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