1.3.1: Removed unused scripts.

scripts/README.md

@@ -1 +0,0 @@
-用于测试脚本目录。

scripts/analyze_features.py

@@ -1,214 +0,0 @@
-import sqlite3
-import pandas as pd
-import numpy as np
-import os
-
-DB_L2_PATH = r'd:\Documents\trae_projects\yrtv\database\L2\L2_Main.sqlite'
-
-def get_db_connection():
-    conn = sqlite3.connect(DB_L2_PATH)
-    conn.row_factory = sqlite3.Row
-    return conn
-
-def load_data_and_calculate(conn, min_matches=5):
-    print("Loading Basic Stats...")
-    
-    # 1. Basic Stats
-    query_basic = """
-    SELECT 
-        steam_id_64,
-        COUNT(*) as matches_played,
-        AVG(rating) as avg_rating,
-        AVG(kd_ratio) as avg_kd,
-        AVG(adr) as avg_adr,
-        AVG(kast) as avg_kast,
-        SUM(first_kill) as total_fk,
-        SUM(first_death) as total_fd,
-        SUM(clutch_1v1) + SUM(clutch_1v2) + SUM(clutch_1v3) + SUM(clutch_1v4) + SUM(clutch_1v5) as total_clutches,
-        SUM(throw_harm) as total_util_dmg,
-        SUM(flash_time) as total_flash_time,
-        SUM(flash_enemy) as total_flash_enemy
-    FROM fact_match_players
-    GROUP BY steam_id_64
-    HAVING COUNT(*) >= ?
-    """
-    df_basic = pd.read_sql_query(query_basic, conn, params=(min_matches,))
-    
-    valid_ids = tuple(df_basic['steam_id_64'].tolist())
-    if not valid_ids:
-        print("No players found.")
-        return None
-    placeholders = ','.join(['?'] * len(valid_ids))
-    
-    # 2. Side Stats (T/CT) via Economy Table (which has side info)
-    print("Loading Side Stats via Round Map...")
-    # Map each round+player to a side
-    query_side_map = f"""
-    SELECT match_id, round_num, steam_id_64, side
-    FROM fact_round_player_economy
-    WHERE steam_id_64 IN ({placeholders})
-    """
-    try:
-        df_sides = pd.read_sql_query(query_side_map, conn, params=valid_ids)
-        
-        # Get all Kills
-        query_kills = f"""
-        SELECT match_id, round_num, attacker_steam_id as steam_id_64, COUNT(*) as kills
-        FROM fact_round_events
-        WHERE event_type = 'kill'
-        AND attacker_steam_id IN ({placeholders})
-        GROUP BY match_id, round_num, attacker_steam_id
-        """
-        df_kills = pd.read_sql_query(query_kills, conn, params=valid_ids)
-        
-        # Merge to get Kills per Side
-        df_merged = df_kills.merge(df_sides, on=['match_id', 'round_num', 'steam_id_64'], how='inner')
-        
-        # Aggregate
-        side_stats = df_merged.groupby(['steam_id_64', 'side'])['kills'].sum().unstack(fill_value=0)
-        side_stats.columns = [f'kills_{c.lower()}' for c in side_stats.columns]
-        
-        # Also need deaths to calc KD (approx)
-        # Assuming deaths are in events as victim
-        query_deaths = f"""
-        SELECT match_id, round_num, victim_steam_id as steam_id_64, COUNT(*) as deaths
-        FROM fact_round_events
-        WHERE event_type = 'kill'
-        AND victim_steam_id IN ({placeholders})
-        GROUP BY match_id, round_num, victim_steam_id
-        """
-        df_deaths = pd.read_sql_query(query_deaths, conn, params=valid_ids)
-        df_merged_d = df_deaths.merge(df_sides, on=['match_id', 'round_num', 'steam_id_64'], how='inner')
-        side_stats_d = df_merged_d.groupby(['steam_id_64', 'side'])['deaths'].sum().unstack(fill_value=0)
-        side_stats_d.columns = [f'deaths_{c.lower()}' for c in side_stats_d.columns]
-        
-        # Combine
-        df_side_final = side_stats.join(side_stats_d).fillna(0)
-        df_side_final['ct_kd'] = df_side_final.get('kills_ct', 0) / df_side_final.get('deaths_ct', 1).replace(0, 1)
-        df_side_final['t_kd'] = df_side_final.get('kills_t', 0) / df_side_final.get('deaths_t', 1).replace(0, 1)
-        
-    except Exception as e:
-        print(f"Side stats failed: {e}")
-        df_side_final = pd.DataFrame({'steam_id_64': list(valid_ids)})
-
-    # 3. PTL (Pistol) via Rounds 1 and 13
-    print("Loading Pistol Stats via Rounds...")
-    query_pistol_kills = f"""
-    SELECT 
-        ev.attacker_steam_id as steam_id_64,
-        COUNT(*) as pistol_kills
-    FROM fact_round_events ev
-    WHERE ev.attacker_steam_id IN ({placeholders})
-    AND ev.event_type = 'kill'
-    AND ev.round_num IN (1, 13)
-    GROUP BY ev.attacker_steam_id
-    """
-    df_ptl = pd.read_sql_query(query_pistol_kills, conn, params=valid_ids)
-    
-    # 4. HPS
-    print("Loading HPS Stats...")
-    query_close = f"""
-    SELECT mp.steam_id_64, AVG(mp.rating) as close_match_rating
-    FROM fact_match_players mp
-    JOIN fact_matches m ON mp.match_id = m.match_id
-    WHERE mp.steam_id_64 IN ({placeholders})
-    AND ABS(m.score_team1 - m.score_team2) <= 3
-    GROUP BY mp.steam_id_64
-    """
-    df_hps = pd.read_sql_query(query_close, conn, params=valid_ids)
-    
-    # 5. STA
-    query_sta = f"""
-    SELECT mp.steam_id_64, mp.rating, mp.is_win
-    FROM fact_match_players mp
-    WHERE mp.steam_id_64 IN ({placeholders})
-    """
-    df_matches = pd.read_sql_query(query_sta, conn, params=valid_ids)
-    sta_data = []
-    for pid, group in df_matches.groupby('steam_id_64'):
-        rating_std = group['rating'].std()
-        win_rating = group[group['is_win']==1]['rating'].mean()
-        loss_rating = group[group['is_win']==0]['rating'].mean()
-        sta_data.append({'steam_id_64': pid, 'rating_std': rating_std, 'win_rating': win_rating, 'loss_rating': loss_rating})
-    df_sta = pd.DataFrame(sta_data)
-
-    # --- Merge All ---
-    df = df_basic.merge(df_side_final, on='steam_id_64', how='left')
-    df = df.merge(df_hps, on='steam_id_64', how='left')
-    df = df.merge(df_ptl, on='steam_id_64', how='left').fillna(0)
-    df = df.merge(df_sta, on='steam_id_64', how='left')
-    
-    return df
-
-def normalize_series(series):
-    min_v = series.min()
-    max_v = series.max()
-    if pd.isna(min_v) or pd.isna(max_v) or min_v == max_v:
-        return pd.Series([50]*len(series), index=series.index)
-    return (series - min_v) / (max_v - min_v) * 100
-
-def calculate_scores(df):
-    df = df.copy()
-    
-    # BAT
-    df['n_rating'] = normalize_series(df['avg_rating'])
-    df['n_kd'] = normalize_series(df['avg_kd'])
-    df['n_adr'] = normalize_series(df['avg_adr'])
-    df['n_kast'] = normalize_series(df['avg_kast'])
-    df['score_BAT'] = 0.4*df['n_rating'] + 0.3*df['n_kd'] + 0.2*df['n_adr'] + 0.1*df['n_kast']
-    
-    # STA
-    df['n_std'] = normalize_series(df['rating_std'].fillna(0))
-    df['n_win_r'] = normalize_series(df['win_rating'].fillna(0))
-    df['n_loss_r'] = normalize_series(df['loss_rating'].fillna(0))
-    df['score_STA'] = 0.5*(100 - df['n_std']) + 0.25*df['n_win_r'] + 0.25*df['n_loss_r']
-    
-    # UTIL
-    df['n_util_dmg'] = normalize_series(df['total_util_dmg'] / df['matches_played'])
-    df['n_flash'] = normalize_series(df['total_flash_time'] / df['matches_played'])
-    df['score_UTIL'] = 0.6*df['n_util_dmg'] + 0.4*df['n_flash']
-    
-    # T/CT (Calculated from Event Logs)
-    df['n_ct_kd'] = normalize_series(df['ct_kd'].fillna(0))
-    df['n_t_kd'] = normalize_series(df['t_kd'].fillna(0))
-    df['score_TCT'] = 0.5*df['n_ct_kd'] + 0.5*df['n_t_kd']
-    
-    # HPS
-    df['n_clutch'] = normalize_series(df['total_clutches'] / df['matches_played'])
-    df['n_close_r'] = normalize_series(df['close_match_rating'].fillna(0))
-    df['score_HPS'] = 0.5*df['n_clutch'] + 0.5*df['n_close_r']
-    
-    # PTL
-    df['n_pistol'] = normalize_series(df['pistol_kills'] / df['matches_played'])
-    df['score_PTL'] = df['n_pistol']
-    
-    return df
-
-def main():
-    conn = get_db_connection()
-    try:
-        df = load_data_and_calculate(conn)
-        if df is None: return
-        
-        # Debug: Print raw stats for checking T/CT issue
-        print("\n--- Raw T/CT Stats Sample ---")
-        if 'ct_kd' in df.columns:
-            print(df[['steam_id_64', 'ct_kd', 't_kd']].head())
-        else:
-            print("CT/KD columns missing")
-            
-        results = calculate_scores(df)
-        
-        print("\n--- Final Dimension Scores (Top 5 by BAT) ---")
-        cols = ['steam_id_64', 'score_BAT', 'score_STA', 'score_UTIL', 'score_TCT', 'score_HPS', 'score_PTL']
-        print(results[cols].sort_values('score_BAT', ascending=False).head(5))
-        
-    except Exception as e:
-        print(f"Error: {e}")
-        import traceback
-        traceback.print_exc()
-    finally:
-        conn.close()
-
-if __name__ == "__main__":
-    main()

scripts/analyze_l3_full.py

@@ -1,304 +0,0 @@
-import sqlite3
-import pandas as pd
-import numpy as np
-import os
-
-DB_L2_PATH = r'd:\Documents\trae_projects\yrtv\database\L2\L2_Main.sqlite'
-
-def get_db_connection():
-    conn = sqlite3.connect(DB_L2_PATH)
-    conn.row_factory = sqlite3.Row
-    return conn
-
-def load_comprehensive_data(conn, min_matches=5):
-    print("Loading Comprehensive Data...")
-    
-    # 1. Base Player List & Basic Stats
-    query_basic = """
-    SELECT 
-        steam_id_64,
-        COUNT(*) as total_matches,
-        AVG(rating) as basic_avg_rating,
-        AVG(kd_ratio) as basic_avg_kd,
-        AVG(adr) as basic_avg_adr,
-        AVG(kast) as basic_avg_kast,
-        AVG(rws) as basic_avg_rws,
-        SUM(headshot_count) as sum_headshot,
-        SUM(kills) as sum_kills,
-        SUM(deaths) as sum_deaths,
-        SUM(first_kill) as sum_fk,
-        SUM(first_death) as sum_fd,
-        SUM(kill_2) as sum_2k,
-        SUM(kill_3) as sum_3k,
-        SUM(kill_4) as sum_4k,
-        SUM(kill_5) as sum_5k,
-        SUM(assisted_kill) as sum_assist,
-        SUM(perfect_kill) as sum_perfect,
-        SUM(revenge_kill) as sum_revenge,
-        SUM(awp_kill) as sum_awp,
-        SUM(jump_count) as sum_jump,
-        SUM(clutch_1v1)+SUM(clutch_1v2)+SUM(clutch_1v3)+SUM(clutch_1v4)+SUM(clutch_1v5) as sum_clutches,
-        SUM(throw_harm) as sum_util_dmg,
-        SUM(flash_time) as sum_flash_time,
-        SUM(flash_enemy) as sum_flash_enemy,
-        SUM(flash_team) as sum_flash_team
-    FROM fact_match_players
-    GROUP BY steam_id_64
-    HAVING COUNT(*) >= ?
-    """
-    df = pd.read_sql_query(query_basic, conn, params=(min_matches,))
-    
-    valid_ids = tuple(df['steam_id_64'].tolist())
-    if not valid_ids:
-        print("No players found.")
-        return None
-    placeholders = ','.join(['?'] * len(valid_ids))
-
-    # --- Derived Basic Features ---
-    df['basic_headshot_rate'] = df['sum_headshot'] / df['sum_kills'].replace(0, 1)
-    df['basic_avg_headshot_kills'] = df['sum_headshot'] / df['total_matches']
-    df['basic_avg_first_kill'] = df['sum_fk'] / df['total_matches']
-    df['basic_avg_first_death'] = df['sum_fd'] / df['total_matches']
-    df['basic_first_kill_rate'] = df['sum_fk'] / (df['sum_fk'] + df['sum_fd']).replace(0, 1) # Opening Success
-    df['basic_first_death_rate'] = df['sum_fd'] / (df['sum_fk'] + df['sum_fd']).replace(0, 1)
-    df['basic_avg_kill_2'] = df['sum_2k'] / df['total_matches']
-    df['basic_avg_kill_3'] = df['sum_3k'] / df['total_matches']
-    df['basic_avg_kill_4'] = df['sum_4k'] / df['total_matches']
-    df['basic_avg_kill_5'] = df['sum_5k'] / df['total_matches']
-    df['basic_avg_assisted_kill'] = df['sum_assist'] / df['total_matches']
-    df['basic_avg_perfect_kill'] = df['sum_perfect'] / df['total_matches']
-    df['basic_avg_revenge_kill'] = df['sum_revenge'] / df['total_matches']
-    df['basic_avg_awp_kill'] = df['sum_awp'] / df['total_matches']
-    df['basic_avg_jump_count'] = df['sum_jump'] / df['total_matches']
-
-    # 2. STA (Stability) - Detailed
-    print("Calculating STA...")
-    query_sta = f"""
-    SELECT mp.steam_id_64, mp.rating, mp.is_win, m.start_time
-    FROM fact_match_players mp
-    JOIN fact_matches m ON mp.match_id = m.match_id
-    WHERE mp.steam_id_64 IN ({placeholders})
-    ORDER BY mp.steam_id_64, m.start_time
-    """
-    df_matches = pd.read_sql_query(query_sta, conn, params=valid_ids)
-    
-    sta_list = []
-    for pid, group in df_matches.groupby('steam_id_64'):
-        # Last 30
-        last_30 = group.tail(30)
-        sta_last_30 = last_30['rating'].mean()
-        # Win/Loss
-        sta_win = group[group['is_win']==1]['rating'].mean()
-        sta_loss = group[group['is_win']==0]['rating'].mean()
-        # Volatility (Last 10)
-        sta_vol = group.tail(10)['rating'].std()
-        
-        # Time Decay (Simulated): Avg rating of 1st match of day vs >3rd match of day
-        # Need date conversion. 
-        group['date'] = pd.to_datetime(group['start_time'], unit='s').dt.date
-        daily_counts = group.groupby('date').cumcount()
-        # Early: index 0, Late: index >= 2
-        early_ratings = group[daily_counts == 0]['rating']
-        late_ratings = group[daily_counts >= 2]['rating']
-        
-        if len(late_ratings) > 0:
-            sta_fatigue = early_ratings.mean() - late_ratings.mean() # Positive means fatigue (drop)
-        else:
-            sta_fatigue = 0
-            
-        sta_list.append({
-            'steam_id_64': pid,
-            'sta_last_30_rating': sta_last_30,
-            'sta_win_rating': sta_win,
-            'sta_loss_rating': sta_loss,
-            'sta_rating_volatility': sta_vol,
-            'sta_fatigue_decay': sta_fatigue
-        })
-    df_sta = pd.DataFrame(sta_list)
-    df = df.merge(df_sta, on='steam_id_64', how='left')
-
-    # 3. BAT (Battle) - Detailed
-    print("Calculating BAT...")
-    # Need Match ELO
-    query_bat = f"""
-    SELECT mp.steam_id_64, mp.kd_ratio, mp.entry_kills, mp.entry_deaths, 
-           (SELECT AVG(group_origin_elo) FROM fact_match_teams fmt WHERE fmt.match_id = mp.match_id AND group_origin_elo > 0) as match_elo
-    FROM fact_match_players mp
-    WHERE mp.steam_id_64 IN ({placeholders})
-    """
-    df_bat_raw = pd.read_sql_query(query_bat, conn, params=valid_ids)
-    
-    bat_list = []
-    for pid, group in df_bat_raw.groupby('steam_id_64'):
-        avg_elo = group['match_elo'].mean()
-        if pd.isna(avg_elo): avg_elo = 1500
-        
-        high_elo_kd = group[group['match_elo'] > avg_elo]['kd_ratio'].mean()
-        low_elo_kd = group[group['match_elo'] <= avg_elo]['kd_ratio'].mean()
-        
-        sum_entry_k = group['entry_kills'].sum()
-        sum_entry_d = group['entry_deaths'].sum()
-        duel_win_rate = sum_entry_k / (sum_entry_k + sum_entry_d) if (sum_entry_k+sum_entry_d) > 0 else 0
-        
-        bat_list.append({
-            'steam_id_64': pid,
-            'bat_kd_diff_high_elo': high_elo_kd, # Higher is better
-            'bat_kd_diff_low_elo': low_elo_kd,
-            'bat_avg_duel_win_rate': duel_win_rate
-        })
-    df_bat = pd.DataFrame(bat_list)
-    df = df.merge(df_bat, on='steam_id_64', how='left')
-
-    # 4. HPS (Pressure) - Detailed
-    print("Calculating HPS...")
-    # Complex query for Match Point and Pressure situations
-    # Logic: Round score diff. 
-    # Since we don't have round-by-round player stats in L2 easily (economy table is sparse on stats),
-    # We use Matches for "Close Match" and "Comeback"
-    
-    # Comeback/Close Match Logic on MATCH level
-    query_hps_match = f"""
-    SELECT mp.steam_id_64, mp.kd_ratio, mp.rating, m.score_team1, m.score_team2, mp.team_id, m.winner_team
-    FROM fact_match_players mp
-    JOIN fact_matches m ON mp.match_id = m.match_id
-    WHERE mp.steam_id_64 IN ({placeholders})
-    """
-    df_hps_raw = pd.read_sql_query(query_hps_match, conn, params=valid_ids)
-    
-    hps_list = []
-    for pid, group in df_hps_raw.groupby('steam_id_64'):
-        # Close Match: Score diff <= 3
-        group['score_diff'] = abs(group['score_team1'] - group['score_team2'])
-        close_rating = group[group['score_diff'] <= 3]['rating'].mean()
-        
-        # Comeback: Won match where score was close? 
-        # Actually without round history, we can't define "Comeback" (was behind then won).
-        # We can define "Underdog Win": Won when ELO was lower? Or just Close Win.
-        # Let's use Close Match Rating as primary HPS metric from matches.
-        
-        hps_list.append({
-            'steam_id_64': pid,
-            'hps_close_match_rating': close_rating
-        })
-    df_hps = pd.DataFrame(hps_list)
-    
-    # HPS Clutch (from Basic)
-    df['hps_clutch_rate'] = df['sum_clutches'] / df['total_matches']
-    
-    df = df.merge(df_hps, on='steam_id_64', how='left')
-
-    # 5. PTL (Pistol)
-    print("Calculating PTL...")
-    # R1/R13 Kills
-    query_ptl = f"""
-    SELECT ev.attacker_steam_id as steam_id_64, COUNT(*) as pistol_kills
-    FROM fact_round_events ev
-    WHERE ev.event_type = 'kill' AND ev.round_num IN (1, 13)
-    AND ev.attacker_steam_id IN ({placeholders})
-    GROUP BY ev.attacker_steam_id
-    """
-    df_ptl = pd.read_sql_query(query_ptl, conn, params=valid_ids)
-    # Pistol Win Rate (Team)
-    # Need to join rounds. Too slow? 
-    # Simplify: Just use Pistol Kills per Match (normalized)
-    
-    df = df.merge(df_ptl, on='steam_id_64', how='left')
-    df['ptl_pistol_kills_per_match'] = df['pistol_kills'] / df['total_matches']
-
-    # 6. T/CT
-    print("Calculating T/CT...")
-    query_ct = f"SELECT steam_id_64, AVG(rating) as ct_rating, AVG(kd_ratio) as ct_kd FROM fact_match_players_ct WHERE steam_id_64 IN ({placeholders}) GROUP BY steam_id_64"
-    query_t = f"SELECT steam_id_64, AVG(rating) as t_rating, AVG(kd_ratio) as t_kd FROM fact_match_players_t WHERE steam_id_64 IN ({placeholders}) GROUP BY steam_id_64"
-    df_ct = pd.read_sql_query(query_ct, conn, params=valid_ids)
-    df_t = pd.read_sql_query(query_t, conn, params=valid_ids)
-    df = df.merge(df_ct, on='steam_id_64', how='left').merge(df_t, on='steam_id_64', how='left')
-
-    # 7. UTIL
-    print("Calculating UTIL...")
-    df['util_avg_dmg'] = df['sum_util_dmg'] / df['total_matches']
-    df['util_avg_flash_time'] = df['sum_flash_time'] / df['total_matches']
-    
-    return df
-
-def normalize(series):
-    s = series.fillna(series.mean())
-    if s.max() == s.min(): return pd.Series([50]*len(s), index=s.index)
-    return (s - s.min()) / (s.max() - s.min()) * 100
-
-def calculate_full_scores(df):
-    df = df.copy()
-    
-    # --- BAT Calculation ---
-    # Components: Rating, KD, ADR, KAST, Duel Win Rate, High ELO KD
-    # Weights: Rating(30), KD(20), ADR(15), KAST(10), Duel(15), HighELO(10)
-    df['n_bat_rating'] = normalize(df['basic_avg_rating'])
-    df['n_bat_kd'] = normalize(df['basic_avg_kd'])
-    df['n_bat_adr'] = normalize(df['basic_avg_adr'])
-    df['n_bat_kast'] = normalize(df['basic_avg_kast'])
-    df['n_bat_duel'] = normalize(df['bat_avg_duel_win_rate'])
-    df['n_bat_high'] = normalize(df['bat_kd_diff_high_elo'])
-    
-    df['score_BAT'] = (0.3*df['n_bat_rating'] + 0.2*df['n_bat_kd'] + 0.15*df['n_bat_adr'] + 
-                       0.1*df['n_bat_kast'] + 0.15*df['n_bat_duel'] + 0.1*df['n_bat_high'])
-
-    # --- STA Calculation ---
-    # Components: Volatility (Neg), Win Rating, Loss Rating, Fatigue (Neg)
-    # Weights: Consistency(40), WinPerf(20), LossPerf(30), Fatigue(10)
-    df['n_sta_vol'] = normalize(df['sta_rating_volatility']) # Lower is better -> 100 - X
-    df['n_sta_win'] = normalize(df['sta_win_rating'])
-    df['n_sta_loss'] = normalize(df['sta_loss_rating'])
-    df['n_sta_fat'] = normalize(df['sta_fatigue_decay']) # Lower (less drop) is better -> 100 - X
-    
-    df['score_STA'] = (0.4*(100-df['n_sta_vol']) + 0.2*df['n_sta_win'] + 
-                       0.3*df['n_sta_loss'] + 0.1*(100-df['n_sta_fat']))
-
-    # --- HPS Calculation ---
-    # Components: Clutch Rate, Close Match Rating
-    df['n_hps_clutch'] = normalize(df['hps_clutch_rate'])
-    df['n_hps_close'] = normalize(df['hps_close_match_rating'])
-    
-    df['score_HPS'] = 0.5*df['n_hps_clutch'] + 0.5*df['n_hps_close']
-
-    # --- PTL Calculation ---
-    # Components: Pistol Kills/Match
-    df['score_PTL'] = normalize(df['ptl_pistol_kills_per_match'])
-
-    # --- T/CT Calculation ---
-    # Components: CT Rating, T Rating
-    df['n_ct'] = normalize(df['ct_rating'])
-    df['n_t'] = normalize(df['t_rating'])
-    df['score_TCT'] = 0.5*df['n_ct'] + 0.5*df['n_t']
-
-    # --- UTIL Calculation ---
-    # Components: Dmg, Flash Time
-    df['n_util_dmg'] = normalize(df['util_avg_dmg'])
-    df['n_util_flash'] = normalize(df['util_avg_flash_time'])
-    df['score_UTIL'] = 0.6*df['n_util_dmg'] + 0.4*df['n_util_flash']
-
-    return df
-
-def main():
-    conn = get_db_connection()
-    try:
-        df = load_comprehensive_data(conn)
-        if df is None: return
-        
-        results = calculate_full_scores(df)
-        
-        print("\n--- Final Full Scores ---")
-        cols = ['steam_id_64', 'score_BAT', 'score_STA', 'score_UTIL', 'score_TCT', 'score_HPS', 'score_PTL']
-        print(results[cols].sort_values('score_BAT', ascending=False).head(5))
-        
-        print("\n--- Available Features Used ---")
-        print("BAT: Rating, KD, ADR, KAST, Duel Win Rate, High ELO Performance")
-        print("STA: Volatility, Win Rating, Loss Rating, Fatigue Decay")
-        print("HPS: Clutch Rate, Close Match Rating")
-        print("PTL: Pistol Kills per Match")
-        print("T/CT: CT Rating, T Rating")
-        print("UTIL: Util Dmg, Flash Duration")
-        
-    finally:
-        conn.close()
-
-if __name__ == "__main__":
-    main()

scripts/analyze_l3_ultimate.py

@@ -1,499 +0,0 @@
-import sqlite3
-import pandas as pd
-import numpy as np
-import os
-
-DB_L2_PATH = r'd:\Documents\trae_projects\yrtv\database\L2\L2_Main.sqlite'
-
-def get_db_connection():
-    conn = sqlite3.connect(DB_L2_PATH)
-    conn.row_factory = sqlite3.Row
-    return conn
-
-def safe_div(a, b):
-    if b == 0: return 0
-    return a / b
-
-def load_and_calculate_ultimate(conn, min_matches=5):
-    print("Loading Ultimate Data Set...")
-    
-    # 1. Basic Stats (Already have)
-    query_basic = """
-    SELECT 
-        steam_id_64,
-        COUNT(*) as matches_played,
-        SUM(round_total) as rounds_played,
-        AVG(rating) as basic_avg_rating,
-        AVG(kd_ratio) as basic_avg_kd,
-        AVG(adr) as basic_avg_adr,
-        AVG(kast) as basic_avg_kast,
-        AVG(rws) as basic_avg_rws,
-        SUM(headshot_count) as sum_hs,
-        SUM(kills) as sum_kills,
-        SUM(deaths) as sum_deaths,
-        SUM(first_kill) as sum_fk,
-        SUM(first_death) as sum_fd,
-        SUM(clutch_1v1) as sum_1v1,
-        SUM(clutch_1v2) as sum_1v2,
-        SUM(clutch_1v3) + SUM(clutch_1v4) + SUM(clutch_1v5) as sum_1v3p,
-        SUM(kill_2) as sum_2k,
-        SUM(kill_3) as sum_3k,
-        SUM(kill_4) as sum_4k,
-        SUM(kill_5) as sum_5k,
-        SUM(assisted_kill) as sum_assist,
-        SUM(perfect_kill) as sum_perfect,
-        SUM(revenge_kill) as sum_revenge,
-        SUM(awp_kill) as sum_awp,
-        SUM(jump_count) as sum_jump,
-        SUM(throw_harm) as sum_util_dmg,
-        SUM(flash_time) as sum_flash_time,
-        SUM(flash_enemy) as sum_flash_enemy,
-        SUM(flash_team) as sum_flash_team
-    FROM fact_match_players
-    GROUP BY steam_id_64
-    HAVING COUNT(*) >= ?
-    """
-    df = pd.read_sql_query(query_basic, conn, params=(min_matches,))
-    valid_ids = tuple(df['steam_id_64'].tolist())
-    if not valid_ids: return None
-    placeholders = ','.join(['?'] * len(valid_ids))
-
-    # --- Basic Derived ---
-    df['basic_headshot_rate'] = df['sum_hs'] / df['sum_kills'].replace(0, 1)
-    df['basic_avg_headshot_kills'] = df['sum_hs'] / df['matches_played']
-    df['basic_avg_first_kill'] = df['sum_fk'] / df['matches_played']
-    df['basic_avg_first_death'] = df['sum_fd'] / df['matches_played']
-    df['basic_first_kill_rate'] = df['sum_fk'] / (df['sum_fk'] + df['sum_fd']).replace(0, 1)
-    df['basic_first_death_rate'] = df['sum_fd'] / (df['sum_fk'] + df['sum_fd']).replace(0, 1)
-    df['basic_avg_kill_2'] = df['sum_2k'] / df['matches_played']
-    df['basic_avg_kill_3'] = df['sum_3k'] / df['matches_played']
-    df['basic_avg_kill_4'] = df['sum_4k'] / df['matches_played']
-    df['basic_avg_kill_5'] = df['sum_5k'] / df['matches_played']
-    df['basic_avg_assisted_kill'] = df['sum_assist'] / df['matches_played']
-    df['basic_avg_perfect_kill'] = df['sum_perfect'] / df['matches_played']
-    df['basic_avg_revenge_kill'] = df['sum_revenge'] / df['matches_played']
-    df['basic_avg_awp_kill'] = df['sum_awp'] / df['matches_played']
-    df['basic_avg_jump_count'] = df['sum_jump'] / df['matches_played']
-    
-    # 2. STA - Detailed Time Series
-    print("Calculating STA (Detailed)...")
-    query_sta = f"""
-    SELECT mp.steam_id_64, mp.rating, mp.is_win, m.start_time, m.duration
-    FROM fact_match_players mp
-    JOIN fact_matches m ON mp.match_id = m.match_id
-    WHERE mp.steam_id_64 IN ({placeholders})
-    ORDER BY mp.steam_id_64, m.start_time
-    """
-    df_matches = pd.read_sql_query(query_sta, conn, params=valid_ids)
-    
-    sta_list = []
-    for pid, group in df_matches.groupby('steam_id_64'):
-        group = group.sort_values('start_time')
-        # Last 30
-        last_30 = group.tail(30)
-        sta_last_30 = last_30['rating'].mean()
-        # Win/Loss
-        sta_win = group[group['is_win']==1]['rating'].mean()
-        sta_loss = group[group['is_win']==0]['rating'].mean()
-        # Volatility
-        sta_vol = group.tail(10)['rating'].std()
-        # Time Correlation (Duration vs Rating)
-        sta_time_corr = group['duration'].corr(group['rating']) if len(group) > 2 else 0
-        # Fatigue
-        group['date'] = pd.to_datetime(group['start_time'], unit='s').dt.date
-        daily = group.groupby('date')['rating'].agg(['first', 'last', 'count'])
-        daily_fatigue = daily[daily['count'] >= 3]
-        if len(daily_fatigue) > 0:
-            fatigue_decay = (daily_fatigue['first'] - daily_fatigue['last']).mean()
-        else:
-            fatigue_decay = 0
-            
-        sta_list.append({
-            'steam_id_64': pid,
-            'sta_last_30_rating': sta_last_30,
-            'sta_win_rating': sta_win,
-            'sta_loss_rating': sta_loss,
-            'sta_rating_volatility': sta_vol,
-            'sta_time_rating_corr': sta_time_corr,
-            'sta_fatigue_decay': fatigue_decay
-        })
-    df = df.merge(pd.DataFrame(sta_list), on='steam_id_64', how='left')
-
-    # 3. BAT - Distance & Advanced
-    print("Calculating BAT (Distance & Context)...")
-    # Distance Logic: Get all kills with positions
-    # We need to map positions.
-    query_dist = f"""
-    SELECT attacker_steam_id as steam_id_64, 
-           attacker_pos_x, attacker_pos_y, attacker_pos_z,
-           victim_pos_x, victim_pos_y, victim_pos_z
-    FROM fact_round_events
-    WHERE event_type = 'kill' 
-    AND attacker_steam_id IN ({placeholders})
-    AND attacker_pos_x IS NOT NULL AND victim_pos_x IS NOT NULL
-    """
-    # Note: This might be heavy. If memory issue, sample or chunk.
-    try:
-        df_dist = pd.read_sql_query(query_dist, conn, params=valid_ids)
-        if not df_dist.empty:
-            # Calc Euclidian Distance
-            df_dist['dist'] = np.sqrt(
-                (df_dist['attacker_pos_x'] - df_dist['victim_pos_x'])**2 +
-                (df_dist['attacker_pos_y'] - df_dist['victim_pos_y'])**2 +
-                (df_dist['attacker_pos_z'] - df_dist['victim_pos_z'])**2
-            )
-            # Units: 1 unit ~ 1 inch. 
-            # Close: < 500 (~12m)
-            # Mid: 500 - 1500 (~12m - 38m)
-            # Far: > 1500
-            df_dist['is_close'] = df_dist['dist'] < 500
-            df_dist['is_mid'] = (df_dist['dist'] >= 500) & (df_dist['dist'] <= 1500)
-            df_dist['is_far'] = df_dist['dist'] > 1500
-            
-            bat_dist = df_dist.groupby('steam_id_64').agg({
-                'is_close': 'mean', # % of kills that are close
-                'is_mid': 'mean',
-                'is_far': 'mean'
-            }).reset_index()
-            bat_dist.columns = ['steam_id_64', 'bat_kill_share_close', 'bat_kill_share_mid', 'bat_kill_share_far']
-            
-            # Note: "Win Rate" by distance requires Deaths by distance.
-            # We can try to get deaths too, but for now Share of Kills is a good proxy for "Preference/Style"
-            # To get "Win Rate", we need to know how many duels occurred at that distance.
-            # Approximation: Win Rate = Kills_at_dist / (Kills_at_dist + Deaths_at_dist)
-            
-            # Fetch Deaths
-            query_dist_d = f"""
-            SELECT victim_steam_id as steam_id_64, 
-                   attacker_pos_x, attacker_pos_y, attacker_pos_z,
-                   victim_pos_x, victim_pos_y, victim_pos_z
-            FROM fact_round_events
-            WHERE event_type = 'kill' 
-            AND victim_steam_id IN ({placeholders})
-            AND attacker_pos_x IS NOT NULL AND victim_pos_x IS NOT NULL
-            """
-            df_dist_d = pd.read_sql_query(query_dist_d, conn, params=valid_ids)
-            df_dist_d['dist'] = np.sqrt(
-                (df_dist_d['attacker_pos_x'] - df_dist_d['victim_pos_x'])**2 +
-                (df_dist_d['attacker_pos_y'] - df_dist_d['victim_pos_y'])**2 +
-                (df_dist_d['attacker_pos_z'] - df_dist_d['victim_pos_z'])**2
-            )
-            
-            # Aggregate Kills Counts
-            k_counts = df_dist.groupby('steam_id_64').agg(
-                k_close=('is_close', 'sum'),
-                k_mid=('is_mid', 'sum'),
-                k_far=('is_far', 'sum')
-            )
-            # Aggregate Deaths Counts
-            df_dist_d['is_close'] = df_dist_d['dist'] < 500
-            df_dist_d['is_mid'] = (df_dist_d['dist'] >= 500) & (df_dist_d['dist'] <= 1500)
-            df_dist_d['is_far'] = df_dist_d['dist'] > 1500
-            d_counts = df_dist_d.groupby('steam_id_64').agg(
-                d_close=('is_close', 'sum'),
-                d_mid=('is_mid', 'sum'),
-                d_far=('is_far', 'sum')
-            )
-            
-            # Merge
-            bat_rates = k_counts.join(d_counts, how='outer').fillna(0)
-            bat_rates['bat_win_rate_close'] = bat_rates['k_close'] / (bat_rates['k_close'] + bat_rates['d_close']).replace(0, 1)
-            bat_rates['bat_win_rate_mid'] = bat_rates['k_mid'] / (bat_rates['k_mid'] + bat_rates['d_mid']).replace(0, 1)
-            bat_rates['bat_win_rate_far'] = bat_rates['k_far'] / (bat_rates['k_far'] + bat_rates['d_far']).replace(0, 1)
-            bat_rates['bat_win_rate_vs_all'] = (bat_rates['k_close']+bat_rates['k_mid']+bat_rates['k_far']) / (bat_rates['k_close']+bat_rates['d_close']+bat_rates['k_mid']+bat_rates['d_mid']+bat_rates['k_far']+bat_rates['d_far']).replace(0, 1)
-
-            df = df.merge(bat_rates[['bat_win_rate_close', 'bat_win_rate_mid', 'bat_win_rate_far', 'bat_win_rate_vs_all']], on='steam_id_64', how='left')
-        else:
-             print("No position data found.")
-    except Exception as e:
-        print(f"Dist calculation error: {e}")
-
-    # High/Low ELO KD
-    query_elo = f"""
-    SELECT mp.steam_id_64, mp.kd_ratio, 
-           (SELECT AVG(group_origin_elo) FROM fact_match_teams fmt WHERE fmt.match_id = mp.match_id AND group_origin_elo > 0) as elo
-    FROM fact_match_players mp
-    WHERE mp.steam_id_64 IN ({placeholders})
-    """
-    df_elo = pd.read_sql_query(query_elo, conn, params=valid_ids)
-    elo_list = []
-    for pid, group in df_elo.groupby('steam_id_64'):
-        avg = group['elo'].mean()
-        if pd.isna(avg): avg = 1000
-        elo_list.append({
-            'steam_id_64': pid,
-            'bat_kd_diff_high_elo': group[group['elo'] > avg]['kd_ratio'].mean(),
-            'bat_kd_diff_low_elo': group[group['elo'] <= avg]['kd_ratio'].mean()
-        })
-    df = df.merge(pd.DataFrame(elo_list), on='steam_id_64', how='left')
-    
-    # Avg Duel Freq
-    df['bat_avg_duel_freq'] = (df['sum_fk'] + df['sum_fd']) / df['rounds_played']
-
-    # 4. HPS - High Pressure Contexts
-    print("Calculating HPS (Contexts)...")
-    # We need round-by-round score evolution.
-    # Join rounds and economy(side) and matches
-    query_hps_ctx = f"""
-    SELECT r.match_id, r.round_num, r.ct_score, r.t_score, r.winner_side, 
-           m.score_team1, m.score_team2, m.winner_team,
-           e.steam_id_64, e.side as player_side,
-           (SELECT COUNT(*) FROM fact_round_events ev WHERE ev.match_id=r.match_id AND ev.round_num=r.round_num AND ev.attacker_steam_id=e.steam_id_64 AND ev.event_type='kill') as kills,
-           (SELECT COUNT(*) FROM fact_round_events ev WHERE ev.match_id=r.match_id AND ev.round_num=r.round_num AND ev.victim_steam_id=e.steam_id_64 AND ev.event_type='kill') as deaths
-    FROM fact_rounds r
-    JOIN fact_matches m ON r.match_id = m.match_id
-    JOIN fact_round_player_economy e ON r.match_id = e.match_id AND r.round_num = e.round_num
-    WHERE e.steam_id_64 IN ({placeholders})
-    """
-    # This is heavy.
-    try:
-        # Optimization: Process per match or use SQL aggregation?
-        # SQL aggregation for specific conditions is better.
-        
-        # 4.1 Match Point Win Rate
-        # Condition: (player_side='CT' AND ct_score >= 12) OR (player_side='T' AND t_score >= 12) (Assuming MR12)
-        # Or just max score of match?
-        # Let's approximate: Rounds where total_score >= 23 (MR12) or 29 (MR15)
-        # Actually, let's use: round_num >= match.round_total - 1? No.
-        # Use: Rounds where One Team Score = Match Win Score - 1.
-        # Since we don't know MR12/MR15 per match easily (some are short), check `game_mode`.
-        # Fallback: Rounds where `ct_score` or `t_score` >= 12.
-        
-        # 4.2 Pressure Entry Rate (Losing Streak)
-        # Condition: Team score < Enemy score - 3.
-        
-        # 4.3 Momentum Multi-kill (Winning Streak)
-        # Condition: Team score > Enemy score + 3.
-        
-        # Let's load a simplified dataframe of rounds
-        df_rounds = pd.read_sql_query(query_hps_ctx, conn, params=valid_ids)
-        
-        hps_stats = []
-        for pid, group in df_rounds.groupby('steam_id_64'):
-            # Determine Player Team Score and Enemy Team Score
-            # If player_side == 'CT', player_score = ct_score
-            group['my_score'] = np.where(group['player_side'] == 'CT', group['ct_score'], group['t_score'])
-            group['enemy_score'] = np.where(group['player_side'] == 'CT', group['t_score'], group['ct_score'])
-            
-            # Match Point (My team or Enemy team at match point)
-            # Simple heuristic: Score >= 12
-            is_match_point = (group['my_score'] >= 12) | (group['enemy_score'] >= 12)
-            mp_rounds = group[is_match_point]
-            # Did we win?
-            # winner_side matches player_side
-            mp_wins = mp_rounds[mp_rounds['winner_side'] == mp_rounds['player_side']]
-            mp_win_rate = len(mp_wins) / len(mp_rounds) if len(mp_rounds) > 0 else 0.5
-            
-            # Pressure (Losing by 3+)
-            is_pressure = (group['enemy_score'] - group['my_score']) >= 3
-            # Entry Rate in pressure? Need FK data. 
-            # We only loaded kills. Let's use Kills per round in pressure.
-            pressure_kpr = group[is_pressure]['kills'].mean() if len(group[is_pressure]) > 0 else 0
-            
-            # Momentum (Winning by 3+)
-            is_momentum = (group['my_score'] - group['enemy_score']) >= 3
-            # Multi-kill rate (>=2 kills)
-            momentum_rounds = group[is_momentum]
-            momentum_multikills = len(momentum_rounds[momentum_rounds['kills'] >= 2])
-            momentum_mk_rate = momentum_multikills / len(momentum_rounds) if len(momentum_rounds) > 0 else 0
-            
-            # Comeback KD Diff
-            # Avg KD in Pressure rounds vs Avg KD overall
-            pressure_deaths = group[is_pressure]['deaths'].sum()
-            pressure_kills = group[is_pressure]['kills'].sum()
-            pressure_kd = pressure_kills / pressure_deaths if pressure_deaths > 0 else pressure_kills
-            
-            overall_deaths = group['deaths'].sum()
-            overall_kills = group['kills'].sum()
-            overall_kd = overall_kills / overall_deaths if overall_deaths > 0 else overall_kills
-            
-            comeback_diff = pressure_kd - overall_kd
-            
-            hps_stats.append({
-                'steam_id_64': pid,
-                'hps_match_point_win_rate': mp_win_rate,
-                'hps_pressure_entry_rate': pressure_kpr, # Proxy
-                'hps_momentum_multikill_rate': momentum_mk_rate,
-                'hps_comeback_kd_diff': comeback_diff,
-                'hps_losing_streak_kd_diff': comeback_diff # Same metric
-            })
-            
-        df = df.merge(pd.DataFrame(hps_stats), on='steam_id_64', how='left')
-        
-        # 4.4 Clutch Win Rates (Detailed)
-        df['hps_clutch_win_rate_1v1'] = df['sum_1v1'] / df['matches_played'] # Normalizing by match for now, ideal is by 1v1 opportunities
-        df['hps_clutch_win_rate_1v2'] = df['sum_1v2'] / df['matches_played']
-        df['hps_clutch_win_rate_1v3_plus'] = df['sum_1v3p'] / df['matches_played']
-        
-        # 4.5 Close Match Rating (from previous)
-        # ... (Already have logic in previous script, reusing)
-        
-    except Exception as e:
-        print(f"HPS Error: {e}")
-
-    # 5. PTL - Pistol Detailed
-    print("Calculating PTL...")
-    # Filter Round 1, 13 (and 16 for MR15?)
-    # Just use 1 and 13 (common for MR12)
-    query_ptl = f"""
-    SELECT 
-        e.steam_id_64,
-        (SELECT COUNT(*) FROM fact_round_events ev WHERE ev.match_id=e.match_id AND ev.round_num=e.round_num AND ev.attacker_steam_id=e.steam_id_64 AND ev.event_type='kill') as kills,
-        (SELECT COUNT(*) FROM fact_round_events ev WHERE ev.match_id=e.match_id AND ev.round_num=e.round_num AND ev.victim_steam_id=e.steam_id_64 AND ev.event_type='kill') as deaths,
-        r.winner_side, e.side as player_side,
-        e.equipment_value
-    FROM fact_round_player_economy e
-    JOIN fact_rounds r ON e.match_id = r.match_id AND e.round_num = r.round_num
-    WHERE e.steam_id_64 IN ({placeholders})
-    AND e.round_num IN (1, 13)
-    """
-    try:
-        df_ptl_raw = pd.read_sql_query(query_ptl, conn, params=valid_ids)
-        ptl_stats = []
-        for pid, group in df_ptl_raw.groupby('steam_id_64'):
-            kills = group['kills'].sum()
-            deaths = group['deaths'].sum()
-            kd = kills / deaths if deaths > 0 else kills
-            
-            wins = len(group[group['winner_side'] == group['player_side']])
-            win_rate = wins / len(group)
-            
-            multikills = len(group[group['kills'] >= 2])
-            
-            # Util Efficiency: Not easy here.
-            
-            ptl_stats.append({
-                'steam_id_64': pid,
-                'ptl_pistol_kills': kills, # Total? Or Avg? Schema says REAL. Let's use Avg per Match later.
-                'ptl_pistol_kd': kd,
-                'ptl_pistol_win_rate': win_rate,
-                'ptl_pistol_multikills': multikills
-            })
-        
-        df_ptl = pd.DataFrame(ptl_stats)
-        df_ptl['ptl_pistol_kills'] = df_ptl['ptl_pistol_kills'] / df['matches_played'].mean() # Approximate
-        df = df.merge(df_ptl, on='steam_id_64', how='left')
-        
-    except Exception as e:
-        print(f"PTL Error: {e}")
-
-    # 6. T/CT & UTIL (Straightforward)
-    print("Calculating T/CT & UTIL...")
-    # T/CT Side Stats
-    query_side = f"""
-    SELECT steam_id_64, 
-        SUM(CASE WHEN side='CT' THEN 1 ELSE 0 END) as ct_rounds,
-        SUM(CASE WHEN side='T' THEN 1 ELSE 0 END) as t_rounds
-    FROM fact_round_player_economy
-    WHERE steam_id_64 IN ({placeholders})
-    GROUP BY steam_id_64
-    """
-    # Combine with aggregated ratings from fact_match_players_ct/t
-    query_side_r = f"""
-    SELECT steam_id_64, AVG(rating) as ct_rating, AVG(kd_ratio) as ct_kd, SUM(first_kill) as ct_fk
-    FROM fact_match_players_ct WHERE steam_id_64 IN ({placeholders}) GROUP BY steam_id_64
-    """
-    df_ct = pd.read_sql_query(query_side_r, conn, params=valid_ids)
-    # Similar for T...
-    
-    # Merge...
-    
-    # UTIL
-    df['util_avg_nade_dmg'] = df['sum_util_dmg'] / df['matches_played']
-    df['util_avg_flash_time'] = df['sum_flash_time'] / df['matches_played']
-    df['util_avg_flash_enemy'] = df['sum_flash_enemy'] / df['matches_played']
-    
-    # Fill NaN
-    df = df.fillna(0)
-    
-    return df
-
-def calculate_ultimate_scores(df):
-    # Normalize Helper
-    def n(col):
-        if col not in df.columns: return 50
-        s = df[col]
-        if s.max() == s.min(): return 50
-        return (s - s.min()) / (s.max() - s.min()) * 100
-
-    df = df.copy()
-    
-    # 1. BAT: Battle (30%)
-    # Weights: Rating(25), KD(20), ADR(15), Duel(10), HighELO(10), CloseRange(10), MultiKill(10)
-    df['score_BAT'] = (
-        0.25 * n('basic_avg_rating') +
-        0.20 * n('basic_avg_kd') +
-        0.15 * n('basic_avg_adr') +
-        0.10 * n('bat_avg_duel_win_rate') + # Need to ensure col exists
-        0.10 * n('bat_kd_diff_high_elo') +
-        0.10 * n('bat_win_rate_close') +
-        0.10 * n('basic_avg_kill_3') # Multi-kill proxy
-    )
-    
-    # 2. STA: Stability (15%)
-    # Weights: Volatility(30), LossRating(30), WinRating(20), TimeCorr(10), Fatigue(10)
-    df['score_STA'] = (
-        0.30 * (100 - n('sta_rating_volatility')) +
-        0.30 * n('sta_loss_rating') +
-        0.20 * n('sta_win_rating') +
-        0.10 * (100 - n('sta_time_rating_corr').abs()) + # Closer to 0 is better (independent of duration)
-        0.10 * (100 - n('sta_fatigue_decay'))
-    )
-    
-    # 3. HPS: Pressure (20%)
-    # Weights: Clutch(30), MatchPoint(20), Comeback(20), PressureEntry(15), CloseMatch(15)
-    df['score_HPS'] = (
-        0.30 * n('sum_1v3p') + # Using high tier clutches
-        0.20 * n('hps_match_point_win_rate') +
-        0.20 * n('hps_comeback_kd_diff') +
-        0.15 * n('hps_pressure_entry_rate') + 
-        0.15 * n('basic_avg_rating') # Fallback if close match rating missing
-    )
-    
-    # 4. PTL: Pistol (10%)
-    # Weights: Kills(40), WinRate(30), KD(30)
-    df['score_PTL'] = (
-        0.40 * n('ptl_pistol_kills') +
-        0.30 * n('ptl_pistol_win_rate') +
-        0.30 * n('ptl_pistol_kd')
-    )
-    
-    # 5. T/CT (15%)
-    # Weights: CT(50), T(50)
-    # Need to load CT/T ratings properly, using basic rating as placeholder if missing
-    df['score_TCT'] = 0.5 * n('basic_avg_rating') + 0.5 * n('basic_avg_rating') 
-    
-    # 6. UTIL (10%)
-    # Weights: Dmg(50), Flash(30), EnemiesFlashed(20)
-    df['score_UTIL'] = (
-        0.50 * n('util_avg_nade_dmg') +
-        0.30 * n('util_avg_flash_time') +
-        0.20 * n('util_avg_flash_enemy')
-    )
-    
-    return df
-
-def main():
-    conn = get_db_connection()
-    try:
-        df = load_and_calculate_ultimate(conn)
-        if df is None: return
-        
-        results = calculate_ultimate_scores(df)
-        
-        print("\n--- Ultimate Scores (Top 5 BAT) ---")
-        cols = ['steam_id_64', 'score_BAT', 'score_STA', 'score_HPS', 'score_PTL', 'score_UTIL']
-        print(results[cols].sort_values('score_BAT', ascending=False).head(5))
-        
-        # Verify coverage
-        print("\n--- Feature Coverage ---")
-        print(f"Total Columns: {len(results.columns)}")
-        print("BAT Distances:", 'bat_win_rate_close' in results.columns)
-        print("HPS Contexts:", 'hps_match_point_win_rate' in results.columns)
-        print("PTL Detailed:", 'ptl_pistol_kd' in results.columns)
-        
-    finally:
-        conn.close()
-
-if __name__ == "__main__":
-    main()

scripts/check_l1a.py

@@ -1,22 +0,0 @@
-import sqlite3
-import os
-
-L1A_DB_PATH = r'd:\Documents\trae_projects\yrtv\database\L1A\L1A.sqlite'
-
-print("Checking L1A...")
-if os.path.exists(L1A_DB_PATH):
-    try:
-        conn = sqlite3.connect(L1A_DB_PATH)
-        cursor = conn.cursor()
-        cursor.execute("SELECT name FROM sqlite_master WHERE type='table'")
-        tables = cursor.fetchall()
-        print(f"Tables: {tables}")
-        
-        cursor.execute("SELECT COUNT(*) FROM raw_iframe_network")
-        count = cursor.fetchone()[0]
-        print(f"L1A Records: {count}")
-        conn.close()
-    except Exception as e:
-        print(f"Error checking L1A: {e}")
-else:
-    print(f"L1A DB not found at {L1A_DB_PATH}")

scripts/check_l3_final.py

@@ -1,19 +0,0 @@
-import sqlite3
-import pandas as pd
-import os
-
-db_path = r'd:\Documents\trae_projects\yrtv\database\L3\L3_Features.sqlite'
-conn = sqlite3.connect(db_path)
-try:
-    print("Checking L3 Obj and KAST:")
-    df = pd.read_sql_query("""
-        SELECT 
-            steam_id_64, 
-            side_obj_t, side_obj_ct,
-            side_kast_t, side_kast_ct
-        FROM dm_player_features
-        LIMIT 5
-    """, conn)
-    print(df)
-finally:
-    conn.close()

scripts/check_l3_variance.py

@@ -1,55 +0,0 @@
-import sqlite3
-import pandas as pd
-import numpy as np
-import os
-
-# Config to match your project structure
-class Config:
-    DB_L3_PATH = r'd:\Documents\trae_projects\yrtv\database\L3\L3_Features.sqlite'
-
-def check_variance():
-    db_path = Config.DB_L3_PATH
-    if not os.path.exists(db_path):
-        print(f"L3 DB not found at {db_path}")
-        return
-
-    conn = sqlite3.connect(db_path)
-    try:
-        # Read all features
-        df = pd.read_sql_query("SELECT * FROM dm_player_features", conn)
-        
-        print(f"Total rows: {len(df)}")
-        if len(df) == 0:
-            print("Table is empty.")
-            return
-
-        numeric_cols = df.select_dtypes(include=['number']).columns
-        
-        print("\n--- Variance Analysis ---")
-        for col in numeric_cols:
-            if col in ['steam_id_64']: continue # Skip ID
-            
-            # Check for all zeros
-            if (df[col] == 0).all():
-                print(f"[ALL ZERO] {col}")
-                continue
-
-            # Check for single value (variance = 0)
-            if df[col].nunique() <= 1:
-                val = df[col].iloc[0]
-                print(f"[SINGLE VAL] {col} = {val}")
-                continue
-
-            # Check for mostly zeros
-            zero_pct = (df[col] == 0).mean()
-            if zero_pct > 0.9:
-                print(f"[MOSTLY ZERO] {col} ({zero_pct:.1%} zeros)")
-                
-            # Basic stats for valid ones
-            # print(f"{col}: min={df[col].min():.2f}, max={df[col].max():.2f}, mean={df[col].mean():.2f}")
-
-    finally:
-        conn.close()
-
-if __name__ == "__main__":
-    check_variance()

scripts/check_round_data.py

@@ -1,45 +0,0 @@
-
-import sqlite3
-import pandas as pd
-
-match_id = 'g161-n-20251222204652101389654'
-
-def check_data():
-    conn = sqlite3.connect('database/L2/L2_Main.sqlite')
-    
-    print(f"--- Check Match: {match_id} ---")
-    
-    # 1. Source Type
-    c = conn.cursor()
-    c.execute("SELECT data_source_type FROM fact_matches WHERE match_id = ?", (match_id,))
-    row = c.fetchone()
-    if row:
-        print(f"Data Source: {row[0]}")
-    else:
-        print("Match not found")
-        return
-
-    # 2. Round Events (Sample)
-    print("\n--- Round Events Sample ---")
-    try:
-        df = pd.read_sql(f"SELECT round_num, event_type, attacker_steam_id, victim_steam_id, weapon FROM fact_round_events WHERE match_id = '{match_id}' LIMIT 5", conn)
-        print(df)
-        if df.empty:
-            print("WARNING: No events found.")
-    except Exception as e:
-        print(e)
-
-    # 3. Economy (Sample)
-    print("\n--- Economy Sample ---")
-    try:
-        df_eco = pd.read_sql(f"SELECT round_num, steam_id_64, equipment_value FROM fact_round_player_economy WHERE match_id = '{match_id}' LIMIT 5", conn)
-        print(df_eco)
-        if df_eco.empty:
-            print("Info: No economy data (Likely Classic source).")
-    except Exception as e:
-        print(e)
-        
-    conn.close()
-
-if __name__ == "__main__":
-    check_data()

scripts/check_side_mapping.py

@@ -1,63 +0,0 @@
-import sqlite3
-import pandas as pd
-import json
-import os
-import sys
-
-# Add parent directory
-sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-from web.config import Config
-
-def check_mapping():
-    conn = sqlite3.connect(Config.DB_L2_PATH)
-    
-    # Join economy and teams via match_id
-    # We need to match steam_id (in eco) to group_uids (in teams)
-    
-    # 1. Get Economy R1 samples
-    query_eco = """
-    SELECT match_id, steam_id_64, side 
-    FROM fact_round_player_economy 
-    WHERE round_num = 1
-    LIMIT 10
-    """
-    eco_rows = pd.read_sql_query(query_eco, conn)
-    
-    if eco_rows.empty:
-        print("No Economy R1 data found.")
-        conn.close()
-        return
-
-    print("Checking Mapping...")
-    for _, row in eco_rows.iterrows():
-        mid = row['match_id']
-        sid = row['steam_id_64']
-        side = row['side']
-        
-        # Get Teams for this match
-        query_teams = "SELECT group_id, group_fh_role, group_uids FROM fact_match_teams WHERE match_id = ?"
-        team_rows = pd.read_sql_query(query_teams, conn, params=(mid,))
-        
-        for _, t_row in team_rows.iterrows():
-            # Check if sid is in group_uids (which contains UIDs, not SteamIDs!)
-            # We need to map SteamID -> UID
-            # Use dim_players or fact_match_players
-            q_uid = "SELECT uid FROM fact_match_players WHERE match_id = ? AND steam_id_64 = ?"
-            uid_res = conn.execute(q_uid, (mid, sid)).fetchone()
-            if not uid_res:
-                continue
-            
-            uid = str(uid_res[0])
-            group_uids = str(t_row['group_uids']).split(',')
-            
-            if uid in group_uids:
-                role = t_row['group_fh_role']
-                print(f"Match {mid}: Steam {sid} (UID {uid}) is on Side {side} in R1.")
-                print(f"  Found in Group {t_row['group_id']} with FH Role {role}.")
-                print(f"  MAPPING: Role {role} = {side}")
-                break
-                
-    conn.close()
-
-if __name__ == "__main__":
-    check_mapping()

scripts/check_tables.py

@@ -1,43 +0,0 @@
-import sqlite3
-import os
-
-DB_PATH = r'd:\Documents\trae_projects\yrtv\database\L2\L2_Main.sqlite'
-
-def check_tables():
-    if not os.path.exists(DB_PATH):
-        print(f"DB not found: {DB_PATH}")
-        return
-
-    conn = sqlite3.connect(DB_PATH)
-    cursor = conn.cursor()
-    
-    tables = [
-        'dim_players', 'dim_maps', 
-        'fact_matches', 'fact_match_teams', 
-        'fact_match_players', 'fact_match_players_ct', 'fact_match_players_t',
-        'fact_rounds', 'fact_round_events', 'fact_round_player_economy'
-    ]
-    
-    print(f"--- L2 Database Check: {DB_PATH} ---")
-    for table in tables:
-        try:
-            cursor.execute(f"SELECT COUNT(*) FROM {table}")
-            count = cursor.fetchone()[0]
-            print(f"{table:<25}: {count:>6} rows")
-            
-            # Simple column check for recently added columns
-            if table == 'fact_match_players':
-                 cursor.execute(f"PRAGMA table_info({table})")
-                 cols = [info[1] for info in cursor.fetchall()]
-                 if 'util_flash_usage' in cols:
-                     print(f"    [OK] util_flash_usage exists")
-                 else:
-                     print(f"    [ERR] util_flash_usage MISSING")
-
-        except Exception as e:
-            print(f"{table:<25}: [ERROR] {e}")
-            
-    conn.close()
-
-if __name__ == "__main__":
-    check_tables()

scripts/debug_db.py

@@ -1,63 +0,0 @@
-import sqlite3
-import pandas as pd
-import os
-
-L2_PATH = r'd:\Documents\trae_projects\yrtv\database\L2\L2_Main.sqlite'
-WEB_PATH = r'd:\Documents\trae_projects\yrtv\database\Web\Web_App.sqlite'
-
-def debug_db():
-    # --- L2 Checks ---
-    conn = sqlite3.connect(L2_PATH)
-    
-    print("--- Data Source Type Distribution ---")
-    try:
-        df = pd.read_sql_query("SELECT data_source_type, COUNT(*) as cnt FROM fact_matches GROUP BY data_source_type", conn)
-        print(df)
-    except Exception as e:
-        print(f"Error: {e}")
-        
-    print("\n--- Economy Table Count ---")
-    try:
-        count = conn.execute("SELECT COUNT(*) FROM fact_round_player_economy").fetchone()[0]
-        print(f"Rows: {count}")
-    except Exception as e:
-        print(f"Error: {e}")
-
-    print("\n--- Check util_flash_usage in fact_match_players ---")
-    try:
-        cursor = conn.cursor()
-        cursor.execute("PRAGMA table_info(fact_match_players)")
-        cols = [info[1] for info in cursor.fetchall()]
-        if 'util_flash_usage' in cols:
-            print("Column 'util_flash_usage' EXISTS.")
-            nz = conn.execute("SELECT COUNT(*) FROM fact_match_players WHERE util_flash_usage > 0").fetchone()[0]
-            print(f"Rows with util_flash_usage > 0: {nz}")
-        else:
-            print("Column 'util_flash_usage' MISSING.")
-    except Exception as e:
-        print(f"Error: {e}")
-
-    conn.close()
-
-    # --- Web DB Checks ---
-    print("\n--- Web DB Check ---")
-    if not os.path.exists(WEB_PATH):
-        print(f"Web DB not found at {WEB_PATH}")
-        return
-
-    try:
-        conn_web = sqlite3.connect(WEB_PATH)
-        cursor = conn_web.cursor()
-        cursor.execute("SELECT name FROM sqlite_master WHERE type='table'")
-        tables = cursor.fetchall()
-        print(f"Tables: {[t[0] for t in tables]}")
-        
-        if 'player_metadata' in [t[0] for t in tables]:
-             count = conn_web.execute("SELECT COUNT(*) FROM player_metadata").fetchone()[0]
-             print(f"player_metadata rows: {count}")
-        conn_web.close()
-    except Exception as e:
-        print(f"Error checking Web DB: {e}")
-
-if __name__ == "__main__":
-    debug_db()

scripts/debug_integrity.py

@@ -1,34 +0,0 @@
-import sqlite3
-import os
-
-BASE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
-L2_PATH = os.path.join(BASE_DIR, 'database', 'L2', 'L2_Main.sqlite')
-
-def check_db_integrity():
-    print(f"Checking DB at: {L2_PATH}")
-    if not os.path.exists(L2_PATH):
-        print("CRITICAL: Database file does not exist!")
-        return
-
-    try:
-        conn = sqlite3.connect(L2_PATH)
-        cursor = conn.cursor()
-        
-        # Check integrity
-        print("Running PRAGMA integrity_check...")
-        cursor.execute("PRAGMA integrity_check")
-        print(f"Integrity: {cursor.fetchone()}")
-        
-        # Check specific user again
-        cursor.execute("SELECT steam_id_64, username FROM dim_players WHERE username LIKE '%jacky%'")
-        rows = cursor.fetchall()
-        print(f"Direct DB check found {len(rows)} rows matching '%jacky%':")
-        for r in rows:
-            print(r)
-            
-        conn.close()
-    except Exception as e:
-        print(f"DB Error: {e}")
-
-if __name__ == '__main__':
-    check_db_integrity()

scripts/debug_jacky.py

@@ -1,39 +0,0 @@
-import sqlite3
-import os
-
-BASE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
-L2_PATH = os.path.join(BASE_DIR, 'database', 'L2', 'L2_Main.sqlite')
-
-def check_jacky():
-    print(f"Checking L2 database at: {L2_PATH}")
-    conn = sqlite3.connect(L2_PATH)
-    cursor = conn.cursor()
-    
-    search_term = 'jacky'
-    print(f"\nSearching for '%{search_term}%' (Case Insensitive test):")
-    
-    # Standard LIKE
-    cursor.execute("SELECT steam_id_64, username FROM dim_players WHERE username LIKE ?", (f'%{search_term}%',))
-    results = cursor.fetchall()
-    print(f"LIKE results: {len(results)}")
-    for r in results:
-        print(r)
-        
-    # Case insensitive explicit
-    print("\nSearching with LOWER():")
-    cursor.execute("SELECT steam_id_64, username FROM dim_players WHERE LOWER(username) LIKE LOWER(?)", (f'%{search_term}%',))
-    results_lower = cursor.fetchall()
-    print(f"LOWER() results: {len(results_lower)}")
-    for r in results_lower:
-        print(r)
-
-    # Check jacky0987 specifically
-    print("\nChecking specific username 'jacky0987':")
-    cursor.execute("SELECT steam_id_64, username FROM dim_players WHERE username = 'jacky0987'")
-    specific = cursor.fetchone()
-    print(f"Specific match: {specific}")
-
-    conn.close()
-
-if __name__ == '__main__':
-    check_jacky()

scripts/init_web_db.py

@@ -1,84 +0,0 @@
-import sqlite3
-import os
-
-# Define database path
-BASE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
-DB_PATH = os.path.join(BASE_DIR, 'database', 'Web', 'Web_App.sqlite')
-
-def init_db():
-    print(f"Initializing Web database at: {DB_PATH}")
-    
-    # Create directory if not exists
-    os.makedirs(os.path.dirname(DB_PATH), exist_ok=True)
-    
-    conn = sqlite3.connect(DB_PATH)
-    cursor = conn.cursor()
-    
-    # Create Tables
-    tables = [
-        """
-        CREATE TABLE IF NOT EXISTS team_lineups (
-            id INTEGER PRIMARY KEY AUTOINCREMENT,
-            name TEXT NOT NULL,
-            description TEXT,
-            player_ids_json TEXT,
-            created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
-        );
-        """,
-        """
-        CREATE TABLE IF NOT EXISTS player_metadata (
-            steam_id_64 TEXT PRIMARY KEY,
-            notes TEXT,
-            tags TEXT,
-            updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
-        );
-        """,
-        """
-        CREATE TABLE IF NOT EXISTS strategy_boards (
-            id INTEGER PRIMARY KEY AUTOINCREMENT,
-            title TEXT,
-            map_name TEXT,
-            data_json TEXT,
-            created_by TEXT,
-            created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
-        );
-        """,
-        """
-        CREATE TABLE IF NOT EXISTS wiki_pages (
-            id INTEGER PRIMARY KEY AUTOINCREMENT,
-            path TEXT UNIQUE,
-            title TEXT,
-            content TEXT,
-            updated_by TEXT,
-            updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
-            created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
-        );
-        """,
-        """
-        CREATE TABLE IF NOT EXISTS comments (
-            id INTEGER PRIMARY KEY AUTOINCREMENT,
-            user_id TEXT,
-            username TEXT,
-            target_type TEXT,
-            target_id TEXT,
-            content TEXT,
-            likes INTEGER DEFAULT 0,
-            is_hidden INTEGER DEFAULT 0,
-            created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
-        );
-        """
-    ]
-    
-    for sql in tables:
-        try:
-            cursor.execute(sql)
-            print("Executed SQL successfully.")
-        except Exception as e:
-            print(f"Error executing SQL: {e}")
-            
-    conn.commit()
-    conn.close()
-    print("Web database initialized successfully.")
-
-if __name__ == '__main__':
-    init_db()

scripts/run_rebuild.py

@@ -1,18 +0,0 @@
-import sys
-import os
-
-# Add project root to path
-current_dir = os.path.dirname(os.path.abspath(__file__))
-project_root = os.path.dirname(current_dir)
-sys.path.append(project_root)
-
-from web.services.feature_service import FeatureService
-
-print("Starting Rebuild...")
-try:
-    count = FeatureService.rebuild_all_features(min_matches=1)
-    print(f"Rebuild Complete. Processed {count} players.")
-except Exception as e:
-    print(f"Error: {e}")
-    import traceback
-    traceback.print_exc()

scripts/run_rebuild_fix.py

@@ -1,14 +0,0 @@
-from web.app import create_app
-from web.services.feature_service import FeatureService
-import sys
-import os
-
-# Ensure project root is in path
-sys.path.append(os.getcwd())
-
-app = create_app()
-
-with app.app_context():
-    print("Starting Feature Rebuild...")
-    count = FeatureService.rebuild_all_features()
-    print(f"Rebuild Complete. Processed {count} players.")

scripts/update_l2_schema_utility.py

@@ -1,30 +0,0 @@
-import sys
-import os
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-
-import sqlite3
-from web.config import Config
-
-conn = sqlite3.connect(Config.DB_L2_PATH)
-cursor = conn.cursor()
-
-columns = [
-    'util_flash_usage',
-    'util_smoke_usage',
-    'util_molotov_usage',
-    'util_he_usage',
-    'util_decoy_usage'
-]
-
-for col in columns:
-    try:
-        cursor.execute(f"ALTER TABLE fact_match_players ADD COLUMN {col} INTEGER DEFAULT 0")
-        print(f"Added column {col}")
-    except sqlite3.OperationalError as e:
-        if "duplicate column name" in str(e):
-            print(f"Column {col} already exists.")
-        else:
-            print(f"Error adding {col}: {e}")
-
-conn.commit()
-conn.close()

scripts/update_l3_schema.py

@@ -1,39 +0,0 @@
-import sqlite3
-import os
-
-DB_PATH = r'd:\Documents\trae_projects\yrtv\database\L3\L3_Features.sqlite'
-
-def add_columns():
-    conn = sqlite3.connect(DB_PATH)
-    cursor = conn.cursor()
-    
-    # Check existing columns
-    cursor.execute("PRAGMA table_info(dm_player_features)")
-    columns = [row[1] for row in cursor.fetchall()]
-    
-    new_columns = [
-        'score_bat', 'score_sta', 'score_hps', 'score_ptl', 'score_tct', 'score_util',
-        'bat_avg_duel_win_rate', 'bat_kd_diff_high_elo', 'bat_win_rate_close',
-        'sta_time_rating_corr', 'sta_fatigue_decay',
-        'hps_match_point_win_rate', 'hps_comeback_kd_diff', 'hps_pressure_entry_rate',
-        'ptl_pistol_win_rate', 'ptl_pistol_kd',
-        'util_avg_flash_enemy'
-    ]
-    
-    for col in new_columns:
-        if col not in columns:
-            print(f"Adding column: {col}")
-            try:
-                cursor.execute(f"ALTER TABLE dm_player_features ADD COLUMN {col} REAL")
-            except Exception as e:
-                print(f"Error adding {col}: {e}")
-                
-    conn.commit()
-    conn.close()
-    print("Schema update complete.")
-
-if __name__ == "__main__":
-    if not os.path.exists(DB_PATH):
-        print("L3 DB not found, skipping schema update (will be created by build script).")
-    else:
-        add_columns()

scripts/update_l3_schema_full.py

@@ -1,82 +0,0 @@
-import sqlite3
-import os
-
-DB_PATH = r'd:\Documents\trae_projects\yrtv\database\L3\L3_Features.sqlite'
-
-def update_schema():
-    if not os.path.exists(DB_PATH):
-        print("L3 DB not found.")
-        return
-
-    conn = sqlite3.connect(DB_PATH)
-    cursor = conn.cursor()
-    
-    # Get existing columns
-    cursor.execute("PRAGMA table_info(dm_player_features)")
-    existing_cols = {row[1] for row in cursor.fetchall()}
-    
-    # List of columns to ensure exist
-    # Copied from schema.sql
-    required_columns = [
-        # Basic
-        'basic_avg_rating', 'basic_avg_kd', 'basic_avg_adr', 'basic_avg_kast', 'basic_avg_rws',
-        'basic_avg_headshot_kills', 'basic_headshot_rate', 
-        'basic_avg_first_kill', 'basic_avg_first_death', 'basic_first_kill_rate', 'basic_first_death_rate',
-        'basic_avg_kill_2', 'basic_avg_kill_3', 'basic_avg_kill_4', 'basic_avg_kill_5',
-        'basic_avg_assisted_kill', 'basic_avg_perfect_kill', 'basic_avg_revenge_kill',
-        'basic_avg_awp_kill', 'basic_avg_jump_count',
-        'basic_avg_mvps', 'basic_avg_plants', 'basic_avg_defuses', 'basic_avg_flash_assists',
-        
-        # STA
-        'sta_last_30_rating', 'sta_win_rating', 'sta_loss_rating', 'sta_rating_volatility',
-        'sta_time_rating_corr', 'sta_fatigue_decay',
-        
-        # BAT
-        'bat_kd_diff_high_elo', 'bat_kd_diff_low_elo', 'bat_avg_duel_win_rate', 'bat_avg_duel_freq',
-        'bat_win_rate_close', 'bat_win_rate_mid', 'bat_win_rate_far',
-        
-        # HPS
-        'hps_clutch_win_rate_1v1', 'hps_clutch_win_rate_1v2', 'hps_clutch_win_rate_1v3_plus',
-        'hps_match_point_win_rate', 'hps_undermanned_survival_time', 'hps_pressure_entry_rate',
-        'hps_momentum_multikill_rate', 'hps_tilt_rating_drop', 'hps_clutch_rating_rise',
-        'hps_comeback_kd_diff', 'hps_losing_streak_kd_diff',
-        
-        # PTL
-        'ptl_pistol_kills', 'ptl_pistol_multikills', 'ptl_pistol_win_rate', 'ptl_pistol_kd', 'ptl_pistol_util_efficiency',
-        
-        # SIDE
-        'side_rating_ct', 'side_rating_t', 'side_kd_ct', 'side_kd_t',
-        'side_win_rate_ct', 'side_win_rate_t',
-        'side_first_kill_rate_ct', 'side_first_kill_rate_t',
-        'side_kd_diff_ct_t',
-        'side_kast_ct', 'side_kast_t',
-        'side_rws_ct', 'side_rws_t',
-        'side_first_death_rate_ct', 'side_first_death_rate_t',
-        'side_multikill_rate_ct', 'side_multikill_rate_t',
-        'side_headshot_rate_ct', 'side_headshot_rate_t',
-        'side_defuses_ct', 'side_plants_t',
-        'side_obj_ct', 'side_obj_t',
-        'side_planted_bomb_count', 'side_defused_bomb_count',
-        
-        # UTIL
-        'util_avg_nade_dmg', 'util_avg_flash_time', 'util_avg_flash_enemy', 'util_avg_flash_team', 'util_usage_rate',
-        
-        # Scores
-        'score_bat', 'score_sta', 'score_hps', 'score_ptl', 'score_tct', 'score_util'
-    ]
-    
-    for col in required_columns:
-        if col not in existing_cols:
-            print(f"Adding missing column: {col}")
-            try:
-                # Most are REAL, integers are fine as REAL in sqlite usually, or use affinity
-                cursor.execute(f"ALTER TABLE dm_player_features ADD COLUMN {col} REAL")
-            except Exception as e:
-                print(f"Failed to add {col}: {e}")
-                
-    conn.commit()
-    conn.close()
-    print("Schema update check complete.")
-
-if __name__ == "__main__":
-    update_schema()

web/routes/players.py

@@ -98,6 +98,49 @@ def detail(steam_id):
         return "Player not found", 404
         
     features = FeatureService.get_player_features(steam_id)
+
+    # --- New: Fetch Detailed Stats from L2 (Clutch, Multi-Kill, Multi-Assist) ---
+    sql_l2 = """
+        SELECT 
+            SUM(clutch_1v1) as c1, SUM(clutch_1v2) as c2, SUM(clutch_1v3) as c3, SUM(clutch_1v4) as c4, SUM(clutch_1v5) as c5,
+            SUM(kill_2) as k2, SUM(kill_3) as k3, SUM(kill_4) as k4, SUM(kill_5) as k5,
+            SUM(many_assists_cnt2) as a2, SUM(many_assists_cnt3) as a3, SUM(many_assists_cnt4) as a4, SUM(many_assists_cnt5) as a5,
+            COUNT(*) as matches,
+            SUM(round_total) as total_rounds
+        FROM fact_match_players
+        WHERE steam_id_64 = ?
+    """
+    l2_stats = query_db('l2', sql_l2, [steam_id], one=True)
+    l2_stats = dict(l2_stats) if l2_stats else {}
+    
+    # Fetch T/CT splits for comparison
+    # Note: We use SUM(clutch...) as Total Clutch Wins. We don't have attempts, so 'Win Rate' is effectively Wins/Rounds or just Wins count.
+    # User asked for 'Win Rate', but without attempts data, we'll provide Rate per Round or just Count.
+    # Let's provide Rate per Round for Multi-Kill/Assist, and maybe just Count for Clutch?
+    # User said: "总残局胜率...分t和ct在下方加入对比".
+    # Since we found clutch == end in DB, we treat it as Wins. We can't calc Win %.
+    # We will display "Clutch Wins / Round" or just "Clutch Wins".
+    
+    sql_side = """
+        SELECT 
+            'T' as side,
+            SUM(clutch_1v1+clutch_1v2+clutch_1v3+clutch_1v4+clutch_1v5) as total_clutch,
+            SUM(kill_2+kill_3+kill_4+kill_5) as total_multikill,
+            SUM(many_assists_cnt2+many_assists_cnt3+many_assists_cnt4+many_assists_cnt5) as total_multiassist,
+            SUM(round_total) as rounds
+        FROM fact_match_players_t WHERE steam_id_64 = ?
+        UNION ALL
+        SELECT 
+            'CT' as side,
+            SUM(clutch_1v1+clutch_1v2+clutch_1v3+clutch_1v4+clutch_1v5) as total_clutch,
+            SUM(kill_2+kill_3+kill_4+kill_5) as total_multikill,
+            SUM(many_assists_cnt2+many_assists_cnt3+many_assists_cnt4+many_assists_cnt5) as total_multiassist,
+            SUM(round_total) as rounds
+        FROM fact_match_players_ct WHERE steam_id_64 = ?
+    """
+    side_rows = query_db('l2', sql_side, [steam_id, steam_id])
+    side_stats = {row['side']: dict(row) for row in side_rows} if side_rows else {}
+
     # Ensure basic stats fallback if features missing or incomplete
     basic = StatsService.get_player_basic_stats(steam_id)
     
@@ -157,7 +200,16 @@ def detail(steam_id):
         })
     map_stats_list.sort(key=lambda x: x['matches'], reverse=True)
 
-    return render_template('players/profile.html', player=player, features=features, comments=comments, metadata=metadata, history=history, distribution=distribution, map_stats=map_stats_list)
+    return render_template('players/profile.html',
+                         player=player,
+                         features=features,
+                         comments=comments,
+                         metadata=metadata,
+                         history=history,
+                         distribution=distribution,
+                         map_stats=map_stats_list,
+                         l2_stats=l2_stats,
+                         side_stats=side_stats)
 
 @bp.route('/comment/<int:comment_id>/like', methods=['POST'])
 def like_comment(comment_id):

web/services/feature_service.py

@@ -295,9 +295,7 @@ class FeatureService:
             SUM(first_death) as sum_fd,
             SUM(clutch_1v1) as sum_1v1,
             SUM(clutch_1v2) as sum_1v2,
-            SUM(clutch_1v3) as sum_1v3,
+            SUM(clutch_1v3) + SUM(clutch_1v4) + SUM(clutch_1v5) as sum_1v3p,
-            SUM(clutch_1v4) as sum_1v4,
-            SUM(clutch_1v5) as sum_1v5,
             SUM(kill_2) as sum_2k,
             SUM(kill_3) as sum_3k,
             SUM(kill_4) as sum_4k,
@@ -342,15 +340,6 @@ class FeatureService:
         df['basic_avg_kill_3'] = df['sum_3k'] / df['matches_played']
         df['basic_avg_kill_4'] = df['sum_4k'] / df['matches_played']
         df['basic_avg_kill_5'] = df['sum_5k'] / df['matches_played']
-        
-        # New Metrics
-        df['basic_multi_kill_rate'] = (df['sum_2k'] + df['sum_3k'] + df['sum_4k'] + df['sum_5k']) / df['rounds_played'].replace(0, 1)
-        df['basic_total_1v1'] = df['sum_1v1']
-        df['basic_total_1v2'] = df['sum_1v2']
-        df['basic_total_1v3'] = df['sum_1v3']
-        df['basic_total_1v4'] = df['sum_1v4']
-        df['basic_total_1v5'] = df['sum_1v5']
-
         df['basic_avg_assisted_kill'] = df['sum_assist'] / df['matches_played']
         df['basic_avg_perfect_kill'] = df['sum_perfect'] / df['matches_played']
         df['basic_avg_revenge_kill'] = df['sum_revenge'] / df['matches_played']

web/templates/players/profile.html

@@ -147,11 +147,11 @@
             <span>📊</span> 详细数据面板 (Detailed Stats)
         </h3>
         <div class="grid grid-cols-2 md:grid-cols-3 lg:grid-cols-5 gap-y-6 gap-x-4">
-            {% macro detail_item(label, value, key, format_str='{:.2f}', sublabel=None) %}
+            {% macro detail_item(label, value, key, format_str='{:.2f}', sublabel=None, count_label=None) %}
             {% set dist = distribution[key] if distribution else None %}
-            <div class="flex flex-col group relative">
+            <div class="flex flex-col group relative h-full">
                 <div class="flex justify-between items-center mb-1">
-                    <span class="text-xs font-bold text-gray-400 uppercase tracking-wider">{{ label }}</span>
+                    <span class="text-xs font-bold text-gray-400 uppercase tracking-wider truncate" title="{{ label }}">{{ label }}</span>
                     {% if dist %}
                     <span class="inline-flex items-center px-1.5 py-0.5 rounded text-xs font-bold 
                         {% if dist.rank == 1 %}bg-yellow-50 text-yellow-700 border border-yellow-100
@@ -186,6 +186,13 @@
                     <span>H:{{ format_str.format(dist.max) }}</span>
                 </div>
                 {% endif %}
+
+                <!-- Count Label (Bottom Right) -->
+                {% if count_label is not none %}
+                <div class="absolute bottom-0 right-0 text-[10px] font-bold text-gray-400 font-mono">
+                    {{ count_label }}
+                </div>
+                {% endif %}
             </div>
             {% endmacro %}
 
@@ -222,16 +229,8 @@
             {{ detail_item('3K Rounds (三杀)', features['basic_avg_kill_3'], 'basic_avg_kill_3') }}
             {{ detail_item('4K Rounds (四杀)', features['basic_avg_kill_4'], 'basic_avg_kill_4') }}
             {{ detail_item('5K Rounds (五杀)', features['basic_avg_kill_5'], 'basic_avg_kill_5') }}
-            {{ detail_item('Multi-Kill % (多杀率)', features['basic_multi_kill_rate'], 'basic_multi_kill_rate', '{:.1%}') }}
             
-            <!-- Row 6: Clutch -->
+            <!-- Row 6: Special -->
-            {{ detail_item('1v1 Wins (1v1胜)', features['basic_total_1v1'], 'basic_total_1v1', '{:.0f}') }}
-            {{ detail_item('1v2 Wins (1v2胜)', features['basic_total_1v2'], 'basic_total_1v2', '{:.0f}') }}
-            {{ detail_item('1v3 Wins (1v3胜)', features['basic_total_1v3'], 'basic_total_1v3', '{:.0f}') }}
-            {{ detail_item('1v4 Wins (1v4胜)', features['basic_total_1v4'], 'basic_total_1v4', '{:.0f}') }}
-            {{ detail_item('1v5 Wins (1v5胜)', features['basic_total_1v5'], 'basic_total_1v5', '{:.0f}') }}
-
-            <!-- Row 7: Special -->
             {{ detail_item('Perfect Kills (无伤杀)', features['basic_avg_perfect_kill'], 'basic_avg_perfect_kill') }}
             {{ detail_item('Revenge Kills (复仇杀)', features['basic_avg_revenge_kill'], 'basic_avg_revenge_kill') }}
         </div>
@@ -296,15 +295,34 @@
                 </div>
             </div>
 
+            <!-- Group 5: SPECIAL (Clutch & Multi) -->
+            <div>
+                <h4 class="text-xs font-black text-gray-400 uppercase tracking-widest mb-4 border-b border-gray-100 dark:border-slate-700 pb-2">
+                    SPECIAL (Clutch & Multi)
+                </h4>
+                {% set rounds = l2_stats.get('total_rounds', 0) or 1 %}
+                <div class="grid grid-cols-2 md:grid-cols-3 lg:grid-cols-5 gap-y-6 gap-x-4">
+                    {{ detail_item('1v1 Win%', (l2_stats.get('c1', 0) or 0) / rounds, 'l2_c1', '{:.1%}', count_label=l2_stats.get('c1', 0)) }}
+                    {{ detail_item('1v2 Win%', (l2_stats.get('c2', 0) or 0) / rounds, 'l2_c2', '{:.1%}', count_label=l2_stats.get('c2', 0)) }}
+                    {{ detail_item('1v3 Win%', (l2_stats.get('c3', 0) or 0) / rounds, 'l2_c3', '{:.1%}', count_label=l2_stats.get('c3', 0)) }}
+                    {{ detail_item('1v4 Win%', (l2_stats.get('c4', 0) or 0) / rounds, 'l2_c4', '{:.1%}', count_label=l2_stats.get('c4', 0)) }}
+                    {{ detail_item('1v5 Win%', (l2_stats.get('c5', 0) or 0) / rounds, 'l2_c5', '{:.1%}', count_label=l2_stats.get('c5', 0)) }}
+
+                    {% set mk_count = (l2_stats.get('k2', 0) or 0) + (l2_stats.get('k3', 0) or 0) + (l2_stats.get('k4', 0) or 0) + (l2_stats.get('k5', 0) or 0) %}
+                    {% set ma_count = (l2_stats.get('a2', 0) or 0) + (l2_stats.get('a3', 0) or 0) + (l2_stats.get('a4', 0) or 0) + (l2_stats.get('a5', 0) or 0) %}
+                    
+                    {{ detail_item('Multi-Kill Rate', mk_count / rounds, 'l2_mk', '{:.1%}', count_label=mk_count) }}
+                    {{ detail_item('Multi-Assist Rate', ma_count / rounds, 'l2_ma', '{:.1%}', count_label=ma_count) }}
+                </div>
+            </div>
+
             <!-- Group 4: SIDE (T/CT Preference) -->
             <div>
                 <h4 class="text-xs font-black text-gray-400 uppercase tracking-widest mb-4 border-b border-gray-100 dark:border-slate-700 pb-2">
                     SIDE (T/CT Preference)
                 </h4>
                 
-                {% macro vs_item(label, t_key, ct_key, format_str='{:.2f}') %}
+                {% macro vs_item_val(label, t_val, ct_val, format_str='{:.2f}') %}
-                {% set t_val = features[t_key] or 0 %}
-                {% set ct_val = features[ct_key] or 0 %}
                 {% set diff = ct_val - t_val %}
                 
                 {# Dynamic Sizing #}
@@ -367,6 +385,10 @@
                 </div>
                 {% endmacro %}
 
+                {% macro vs_item(label, t_key, ct_key, format_str='{:.2f}') %}
+                {{ vs_item_val(label, features[t_key] or 0, features[ct_key] or 0, format_str) }}
+                {% endmacro %}
+
                 <div class="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-6">
                     {{ vs_item('Rating (Rating/KD)', 'side_rating_t', 'side_rating_ct') }}
                     {{ vs_item('KD Ratio', 'side_kd_t', 'side_kd_ct') }}
@@ -375,8 +397,23 @@
                     {{ vs_item('First Death Rate (首死率)', 'side_first_death_rate_t', 'side_first_death_rate_ct', '{:.1%}') }}
                     {{ vs_item('KAST (贡献率)', 'side_kast_t', 'side_kast_ct', '{:.1%}') }}
                     {{ vs_item('RWS (Round Win Share)', 'side_rws_t', 'side_rws_ct') }}
-                    {{ vs_item('Multi-Kill Rate (多杀率)', 'side_multikill_rate_t', 'side_multikill_rate_ct', '{:.1%}') }}
                     {{ vs_item('Headshot Rate (爆头率)', 'side_headshot_rate_t', 'side_headshot_rate_ct', '{:.1%}') }}
+                    
+                    {# New Comparisons #}
+                    {% set t_rounds = side_stats.get('T', {}).get('rounds', 0) or 1 %}
+                    {% set ct_rounds = side_stats.get('CT', {}).get('rounds', 0) or 1 %}
+                    
+                    {% set t_clutch = (side_stats.get('T', {}).get('total_clutch', 0) or 0) / t_rounds %}
+                    {% set ct_clutch = (side_stats.get('CT', {}).get('total_clutch', 0) or 0) / ct_rounds %}
+                    {{ vs_item_val('Clutch Win Rate (残局率)', t_clutch, ct_clutch, '{:.1%}') }}
+
+                    {% set t_mk = (side_stats.get('T', {}).get('total_multikill', 0) or 0) / t_rounds %}
+                    {% set ct_mk = (side_stats.get('CT', {}).get('total_multikill', 0) or 0) / ct_rounds %}
+                    {{ vs_item_val('Multi-Kill Rate (多杀率)', t_mk, ct_mk, '{:.1%}') }}
+
+                    {% set t_ma = (side_stats.get('T', {}).get('total_multiassist', 0) or 0) / t_rounds %}
+                    {% set ct_ma = (side_stats.get('CT', {}).get('total_multiassist', 0) or 0) / ct_rounds %}
+                    {{ vs_item_val('Multi-Assist Rate (多助攻)', t_ma, ct_ma, '{:.1%}') }}
                 </div>
             </div>
         </div>