","Chase%","Whiff%","Z-Con%","K%","BB%")

  metrics <- combined %>%
    pivot_longer(cols = everything(), names_to = "Metric_Type", values_to = "Value") %>%
    mutate(
      Metric = str_remove(Metric_Type, "_percentile"),
      Type   = ifelse(str_detect(Metric_Type, "_percentile"), "Percentile", "Value")
    ) %>%
    select(Metric, Type, Value) %>%
    pivot_wider(names_from = Type, values_from = Value) %>%
    filter(!is.na(Percentile) & !is.na(Value)) %>%
    mutate(
      Metric = factor(Metric, levels = custom_order),
      color  = gradient_n_pal(c("#164EA7","#c7dcdc","#C8102E"))(Percentile / 100),
      Value  = case_when(
        Metric %in% c("xwOBA","xBA") ~ sprintf("%.3f", Value),
        TRUE ~ sprintf("%.1f", Value)
      )
    )

  ggplot(metrics, aes(y = factor(Metric, levels = rev(custom_order)))) +
    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 = length(custom_order) + 0.35,
             color = "white", linewidth = 1.5, alpha = 0.5) +
    geom_segment(aes(x = -2,   xend = -16,
                     y    = as.numeric(factor(Metric, levels = rev(custom_order))) - 0.3,
                     yend = as.numeric(factor(Metric, levels = rev(custom_order))) - 0.3),
                 linetype = "longdash", color = "#399098", linewidth = 1) +
    geom_segment(aes(x = 102,  xend = 113,
                     y    = as.numeric(factor(Metric, levels = rev(custom_order))) - 0.3,
                     yend = as.numeric(factor(Metric, levels = rev(custom_order))) - 0.3),
                 linetype = "longdash", color = "#399098", linewidth = 1) +
    geom_text(aes(x = -3,  label = Metric), hjust = 1, size = 5) +
    geom_text(aes(x = 103, label = Value),  hjust = 0, size = 5) +
    geom_point(aes(x = Percentile, fill = color),
               color = "white", size = 12, shape = 21, stroke = 2.25) +
    geom_text(aes(x = Percentile, label = Percentile),
              size = 4.5, color = "white", fontface = "bold") +
    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())
}

# hits spray chart
spray_chart <- function(data, batter_name) {
  hits <- data %>%
    filter(Batter == batter_name,
           PlayResult %in% c("Single","Double","Triple","Homerun","HomeRun", "Out", "Error",
                             "Sacrifice", "FieldersChoice")) %>%
    mutate(
      hit_type = case_when(
        PlayResult == "Single"                    ~ "SINGLE",
        PlayResult == "Double"                    ~ "DOUBLE",
        PlayResult == "Triple"                    ~ "TRIPLE",
        PlayResult %in% c("Homerun","HomeRun")    ~ "HOME RUN",
        PlayResult %in% c("Out", "Sacrifice", "FieldersChoice") ~ "OUT",
        PlayResult == "Error" ~ "REACH ON ERROR"
      ),
      hit_type = factor(hit_type, levels = c("SINGLE","DOUBLE","TRIPLE","HOME RUN", "OUT", "REACH ON ERROR")),
      spray_x  = Distance * sin(Bearing * pi / 180),
      spray_y  = Distance * cos(Bearing * pi / 180)
    )

  curve1 <- make_curve_segments(89.095,  89.095, -3, 160, curvature =  0.36, n = 60)
  curve2 <- make_curve_segments(-89.095, 89.095,  3, 160, curvature = -0.36, n = 60)

  fence_x <- c(0, curve1$x, curve1$xend[nrow(curve1)],
               curve2$xend[nrow(curve2)], rev(curve2$x), 0)
  fence_y <- c(0, curve1$y, curve1$yend[nrow(curve1)],
               curve2$yend[nrow(curve2)], rev(curve2$y), 0)

  theta  <- seq(-45, 45, length.out = 100) * pi / 180
  fair_x <- c(0, 350 * sin(theta), 0)
  fair_y <- c(0, 350 * cos(theta), 0)

  colors <- c("SINGLE" = "yellow", "DOUBLE" = "orange",
              "TRIPLE" = "purple", "HOME RUN" = "red",
              "OUT" = "#808080", "REACH ON ERROR" = "green2")

  p <- ggplot() +
    geom_polygon(aes(x = fair_x, y = fair_y), fill = "#50BA49", alpha = 0.5) +
    geom_polygon(aes(x = fence_x, y = fence_y), fill = "#B06F63", alpha = 0.5) +
    geom_segment(aes(x =  0,      y = 0,      xend =  63.64, yend =  63.64), color = "black", linewidth = .5) +
    geom_segment(aes(x =  63.64,  y = 63.64,  xend =  0,     yend = 127.28), color = "black", linewidth = .5) +
    geom_segment(aes(x =  0,      y = 127.28, xend = -63.64, yend =  63.64), color = "black", linewidth = .5) +
    geom_segment(aes(x = -63.64,  y = 63.64,  xend =  0,     yend =   0),    color = "black", linewidth = .5) +
    geom_segment(data = curve1, aes(x=x,y=y,xend=xend,yend=yend),
                 linewidth=0.6, color="black", lineend="round") +
    geom_segment(data = curve2, aes(x=x,y=y,xend=xend,yend=yend),
                 linewidth=0.6, color="black", lineend="round") +
    geom_segment(aes(x=-1, y=160, xend=1, yend=160), color="black") +
    annotate("text", x=c(-155,155),   y=135, label="200", size=2.5, color="black") +
    annotate("text", x=c(-190,190),   y=170, label="250", size=2.5, color="black") +
    annotate("text", x=c(-227,227),   y=205, label="300", size=2.5, color="black") +
    annotate("text", x=c(-262,262),   y=242, label="350", size=2.5, color="black") +
    annotate("text", x=c(-297,297),   y=277, label="400", size=2.5, color="black") +
    geom_segment(aes(x=0,y=0,xend= 318.2, yend=318.2), color="black", linewidth=0.5) +
    geom_segment(aes(x=0,y=0,xend=-318.2, yend=318.2), color="black", linewidth=0.5) +
    geom_point(data = hits,
               aes(x=spray_x, y=spray_y, fill=hit_type,
                   text=paste0(hit_type,"<br>",
                               "EV: ",   round(ExitSpeed,1),"<br>",
                               "LA: ",   round(Angle,1),    "<br>",
                               "Dist: ", round(Distance,0), " ft<br>",
                               "xwOBA: ",round(xwOBA,3),   "<br>",
                               "Pitch: ",TaggedPitchType)),
               color="black", shape=21, size=3, alpha=0.85, stroke=0.8) +
    scale_fill_manual(values = colors, name = "") +
    labs(title = paste0("Hits Spray Chart — ", batter_name)) +
    ylim(-30, 400) + xlim(-330, 330) + coord_equal() +
    theme_void() +
    theme(plot.title     = element_text(hjust=0.5, face="bold", size=16),
          legend.position = "right",
          plot.background = element_rect(fill="white", color=NA))

  ggplotly(p, tooltip="text") %>% layout(hovermode="closest")
}

# ============================================================
# PITCHER OVERVIEW FUNCTIONS
# ============================================================

# movement plot
movement_plot <- function(data, pitcher_name) {
  data <- data %>% filter(Pitcher == pitcher_name, TaggedPitchType != "Other")

  circle_shape <- function(r) {
    list(type = "circle", x0 = -r, x1 = r, y0 = -r, y1 = r,
         line = list(color = "rgba(128,128,128,0.5)", width = 1,
                     dash = if (r %in% c(6, 18)) "dot" else "solid"),
         fillcolor = "transparent")
  }

  shapes <- c(
    list(
      list(type = "line", x0 = 0, x1 = 0, y0 = -28, y1 = 28,
           line = list(color = "rgba(128,128,128,0.6)", dash = "dash", width = 1)),
      list(type = "line", x0 = -28, x1 = 28, y0 = 0, y1 = 0,
           line = list(color = "rgba(128,128,128,0.6)", dash = "dash", width = 1))
    ),
    lapply(c(6, 12, 18, 24), circle_shape)
  )

  tick_labels <- c(
    lapply(c(24, 12, 12, 24), function(val) {
      list(x = val, y = 0, text = as.character(val), showarrow = FALSE,
           yshift = -12, font = list(size = 10, color = "grey40"))
    }),
    lapply(c(24, 12, 12, 24), function(val) {
      list(x = 0, y = val, text = as.character(val), showarrow = FALSE,
           xshift = -12, font = list(size = 10, color = "grey40"))
    }),
    list(
      list(x = 0.5, y = 0, xref = "paper", yref = "paper",
           text = "Horizontal Break (inches)", showarrow = FALSE,
           yshift = -15, font = list(size = 13, color = "black")),
      list(x = 0, y = 0.5, xref = "paper", yref = "paper",
           text = "Induced Vertical Break (inches)", showarrow = FALSE,
           xanchor = "center", yanchor = "middle", xshift = 75,
           textangle = -90, font = list(size = 13, color = "black"))
    )
  )

  plot_ly(data, x = ~HorzBreak, y = ~InducedVertBreak, color = ~TaggedPitchType,
          colors = pitch_colors, type = "scatter", mode = "markers",
          marker = list(size = 10, opacity = 0.8), hoverinfo = "text",
          text = ~paste("Pitch:", TaggedPitchType,
                        "<br>HB:", round(HorzBreak, 1),
                        "<br>IVB:", round(InducedVertBreak, 1),
                        "<br>Velo:", round(RelSpeed, 1),
                        "<br>Stuff Plus:", round(stuff_plus, 1))) %>%
    layout(
      title = list(text = paste0("Movement Plot — ", pitcher_name),
                   font = list(size = 18), x = 0.5, y = 0.98,
                   xanchor = "center", xref = "paper"),
      xaxis = list(title = "", showticklabels = FALSE, showgrid = FALSE,
                   zeroline = FALSE, range = c(-30, 30),
                   scaleanchor = "y", scaleratio = 1),
      yaxis = list(title = "", showticklabels = FALSE, showgrid = FALSE,
                   zeroline = FALSE, range = c(-30, 30)),
      shapes = shapes, annotations = tick_labels,
      plot_bgcolor = "white", paper_bgcolor = "white",
      legend = list(orientation = "v", x = 0.85, y = 0.95, xanchor = "left",
                    yanchor = "top", font = list(size = 12),
                    bgcolor = "rgba(255,255,255,0.8)"),
      autosize = TRUE, margin = list(l = 40, r = 10, t = 40, b = 40),
      showlegend = TRUE
    )
}

# Pitcher league reference for conditional formatting
pitcher_reference_stats <- function(df) {
  df %>% summarise(
    `WHIFF%`  = 100 * mean(is_whiff, na.rm = TRUE),
    `IZW%`    = 100 * sum(is_whiff[in_zone == 1], na.rm = TRUE) /
                      sum(is_swing[in_zone == 1], na.rm = TRUE),
    `CSW%`    = 100 * mean(is_csw,  na.rm = TRUE),
    `CHASE%`  = 100 * mean(chase,   na.rm = TRUE),
    `ZONE%`   = 100 * mean(in_zone, na.rm = TRUE),
    `STUFF+`  = mean(stuff_plus, na.rm = TRUE),
    EV        = mean(ExitSpeed, na.rm = TRUE),
    EV50      = mean(ExitSpeed[!is.na(ExitSpeed) &
                       ExitSpeed >= quantile(ExitSpeed, 0.5, na.rm = TRUE)], na.rm = TRUE),
    EV90      = mean(ExitSpeed[!is.na(ExitSpeed) &
                       ExitSpeed >= quantile(ExitSpeed, 0.9, na.rm = TRUE)], na.rm = TRUE),
    xwOBA     = mean(xwOBA, na.rm = TRUE),
    xwOBAcon  = mean(xwOBA[PitchCall == "InPlay"], na.rm = TRUE)
  )
}

# pitcher summary overview table
pitcher_summary <- function(data, pitcher_name) {
  pitchdata <- data %>% filter(Pitcher == pitcher_name, TaggedPitchType != "Other")

  pitch_summary <- pitchdata %>%
    group_by(TaggedPitchType) %>%
    summarise(
      usage_temp = round((n() / nrow(pitchdata)) * 100, 1),
      `USAGE%` = round((n() / nrow(pitchdata)) * 100, 1),
      NUM = n(),
      BBE = sum(event_type %in% c("Field Out", "Home Run", "Single", "Double", "Triple")),
      VELO = round(mean(RelSpeed, na.rm = TRUE), 1),
      `MAX VELO` = round(max(RelSpeed, na.rm = TRUE), 1),
      SPIN = round(mean(SpinRate, na.rm = TRUE), 0),
      IVB = round(mean(InducedVertBreak, na.rm = TRUE), 1),
      HB = round(mean(HorzBreak, na.rm = TRUE), 1),
      VAA = round(mean(VertApprAngle, na.rm = TRUE), 2),
      EXT = round(mean(Extension, na.rm = TRUE), 1),
      RELS = round(mean(RelSide, na.rm = TRUE), 1),
      RELH = round(mean(RelHeight, na.rm = TRUE), 1),
      EV = round(mean(ExitSpeed, na.rm = TRUE), 1),
      EV50 = round(mean(ExitSpeed[!is.na(ExitSpeed) &
                                    ExitSpeed >= quantile(ExitSpeed, 0.5, na.rm = TRUE)], na.rm = TRUE), 1),
      EV90 = round(mean(ExitSpeed[!is.na(ExitSpeed) &
                                    ExitSpeed >= quantile(ExitSpeed, 0.9, na.rm = TRUE)], na.rm = TRUE), 1),
      `WHIFF%` = round(100 * mean(is_whiff, na.rm = TRUE), 1),
      `IZW%` = round(100 * sum(is_whiff[in_zone == 1], na.rm = TRUE) /
                       sum(is_swing[in_zone == 1], na.rm = TRUE), 1),
      `CSW%` = round(100 * mean(is_csw, na.rm = TRUE), 1),
      `CHASE%` = round(100 * mean(chase, na.rm = TRUE), 1),
      `ZONE%` = round(100 * mean(in_zone, na.rm = TRUE), 1),
      `GB%`     = round(100 * (sum((AutoHitType == "GroundBall" & PitchCall == "InPlay"), na.rm = TRUE) /
                         sum(PitchCall == "InPlay", na.rm = TRUE)), 2),
      xwOBAcon = round(mean(xwOBA[PitchCall == "InPlay"], na.rm = TRUE), 3),
      xwOBA = round(mean(xwOBA, na.rm = TRUE), 3),
      `STUFF+` = round(mean(stuff_plus, na.rm = TRUE), 0),
      .groups = "drop"
    ) %>%
    rename(GROUP = TaggedPitchType) %>%
    arrange(desc(usage_temp)) %>%
    select(-usage_temp)

  handedness_summary <- pitchdata %>%
    group_by(BatterSide) %>%
    summarise(
      `USAGE%` = round((n() / nrow(pitchdata)) * 100, 1), NUM = n(),
      BBE = sum(event_type %in% c("Field Out", "Home Run", "Single", "Double", "Triple")),
      VELO = NA_real_, `MAX VELO` = NA_real_, SPIN = NA_real_,
      IVB = NA_real_, HB = NA_real_, EXT = NA_real_, RELS = NA_real_, RELH = NA_real_,
      EV = round(mean(ExitSpeed, na.rm = TRUE), 1),
      EV50 = round(mean(ExitSpeed[!is.na(ExitSpeed) &
                                    ExitSpeed >= quantile(ExitSpeed, 0.5, na.rm = TRUE)], na.rm = TRUE), 1),
      EV90 = round(mean(ExitSpeed[!is.na(ExitSpeed) &
                                    ExitSpeed >= quantile(ExitSpeed, 0.9, na.rm = TRUE)], na.rm = TRUE), 1),
      `WHIFF%` = round(100 * mean(is_whiff, na.rm = TRUE), 1),
      `IZW%` = round(100 * sum(is_whiff[in_zone == 1], na.rm = TRUE) /
                       sum(is_swing[in_zone == 1], na.rm = TRUE), 1),
      `CSW%` = round(100 * mean(is_csw, na.rm = TRUE), 1),
      `CHASE%` = round(100 * mean(chase, na.rm = TRUE), 1),
      `ZONE%` = round(100 * mean(in_zone, na.rm = TRUE), 1),
      `GB%`     = round(100 * (sum((AutoHitType == "GroundBall" & PitchCall == "InPlay"), na.rm = TRUE) /
                         sum(PitchCall == "InPlay", na.rm = TRUE)), 2),
      xwOBAcon = round(mean(xwOBA[PitchCall == "InPlay"], na.rm = TRUE), 3),
      xwOBA = round(mean(xwOBA, na.rm = TRUE), 3),
      `STUFF+` = round(mean(stuff_plus, na.rm = TRUE), 0),
      .groups = "drop"
    ) %>% rename(GROUP = BatterSide)

  overall_summary <- data %>%
    filter(Pitcher == pitcher_name) %>%
    summarise(
      `USAGE%` = NA_real_, NUM = n(),
      BBE = sum(event_type %in% c("Field Out", "Home Run", "Single", "Double", "Triple")),
      VELO = NA_real_, `MAX VELO` = NA_real_, SPIN = NA_real_,
      IVB = NA_real_, HB = NA_real_, EXT = NA_real_, RELS = NA_real_, RELH = NA_real_,
      EV = round(mean(ExitSpeed, na.rm = TRUE), 1),
      EV50 = round(mean(ExitSpeed[!is.na(ExitSpeed) &
                                    ExitSpeed >= quantile(ExitSpeed, 0.5, na.rm = TRUE)], na.rm = TRUE), 1),
      EV90 = round(mean(ExitSpeed[!is.na(ExitSpeed) &
                                    ExitSpeed >= quantile(ExitSpeed, 0.9, na.rm = TRUE)], na.rm = TRUE), 1),
      `WHIFF%` = round(100 * mean(is_whiff, na.rm = TRUE), 1),
      `IZW%` = round(100 * sum(is_whiff[in_zone == 1], na.rm = TRUE) /
                       sum(is_swing[in_zone == 1], na.rm = TRUE), 1),
      `CSW%` = round(100 * mean(is_csw, na.rm = TRUE), 1),
      `CHASE%` = round(100 * mean(chase, na.rm = TRUE), 1),
      `ZONE%` = round(100 * mean(in_zone, na.rm = TRUE), 1),
      `GB%`     = round(100 * (sum((AutoHitType == "GroundBall" & PitchCall == "InPlay"), na.rm = TRUE) /
                         sum(PitchCall == "InPlay", na.rm = TRUE)), 2),
      xwOBAcon = round(mean(xwOBA[PitchCall == "InPlay"], na.rm = TRUE), 3),
      xwOBA = round(mean(xwOBA, na.rm = TRUE), 3),
      `STUFF+` = round(mean(stuff_plus, na.rm = TRUE), 0)
    ) %>% mutate(GROUP = "Overall") %>% select(GROUP, everything())

  full_summary <- bind_rows(pitch_summary, handedness_summary, overall_summary)

  full_summary %>%
    gt() %>%
    gt_theme_espn() %>%
    tab_header(title = paste0("Pitch Summary — ", pitcher_name)) %>%
    tab_options(heading.align = "center") %>%
    sub_missing(missing_text = "—") %>%
    tab_style(style = cell_text(color = "#002855"),
              locations = cells_body(columns = everything(), rows = everything())) %>%
    tab_style(style = cell_text(color = "#002855"),
              locations = cells_column_labels(columns = everything())) %>%
    tab_style(style = cell_text(color = "#002855", size = px(22)),
              locations = cells_title(groups = "title")) %>%
    tab_style(style = cell_borders(sides = c("top","bottom","left","right"),
                                   color = "#002855", weight = px(.5)),
              locations = cells_body(columns = everything(), rows = everything())) %>%
    tab_style(style = cell_borders(sides = "top", color = "#002855", weight = px(10)),
              locations = cells_body(rows = GROUP %in% c("Left"))) %>%
    tab_style(style = cell_borders(sides = "bottom", color = "#002855", weight = px(10)),
              locations = cells_body(rows = GROUP %in% c("Right"))) %>%
    tab_style(style = list(cell_text(weight = "bold"), cell_fill(color = "#f0f0f0")),
              locations = cells_body(rows = GROUP == "Overall")) %>%
    tab_style(style = list(cell_fill(color = "red"), cell_text(color = "white", weight = "bold")),
              locations = cells_body(columns = GROUP, rows = GROUP == "Fastball")) %>%
    tab_style(style = list(cell_fill(color = "green4"), cell_text(color = "white", weight = "bold")),
              locations = cells_body(columns = GROUP, rows = GROUP == "Changeup")) %>%
    tab_style(style = list(cell_fill(color = "blue"), cell_text(color = "white", weight = "bold")),
              locations = cells_body(columns = GROUP, rows = GROUP == "Curveball")) %>%
    tab_style(style = list(cell_fill(color = "gold"), cell_text(color = "black", weight = "bold")),
              locations = cells_body(columns = GROUP, rows = GROUP == "Slider")) %>%
    tab_style(style = list(cell_fill(color = "maroon2"), cell_text(color = "white", weight = "bold")),
              locations = cells_body(columns = GROUP, rows = GROUP == "Sweeper")) %>%
    tab_style(style = list(cell_fill(color = "lightblue"), cell_text(color = "black", weight = "bold")),
              locations = cells_body(columns = GROUP, rows = GROUP == "Splitter")) %>%
    tab_style(style = list(cell_fill(color = "brown"), cell_text(color = "white", weight = "bold")),
              locations = cells_body(columns = GROUP, rows = GROUP == "Cutter")) %>%
    tab_style(style = list(cell_fill(color = "orange"), cell_text(color = "white", weight = "bold")),
              locations = cells_body(columns = GROUP, rows = GROUP == "Sinker")) %>%
    tab_style(style = list(cell_fill(color = "#6A89A7"), cell_text(color = "white", weight = "bold")),
              locations = cells_body(columns = GROUP, rows = GROUP == "Knuckleball")) %>%
    tab_style(style = list(cell_fill(color = "#2962FF"), cell_text(color = "white", weight = "bold")),
              locations = cells_body(columns = GROUP, rows = GROUP == "Right")) %>%
    tab_style(style = list(cell_fill(color = "#C62828"), cell_text(color = "white", weight = "bold")),
              locations = cells_body(columns = GROUP, rows = GROUP == "Left")) %>%
    tab_style(style = list(cell_fill(color = "#333333"), cell_text(color = "white", weight = "bold")),
              locations = cells_body(columns = GROUP, rows = GROUP == "Overall")) %>%
    data_color(columns = `WHIFF%`,  fn = make_color_fn(pitch_lg_ref$`WHIFF%`  - 12.5, pitch_lg_ref$`WHIFF%`  + 12.5)) %>%
    data_color(columns = `IZW%`,    fn = make_color_fn(pitch_lg_ref$`IZW%`    - 10,   pitch_lg_ref$`IZW%`    + 10))   %>%
    data_color(columns = `CSW%`,    fn = make_color_fn(pitch_lg_ref$`CSW%`    - 10,   pitch_lg_ref$`CSW%`    + 10))   %>%
    data_color(columns = `CHASE%`,  fn = make_color_fn(pitch_lg_ref$`CHASE%`  - 12.5, pitch_lg_ref$`CHASE%`  + 12.5)) %>%
    data_color(columns = `ZONE%`,   fn = make_color_fn(pitch_lg_ref$`ZONE%`   - 15,   pitch_lg_ref$`ZONE%`   + 15))   %>%
    data_color(columns = `STUFF+`,  fn = make_color_fn(pitch_lg_ref$`STUFF+`  - 15,   pitch_lg_ref$`STUFF+`  + 15))   %>%
    data_color(columns = EV,        fn = make_color_fn_reverse(pitch_lg_ref$EV       - 12.5, pitch_lg_ref$EV       + 12.5)) %>%
    data_color(columns = EV50,      fn = make_color_fn_reverse(pitch_lg_ref$EV50     - 10,   pitch_lg_ref$EV50     + 10))   %>%
    data_color(columns = EV90,      fn = make_color_fn_reverse(pitch_lg_ref$EV90     - 10,   pitch_lg_ref$EV90     + 10))   %>%
    data_color(columns = xwOBA,     fn = make_color_fn_reverse(pitch_lg_ref$xwOBA    - .10,  pitch_lg_ref$xwOBA    + .10))  %>%
    data_color(columns = xwOBAcon,  fn = make_color_fn_reverse(pitch_lg_ref$xwOBAcon - .10,  pitch_lg_ref$xwOBAcon + .10))  %>%
    fmt_number(columns = xwOBA, decimals = 3) %>%
    fmt_number(columns = xwOBAcon, decimals = 3) %>%
    fmt_number(columns = c(`WHIFF%`, `IZW%`, `CSW%`, `CHASE%`, `ZONE%`, EV, EV50, EV90), decimals = 1)
}

# reference df for the savant-style percentile chart
get_percentile_refs_pitcher <- function(data) {
  data %>%
    group_by(Pitcher) %>%
    summarise(
      xBAcon = 1 - mean(prob_0, na.rm = TRUE),
      xwOBA = mean(xwOBA, na.rm = TRUE),
      `Stuff+` = round(mean(stuff_plus, na.rm = TRUE)),
      `FB Velo` = mean(RelSpeed[TaggedPitchType == "Fastball"], na.rm = TRUE),
      `Avg EV` = mean(ExitSpeed, na.rm = TRUE),
      EV50 = mean(ExitSpeed[!is.na(ExitSpeed) &
                              ExitSpeed >= quantile(ExitSpeed, 0.5, na.rm = TRUE)], na.rm = TRUE),
      EV90 = mean(ExitSpeed[!is.na(ExitSpeed) &
                              ExitSpeed >= quantile(ExitSpeed, 0.9, na.rm = TRUE)], na.rm = TRUE),
      `HH%` = 100 * mean(is_hard_hit, na.rm = TRUE),
      `GB%` = 100 * sum(AutoHitType == "GroundBall" & PitchCall == "InPlay", na.rm = TRUE) /
        sum(PitchCall == "InPlay", na.rm = TRUE),
      `Chase%` = 100 * mean(chase, na.rm = TRUE),
      `Whiff%` = 100 * mean(is_whiff, na.rm = TRUE),
      `IZW%` = 100 * sum(is_whiff[in_zone == 1], na.rm = TRUE) /
        sum(is_swing[in_zone == 1], na.rm = TRUE),
      `K%` = 100 * mean(is_k, na.rm = TRUE),
      `BB%` = 100 * mean(is_walk, na.rm = TRUE),
      `Ext` = mean(Extension, na.rm = TRUE),
      number = sum(is_plate_appearance, na.rm = TRUE),
      .groups = "drop"
    ) %>%
    select(-Pitcher) %>%
    filter(number >= 30) %>%
    select(-number) %>%
    mutate(across(everything(), as.numeric))
}

# savant-style percentile chart
pitcher_percentiles <- function(df, pitcher, summary_ref) {
  pitcher_stats <- df %>%
    filter(Pitcher == pitcher) %>%
    summarise(
      xBAcon = 1 - mean(prob_0, na.rm = TRUE),
      xwOBA = mean(xwOBA, na.rm = TRUE),
      `Stuff+` = mean(stuff_plus, na.rm = TRUE),
      `FB Velo` = mean(RelSpeed[TaggedPitchType == "Fastball"], na.rm = TRUE),
      `Avg EV` = mean(ExitSpeed, na.rm = TRUE),
      EV50 = mean(ExitSpeed[!is.na(ExitSpeed) &
                              ExitSpeed >= quantile(ExitSpeed, 0.5, na.rm = TRUE)], na.rm = TRUE),
      EV90 = mean(ExitSpeed[!is.na(ExitSpeed) &
                              ExitSpeed >= quantile(ExitSpeed, 0.9, na.rm = TRUE)], na.rm = TRUE),
      `HH%` = 100 * mean(is_hard_hit, na.rm = TRUE),
      `GB%` = 100 * sum(AutoHitType == "GroundBall" & PitchCall == "InPlay", na.rm = TRUE) /
        sum(PitchCall == "InPlay", na.rm = TRUE),
      `Chase%` = 100 * mean(chase, na.rm = TRUE),
      `Whiff%` = 100 * mean(is_whiff, na.rm = TRUE),
      `IZW%` = 100 * sum(is_whiff[in_zone == 1], na.rm = TRUE) /
        sum(is_swing[in_zone == 1], na.rm = TRUE),
      `K%` = 100 * mean(is_k, na.rm = TRUE),
      `BB%` = 100 * mean(is_walk, na.rm = TRUE),
      `Ext` = mean(Extension, na.rm = TRUE),
      .groups = "drop"
    ) %>%
    mutate(across(everything(), as.numeric))

  lower_better <- c("xwOBA", "xBAcon", "Avg EV", "EV50", "EV90", "HH%", "BB%")

  pitcher_pctiles <- pitcher_stats %>%
    mutate(across(everything(), ~ {
      col <- cur_column()
      if (col %in% lower_better) {
        round(mean(summary_ref[[col]] >= .x, na.rm = TRUE) * 100)
      } else {
        round(mean(summary_ref[[col]] <= .x, na.rm = TRUE) * 100)
      }
    }, .names = "{.col}_percentile")) %>%
    select(matches("_percentile"))

  custom_order <- c("xBAcon", "xwOBA", "Stuff+", "FB Velo", "Avg EV", "EV50", "EV90",
                    "HH%", "GB%", "Chase%", "Whiff%", "IZW%", "K%", "BB%", "Ext")

  metrics <- bind_cols(pitcher_stats, pitcher_pctiles) %>%
    pivot_longer(everything(), names_to = "Metric_Type", values_to = "Value") %>%
    mutate(Metric = str_remove(Metric_Type, "_percentile"),
           Type = ifelse(str_detect(Metric_Type, "_percentile"), "Percentile", "Value")) %>%
    select(Metric, Type, Value) %>%
    pivot_wider(names_from = Type, values_from = Value) %>%
    filter(!is.na(Percentile) & !is.na(Value)) %>%
    mutate(
      Metric = factor(Metric, levels = custom_order),
      color = gradient_n_pal(c("#3661ad", "#90A4AE", "#d82029"))(Percentile / 100),
      Value = case_when(
        Metric %in% c("xwOBA", "xBAcon") ~ sprintf("%.3f", Value),
        Metric == "Stuff+" ~ as.character(round(Value)),
        TRUE ~ sprintf("%.1f", Value)
      )
    )

  ggplot(metrics, aes(y = factor(Metric, levels = rev(levels(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 = length(custom_order) + 0.35,
             color = "white", linewidth = 1.5, alpha = 0.5) +
    geom_segment(aes(x = -2, xend = -16,
                     y = as.numeric(factor(Metric, levels = rev(levels(Metric)))) - 0.3,
                     yend = as.numeric(factor(Metric, levels = rev(levels(Metric)))) - 0.3),
                 linetype = "longdash", color = "#399098", linewidth = 1) +
    geom_segment(aes(x = 102, xend = 113,
                     y = as.numeric(factor(Metric, levels = rev(levels(Metric)))) - 0.3,
                     yend = as.numeric(factor(Metric, levels = rev(levels(Metric)))) - 0.3),
                 linetype = "longdash", color = "#399098", linewidth = 1) +
    geom_text(aes(x = -3, label = Metric), hjust = 1, size = 5) +
    geom_text(aes(x = 103, label = Value), hjust = 0, size = 5) +
    geom_point(aes(x = Percentile, fill = color),
               color = "white", size = 12, shape = 21, stroke = 2.25) +
    geom_text(aes(x = Percentile, label = Percentile),
              size = 4.5, color = "white", fontface = "bold") +
    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())
}

# inferred arm angle plot using a silhouette image
arm_angle_plot <- function(data, pitcher_name) {
  d <- data %>%
    filter(Pitcher == pitcher_name, TaggedPitchType != "Other",
           !is.na(RelHeight), !is.na(RelSide))

  # throwing hand drives the mirror
  h      <- d$PitcherThrows[!is.na(d$PitcherThrows)]
  is_lhp <- length(h) > 0 && names(sort(table(h), decreasing = TRUE))[1] == "Left"
  m      <- if (is_lhp) -1 else 1

  arm_points <- d %>%
    group_by(pitch_type = TaggedPitchType) %>%
    summarise(mean_height = mean(RelHeight, na.rm = TRUE),
              mean_side   = -mean(RelSide, na.rm = TRUE),
              .groups = "drop")

  anchor_x <- -0.14 * m
  anchor_y <-  4.75

  img_use  <- if (is_lhp) img_raster_L else img_raster_R
  img_xmin <- if (is_lhp) -1.6 else -0.4
  img_xmax <- if (is_lhp)  0.4 else  1.6
  xlim_use <- if (is_lhp) c(-2.5, 3) else c(-3, 2.5)

  mean_h <- round(mean(d$RelHeight, na.rm = TRUE), 1)
  mean_s <- round(mean(d$RelSide,   na.rm = TRUE), 1)

  ggplot() +
    annotate("rect", xmin = -4.5, xmax = 4.5, ymin = 0,   ymax = 0.6, fill = "#8B4513", alpha = 0.7) +
    annotate("rect", xmin = -4.5, xmax = 4.5, ymin = 0.6, ymax = 4.3, fill = "#164EA7", alpha = 0.9) +
    annotate("rect", xmin = -4.5, xmax = 4.5, ymin = 4.3, ymax = 4.5, fill = "#F25B00", alpha = 0.9) +
    annotate("rect", xmin = -0.5, xmax = 0.5, ymin = 0.5, ymax = 0.65,
             fill = "white", color = "black", linewidth = 0.5) +
    annotation_raster(img_use, xmin = img_xmin, xmax = img_xmax, ymin = 0.06, ymax = 5.85) +
    geom_segment(data = arm_points,
                 aes(x = anchor_x, y = anchor_y, xend = mean_side, yend = mean_height,
                     color = pitch_type), linewidth = 1.5) +
    geom_point(data = arm_points,
               aes(x = mean_side, y = mean_height, color = pitch_type), size = 4) +
    scale_color_manual(values = pitch_colors, name = "Pitch Type") +
    labs(title = paste0("Inferred Arm Angle \u2014 ", ifelse(is_lhp, "LHP", "RHP")),
         x = "Horizontal (ft)", y = "Vertical (ft)",
          subtitle = "Player height assumed to be 6 ft") +
    coord_fixed(xlim = xlim_use, ylim = c(0, 7)) +
    annotate("text", x = -2.5 * m, y = 6.8, label = "1B", size = 2.5, fontface = "bold") +
    annotate("text", x =  2.5 * m, y = 6.8, label = "3B", size = 2.5, fontface = "bold") +
    annotate("label", x = 0, y = 0.25,
             label = paste0("Rel Height: ", mean_h, ", Rel Side: ", mean_s),
             fill = "#EF3340", color = "white", fontface = "bold", size = 3.5,
             label.padding = unit(0.15, "lines")) +
    theme_minimal() +
    theme(axis.title.y = element_text(size = 14),
          axis.title.x = element_text(size = 14),
          axis.ticks   = element_blank(),
          plot.title   = element_text(size = 16, hjust = 0.5, vjust = -1),
          panel.grid   = element_blank(),
          axis.line    = element_blank(),
          legend.position = "none",
          plot.margin = margin(5, 5, 5, 5)) +
  theme(plot.subtitle = element_text(hjust = 0.5))
}

# ============================================================
# Precomputed references + player lists
# ============================================================
lg_ref                 <- reference_stats(all_data)
pitch_lg_ref           <- pitcher_reference_stats(all_data)
percentile_ref_hitter  <- get_percentile_refs_hitter(all_data)
percentile_ref_pitcher <- get_percentile_refs_pitcher(all_data)

ALL_BATTERS  <- sort(unique(all_data$Batter))
ALL_PITCHERS <- sort(unique(all_data$Pitcher))

HF_PASSWORD <- Sys.getenv("APP_PASSWORD")

# ============================================================
# Tab content
# ============================================================
tab_hitter_overview <- function() tagList(
  tags$div(class = "data-card", style = "margin-bottom:16px;",
           tags$div(class = "data-card-hdr",
                    tags$span(class = "data-card-title", "Batter summary"),
                    tags$span(class = "data-card-sub", "By pitch group, handedness, opponent level")
           ),
           tags$div(class = "data-card-body", gt_output("gt_batter_summary"))
  ),
  tags$div(style = "display:grid; grid-template-columns:1fr 1fr; gap:14px;",
           tags$div(class = "data-card",
                    tags$div(class = "data-card-hdr",
                             tags$span(class = "data-card-title", "Percentile profile"),
                             tags$span(class = "data-card-sub", "vs. lower-level 2026 dataset")
                    ),
                    tags$div(class = "data-card-body", plotOutput("plot_hitter_percentiles", height = "420px"))
           ),
           tags$div(class = "data-card",
                    tags$div(class = "data-card-hdr",
                             tags$span(class = "data-card-title", "Spray chart"),
                             tags$span(class = "data-card-sub", "Hits only")
                    ),
                    tags$div(class = "data-card-body", plotlyOutput("plot_spray_overview", height = "420px"))
           )
  )
)

tab_pitcher_overview <- function() tagList(
  tags$div(class = "data-card", style = "margin-bottom:16px;",
           tags$div(class = "data-card-hdr",
                    tags$span(class = "data-card-title", "Pitch summary"),
                    tags$span(class = "data-card-sub", "By pitch type, handedness, opponent level")
           ),
           tags$div(class = "data-card-body", gt_output("gt_pitch_summary"))
  ),
  tags$div(style = "display:grid; grid-template-columns:1fr 1fr; gap:14px; margin-bottom:16px;",
           tags$div(class = "data-card",
                    tags$div(class = "data-card-hdr",
                             tags$span(class = "data-card-title", "Movement plot"),
                             tags$span(class = "data-card-sub", "HB vs IVB by pitch")
                    ),
                    tags$div(class = "data-card-body", plotlyOutput("plot_movement", height = "440px"))
           ),
           tags$div(class = "data-card",
                    tags$div(class = "data-card-hdr",
                             tags$span(class = "data-card-title", "Percentile profile"),
                             tags$span(class = "data-card-sub", "vs. lower-level 2026 pitchers")
                    ),
                    tags$div(class = "data-card-body", plotOutput("plot_pitcher_percentiles", height = "440px"))
           )
  ),
  tags$div(class = "data-card",
           tags$div(class = "data-card-hdr",
                    tags$span(class = "data-card-title", "Inferred arm angle"),
                    tags$span(class = "data-card-sub", "Mean release by pitch")
           ),
           tags$div(class = "data-card-body", plotOutput("plot_arm_angle", height = "440px"))
  )
)

TABS <- list(
  list(id = "hitter",  label = "Hitter Overview",  icon = "ti-chart-bar"),
  list(id = "pitcher", label = "Pitcher Overview", icon = "ti-target")
)

# ============================================================
# Login
# ============================================================
login_css <- tags$style(HTML("
  #login_screen { position:fixed; top:0; left:0; right:0; bottom:0; z-index:9999;
    display:flex; flex-direction:column; align-items:center; justify-content:center;
    background:linear-gradient(135deg,#001a3a 0%,#003366 40%,#0a4a8a 100%);
    transition:opacity .8s ease, transform .8s ease; }
  #login_screen.fade-out { opacity:0; transform:scale(1.02); pointer-events:none; }
  #login_screen .login-logo { width:120px; height:120px; margin-bottom:20px; border-radius:50%;
    object-fit:cover; border:3px solid rgba(255,165,0,.6); box-shadow:0 0 30px rgba(255,165,0,.2); }
  #login_screen .login-title { font-size:42px; font-weight:800; color:#fff; letter-spacing:1.5px;
    margin-bottom:6px; text-align:center; }
  #login_screen .login-subtitle { font-size:16px; color:rgba(255,165,0,.7); letter-spacing:3px;
    text-transform:uppercase; margin-bottom:40px; }
  #login_screen .login-accent { width:60px; height:3px; background:#FF8C00; margin:0 auto 30px;
    border-radius:2px; }
  #login_screen .login-box { background:rgba(255,255,255,.06); border:1px solid rgba(255,165,0,.2);
    border-radius:12px; padding:32px 40px; backdrop-filter:blur(10px); width:340px; text-align:center; }
  #login_screen .login-box input[type='password'] { width:100%; padding:12px 16px;
    border:1px solid rgba(255,165,0,.3); border-radius:6px; background:rgba(255,255,255,.08);
    color:#fff; font-size:15px; margin-bottom:16px; outline:none; transition:border-color .3s;
    text-align:center; letter-spacing:2px; }
  #login_screen .login-box input[type='password']:focus { border-color:#FF8C00; }
  #login_screen .login-box input[type='password']::placeholder { color:rgba(255,255,255,.35); letter-spacing:1px; }
  #login_screen .login-btn { width:100%; padding:12px; border:none; border-radius:6px;
    background:#FF8C00; color:#fff; font-size:14px; font-weight:700; letter-spacing:1px;
    text-transform:uppercase; cursor:pointer; transition:background .3s, transform .15s; }
  #login_screen .login-btn:hover { background:#e67e00; transform:translateY(-1px); }
  #login_screen .login-error { color:#ff6b6b; font-size:13px; margin-top:12px; min-height:20px; }
"))

login_overlay <- tags$div(id = "login_screen",
  tags$img(class = "login-logo", src = logo_uri),
  tags$div(class = "login-title", "Lower Level"),
  tags$div(class = "login-accent"),
  tags$div(class = "login-subtitle", "Hyannis Harbor Hawks"),
  tags$div(class = "login-box",
    tags$input(id = "login_pw", type = "password", placeholder = "Enter password"),
    tags$button(id = "login_btn", class = "login-btn", "Enter"),
    tags$div(id = "login_error", class = "login-error")
  )
)

login_js <- tags$script(HTML(paste0("
  var APP_PASSWORD = '", HF_PASSWORD, "';
  function tryLogin() {
    var pw = document.getElementById('login_pw').value;
    if (pw === APP_PASSWORD) {
      document.getElementById('login_screen').classList.add('fade-out');
      setTimeout(function(){ document.getElementById('login_screen').style.display = 'none'; }, 800);
    } else {
      document.getElementById('login_error').textContent = 'Incorrect password';
      document.getElementById('login_pw').value = '';
      document.getElementById('login_pw').style.borderColor = '#ff6b6b';
      setTimeout(function(){
        document.getElementById('login_error').textContent = '';
        document.getElementById('login_pw').style.borderColor = 'rgba(255,165,0,0.3)';
      }, 2000);
    }
  }
  document.addEventListener('DOMContentLoaded', function(){
    document.getElementById('login_btn').addEventListener('click', tryLogin);
    document.getElementById('login_pw').addEventListener('keydown', function(e){
      if (e.key === 'Enter') tryLogin();
    });
  });
")))

# ============================================================
# UI
# ============================================================
ui <- fluidPage(
  theme = hawks_theme,
  hawks_css,
  tags$link(rel = "stylesheet",
    href = "https://cdn.jsdelivr.net/npm/@tabler/icons-webfont@latest/dist/tabler-icons.min.css"),
  login_css,

  tags$style(HTML(glue("
    body {{ margin:0; overflow:hidden; background:{HAWKS_LIGHT}; }}
    .hawks-topbar {{
      background:{HAWKS_DARK}; padding:0 24px; height:auto; display:flex;
      align-items:center; justify-content:space-between; flex-shrink:0;
      flex-wrap:wrap; gap:0; border-bottom:3px solid {HAWKS_ORANGE};
    }}
    .topbar-left {{ display:flex; align-items:center; gap:20px; padding:14px 0; }}
    .topbar-title {{
      color:#fff; font-size:40px; font-weight:800; letter-spacing:-.3px;
      text-align:center; flex:1; white-space:nowrap;
    }}
    .topbar-brand {{ display:flex; align-items:center; gap:10px; padding:14px 0; }}
    .topbar-brand .app-title {{ color:#fff; font-size:16px; font-weight:800; letter-spacing:-.2px; }}
    .topbar-player {{ display:flex; flex-direction:column; align-items:flex-start; gap:3px; padding:10px 0; }}
    .topbar-player .player-label {{ font-size:9px; font-weight:700; text-transform:uppercase;
      letter-spacing:.1em; color:#ffffff55; }}
    .topbar-player .selectize-input {{
      background:#ffffff14 !important; border:1px solid #ffffff30 !important;
      border-radius:6px !important; color:#fff !important; font-size:13px !important;
      font-weight:600 !important; min-width:200px; padding:6px 10px !important; }}
    .topbar-player .selectize-input.focus {{ border-color:{HAWKS_ORANGE} !important; box-shadow:none !important; }}
    .topbar-player .selectize-dropdown {{ border:1px solid {HAWKS_BORDER}; border-radius:6px; font-size:12px; }}
    .topbar-player .selectize-input input {{
      color: #fff !important;
      min-width: 120px !important;
      width: auto !important;
    }}
    .topbar-player .selectize-input input::placeholder {{
      color: rgba(255,255,255,.35) !important;
    }}
    .hawks-tabs {{ background:#fff; border-bottom:1px solid {HAWKS_BORDER}; padding:0 20px;
      display:flex; flex-shrink:0; overflow-x:auto; }}
    .h-tab {{ padding:11px 15px; font-size:12px; font-weight:500; color:{HAWKS_GRAY};
      border-bottom:2px solid transparent; cursor:pointer; white-space:nowrap;
      display:flex; align-items:center; gap:6px; transition:color .15s, border-color .15s; }}
    .h-tab:hover  {{ color:#374151; }}
    .h-tab.active {{ color:{HAWKS_BLUE}; border-bottom-color:{HAWKS_BLUE}; font-weight:600; }}
    .hawks-body  {{ display:flex; flex:1; overflow:hidden; }}
    .hawks-content {{ flex:1; overflow-y:auto; padding:20px; }}
    .hawks-shell {{ display:flex; flex-direction:column; height:100vh; overflow:hidden; }}
    .hawks-main  {{ display:flex; flex-direction:column; flex:1; overflow:hidden; }}
  "))),

  tags$script(HTML(glue("
    function setTab(tabId) {{
      document.querySelectorAll('.h-tab').forEach(function(el) {{
        el.classList.toggle('active', el.dataset.tab === tabId);
      }});
      Shiny.setInputValue('active_tab', tabId, {{priority:'event'}});
    }}
  "))),

  tags$div(
    class = "hawks-shell",

    tags$div(
      class = "hawks-topbar",
      tags$div(class = "topbar-left",
        tags$div(class = "topbar-brand",
          tags$svg(width="24",height="24",viewBox="0 0 30 30",fill="none",
            tags$polygon(points="15,2 28,26 15,21 2,26",fill=HAWKS_ORANGE,opacity="0.9"),
            tags$polygon(points="15,2 15,21 2,26",fill="#fff",opacity="0.18")
          ),
          tags$span(class="app-title","Lower Level")
        )
      ),
      tags$div(class = "topbar-title", "Hyannis Harbor Hawks Analytics"),
      tags$div(class = "topbar-player",
        tags$div(class="player-label","Player"),
        selectizeInput("selected_player", NULL,
                       choices = NULL, width = "210px")
      )
    ),

    tags$div(class = "hawks-main",
      tags$div(class = "hawks-tabs",
        tagList(lapply(TABS, function(t) {
          tags$div(
            class = paste("h-tab", if (t$id == "hitter") "active" else ""),
            `data-tab` = t$id,
            onclick = glue("setTab('{t$id}')"),
            tags$i(class=glue("ti {t$icon}"), style="font-size:13px;"),
            t$label
          )
        }))
      ),
      tags$div(class = "hawks-body",
        tags$div(class="hawks-content", uiOutput("ui_tab_content"))
      )
    )
  ),
  login_overlay,
  login_js
)

# ============================================================
# Server
# ============================================================
server <- function(input, output, session) {

  # Populate the player search server-side (default tab is hitter)
  updateSelectizeInput(session, "selected_player",
                       choices = ALL_BATTERS, selected = ALL_BATTERS[1], server = TRUE)

  # Swap the player dropdown between batters and pitchers on tab change
  observeEvent(input$active_tab, {
    if ((input$active_tab %||% "hitter") == "pitcher") {
      updateSelectizeInput(session, "selected_player",
                           choices = ALL_PITCHERS, selected = ALL_PITCHERS[1], server = TRUE)
    } else {
      updateSelectizeInput(session, "selected_player",
                           choices = ALL_BATTERS, selected = ALL_BATTERS[1], server = TRUE)
    }
  }, ignoreInit = TRUE)

  # Player data, filtered on the correct identity column for the active tab
  player_data <- reactive({
    req(input$selected_player)
    if ((input$active_tab %||% "hitter") == "pitcher") {
      all_data %>% filter(Pitcher == input$selected_player)
    } else {
      all_data %>% filter(Batter == input$selected_player)
    }
  })

  # Header sub-lines
  player_team <- reactive({
    req(input$selected_player)
    df <- all_data %>% filter(Batter == input$selected_player)
    if (nrow(df) == 0) return("")
    team <- df %>% count(BatterTeam) %>% slice_max(n, n = 1) %>% pull(BatterTeam)
    if (length(team) == 0 || is.na(team[1])) return("")
    as.character(team[1])
  })

  pitcher_info <- reactive({
    req(input$selected_player)
    df <- all_data %>% filter(Pitcher == input$selected_player)
    if (nrow(df) == 0) return("")
    hand <- df %>% count(PitcherThrows) %>% slice_max(n, n = 1) %>% pull(PitcherThrows)
    if (length(hand) == 0 || is.na(hand[1])) return("")
    if (hand[1] == "Right") "Right-Handed Pitcher"
    else if (hand[1] == "Left") "Left-Handed Pitcher"
    else as.character(hand[1])
  })

  output$ui_tab_content <- renderUI({
    tab <- input$active_tab %||% "hitter"

    if (tab == "pitcher") {
      header <- tags$div(
        style = glue("margin-bottom:18px; padding-bottom:14px; border-bottom:1px solid {HAWKS_BORDER};"),
        tags$div(style = glue("font-size:22px; font-weight:800; color:{HAWKS_DARK}; line-height:1;"),
                 input$selected_player),
        tags$div(style = glue("font-size:13px; color:{HAWKS_GRAY}; margin-top:4px;"),
                 pitcher_info())
      )
      tagList(header, tab_pitcher_overview())
    } else {
      header <- tags$div(
        style = glue("margin-bottom:18px; padding-bottom:14px; border-bottom:1px solid {HAWKS_BORDER};"),
        tags$div(style = glue("font-size:22px; font-weight:800; color:{HAWKS_DARK}; line-height:1;"),
                 input$selected_player),
        tags$div(style = glue("font-size:13px; color:{HAWKS_GRAY}; margin-top:4px;"),
                 player_team())
      )
      tagList(header, tab_hitter_overview())
    }
  })

  # ---- HITTER OVERVIEW ----
  output$gt_batter_summary <- render_gt({
    df <- player_data(); req(nrow(df) > 0)
    batter_summary(df, input$selected_player)
  })

  output$plot_hitter_percentiles <- renderPlot({
    df <- player_data(); req(nrow(df) > 0)
    batter_percentiles(df, input$selected_player, percentile_ref_hitter)
  }, bg = "white")

  output$plot_spray_overview <- renderPlotly({
    df <- player_data(); req(nrow(df) > 0)
    spray_chart(df, input$selected_player)
  })

  # ---- PITCHER OVERVIEW ----
  output$gt_pitch_summary <- render_gt({
    df <- player_data(); req(nrow(df) > 0)
    pitcher_summary(df, input$selected_player)
  })

  output$plot_movement <- renderPlotly({
    df <- player_data(); req(nrow(df) > 0)
    movement_plot(df, input$selected_player)
  })

  output$plot_pitcher_percentiles <- renderPlot({
    df <- player_data(); req(nrow(df) > 0)
    pitcher_percentiles(df, input$selected_player, percentile_ref_pitcher)
  }, bg = "white")

  output$plot_arm_angle <- renderPlot({
    df <- player_data(); req(nrow(df) > 0)
    arm_angle_plot(df, input$selected_player)
  }, bg = "white")
}

shinyApp(ui, server)