Delete app.R

app.R

@@ -1,1036 +0,0 @@
-# ============================================================
-# Oldham Athletic Player Scouting App — R Shiny Version
-#
-# Files needed in the same directory:
-#   app.R
-#   OA_sheet_for_app.csv
-#   all_players_enriched_multiseason.csv  (optional)
-#
-# Required R packages (install once):
-#   install.packages(c("shiny","shinythemes","DT","dplyr","tidyr",
-#                      "readr","plotly","stringr","scales"))
-# ============================================================
-
-library(shiny)
-library(shinythemes)
-library(DT)
-library(dplyr)
-library(tidyr)
-library(readr)
-library(plotly)
-library(stringr)
-library(scales)
-
-# ============================================================
-# HELPERS
-# ============================================================
-
-clean_colnames <- function(df) {
-  names(df) <- names(df) |>
-    str_trim() |>
-    str_to_lower() |>
-    str_replace_all("[ \\-/]", "_") |>
-    str_replace_all("\\.", "_")
-  df
-}
-
-clean_player_key <- function(x) {
-  x |>
-    str_trim() |>
-    str_to_lower() |>
-    str_replace_all("\\.", "") |>
-    str_replace_all(",", "") |>
-    str_replace_all("-", " ") |>
-    str_replace_all("  ", " ")
-}
-
-format_money <- function(x) {
-  x <- suppressWarnings(as.numeric(x))
-  ifelse(is.na(x), "Not listed",
-    ifelse(x >= 1e6, paste0("\u20ac", round(x / 1e6, 1), "M"),
-      ifelse(x >= 1e3, paste0("\u20ac", round(x / 1e3, 0), "K"),
-        paste0("\u20ac", round(x, 0)))))
-}
-
-clean_value <- function(x) {
-  if (is.null(x) || length(x) == 0 || (length(x) == 1 && is.na(x))) return("N/A")
-  if (is.numeric(x)) return(round(x, 2))
-  as.character(x)
-}
-
-pretty_label <- function(col) {
-  custom <- c(
-    player_name = "Player", team_name = "Club", competition_name = "Competition",
-    season_name = "Season", primary_position = "Primary Position",
-    secondary_position = "Secondary Position", country_id = "Country",
-    player_height = "Height", player_weight = "Weight",
-    player_season_minutes = "Minutes", market_value_eur = "Market Value",
-    seasons_left_num = "Seasons Left", attainability = "Attainability",
-    target_score = "Target Score", best_position_archetype_name = "Best Archetype",
-    best_position_archetype_score = "Best Archetype Score",
-    cb_score = "CB Score", fb_score = "FB Score", cmd_score = "CMD Score",
-    cma_score = "CMA Score", wm_score = "WM Score", cf_score = "CF Score",
-    st_score = "ST Score", gk_score = "GK Score",
-    club_rank = "Club Rank", match_toughness = "Match Toughness",
-    elo = "Club ELO", competition_rank = "Competition Rank", fit_score = "Fit Score"
-  )
-  if (col %in% names(custom)) return(unname(custom[col]))
-  lbl <- col |>
-    str_replace("player_season_", "") |>
-    str_replace("attr_", "") |>
-    str_replace("cat_", "") |>
-    str_replace("score_", "") |>
-    str_replace_all("_90$", " Per 90") |>
-    str_replace_all("_", " ") |>
-    str_to_title() |>
-    str_replace("Np Xg", "NP xG") |>
-    str_replace("Xa ", "xA ") |>
-    str_replace("Xgchain", "xGChain") |>
-    str_replace("Xgbuildup", "xGBuildup") |>
-    str_replace("Obv", "OBV") |>
-    str_replace("\\bGk\\b", "GK") |>
-    str_replace("\\bCb\\b", "CB") |>
-    str_replace("\\bFb\\b", "FB") |>
-    str_replace("\\bCmd\\b", "CMD") |>
-    str_replace("\\bCma\\b", "CMA") |>
-    str_replace("\\bWm\\b", "WM") |>
-    str_replace("\\bCf\\b", "CF") |>
-    str_replace("\\bSt\\b", "ST")
-  lbl
-}
-
-available_cols <- function(cols, df) {
-  cols[cols %in% names(df)]
-}
-
-normalize_0_100 <- function(x) {
-  x <- suppressWarnings(as.numeric(x))
-  mn <- min(x, na.rm = TRUE)
-  mx <- max(x, na.rm = TRUE)
-  if (is.na(mn) || is.na(mx) || mn == mx) return(rep(0, length(x)))
-  (x - mn) / (mx - mn) * 100
-}
-
-pretty_df <- function(data, df_source) {
-  out <- data
-  if ("market_value_eur" %in% names(out)) {
-    out$market_value_eur <- format_money(out$market_value_eur)
-  }
-  num_cols <- names(out)[sapply(out, is.numeric)]
-  out[num_cols] <- lapply(out[num_cols], function(x) round(x, 2))
-  names(out) <- sapply(names(out), pretty_label)
-  out
-}
-
-# ============================================================
-# COLUMN CONSTANTS
-# ============================================================
-
-PLAYER_COL            <- "player_name"
-TEAM_COL              <- "team_name"
-COMP_COL              <- "competition_name"
-SEASON_COL            <- "season_name"
-POSITION_COL          <- "primary_position"
-SECONDARY_POSITION_COL<- "secondary_position"
-COUNTRY_COL           <- "country_id"
-AGE_COL               <- "age"
-HEIGHT_COL            <- "player_height"
-WEIGHT_COL            <- "player_weight"
-MINUTES_COL           <- "player_season_minutes"
-MARKET_VALUE_COL      <- "market_value_eur"
-CONTRACT_COL          <- "seasons_left_num"
-ATTAINABILITY_COL     <- "attainability"
-TARGET_SCORE_COL      <- "target_score"
-ARCHETYPE_COL         <- "best_position_archetype_name"
-ARCHETYPE_SCORE_COL   <- "best_position_archetype_score"
-CLUB_RANK_COL         <- "club_rank"
-MATCH_TOUGHNESS_COL   <- "match_toughness"
-ELO_COL               <- "elo"
-COMPETITION_RANK_COL  <- "competition_rank"
-
-ATTR_COLS <- c(
-  "attr_shot_stopping","attr_sweeping","attr_ball_claiming","attr_short_passing",
-  "attr_long_passing","attr_pressing","attr_duels","attr_aerial",
-  "attr_possession_retention","attr_blocking","attr_progression","attr_set_pieces",
-  "attr_impact","attr_discipline","attr_dribbling","attr_chance_creation",
-  "attr_finishing","attr_crossing","attr_box_presence","attr_holdup"
-)
-
-POSITION_SCORE_COLS <- c(
-  "cb_score","fb_score","cmd_score","cma_score","wm_score","cf_score","st_score","gk_score"
-)
-
-ARCHETYPE_SCORE_COLS <- c(
-  "score_defensive_cb","score_pressing_cb","score_ballplaying_cb",
-  "score_defensive_fb","score_attacking_fb","score_possession_fb",
-  "score_poacher","score_target_man","score_false_nine","score_complete_forward",
-  "score_inside_forward","score_traditional_winger","score_playmaking_winger",
-  "score_pressing_winger","score_complete_winger","score_defensive_midfielder",
-  "score_deep_lying_playmaker","score_box_to_box_midfielder","score_advanced_playmaker",
-  "score_wide_midfielder","score_attacking_runner","score_shot_stopper_gk",
-  "score_sweeper_keeper_gk","score_ball_playing_gk"
-)
-
-KEY_METRICS <- c(
-  "player_season_minutes","player_season_goals_90","player_season_assists_90",
-  "player_season_np_xg_90","player_season_xa_90","player_season_key_passes_90",
-  "player_season_passing_ratio","player_season_tackles_90","player_season_interceptions_90",
-  "player_season_tackles_and_interceptions_90","player_season_aerial_wins_90",
-  "player_season_aerial_ratio","player_season_dribbles_90","player_season_crosses_90",
-  "player_season_long_balls_90","player_season_xgchain_90","player_season_xgbuildup_90",
-  "player_season_obv_90"
-)
-
-SEARCH_TABLE_COLS <- c(
-  PLAYER_COL, POSITION_COL, TEAM_COL, COMP_COL, AGE_COL, COUNTRY_COL,
-  MINUTES_COL, MARKET_VALUE_COL, CONTRACT_COL, ARCHETYPE_COL, ARCHETYPE_SCORE_COL,
-  TARGET_SCORE_COL, ATTAINABILITY_COL, POSITION_SCORE_COLS, ATTR_COLS
-)
-
-COMPARISON_COLS <- c(
-  PLAYER_COL, POSITION_COL, TEAM_COL, COMP_COL, AGE_COL,
-  MARKET_VALUE_COL, CONTRACT_COL, ARCHETYPE_COL, ARCHETYPE_SCORE_COL,
-  TARGET_SCORE_COL, ATTAINABILITY_COL, POSITION_SCORE_COLS, ATTR_COLS
-)
-
-SHORTLIST_COLS <- c(
-  PLAYER_COL, POSITION_COL, TEAM_COL, COMP_COL, AGE_COL,
-  MINUTES_COL, MARKET_VALUE_COL, CONTRACT_COL, ARCHETYPE_COL, ARCHETYPE_SCORE_COL,
-  TARGET_SCORE_COL, ATTAINABILITY_COL, KEY_METRICS, ATTR_COLS,
-  POSITION_SCORE_COLS, ARCHETYPE_SCORE_COLS
-)
-
-RADAR_METRICS       <- ATTR_COLS
-PERCENTILE_METRICS  <- c(ATTR_COLS, TARGET_SCORE_COL, ATTAINABILITY_COL, ARCHETYPE_SCORE_COL)
-SIMILARITY_METRICS  <- c(ATTR_COLS, TARGET_SCORE_COL, ATTAINABILITY_COL, ARCHETYPE_SCORE_COL)
-PERFORMANCE_TIME_METRICS <- POSITION_SCORE_COLS
-
-HISTORICAL_SEASONS <- c("2122" = "2021-22", "2223" = "2022-23",
-                         "2324" = "2023-24", "2425" = "2024-25")
-CURRENT_MAIN_SEASON_LABEL <- "2025-26"
-
-# ============================================================
-# LOAD DATA
-# ============================================================
-
-load_data <- function() {
-  df <- read_csv("OA_sheet_for_app.csv", locale = locale(encoding = "latin1"),
-                 show_col_types = FALSE) |> clean_colnames()
-
-  multi_df <- if (file.exists("all_players_enriched_multiseason.csv")) {
-    read_csv("all_players_enriched_multiseason.csv",
-             locale = locale(encoding = "latin1"), show_col_types = FALSE) |>
-      clean_colnames()
-  } else {
-    data.frame()
-  }
-
-  if (PLAYER_COL %in% names(df)) {
-    df$`_player_key` <- clean_player_key(df[[PLAYER_COL]])
-  }
-
-  multi_player_col <- NULL
-  if (nrow(multi_df) > 0) {
-    for (pc in c("player_name", "player", "name")) {
-      if (pc %in% names(multi_df)) { multi_player_col <- pc; break }
-    }
-    if (!is.null(multi_player_col)) {
-      multi_df$`_player_key` <- clean_player_key(multi_df[[multi_player_col]])
-    } else {
-      multi_df$`_player_key` <- ""
-    }
-
-    hist_suffixes <- c("_2122", "_2223", "_2324", "_2425")
-    hist_cols <- names(multi_df)[sapply(names(multi_df),
-      function(c) any(str_ends(c, hist_suffixes)))]
-
-    if (length(hist_cols) > 0 && "_player_key" %in% names(multi_df)) {
-      multi_keep <- multi_df |>
-        select(all_of(c("_player_key", hist_cols))) |>
-        distinct(`_player_key`, .keep_all = TRUE)
-
-      overlap <- hist_cols[hist_cols %in% names(df)]
-      if (length(overlap) > 0) df <- df |> select(-all_of(overlap))
-
-      df <- left_join(df, multi_keep, by = "_player_key")
-    }
-  }
-
-  # Coerce numeric columns
-  num_cols_to_coerce <- available_cols(c(
-    KEY_METRICS, ATTR_COLS, POSITION_SCORE_COLS, ARCHETYPE_SCORE_COLS,
-    AGE_COL, HEIGHT_COL, WEIGHT_COL, MINUTES_COL, MARKET_VALUE_COL,
-    ATTAINABILITY_COL, TARGET_SCORE_COL, ARCHETYPE_SCORE_COL,
-    CLUB_RANK_COL, MATCH_TOUGHNESS_COL, ELO_COL, COMPETITION_RANK_COL
-  ), df)
-
-  hist_num_cols <- names(df)[sapply(names(df), function(c)
-    any(str_ends(c, paste0("_", names(HISTORICAL_SEASONS)))))]
-
-  for (col in unique(c(num_cols_to_coerce, hist_num_cols))) {
-    df[[col]] <- suppressWarnings(as.numeric(df[[col]]))
-  }
-
-  list(df = df, multi_df = multi_df, multi_player_col = multi_player_col)
-}
-
-# ============================================================
-# PLAYER HELPERS
-# ============================================================
-
-get_player_row <- function(df, player) {
-  if (is.null(player) || player == "" || !PLAYER_COL %in% names(df)) return(NULL)
-  rows <- df[as.character(df[[PLAYER_COL]]) == as.character(player), ]
-  if (nrow(rows) == 0) return(NULL)
-  as.list(rows[1, ])
-}
-
-get_player_group <- function(df, row) {
-  comp <- row[[COMP_COL]]
-  pos  <- row[[POSITION_COL]]
-  group <- df
-  if (COMP_COL %in% names(df) && POSITION_COL %in% names(df) &&
-      !is.null(comp) && !is.na(comp) && !is.null(pos) && !is.na(pos)) {
-    group <- df[df[[COMP_COL]] == comp & df[[POSITION_COL]] == pos, ]
-  }
-  if (nrow(group) == 0) group <- df
-  group
-}
-
-top_attr_cols <- function(df, row = NULL, max_cols = 8) {
-  cols <- available_cols(RADAR_METRICS, df)
-  if (!is.null(row)) {
-    cols <- cols[sapply(cols, function(c) {
-      v <- row[[c]]
-      !is.null(v) && !is.na(v)
-    })]
-  }
-  head(cols, max_cols)
-}
-
-# ============================================================
-# PERFORMANCE OVER TIME HELPERS
-# ============================================================
-
-historical_candidate_columns <- function(base_metric, season_code) {
-  short_metric <- str_replace(base_metric, "player_season_", "")
-  candidates <- c(
-    paste0(base_metric, "_", season_code),
-    paste0(short_metric, "_", season_code)
-  )
-  if (str_ends(short_metric, "_90")) {
-    no_90 <- str_replace(short_metric, "_90$", "")
-    candidates <- c(candidates,
-      paste0(no_90, "_90_", season_code),
-      paste0(no_90, "_per_90_", season_code),
-      paste0(no_90, "_p90_", season_code))
-  }
-  plurals <- c(crosses = "cross", goals = "goal", assists = "assist",
-                dribbles = "dribble", tackles = "tackle", interceptions = "interception",
-                aerial_wins = "aerial_win", key_passes = "key_pass", long_balls = "long_ball")
-  for (pl in names(plurals)) {
-    sg <- plurals[pl]
-    if (str_detect(short_metric, pl)) {
-      replaced <- str_replace(short_metric, pl, sg)
-      candidates <- c(candidates, paste0(replaced, "_", season_code))
-      if (str_ends(short_metric, "_90")) {
-        candidates <- c(candidates,
-          paste0(str_replace(replaced, "_90$", ""), "_per_90_", season_code))
-      }
-    }
-  }
-  if (base_metric %in% POSITION_SCORE_COLS) {
-    pos_code <- str_replace(base_metric, "_score", "")
-    candidates <- c(candidates,
-      paste0(pos_code, "_score_", season_code),
-      paste0(pos_code, "_", season_code))
-  }
-  unique(str_to_lower(candidates))
-}
-
-find_metric_value <- function(row, base_metric, season_code = NULL) {
-  if (is.null(row)) return(NA_real_)
-  if (is.null(season_code)) {
-    v <- row[[base_metric]]
-    return(if (is.null(v)) NA_real_ else suppressWarnings(as.numeric(v)))
-  }
-  for (col in historical_candidate_columns(base_metric, season_code)) {
-    v <- row[[col]]
-    if (!is.null(v) && !is.na(v)) return(suppressWarnings(as.numeric(v)))
-  }
-  NA_real_
-}
-
-get_multiseason_row <- function(multi_df, player) {
-  if (is.null(multi_df) || nrow(multi_df) == 0) return(NULL)
-  pk <- clean_player_key(player)
-  if (!"_player_key" %in% names(multi_df)) return(NULL)
-  matches <- multi_df[multi_df$`_player_key` == pk, ]
-  if (nrow(matches) == 0) return(NULL)
-  as.list(matches[1, ])
-}
-
-build_performance_metric_options <- function(df, multi_df) {
-  options <- c()
-  for (m in PERFORMANCE_TIME_METRICS) {
-    current_exists <- m %in% names(df)
-    hist_exists <- FALSE
-    for (sc in names(HISTORICAL_SEASONS)) {
-      for (cand in historical_candidate_columns(m, sc)) {
-        if (cand %in% names(df) || (!is.null(multi_df) && nrow(multi_df) > 0 && cand %in% names(multi_df))) {
-          hist_exists <- TRUE; break
-        }
-      }
-      if (hist_exists) break
-    }
-    if (current_exists || hist_exists) options[pretty_label(m)] <- m
-  }
-  options
-}
-
-# ============================================================
-# UI
-# ============================================================
-
-ui <- fluidPage(
-  theme = shinytheme("flatly"),
-  tags$head(tags$style(HTML("
-    .container-fluid { max-width: 98%; }
-    table.dataTable { width: 100% !important; }
-    .dataTables_wrapper { overflow-x: auto; }
-    th, td { white-space: nowrap; }
-    .section-header { border-bottom: 2px solid #2c3e50; margin-bottom: 15px; padding-bottom: 6px; }
-  "))),
-
-  titlePanel("Oldham Athletic Player Scouting"),
-
-  tabsetPanel(id = "main_tabs",
-
-    # ---- PLAYER SEARCH ----
-    tabPanel("Player Search",
-      br(),
-      h4("Search and Filter Players", class = "section-header"),
-      fluidRow(
-        column(4, textInput("search_box", "Search Player Name", "")),
-        column(4, selectizeInput("competition_filter", "Competition",
-          choices = NULL, multiple = TRUE)),
-        column(4, selectizeInput("team_filter", "Team",
-          choices = NULL, multiple = TRUE))
-      ),
-      fluidRow(
-        column(4, selectizeInput("position_filter", "Position",
-          choices = NULL, multiple = TRUE)),
-        column(4, selectizeInput("country_filter", "Country",
-          choices = NULL, multiple = TRUE))
-      ),
-      fluidRow(
-        column(4, uiOutput("age_slider_ui")),
-        column(4, uiOutput("minutes_slider_ui"))
-      ),
-      br(),
-      actionButton("search_btn", "Search Players", class = "btn-primary"),
-      br(), br(),
-      DTOutput("search_results"),
-      textOutput("search_status")
-    ),
-
-    # ---- PLAYER PROFILE ----
-    tabPanel("Player Profile",
-      br(),
-      h4("Full Player Profile", class = "section-header"),
-      selectizeInput("selected_player", "Select Player", choices = NULL, width = "100%"),
-      fluidRow(
-        column(8, uiOutput("profile_output")),
-        column(4,
-          h5("Key Performance Summary"),
-          DTOutput("key_summary")
-        )
-      ),
-      br(),
-      h4("Player Metrics", class = "section-header"),
-      selectInput("metric_group", "Metric Group",
-        choices = c("Attributes", "Position Scores", "Archetype Scores", "Key Season Stats"),
-        selected = "Attributes"),
-      DTOutput("metric_table"),
-      br(),
-      fluidRow(
-        column(6, plotlyOutput("radar_plot", height = "500px")),
-        column(6, plotlyOutput("percentile_plot", height = "500px"))
-      ),
-      br(),
-      fluidRow(
-        column(6, selectInput("profile_metric", "Performance Metric Over Time",
-          choices = NULL)),
-        column(6, actionButton("trend_btn", "Show Performance Chart", class = "btn-info"))
-      ),
-      plotlyOutput("trend_plot"),
-      br(),
-      textAreaInput("scout_notes", "Scout Notes", rows = 4,
-        placeholder = "Enter notes to include in the scouting report."),
-      fluidRow(
-        column(3, downloadButton("report_btn", "Download Scouting Report (CSV)")),
-        column(3, actionButton("shortlist_btn", "Add Player to Shortlist", class = "btn-success"))
-      ),
-      br(),
-      DTOutput("shortlist_from_profile")
-    ),
-
-    # ---- PLAYER COMPARISON ----
-    tabPanel("Player Comparison Tool",
-      br(),
-      h4("Compare Up To Three Players", class = "section-header"),
-      fluidRow(
-        column(4, selectizeInput("compare_1", "Player 1", choices = NULL)),
-        column(4, selectizeInput("compare_2", "Player 2", choices = NULL)),
-        column(4, selectizeInput("compare_3", "Player 3", choices = NULL))
-      ),
-      actionButton("compare_btn", "Compare Players", class = "btn-primary"),
-      br(), br(),
-      DTOutput("comparison_table"),
-      br(),
-      plotlyOutput("comparison_radar", height = "550px")
-    ),
-
-    # ---- FIT SCORE ----
-    tabPanel("Fit Score Calculator",
-      br(),
-      h4("Fit Score Calculator", class = "section-header"),
-      p("Select competitions and positions, then adjust trait weights to generate ranked recommendations."),
-      fluidRow(
-        column(6, selectizeInput("fit_competition_filter", "Competitions to Search",
-          choices = NULL, multiple = TRUE)),
-        column(6, selectizeInput("fit_position_filter", "Positions to Search",
-          choices = NULL, multiple = TRUE))
-      ),
-      fluidRow(
-        column(4, sliderInput("pressing_w", "Pressing", 0, 10, 5, step = 1)),
-        column(4, sliderInput("duels_w", "Duels", 0, 10, 5, step = 1)),
-        column(4, sliderInput("aerial_w", "Aerial", 0, 10, 4, step = 1))
-      ),
-      fluidRow(
-        column(4, sliderInput("possession_w", "Possession Retention", 0, 10, 5, step = 1)),
-        column(4, sliderInput("blocking_w", "Blocking", 0, 10, 4, step = 1)),
-        column(4, sliderInput("progression_w", "Progression", 0, 10, 6, step = 1))
-      ),
-      fluidRow(
-        column(4, sliderInput("impact_w", "Impact", 0, 10, 6, step = 1)),
-        column(4, sliderInput("discipline_w", "Discipline", 0, 10, 3, step = 1)),
-        column(4, sliderInput("dribbling_w", "Dribbling", 0, 10, 4, step = 1))
-      ),
-      fluidRow(
-        column(4, sliderInput("chance_w", "Chance Creation", 0, 10, 5, step = 1)),
-        column(4, sliderInput("finishing_w", "Finishing", 0, 10, 3, step = 1)),
-        column(4, sliderInput("crossing_w", "Crossing", 0, 10, 3, step = 1))
-      ),
-      fluidRow(
-        column(4, sliderInput("box_w", "Box Presence", 0, 10, 3, step = 1)),
-        column(4, sliderInput("holdup_w", "Holdup", 0, 10, 3, step = 1)),
-        column(4, sliderInput("target_w", "Target Score", 0, 10, 7, step = 1))
-      ),
-      fluidRow(
-        column(4, sliderInput("attain_w", "Attainability", 0, 10, 6, step = 1))
-      ),
-      actionButton("fit_btn", "Generate Ranked Recommendations", class = "btn-primary"),
-      br(), br(),
-      DTOutput("fit_table")
-    ),
-
-    # ---- SIMILAR PLAYERS ----
-    tabPanel("Similar Player Finder",
-      br(),
-      h4("Find Similar Players", class = "section-header"),
-      selectizeInput("similar_player_select", "Select Player", choices = NULL, width = "60%"),
-      actionButton("similar_btn", "Find Similar Players", class = "btn-primary"),
-      br(), br(),
-      DTOutput("similar_table")
-    ),
-
-    # ---- SHORTLIST ----
-    tabPanel("Shortlist Manager",
-      br(),
-      h4("Shortlist Manager", class = "section-header"),
-      fluidRow(
-        column(4, selectizeInput("shortlist_player", "Add Player", choices = NULL)),
-        column(2, br(), actionButton("add_shortlist_btn", "Add to Shortlist", class = "btn-success")),
-        column(2, br(), actionButton("clear_shortlist_btn", "Clear Shortlist", class = "btn-danger")),
-        column(2, br(), downloadButton("export_shortlist_btn", "Export CSV"))
-      ),
-      br(),
-      DTOutput("shortlist_table")
-    )
-  )
-)
-
-# ============================================================
-# SERVER
-# ============================================================
-
-server <- function(input, output, session) {
-
-  # --- Load data once ---
-  app_data <- tryCatch(load_data(), error = function(e) {
-    showNotification(paste("Error loading data:", e$message), type = "error", duration = NULL)
-    list(df = data.frame(), multi_df = data.frame(), multi_player_col = NULL)
-  })
-
-  df       <- app_data$df
-  multi_df <- app_data$multi_df
-
-  # Session shortlist
-  shortlist <- reactiveVal(character(0))
-
-  # --- Populate dropdowns on startup ---
-  observe({
-    req(nrow(df) > 0)
-
-    comp_opts     <- if (COMP_COL %in% names(df)) sort(unique(na.omit(as.character(df[[COMP_COL]])))) else character(0)
-    team_opts     <- if (TEAM_COL %in% names(df)) sort(unique(na.omit(as.character(df[[TEAM_COL]])))) else character(0)
-    pos_opts      <- if (POSITION_COL %in% names(df)) sort(unique(na.omit(as.character(df[[POSITION_COL]])))) else character(0)
-    country_opts  <- if (COUNTRY_COL %in% names(df)) sort(unique(na.omit(as.character(df[[COUNTRY_COL]])))) else character(0)
-
-    # Build named player choices: "Name | Position | Team" -> player_name
-    player_rows <- df[, available_cols(c(PLAYER_COL, POSITION_COL, TEAM_COL), df), drop = FALSE] |> distinct()
-    player_choices <- setNames(
-      as.character(player_rows[[PLAYER_COL]]),
-      paste0(player_rows[[PLAYER_COL]], " | ",
-             if (POSITION_COL %in% names(player_rows)) player_rows[[POSITION_COL]] else "",
-             " | ",
-             if (TEAM_COL %in% names(player_rows)) player_rows[[TEAM_COL]] else "")
-    )
-    player_choices <- player_choices[order(names(player_choices))]
-
-    perf_opts <- build_performance_metric_options(df, multi_df)
-
-    updateSelectizeInput(session, "competition_filter", choices = comp_opts, server = TRUE)
-    updateSelectizeInput(session, "team_filter", choices = team_opts, server = TRUE)
-    updateSelectizeInput(session, "position_filter", choices = pos_opts, server = TRUE)
-    updateSelectizeInput(session, "country_filter", choices = country_opts, server = TRUE)
-    updateSelectizeInput(session, "selected_player", choices = player_choices, server = TRUE)
-    updateSelectizeInput(session, "compare_1", choices = c("" = "", player_choices), server = TRUE)
-    updateSelectizeInput(session, "compare_2", choices = c("" = "", player_choices), server = TRUE)
-    updateSelectizeInput(session, "compare_3", choices = c("" = "", player_choices), server = TRUE)
-    updateSelectizeInput(session, "fit_competition_filter", choices = comp_opts, server = TRUE)
-    updateSelectizeInput(session, "fit_position_filter", choices = pos_opts, server = TRUE)
-    updateSelectizeInput(session, "similar_player_select", choices = player_choices, server = TRUE)
-    updateSelectizeInput(session, "shortlist_player", choices = player_choices, server = TRUE)
-    updateSelectInput(session, "profile_metric", choices = perf_opts)
-  })
-
-  # --- Dynamic sliders ---
-  output$age_slider_ui <- renderUI({
-    age_min <- if (AGE_COL %in% names(df)) floor(min(df[[AGE_COL]], na.rm = TRUE)) else 15
-    age_max <- if (AGE_COL %in% names(df)) ceiling(max(df[[AGE_COL]], na.rm = TRUE)) else 45
-    tagList(
-      sliderInput("min_age_filter", "Minimum Age", age_min, age_max, age_min, step = 1),
-      sliderInput("max_age_filter", "Maximum Age", age_min, age_max, age_max, step = 1)
-    )
-  })
-
-  output$minutes_slider_ui <- renderUI({
-    min_max <- if (MINUTES_COL %in% names(df)) ceiling(max(df[[MINUTES_COL]], na.rm = TRUE)) else 5000
-    sliderInput("minutes_filter", "Minimum Minutes", 0, min_max, 0, step = 100)
-  })
-
-  # ---- SEARCH ----
-  search_result_df <- eventReactive(input$search_btn, {
-    data <- df
-
-    if (!is.null(input$search_box) && nchar(input$search_box) > 0 && PLAYER_COL %in% names(data)) {
-      data <- data[str_detect(as.character(data[[PLAYER_COL]]),
-                              regex(input$search_box, ignore_case = TRUE)), ]
-    }
-    if (length(input$competition_filter) > 0 && COMP_COL %in% names(data))
-      data <- data[data[[COMP_COL]] %in% input$competition_filter, ]
-    if (length(input$team_filter) > 0 && TEAM_COL %in% names(data))
-      data <- data[data[[TEAM_COL]] %in% input$team_filter, ]
-    if (length(input$position_filter) > 0 && POSITION_COL %in% names(data))
-      data <- data[data[[POSITION_COL]] %in% input$position_filter, ]
-    if (length(input$country_filter) > 0 && COUNTRY_COL %in% names(data))
-      data <- data[data[[COUNTRY_COL]] %in% input$country_filter, ]
-
-    min_age <- if (!is.null(input$min_age_filter)) input$min_age_filter else -Inf
-    max_age <- if (!is.null(input$max_age_filter)) input$max_age_filter else  Inf
-    if (AGE_COL %in% names(data))
-      data <- data[!is.na(data[[AGE_COL]]) &
-                   data[[AGE_COL]] >= min_age & data[[AGE_COL]] <= max_age, ]
-
-    min_min <- if (!is.null(input$minutes_filter)) input$minutes_filter else 0
-    if (MINUTES_COL %in% names(data))
-      data <- data[!is.na(data[[MINUTES_COL]]) & data[[MINUTES_COL]] >= min_min, ]
-
-    cols <- available_cols(SEARCH_TABLE_COLS, data)
-    out  <- data[, cols, drop = FALSE]
-    if (nrow(out) == 0) return(data.frame(Message = "No players found."))
-
-    sort_col <- if (TARGET_SCORE_COL %in% names(out)) TARGET_SCORE_COL else ATTAINABILITY_COL
-    if (sort_col %in% names(out))
-      out <- out[order(-out[[sort_col]], na.last = TRUE), ]
-
-    pretty_df(out, df)
-  })
-
-  output$search_results <- renderDT({
-    req(search_result_df())
-    datatable(search_result_df(), selection = "single", rownames = FALSE,
-              options = list(scrollX = TRUE, pageLength = 25))
-  })
-
-  output$search_status <- renderText({
-    sel <- input$search_results_rows_selected
-    if (!is.null(sel) && length(sel) > 0) {
-      player <- search_result_df()[sel, "Player"]
-      updateSelectizeInput(session, "selected_player", selected = player)
-      paste("Loaded", player, "into the Player Profile tab.")
-    } else {
-      "Click a player row to load them into the Player Profile tab."
-    }
-  })
-
-  # ---- PLAYER PROFILE ----
-  current_player_row <- reactive({
-    get_player_row(df, input$selected_player)
-  })
-
-  output$profile_output <- renderUI({
-    row <- current_player_row()
-    if (is.null(row)) return(p("Select a player to view their profile."))
-    tagList(
-      h2(row[[PLAYER_COL]]),
-      h4(paste0(row[[TEAM_COL]], " | ", row[[COMP_COL]])),
-      h4("Player Details"),
-      tags$ul(
-        tags$li(strong("Primary Position: "), clean_value(row[[POSITION_COL]])),
-        tags$li(strong("Secondary Position: "), clean_value(row[[SECONDARY_POSITION_COL]])),
-        tags$li(strong("Age: "), clean_value(row[[AGE_COL]])),
-        tags$li(strong("Country: "), clean_value(row[[COUNTRY_COL]])),
-        tags$li(strong("Height: "), paste0(clean_value(row[[HEIGHT_COL]]), " cm")),
-        tags$li(strong("Weight: "), paste0(clean_value(row[[WEIGHT_COL]]), " kg")),
-        tags$li(strong("Market Value: "), format_money(row[[MARKET_VALUE_COL]])),
-        tags$li(strong("Contract: "), clean_value(row[[CONTRACT_COL]])),
-        tags$li(strong("Minutes: "), clean_value(row[[MINUTES_COL]]))
-      )
-    )
-  })
-
-  output$key_summary <- renderDT({
-    row <- current_player_row()
-    if (is.null(row)) return(datatable(data.frame(Metric = "Select a player", Value = "")))
-    out <- data.frame(
-      Metric = c("Best Archetype","Best Archetype Score","Target Score",
-                 "Attainability","Club Rank","Match Toughness","Club ELO"),
-      Value  = c(clean_value(row[[ARCHETYPE_COL]]),
-                 clean_value(row[[ARCHETYPE_SCORE_COL]]),
-                 clean_value(row[[TARGET_SCORE_COL]]),
-                 clean_value(row[[ATTAINABILITY_COL]]),
-                 clean_value(row[[CLUB_RANK_COL]]),
-                 clean_value(row[[MATCH_TOUGHNESS_COL]]),
-                 clean_value(row[[ELO_COL]]))
-    )
-    datatable(out, rownames = FALSE, options = list(dom = "t", paging = FALSE))
-  })
-
-  output$metric_table <- renderDT({
-    row <- current_player_row()
-    if (is.null(row)) return(datatable(data.frame(Metric = "Select a player", Score = "")))
-    cols <- switch(input$metric_group,
-      "Attributes"       = ATTR_COLS,
-      "Position Scores"  = POSITION_SCORE_COLS,
-      "Archetype Scores" = ARCHETYPE_SCORE_COLS,
-      "Key Season Stats" = KEY_METRICS,
-      ATTR_COLS
-    )
-    cols <- available_cols(cols, df)
-    rows_data <- lapply(cols, function(c) {
-      v <- row[[c]]
-      if (!is.null(v) && !is.na(v))
-        data.frame(Metric = pretty_label(c),
-                   Score  = round(as.numeric(v), 2))
-    })
-    out <- do.call(rbind, Filter(Negate(is.null), rows_data))
-    if (is.null(out)) out <- data.frame(Metric = "No metrics available", Score = NA)
-    out <- out[order(-out$Score, na.last = TRUE), ]
-    datatable(out, rownames = FALSE, options = list(scrollX = TRUE, pageLength = 25))
-  })
-
-  output$radar_plot <- renderPlotly({
-    row <- current_player_row()
-    if (is.null(row)) return(plot_ly() |> layout(title = "Select a player"))
-    metrics <- top_attr_cols(df, row, max_cols = 8)
-    if (length(metrics) < 3) return(plot_ly() |> layout(title = "Not enough attributes"))
-    group  <- get_player_group(df, row)
-    labels <- sapply(metrics, pretty_label)
-    player_vals <- sapply(metrics, function(m) {
-      v <- row[[m]]; if (is.null(v) || is.na(v)) 0 else as.numeric(v)
-    })
-    avg_vals <- sapply(metrics, function(m) {
-      if (m %in% names(group)) mean(group[[m]], na.rm = TRUE) else 0
-    })
-    max_val <- max(100, max(player_vals, avg_vals, na.rm = TRUE) * 1.1, na.rm = TRUE)
-    plot_ly(type = "scatterpolar", fill = "toself") |>
-      add_trace(r = c(player_vals, player_vals[1]),
-                theta = c(labels, labels[1]),
-                name = as.character(input$selected_player)) |>
-      add_trace(r = c(avg_vals, avg_vals[1]),
-                theta = c(labels, labels[1]),
-                name = "Position/Competition Avg") |>
-      layout(title = paste(input$selected_player, "Attribute Radar"),
-             polar = list(radialaxis = list(range = c(0, max_val))),
-             legend = list(orientation = "h"))
-  })
-
-  output$percentile_plot <- renderPlotly({
-    row <- current_player_row()
-    if (is.null(row)) return(plot_ly() |> layout(title = "Select a player"))
-    group <- get_player_group(df, row)
-    rows_data <- lapply(available_cols(PERCENTILE_METRICS, df), function(m) {
-      v      <- suppressWarnings(as.numeric(row[[m]]))
-      vals   <- suppressWarnings(as.numeric(group[[m]]))
-      vals   <- vals[!is.na(vals)]
-      if (!is.null(v) && !is.na(v) && length(vals) > 1) {
-        pct <- mean(vals < v, na.rm = TRUE) * 100
-        data.frame(Metric = pretty_label(m), Percentile = round(pct, 1))
-      }
-    })
-    plot_df <- do.call(rbind, Filter(Negate(is.null), rows_data))
-    if (is.null(plot_df) || nrow(plot_df) == 0)
-      return(plot_ly() |> layout(title = "No percentile data"))
-    plot_df <- plot_df[order(plot_df$Percentile), ]
-    plot_ly(plot_df, x = ~Percentile, y = ~Metric, type = "bar", orientation = "h",
-            text = ~paste0(Percentile, "%"), textposition = "outside") |>
-      layout(title = paste(input$selected_player, "Percentiles vs Same Position and Competition"),
-             xaxis = list(range = c(0, 110)), yaxis = list(title = ""),
-             height = max(450, 32 * nrow(plot_df)))
-  })
-
-  observeEvent(input$trend_btn, {
-    row       <- current_player_row()
-    multi_row <- get_multiseason_row(multi_df, input$selected_player)
-    metric    <- input$profile_metric
-
-    output$trend_plot <- renderPlotly({
-      if (is.null(row) || is.null(metric) || metric == "")
-        return(plot_ly() |> layout(title = "Select a player and metric."))
-      rows_data <- lapply(names(HISTORICAL_SEASONS), function(sc) {
-        v <- NA_real_
-        if (!is.null(multi_row)) v <- find_metric_value(multi_row, metric, sc)
-        if (is.na(v)) v <- find_metric_value(row, metric, sc)
-        if (!is.na(v)) data.frame(Season = HISTORICAL_SEASONS[sc], Score = v)
-      })
-      plot_df <- do.call(rbind, Filter(Negate(is.null), rows_data))
-      curr_val <- find_metric_value(row, metric, NULL)
-      if (!is.na(curr_val)) {
-        plot_df <- plot_df[plot_df$Season != CURRENT_MAIN_SEASON_LABEL, ]
-        plot_df <- rbind(plot_df, data.frame(Season = CURRENT_MAIN_SEASON_LABEL, Score = curr_val))
-      }
-      if (is.null(plot_df) || nrow(plot_df) == 0)
-        return(plot_ly() |> layout(title = "No performance data found."))
-      season_order <- c("2021-22","2022-23","2023-24","2024-25","2025-26")
-      plot_df$Season <- factor(plot_df$Season, levels = season_order)
-      plot_df <- plot_df[order(plot_df$Season), ]
-      plot_ly(plot_df, x = ~Season, y = ~Score, type = "scatter", mode = "lines+markers+text",
-              text = ~round(Score, 2), textposition = "top center") |>
-        layout(title = paste0(input$selected_player, ": ", pretty_label(metric), " Over Time"))
-    })
-  })
-
-  # --- PDF/CSV Report download ---
-  output$report_btn <- downloadHandler(
-    filename = function() {
-      safe <- str_replace_all(input$selected_player, "[^A-Za-z0-9_]", "_")
-      paste0(safe, "_scouting_report.csv")
-    },
-    content = function(file) {
-      row <- current_player_row()
-      if (is.null(row)) {
-        write.csv(data.frame(Message = "No player selected"), file, row.names = FALSE)
-        return()
-      }
-      all_cols <- available_cols(c(PLAYER_COL, TEAM_COL, COMP_COL, POSITION_COL,
-                                   AGE_COL, COUNTRY_COL, HEIGHT_COL, WEIGHT_COL,
-                                   MARKET_VALUE_COL, CONTRACT_COL, MINUTES_COL,
-                                   ARCHETYPE_COL, ARCHETYPE_SCORE_COL, TARGET_SCORE_COL,
-                                   ATTAINABILITY_COL, CLUB_RANK_COL, MATCH_TOUGHNESS_COL,
-                                   ELO_COL, ATTR_COLS, KEY_METRICS, POSITION_SCORE_COLS,
-                                   ARCHETYPE_SCORE_COLS), df)
-      out <- df[as.character(df[[PLAYER_COL]]) == as.character(input$selected_player),
-                all_cols, drop = FALSE]
-      notes_row <- data.frame(Section = "Scout Notes", Notes = input$scout_notes)
-      write.csv(out, file, row.names = FALSE)
-    }
-  )
-
-  # --- Shortlist ---
-  observeEvent(input$shortlist_btn, {
-    p <- input$selected_player
-    if (!is.null(p) && p != "" && !p %in% shortlist()) {
-      shortlist(c(shortlist(), p))
-    }
-  })
-
-  view_shortlist <- reactive({
-    sl <- shortlist()
-    if (length(sl) == 0) return(data.frame(Message = "No players added yet."))
-    data <- df[as.character(df[[PLAYER_COL]]) %in% sl, ]
-    cols <- available_cols(SHORTLIST_COLS, data)
-    out  <- data[, cols, drop = FALSE]
-    if (nrow(out) == 0) return(data.frame(Message = "Shortlist is empty."))
-    pretty_df(out, df)
-  })
-
-  output$shortlist_from_profile <- renderDT({
-    datatable(view_shortlist(), rownames = FALSE,
-              options = list(scrollX = TRUE, pageLength = 15))
-  })
-
-  # ---- COMPARISON ----
-  comparison_df <- eventReactive(input$compare_btn, {
-    players <- c(input$compare_1, input$compare_2, input$compare_3)
-    players <- players[!is.null(players) & players != ""]
-    if (length(players) == 0) return(data.frame(Message = "Select at least one player."))
-    data <- df[as.character(df[[PLAYER_COL]]) %in% players, ]
-    cols <- available_cols(COMPARISON_COLS, data)
-    pretty_df(data[, cols, drop = FALSE], df)
-  })
-
-  output$comparison_table <- renderDT({
-    datatable(comparison_df(), selection = "single", rownames = FALSE,
-              options = list(scrollX = TRUE, pageLength = 25))
-  })
-
-  output$comparison_radar <- renderPlotly({
-    players <- c(input$compare_1, input$compare_2, input$compare_3)
-    players <- players[!is.null(players) & players != ""]
-    if (length(players) == 0) return(plot_ly() |> layout(title = "Select players to compare."))
-    first_row <- get_player_row(df, players[1])
-    if (is.null(first_row)) return(plot_ly())
-    metrics <- top_attr_cols(df, first_row, max_cols = 8)
-    if (length(metrics) < 3) return(plot_ly() |> layout(title = "Not enough attributes."))
-    labels <- sapply(metrics, pretty_label)
-    fig <- plot_ly(type = "scatterpolar", fill = "toself")
-    for (p in players) {
-      row <- get_player_row(df, p)
-      if (!is.null(row)) {
-        vals <- sapply(metrics, function(m) {
-          v <- row[[m]]; if (is.null(v) || is.na(v)) 0 else as.numeric(v)
-        })
-        fig <- fig |> add_trace(r = c(vals, vals[1]), theta = c(labels, labels[1]), name = p)
-      }
-    }
-    fig |> layout(title = "Player Attribute Radar Comparison",
-                  polar = list(radialaxis = list(range = c(0, 110))),
-                  legend = list(orientation = "h"))
-  })
-
-  # ---- FIT SCORE ----
-  fit_result_df <- eventReactive(input$fit_btn, {
-    data <- df
-    if (length(input$fit_competition_filter) > 0 && COMP_COL %in% names(data))
-      data <- data[data[[COMP_COL]] %in% input$fit_competition_filter, ]
-    if (length(input$fit_position_filter) > 0 && POSITION_COL %in% names(data))
-      data <- data[data[[POSITION_COL]] %in% input$fit_position_filter, ]
-    if (nrow(data) == 0) return(data.frame(Message = "No players found for selected filters."))
-
-    weights <- c(
-      attr_pressing = input$pressing_w, attr_duels = input$duels_w,
-      attr_aerial = input$aerial_w, attr_possession_retention = input$possession_w,
-      attr_blocking = input$blocking_w, attr_progression = input$progression_w,
-      attr_impact = input$impact_w, attr_discipline = input$discipline_w,
-      attr_dribbling = input$dribbling_w, attr_chance_creation = input$chance_w,
-      attr_finishing = input$finishing_w, attr_crossing = input$crossing_w,
-      attr_box_presence = input$box_w, attr_holdup = input$holdup_w
-    )
-    weights <- c(weights, setNames(c(input$target_w, input$attain_w),
-                                   c(TARGET_SCORE_COL, ATTAINABILITY_COL)))
-    total_weight <- sum(weights)
-    if (total_weight == 0) return(data.frame(Message = "At least one weight must be > 0."))
-
-    fit_vals <- rep(0, nrow(data))
-    for (col in names(weights)) {
-      w <- weights[col]
-      if (col %in% names(data) && w > 0) {
-        fit_vals <- fit_vals + normalize_0_100(data[[col]]) * w
-      }
-    }
-    data$fit_score <- fit_vals / total_weight
-    cols <- available_cols(c(PLAYER_COL, POSITION_COL, TEAM_COL, COMP_COL,
-                             AGE_COL, MINUTES_COL, MARKET_VALUE_COL, CONTRACT_COL,
-                             ARCHETYPE_COL, ARCHETYPE_SCORE_COL,
-                             TARGET_SCORE_COL, ATTAINABILITY_COL,
-                             names(weights)), data)
-    cols <- c(cols, "fit_score")
-    out  <- data[order(-data$fit_score, na.last = TRUE), cols, drop = FALSE]
-    pretty_df(head(out, 50), df)
-  })
-
-  output$fit_table <- renderDT({
-    datatable(fit_result_df(), selection = "single", rownames = FALSE,
-              options = list(scrollX = TRUE, pageLength = 25))
-  })
-
-  # ---- SIMILAR PLAYERS ----
-  similar_result_df <- eventReactive(input$similar_btn, {
-    row <- get_player_row(df, input$similar_player_select)
-    if (is.null(row)) return(data.frame(Message = "Select a player."))
-    metrics <- available_cols(SIMILARITY_METRICS, df)
-    metrics <- metrics[sapply(metrics, function(m) {
-      v <- row[[m]]; !is.null(v) && !is.na(v)
-    })]
-    metrics <- head(metrics, 24)
-    if (length(metrics) == 0) return(data.frame(Message = "No similarity metrics available."))
-    pos <- row[[POSITION_COL]]
-    candidates <- df[as.character(df[[PLAYER_COL]]) != as.character(input$similar_player_select), ]
-    if (POSITION_COL %in% names(df) && !is.null(pos) && !is.na(pos)) {
-      sub <- candidates[candidates[[POSITION_COL]] == pos, ]
-      if (nrow(sub) > 0) candidates <- sub
-    }
-    dist_vals <- rep(0, nrow(candidates))
-    for (m in metrics) {
-      all_vals <- suppressWarnings(as.numeric(df[[m]]))
-      sd_val   <- sd(all_vals, na.rm = TRUE)
-      cand_vals <- suppressWarnings(as.numeric(candidates[[m]]))
-      ref_val   <- suppressWarnings(as.numeric(row[[m]]))
-      if (!is.na(sd_val) && sd_val > 0) {
-        dist_vals <- dist_vals + ((cand_vals - ref_val) / sd_val)^2
-      }
-    }
-    candidates$similarity_score <- 100 / (1 + dist_vals)
-    cols <- c(available_cols(c(PLAYER_COL, TEAM_COL, COMP_COL, POSITION_COL,
-                               AGE_COL, MARKET_VALUE_COL, ARCHETYPE_COL,
-                               ARCHETYPE_SCORE_COL, TARGET_SCORE_COL,
-                               ATTAINABILITY_COL), candidates), "similarity_score")
-    out <- candidates[order(-candidates$similarity_score), cols, drop = FALSE]
-    pretty_df(head(out, 10), df)
-  })
-
-  output$similar_table <- renderDT({
-    datatable(similar_result_df(), selection = "single", rownames = FALSE,
-              options = list(scrollX = TRUE, pageLength = 15))
-  })
-
-  # ---- SHORTLIST MANAGER ----
-  observeEvent(input$add_shortlist_btn, {
-    p <- input$shortlist_player
-    if (!is.null(p) && p != "" && !p %in% shortlist()) {
-      shortlist(c(shortlist(), p))
-    }
-  })
-
-  observeEvent(input$clear_shortlist_btn, {
-    shortlist(character(0))
-  })
-
-  output$shortlist_table <- renderDT({
-    datatable(view_shortlist(), rownames = FALSE,
-              options = list(scrollX = TRUE, pageLength = 25))
-  })
-
-  output$export_shortlist_btn <- downloadHandler(
-    filename = function() "shortlist_export.csv",
-    content  = function(file) {
-      sl <- view_shortlist()
-      write.csv(sl, file, row.names = FALSE)
-    }
-  )
-}
-
-shinyApp(ui, server)