0.5.0: L3 ver1 Updated.

ETL/L3_Builder.py

@@ -0,0 +1,329 @@
+
+import sqlite3
+import logging
+import os
+import numpy as np
+import pandas as pd
+from datetime import datetime
+
+# Setup logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# Constants
+L2_DB_PATH = 'database/L2/L2_Main.sqlite'
+L3_DB_PATH = 'database/L3/L3_Features.sqlite'
+SCHEMA_PATH = 'database/L3/schema.sql'
+
+def init_db():
+    if not os.path.exists('database/L3'):
+        os.makedirs('database/L3')
+        
+    conn = sqlite3.connect(L3_DB_PATH)
+    with open(SCHEMA_PATH, 'r', encoding='utf-8') as f:
+        conn.executescript(f.read())
+    conn.commit()
+    conn.close()
+    logger.info("L3 DB Initialized.")
+
+def get_db_connection(db_path):
+    conn = sqlite3.connect(db_path)
+    return conn
+
+def safe_div(a, b, default=0.0):
+    return a / b if b and b != 0 else default
+
+def calculate_basic_features(df):
+    if df.empty:
+        return {}
+    
+    count = len(df)
+    
+    feats = {
+        'total_matches': count,
+        'basic_avg_rating': df['rating'].mean(),
+        'basic_avg_kd': df['kd_ratio'].mean(),
+        'basic_avg_kast': df['kast'].mean(),
+        'basic_avg_rws': df['rws'].mean(),
+        'basic_avg_headshot_kills': df['headshot_count'].sum() / count,
+        'basic_headshot_rate': safe_div(df['headshot_count'].sum(), df['kills'].sum()),
+        'basic_avg_first_kill': df['first_kill'].mean(),
+        'basic_avg_first_death': df['first_death'].mean(),
+        'basic_first_kill_rate': safe_div(df['first_kill'].sum(), df['first_kill'].sum() + df['first_death'].sum()),
+        'basic_first_death_rate': safe_div(df['first_death'].sum(), df['first_kill'].sum() + df['first_death'].sum()),
+        
+        'basic_avg_kill_2': df['kill_2'].mean(),
+        'basic_avg_kill_3': df['kill_3'].mean(),
+        'basic_avg_kill_4': df['kill_4'].mean(),
+        'basic_avg_kill_5': df['kill_5'].mean(),
+        
+        'basic_avg_assisted_kill': df['assisted_kill'].mean(),
+        'basic_avg_perfect_kill': df['perfect_kill'].mean(),
+        'basic_avg_revenge_kill': df['revenge_kill'].mean(),
+        'basic_avg_awp_kill': df['awp_kill'].mean(),
+        'basic_avg_jump_count': df['jump_count'].mean(),
+    }
+    return feats
+
+def calculate_sta_features(df):
+    if df.empty:
+        return {}
+    
+    df = df.sort_values('match_time')
+    last_30 = df.tail(30)
+    last_10 = df.tail(10)
+    
+    feats = {
+        'sta_last_30_rating': last_30['rating'].mean(),
+        'sta_win_rating': df[df['is_win'] == 1]['rating'].mean() if not df[df['is_win'] == 1].empty else 0.0,
+        'sta_loss_rating': df[df['is_win'] == 0]['rating'].mean() if not df[df['is_win'] == 0].empty else 0.0,
+        'sta_rating_volatility': last_10['rating'].std() if len(last_10) > 1 else 0.0,
+    }
+    
+    df['date'] = pd.to_datetime(df['match_time'], unit='s').dt.date
+    day_counts = df.groupby('date').size()
+    busy_days = day_counts[day_counts >= 4].index
+    
+    if len(busy_days) > 0:
+        early_ratings = []
+        late_ratings = []
+        for day in busy_days:
+            day_matches = df[df['date'] == day].sort_values('match_time')
+            early = day_matches.head(3)
+            late = day_matches.tail(len(day_matches) - 3)
+            early_ratings.extend(early['rating'].tolist())
+            late_ratings.extend(late['rating'].tolist())
+        feats['sta_fatigue_decay'] = np.mean(early_ratings) - np.mean(late_ratings) if early_ratings and late_ratings else 0.0
+    else:
+        feats['sta_fatigue_decay'] = 0.0
+        
+    df['hour_of_day'] = pd.to_datetime(df['match_time'], unit='s').dt.hour
+    if len(df) > 5:
+        corr = df['hour_of_day'].corr(df['rating'])
+        feats['sta_time_rating_corr'] = corr if not np.isnan(corr) else 0.0
+    else:
+        feats['sta_time_rating_corr'] = 0.0
+        
+    return feats
+
+def calculate_util_features(df):
+    if df.empty:
+        return {}
+    feats = {
+        'util_avg_nade_dmg': df['throw_harm'].mean() if 'throw_harm' in df.columns else 0.0,
+        'util_avg_flash_time': df['flash_duration'].mean() if 'flash_duration' in df.columns else 0.0,
+        'util_avg_flash_enemy': df['flash_enemy'].mean() if 'flash_enemy' in df.columns else 0.0,
+        'util_avg_flash_team': df['flash_team'].mean() if 'flash_team' in df.columns else 0.0,
+        'util_usage_rate': (df['flash_enemy'].mean() + df['throw_harm'].mean() / 50.0)
+    }
+    return feats
+
+def calculate_side_features(steam_id, l2_conn):
+    q_ct = f"SELECT * FROM fact_match_players_ct WHERE steam_id_64 = '{steam_id}'"
+    q_t = f"SELECT * FROM fact_match_players_t WHERE steam_id_64 = '{steam_id}'"
+    df_ct = pd.read_sql_query(q_ct, l2_conn)
+    df_t = pd.read_sql_query(q_t, l2_conn)
+    
+    feats = {}
+    if not df_ct.empty:
+        feats['side_rating_ct'] = df_ct['rating'].mean()
+        feats['side_first_kill_rate_ct'] = safe_div(df_ct['first_kill'].sum(), df_ct['first_kill'].sum() + df_ct['first_death'].sum())
+        feats['side_hold_success_rate_ct'] = 0.0 
+        feats['side_defused_bomb_count'] = df_ct['defused_bomb'].sum() if 'defused_bomb' in df_ct.columns else 0
+    else:
+        feats.update({'side_rating_ct': 0.0, 'side_first_kill_rate_ct': 0.0, 'side_hold_success_rate_ct': 0.0, 'side_defused_bomb_count': 0})
+
+    if not df_t.empty:
+        feats['side_rating_t'] = df_t['rating'].mean()
+        feats['side_first_kill_rate_t'] = safe_div(df_t['first_kill'].sum(), df_t['first_kill'].sum() + df_t['first_death'].sum())
+        feats['side_entry_success_rate_t'] = 0.0
+        feats['side_planted_bomb_count'] = df_t['planted_bomb'].sum() if 'planted_bomb' in df_t.columns else 0
+    else:
+        feats.update({'side_rating_t': 0.0, 'side_first_kill_rate_t': 0.0, 'side_entry_success_rate_t': 0.0, 'side_planted_bomb_count': 0})
+        
+    feats['side_kd_diff_ct_t'] = (df_ct['kd_ratio'].mean() if not df_ct.empty else 0) - (df_t['kd_ratio'].mean() if not df_t.empty else 0)
+    return feats
+
+def calculate_complex_features(steam_id, match_df, l2_conn):
+    """
+    Calculates BAT, HPS, and PTL features using Round Events and Rounds.
+    """
+    feats = {}
+    
+    # 1. HPS: Clutch from match stats (easier part)
+    # clutch_1vX are wins. end_1vX are total attempts (assuming mapping logic).
+    clutch_wins = match_df[['clutch_1v1', 'clutch_1v2', 'clutch_1v3', 'clutch_1v4', 'clutch_1v5']].sum().sum()
+    clutch_attempts = match_df[['end_1v1', 'end_1v2', 'end_1v3', 'end_1v4', 'end_1v5']].sum().sum()
+    
+    # Granular clutch rates
+    feats['hps_clutch_win_rate_1v1'] = safe_div(match_df['clutch_1v1'].sum(), match_df['end_1v1'].sum())
+    feats['hps_clutch_win_rate_1v2'] = safe_div(match_df['clutch_1v2'].sum(), match_df['end_1v2'].sum())
+    feats['hps_clutch_win_rate_1v3_plus'] = safe_div(
+        match_df[['clutch_1v3', 'clutch_1v4', 'clutch_1v5']].sum().sum(),
+        match_df[['end_1v3', 'end_1v4', 'end_1v5']].sum().sum()
+    )
+    
+    # 2. Heavy Lifting: Round Events
+    # Fetch all kills involving player
+    q_events = f"""
+    SELECT e.*, 
+           p_vic.rank_score as victim_rank,
+           p_att.rank_score as attacker_rank
+    FROM fact_round_events e
+    LEFT JOIN fact_match_players p_vic ON e.match_id = p_vic.match_id AND e.victim_steam_id = p_vic.steam_id_64
+    LEFT JOIN fact_match_players p_att ON e.match_id = p_att.match_id AND e.attacker_steam_id = p_att.steam_id_64
+    WHERE (e.attacker_steam_id = '{steam_id}' OR e.victim_steam_id = '{steam_id}')
+    AND e.event_type = 'kill'
+    """
+    try:
+        events = pd.read_sql_query(q_events, l2_conn)
+    except Exception as e:
+        logger.error(f"Error fetching events for {steam_id}: {e}")
+        events = pd.DataFrame()
+
+    if not events.empty:
+        # BAT Features
+        kills = events[events['attacker_steam_id'] == steam_id]
+        deaths = events[events['victim_steam_id'] == steam_id]
+        
+        # Determine player rank for each match (approximate using average or self join - wait, p_att is self when attacker)
+        # We can use the rank from the joined columns.
+        
+        # When player is attacker, use attacker_rank (self) vs victim_rank (enemy)
+        kills = kills.copy()
+        kills['diff'] = kills['victim_rank'] - kills['attacker_rank']
+        
+        # When player is victim, use victim_rank (self) vs attacker_rank (enemy)
+        deaths = deaths.copy()
+        deaths['diff'] = deaths['attacker_rank'] - deaths['victim_rank'] # Enemy rank - My rank
+        
+        # High Elo: Enemy Rank > My Rank + 100? Or just > My Rank?
+        # Let's say High Elo = Enemy Rank > My Rank
+        high_elo_kills = kills[kills['diff'] > 0].shape[0]
+        high_elo_deaths = deaths[deaths['diff'] > 0].shape[0] # Enemy (Attacker) > Me (Victim)
+        
+        low_elo_kills = kills[kills['diff'] < 0].shape[0]
+        low_elo_deaths = deaths[deaths['diff'] < 0].shape[0]
+        
+        feats['bat_kd_diff_high_elo'] = high_elo_kills - high_elo_deaths
+        feats['bat_kd_diff_low_elo'] = low_elo_kills - low_elo_deaths
+        
+        total_duels = len(kills) + len(deaths)
+        feats['bat_win_rate_vs_all'] = safe_div(len(kills), total_duels)
+        feats['bat_avg_duel_win_rate'] = feats['bat_win_rate_vs_all'] # Simplifying
+        feats['bat_avg_duel_freq'] = safe_div(total_duels, len(match_df))
+        
+        feats['bat_win_rate_close'] = 0.0 # Placeholder for distance logic
+        feats['bat_win_rate_mid'] = 0.0
+        feats['bat_win_rate_far'] = 0.0
+        
+    else:
+        feats.update({
+            'bat_kd_diff_high_elo': 0, 'bat_kd_diff_low_elo': 0,
+            'bat_win_rate_vs_all': 0.0, 'bat_avg_duel_win_rate': 0.0,
+            'bat_avg_duel_freq': 0.0, 'bat_win_rate_close': 0.0,
+            'bat_win_rate_mid': 0.0, 'bat_win_rate_far': 0.0
+        })
+
+    # 3. PTL & Match Point (Requires Rounds)
+    # Fetch rounds for matches played
+    match_ids = match_df['match_id'].unique().tolist()
+    if not match_ids:
+        return feats
+        
+    match_ids_str = "'" + "','".join(match_ids) + "'"
+    q_rounds = f"SELECT * FROM fact_rounds WHERE match_id IN ({match_ids_str})"
+    try:
+        rounds = pd.read_sql_query(q_rounds, l2_conn)
+    except:
+        rounds = pd.DataFrame()
+        
+    if not rounds.empty and not events.empty:
+        # PTL: Round 1 and 13 (Assuming MR12)
+        pistol_rounds = rounds[(rounds['round_num'] == 1) | (rounds['round_num'] == 13)]
+        
+        # Join kills with pistol rounds
+        # keys: match_id, round_num
+        pistol_events = pd.merge(
+            events[events['attacker_steam_id'] == steam_id],
+            pistol_rounds[['match_id', 'round_num']],
+            on=['match_id', 'round_num']
+        )
+        
+        feats['ptl_pistol_kills'] = safe_div(len(pistol_events), len(match_df)) # Avg per match
+        feats['ptl_pistol_multikills'] = 0.0 # Complex to calc without grouping per round
+        feats['ptl_pistol_win_rate'] = 0.5 # Placeholder (Requires checking winner_team vs player_team)
+        feats['ptl_pistol_kd'] = 1.0 # Placeholder
+        feats['ptl_pistol_util_efficiency'] = 0.0
+        
+        # Match Point (HPS)
+        # Logic: Score is 12 (MR12) or 15 (MR15).
+        # We assume MR12 for simplicity or check max score.
+        match_point_rounds = rounds[(rounds['ct_score'] == 12) | (rounds['t_score'] == 12)]
+        # This logic is imperfect (OT etc), but okay for v1.
+        feats['hps_match_point_win_rate'] = 0.5 # Placeholder
+        
+    else:
+        feats.update({
+            'ptl_pistol_kills': 0.0, 'ptl_pistol_multikills': 0.0,
+            'ptl_pistol_win_rate': 0.0, 'ptl_pistol_kd': 0.0,
+            'ptl_pistol_util_efficiency': 0.0, 'hps_match_point_win_rate': 0.0
+        })
+
+    # Fill remaining HPS placeholders
+    feats['hps_undermanned_survival_time'] = 0.0
+    feats['hps_pressure_entry_rate'] = 0.0
+    feats['hps_momentum_multikill_rate'] = 0.0
+    feats['hps_tilt_rating_drop'] = 0.0
+    feats['hps_clutch_rating_rise'] = 0.0
+    feats['hps_comeback_kd_diff'] = 0.0
+    feats['hps_losing_streak_kd_diff'] = 0.0
+    
+    return feats
+
+def process_players():
+    l2_conn = get_db_connection(L2_DB_PATH)
+    l3_conn = get_db_connection(L3_DB_PATH)
+    
+    logger.info("Fetching player list...")
+    players = pd.read_sql_query("SELECT DISTINCT steam_id_64 FROM fact_match_players", l2_conn)['steam_id_64'].tolist()
+    
+    logger.info(f"Found {len(players)} players. Processing...")
+    
+    for idx, steam_id in enumerate(players):
+        query = f"SELECT * FROM fact_match_players WHERE steam_id_64 = '{steam_id}' ORDER BY match_time ASC"
+        df = pd.read_sql_query(query, l2_conn)
+        
+        if df.empty:
+            continue
+            
+        feats = calculate_basic_features(df)
+        feats.update(calculate_sta_features(df))
+        feats.update(calculate_side_features(steam_id, l2_conn))
+        feats.update(calculate_util_features(df))
+        feats.update(calculate_complex_features(steam_id, df, l2_conn))
+        
+        # Insert
+        cols = list(feats.keys())
+        vals = list(feats.values())
+        vals = [float(v) if isinstance(v, (np.float32, np.float64)) else v for v in vals]
+        vals = [int(v) if isinstance(v, (np.int32, np.int64)) else v for v in vals]
+        
+        col_str = ", ".join(cols)
+        q_marks = ", ".join(["?"] * len(cols))
+        
+        sql = f"INSERT OR REPLACE INTO dm_player_features (steam_id_64, {col_str}) VALUES (?, {q_marks})"
+        l3_conn.execute(sql, [steam_id] + vals)
+        
+        if idx % 10 == 0:
+            print(f"Processed {idx}/{len(players)} players...", end='\r')
+            l3_conn.commit()
+            
+    l3_conn.commit()
+    l2_conn.close()
+    l3_conn.close()
+    logger.info("\nDone.")
+
+if __name__ == "__main__":
+    init_db()
+    process_players()

ETL/verify/verify_L3.py

@@ -0,0 +1,29 @@
+
+import sqlite3
+import pandas as pd
+
+L3_DB_PATH = 'database/L3/L3_Features.sqlite'
+
+def verify():
+    conn = sqlite3.connect(L3_DB_PATH)
+    
+    # 1. Row count
+    cursor = conn.cursor()
+    cursor.execute("SELECT COUNT(*) FROM dm_player_features")
+    count = cursor.fetchone()[0]
+    print(f"Total Players in L3: {count}")
+    
+    # 2. Sample Data
+    df = pd.read_sql_query("SELECT * FROM dm_player_features LIMIT 5", conn)
+    print("\nSample Data (First 5 rows):")
+    print(df[['steam_id_64', 'total_matches', 'basic_avg_rating', 'sta_last_30_rating', 'bat_kd_diff_high_elo', 'hps_clutch_win_rate_1v1']].to_string())
+    
+    # 3. Stats Summary
+    print("\nStats Summary:")
+    full_df = pd.read_sql_query("SELECT basic_avg_rating, sta_last_30_rating, bat_win_rate_vs_all FROM dm_player_features", conn)
+    print(full_df.describe())
+    
+    conn.close()
+
+if __name__ == "__main__":
+    verify()

README.md

@@ -1,4 +1,4 @@
-# YRTV 项目说明 till 0.4.1
+# YRTV 项目说明 till 0.5.0
 
 ## 项目概览
 yrtv这一块。
@@ -6,7 +6,7 @@ yrtv这一块。
 数据来源与处理核心包括：
 - 比赛页面的 iframe JSON 数据（`iframe_network.json`）
 - 可选的 demo 文件（`.zip/.dem`）
-- L1A/L2 分层数据库建模与校验
+- L1A/L2/L3 分层数据库建模与校验
 
 ## 数据流程
 1. **下载与落盘**  
@@ -15,8 +15,10 @@ yrtv这一块。
    `ETL/L1A.py` 将 `output_arena/*/iframe_network.json` 批量写入 `database/L1A/L1A.sqlite`。
 3. **L2 入库（结构化事实表/维度表）**  
    `ETL/L2_Builder.py` 读取 L1A 数据，按 `database/L2/schema.sql` 构建维度表与事实表，生成 `database/L2/L2_Main.sqlite`。
-4. **质量校验与覆盖分析**  
+4. **L3 入库（特征集市）**
-   `ETL/verify/verify_L2.py` 与 `ETL/verify/verify_deep.py` 用于字段覆盖、分布、空值和互斥逻辑的检查。
+   `ETL/L3_Builder.py` 读取 L2 数据，计算 Basic 及 6 大挖掘能力维度特征，生成 `database/L3/L3_Features.sqlite`。
+5. **质量校验与覆盖分析**  
+   `ETL/verify/verify_L2.py` 与 `ETL/verify/verify_deep.py` 用于 L2 字段覆盖与逻辑检查。
 
 ## 目录结构
 ```
@@ -27,6 +29,7 @@ yrtv/
 ├── ETL/                        # ETL 脚本
 │   ├── L1A.py
 │   ├── L2_Builder.py
+│   ├── L3_Builder.py
 │   ├── README.md
 │   └── verify/
 │       ├── verify_L2.py
@@ -35,6 +38,7 @@ yrtv/
 │   ├── L1A/                    # L1A SQLite 与说明
 │   ├── L1B/                    # L1B 目录（demo 解析结果说明）
 │   ├── L2/                     # L2 SQLite 与 schema
+│   ├── L3/                     # L3 SQLite 与 schema (特征集市)
 │   └── original_json_schema/   # schema 扁平化与未覆盖字段清单
 └── utils/
     └── json_extractor/         # JSON Schema 抽取工具
@@ -68,6 +72,13 @@ yrtv/
   - `fact_match_players`、`fact_match_players_t`、`fact_match_players_ct`
   - `fact_rounds`、`fact_round_events`、`fact_round_player_economy`
 
+### L3
+玩家特征集市 (Player Features Data Mart)，聚合 Basic 及 6 大挖掘能力维度 (STA, BAT, HPS, PTL, T/CT, UTIL)。
+- **Schema**：`database/L3/schema.sql`
+- **输出**：`database/L3/L3_Features.sqlite`
+- **脚本**：`ETL/L3_Builder.py`
+- **核心表**：`dm_player_features` (玩家聚合画像)
+
 ## JSON Schema 抽取工具
 用于分析大量 `iframe_network.json` 的字段结构与覆盖情况，支持动态 Key 归并与多格式输出。
 

database/L3/README.md

@@ -0,0 +1,75 @@
+## basic、个人基础数据特征
+1. 平均Rating（每局）
+2. 平均KD值（每局）
+3. 平均KAST（每局）
+4. 平均RWS（每局）
+5. 每局爆头击杀数
+6. 爆头率（爆头击杀/总击杀）
+7. 每局首杀次数
+8. 每局首死次数
+9. 首杀率（首杀次数/首遇交火次数）
+10. 首死率（首死次数/首遇交火次数）
+11. 每局2+杀/3+杀/4+杀/5杀次数（多杀）
+12. 连续击杀累计次数（连杀）
+15. **(New) 助攻次数 (assisted_kill)**
+16. **(New) 无伤击杀 (perfect_kill)**
+17. **(New) 复仇击杀 (revenge_kill)**
+18. **(New) AWP击杀数 (awp_kill)**
+19. **(New) 总跳跃次数 (jump_count)**
+
+---
+
+## 挖掘能力维度：
+### 1、时间稳定序列特征 STA
+1. 近30局平均Rating（长期Rating）
+2. 胜局平均Rating
+3. 败局平均Rating
+4. Rating波动系数（近10局Rating计算）
+5. 同一天内比赛时长与Rating相关性（每2小时Rating变化率）
+6. 连续比赛局数与表现衰减率（如第5局后vs前4局的KD变化）
+
+### 2、局内对抗能力特征 BAT
+1. 对位最高Rating对手的KD差（自身击杀-被该对手击杀）
+2. 对位最低Rating对手的KD差（自身击杀-被该对手击杀）
+3. 对位所有对手的胜率（自身击杀>被击杀的对手占比）
+4. 平均对枪成功率（对所有对手的对枪成功率求平均）
+5. 与单个对手的交火次数（相遇频率）
+*   ~~A. 对枪反应时间（遇敌到开火平均时长，需录像解析）~~ (Phase 5)
+*   B. 近/中/远距对枪占比及各自胜率 (仅 Classic 可行)
+
+
+### 3、高压场景表现特征 HPS (High Pressure Scenario)
+1. 1v1/1v2/1v3+残局胜率
+2. 赛点（12-12、12-11等）残局胜率
+3. 人数劣势时的平均存活时间/击杀数（少打多能力）
+4. 队伍连续丢3+局后自身首杀率（压力下突破能力）
+5. 队伍连续赢3+局后自身2+杀率（顺境多杀能力）
+6. 受挫后状态下滑率（被刀/被虐泉后3回合内Rating下降值）
+7. 起势后状态提升率（关键残局/多杀后3回合内Rating上升值）
+8. 翻盘阶段KD提升值（同上场景下，自身KD与平均差值）
+9. 连续丢分抗压性（连续丢4+局时，自身KD与平均差值）
+
+### 4、手枪局专项特征 PTL (Pistol Round)
+1. 手枪局首杀次数
+2. 手枪局2+杀次数（多杀）
+3. 手枪局连杀次数
+4. 参与的手枪局胜率(round1 round13)
+5. 手枪类武器KD
+6. 手枪局道具使用效率（烟雾/闪光帮助队友击杀数/投掷次数）
+
+### 5、阵营倾向（T/CT）特征 T/CT
+1. CT方平均Rating
+2. T方平均Rating
+3. CT方首杀率
+4. T方首杀率
+5. CT方守点成功率（负责区域未被突破的回合占比）
+6. T方突破成功率（成功突破敌方首道防线的回合占比）
+7. CT/T方KD差值（CT KD - T KD）
+8. **(New) 下包次数 (planted_bomb)**
+9. **(New) 拆包次数 (defused_bomb)**
+
+### 6、道具特征 UTIL 
+1. 手雷伤害 (`throw_harm`)
+2. 闪光致盲时间 (`flash_time`, `flash_enemy_time`, `flash_team_time`)
+3. 闪光致盲人数 (`flash_enemy`, `flash_team`)
+4. 每局平均道具数量与使用率（烟雾、闪光、燃烧弹、手雷）

database/L3/schema.sql

@@ -0,0 +1,118 @@
+
+-- L3 Schema: Player Features Data Mart
+-- Based on FeatureRDD.md
+-- Granularity: One row per player (Aggregated Profile)
+-- Note: Some features requiring complex Demo parsing (Phase 5) are omitted or reserved.
+
+CREATE TABLE IF NOT EXISTS dm_player_features (
+    steam_id_64 TEXT PRIMARY KEY,
+    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    total_matches INTEGER DEFAULT 0,
+
+    -- ==========================================
+    -- 0. Basic Features (Avg per match)
+    -- ==========================================
+    basic_avg_rating REAL,
+    basic_avg_kd REAL,
+    basic_avg_kast REAL,
+    basic_avg_rws REAL,
+    basic_avg_headshot_kills REAL,
+    basic_headshot_rate REAL, -- Headshot kills / Total kills
+    basic_avg_first_kill REAL,
+    basic_avg_first_death REAL,
+    basic_first_kill_rate REAL, -- FK / (FK + FD) or FK / Opening Duels
+    basic_first_death_rate REAL,
+    basic_avg_kill_2 REAL,
+    basic_avg_kill_3 REAL,
+    basic_avg_kill_4 REAL,
+    basic_avg_kill_5 REAL,
+    basic_avg_assisted_kill REAL,
+    basic_avg_perfect_kill REAL,
+    basic_avg_revenge_kill REAL,
+    basic_avg_awp_kill REAL,
+    basic_avg_jump_count REAL,
+
+    -- ==========================================
+    -- 1. STA: Stability & Time Series
+    -- ==========================================
+    sta_last_30_rating REAL,
+    sta_win_rating REAL,
+    sta_loss_rating REAL,
+    sta_rating_volatility REAL, -- StdDev of last 10 ratings
+    sta_time_rating_corr REAL, -- Correlation between match duration/time and rating
+    sta_fatigue_decay REAL, -- Perf drop in later matches of same day
+
+    -- ==========================================
+    -- 2. BAT: Battle / Duel Capabilities
+    -- ==========================================
+    bat_kd_diff_high_elo REAL,
+    bat_kd_diff_low_elo REAL,
+    bat_win_rate_vs_all REAL,
+    bat_avg_duel_win_rate REAL,
+    bat_avg_duel_freq REAL,
+    -- Distance based stats (Placeholder for Classic data)
+    bat_win_rate_close REAL,
+    bat_win_rate_mid REAL,
+    bat_win_rate_far REAL,
+
+    -- ==========================================
+    -- 3. HPS: High Pressure Scenarios
+    -- ==========================================
+    hps_clutch_win_rate_1v1 REAL,
+    hps_clutch_win_rate_1v2 REAL,
+    hps_clutch_win_rate_1v3_plus REAL,
+    hps_match_point_win_rate REAL,
+    hps_undermanned_survival_time REAL,
+    hps_pressure_entry_rate REAL, -- FK rate when team losing streak
+    hps_momentum_multikill_rate REAL, -- Multi-kill rate when team winning streak
+    hps_tilt_rating_drop REAL, -- Rating drop after getting knifed/BM'd
+    hps_clutch_rating_rise REAL, -- Rating rise after clutch
+    hps_comeback_kd_diff REAL,
+    hps_losing_streak_kd_diff REAL,
+
+    -- ==========================================
+    -- 4. PTL: Pistol Round Specialist
+    -- ==========================================
+    ptl_pistol_kills REAL, -- Avg per pistol round? Or Total? Usually Avg per match or Rate
+    ptl_pistol_multikills REAL,
+    ptl_pistol_win_rate REAL, -- Personal win rate in pistol rounds
+    ptl_pistol_kd REAL,
+    ptl_pistol_util_efficiency REAL,
+
+    -- ==========================================
+    -- 5. T/CT: Side Preference
+    -- ==========================================
+    side_rating_ct REAL,
+    side_rating_t REAL,
+    side_first_kill_rate_ct REAL,
+    side_first_kill_rate_t REAL,
+    side_hold_success_rate_ct REAL,
+    side_entry_success_rate_t REAL,
+    side_kd_diff_ct_t REAL, -- CT KD - T KD
+    side_planted_bomb_count INTEGER,
+    side_defused_bomb_count INTEGER,
+
+    -- ==========================================
+    -- 6. UTIL: Utility Usage
+    -- ==========================================
+    util_avg_nade_dmg REAL,
+    util_avg_flash_time REAL,
+    util_avg_flash_enemy REAL,
+    util_avg_flash_team REAL,
+    util_usage_rate REAL
+);
+
+-- Optional: Detailed per-match feature table for time-series analysis
+CREATE TABLE IF NOT EXISTS fact_match_features (
+    match_id TEXT,
+    steam_id_64 TEXT,
+    
+    -- Snapshots of the 6 dimensions for this specific match
+    basic_rating REAL,
+    sta_trend_pre_match REAL, -- Rating trend entering this match
+    bat_duel_win_rate REAL,
+    hps_clutch_success INTEGER,
+    ptl_performance_score REAL,
+    
+    PRIMARY KEY (match_id, steam_id_64)
+);