3.0.0 : Reconstructed Database System.

database/L3/L3_Builder.py

@@ -0,0 +1,293 @@
+
+import logging
+import os
+import sys
+import sqlite3
+import json
+
+# Setup logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# Get absolute paths
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))  # Points to database/ directory
+PROJECT_ROOT = os.path.dirname(BASE_DIR)  # Points to project root
+sys.path.insert(0, PROJECT_ROOT)  # Add project root to Python path
+L2_DB_PATH = os.path.join(BASE_DIR, 'L2', 'L2.db')
+L3_DB_PATH = os.path.join(BASE_DIR, 'L3', 'L3.db')
+WEB_DB_PATH = os.path.join(BASE_DIR, 'Web', 'Web_App.sqlite')
+SCHEMA_PATH = os.path.join(BASE_DIR, 'L3', 'schema.sql')
+
+def _get_existing_columns(conn, table_name):
+    cur = conn.execute(f"PRAGMA table_info({table_name})")
+    return {row[1] for row in cur.fetchall()}
+
+def _ensure_columns(conn, table_name, columns):
+    existing = _get_existing_columns(conn, table_name)
+    for col, col_type in columns.items():
+        if col in existing:
+            continue
+        conn.execute(f"ALTER TABLE {table_name} ADD COLUMN {col} {col_type}")
+
+def init_db():
+    """Initialize L3 database with new schema"""
+    l3_dir = os.path.dirname(L3_DB_PATH)
+    if not os.path.exists(l3_dir):
+        os.makedirs(l3_dir)
+    
+    logger.info(f"Initializing L3 database at: {L3_DB_PATH}")
+    conn = sqlite3.connect(L3_DB_PATH)
+    
+    try:
+        with open(SCHEMA_PATH, 'r', encoding='utf-8') as f:
+            schema_sql = f.read()
+            conn.executescript(schema_sql)
+        
+        conn.commit()
+        logger.info("✓ L3 schema created successfully")
+        
+        # Verify tables
+        cursor = conn.cursor()
+        cursor.execute("SELECT name FROM sqlite_master WHERE type='table' ORDER BY name")
+        tables = [row[0] for row in cursor.fetchall()]
+        logger.info(f"✓ Created {len(tables)} tables: {', '.join(tables)}")
+        
+        # Verify dm_player_features columns
+        cursor.execute("PRAGMA table_info(dm_player_features)")
+        columns = cursor.fetchall()
+        logger.info(f"✓ dm_player_features has {len(columns)} columns")
+        
+    except Exception as e:
+        logger.error(f"Error initializing L3 database: {e}")
+        raise
+    finally:
+        conn.close()
+    
+    logger.info("L3 DB Initialized with new 5-tier architecture")
+
+def _get_team_players():
+    """Get list of steam_ids from Web App team lineups"""
+    if not os.path.exists(WEB_DB_PATH):
+        logger.warning(f"Web DB not found at {WEB_DB_PATH}, returning empty list")
+        return set()
+        
+    try:
+        conn = sqlite3.connect(WEB_DB_PATH)
+        cursor = conn.cursor()
+        cursor.execute("SELECT player_ids_json FROM team_lineups")
+        rows = cursor.fetchall()
+        
+        steam_ids = set()
+        for row in rows:
+            if row[0]:
+                try:
+                    ids = json.loads(row[0])
+                    if isinstance(ids, list):
+                        steam_ids.update(ids)
+                except json.JSONDecodeError:
+                    logger.warning(f"Failed to parse player_ids_json: {row[0]}")
+                    
+        conn.close()
+        logger.info(f"Found {len(steam_ids)} unique players in Team Lineups")
+        return steam_ids
+    except Exception as e:
+        logger.error(f"Error reading Web DB: {e}")
+        return set()
+
+def main():
+    """
+    Main L3 feature building pipeline using modular processors
+    """
+    logger.info("========================================")
+    logger.info("Starting L3 Builder with 5-Tier Architecture")
+    logger.info("========================================")
+    
+    # 1. Ensure Schema is up to date
+    init_db()
+    
+    # 2. Import processors
+    try:
+        from database.L3.processors import (
+            BasicProcessor,
+            TacticalProcessor,
+            IntelligenceProcessor,
+            MetaProcessor,
+            CompositeProcessor
+        )
+        logger.info("✓ All 5 processors imported successfully")
+    except ImportError as e:
+        logger.error(f"Failed to import processors: {e}")
+        return
+    
+    # 3. Connect to databases
+    conn_l2 = sqlite3.connect(L2_DB_PATH)
+    conn_l2.row_factory = sqlite3.Row
+    conn_l3 = sqlite3.connect(L3_DB_PATH)
+    
+    try:
+        # 4. Get target players (Team Lineups only)
+        team_players = _get_team_players()
+        if not team_players:
+            logger.warning("No players found in Team Lineups. Aborting L3 build.")
+            return
+
+        # 5. Get distinct players from L2 matching Team Lineups
+        cursor_l2 = conn_l2.cursor()
+        
+        # Build placeholder string for IN clause
+        placeholders = ','.join(['?' for _ in team_players])
+        
+        sql = f"""
+            SELECT DISTINCT steam_id_64 
+            FROM dim_players
+            WHERE steam_id_64 IN ({placeholders})
+            ORDER BY steam_id_64
+        """
+        
+        cursor_l2.execute(sql, list(team_players))
+        
+        players = cursor_l2.fetchall()
+        total_players = len(players)
+        logger.info(f"Found {total_players} matching players in L2 to process")
+        
+        if total_players == 0:
+            logger.warning("No matching players found in dim_players table")
+            return
+        
+        success_count = 0
+        error_count = 0
+        
+        # 6. Process each player
+        for idx, row in enumerate(players, 1):
+            steam_id = row[0]
+            
+            try:
+                # Calculate features from each processor tier by tier
+                features = {}
+                
+                # Tier 1: CORE (41 columns)
+                features.update(BasicProcessor.calculate(steam_id, conn_l2))
+                
+                # Tier 2: TACTICAL (44 columns)
+                features.update(TacticalProcessor.calculate(steam_id, conn_l2))
+                
+                # Tier 3: INTELLIGENCE (53 columns)
+                features.update(IntelligenceProcessor.calculate(steam_id, conn_l2))
+                
+                # Tier 4: META (52 columns)
+                features.update(MetaProcessor.calculate(steam_id, conn_l2))
+                
+                # Tier 5: COMPOSITE (11 columns) - requires previous features
+                features.update(CompositeProcessor.calculate(steam_id, conn_l2, features))
+                
+                # Add metadata
+                match_count = _get_match_count(steam_id, conn_l2)
+                round_count = _get_round_count(steam_id, conn_l2)
+                
+                # Insert/Update features in L3
+                _upsert_features(conn_l3, steam_id, features, match_count, round_count, conn_l2)
+                
+                success_count += 1
+                
+                # Batch commit and progress logging
+                if idx % 50 == 0:
+                    conn_l3.commit()
+                    logger.info(f"Progress: {idx}/{total_players} ({success_count} success, {error_count} errors)")
+                    
+            except Exception as e:
+                error_count += 1
+                logger.error(f"Error processing player {steam_id}: {e}")
+                if error_count <= 3:  # Show details for first 3 errors
+                    import traceback
+                    traceback.print_exc()
+                continue
+        
+        # Final commit
+        conn_l3.commit()
+        
+        logger.info("========================================")
+        logger.info(f"L3 Build Complete!")
+        logger.info(f"  Success: {success_count} players")
+        logger.info(f"  Errors: {error_count} players")
+        logger.info(f"  Total: {total_players} players")
+        logger.info(f"  Success Rate: {success_count/total_players*100:.1f}%")
+        logger.info("========================================")
+        
+    except Exception as e:
+        logger.error(f"Fatal error during L3 build: {e}")
+        import traceback
+        traceback.print_exc()
+        
+    finally:
+        conn_l2.close()
+        conn_l3.close()
+
+
+def _get_match_count(steam_id: str, conn_l2: sqlite3.Connection) -> int:
+    """Get total match count for player"""
+    cursor = conn_l2.cursor()
+    cursor.execute("""
+        SELECT COUNT(*) FROM fact_match_players
+        WHERE steam_id_64 = ?
+    """, (steam_id,))
+    return cursor.fetchone()[0]
+
+
+def _get_round_count(steam_id: str, conn_l2: sqlite3.Connection) -> int:
+    """Get total round count for player"""
+    cursor = conn_l2.cursor()
+    cursor.execute("""
+        SELECT COALESCE(SUM(round_total), 0) FROM fact_match_players
+        WHERE steam_id_64 = ?
+    """, (steam_id,))
+    return cursor.fetchone()[0]
+
+
+def _upsert_features(conn_l3: sqlite3.Connection, steam_id: str, features: dict,
+                     match_count: int, round_count: int, conn_l2: sqlite3.Connection):
+    """
+    Insert or update player features in dm_player_features
+    """
+    cursor_l3 = conn_l3.cursor()
+    cursor_l2 = conn_l2.cursor()
+    
+    # Get first and last match dates from L2
+    cursor_l2.execute("""
+        SELECT MIN(m.start_time), MAX(m.start_time)
+        FROM fact_match_players p
+        JOIN fact_matches m ON p.match_id = m.match_id
+        WHERE p.steam_id_64 = ?
+    """, (steam_id,))
+    date_row = cursor_l2.fetchone()
+    first_match_date = date_row[0] if date_row and date_row[0] else None
+    last_match_date = date_row[1] if date_row and date_row[1] else None
+    
+    # Add metadata to features
+    features['total_matches'] = match_count
+    features['total_rounds'] = round_count
+    features['first_match_date'] = first_match_date
+    features['last_match_date'] = last_match_date
+    
+    # Build dynamic column list from features dict
+    columns = ['steam_id_64'] + list(features.keys())
+    placeholders = ','.join(['?' for _ in columns])
+    columns_sql = ','.join(columns)
+    
+    # Build UPDATE SET clause for ON CONFLICT
+    update_clauses = [f"{col}=excluded.{col}" for col in features.keys()]
+    update_clause_sql = ','.join(update_clauses)
+    
+    values = [steam_id] + [features[k] for k in features.keys()]
+    
+    sql = f"""
+        INSERT INTO dm_player_features ({columns_sql})
+        VALUES ({placeholders})
+        ON CONFLICT(steam_id_64) DO UPDATE SET
+            {update_clause_sql},
+            last_updated=CURRENT_TIMESTAMP
+    """
+    
+    cursor_l3.execute(sql, values)
+
+if __name__ == "__main__":
+    main()

database/L3/README.md

@@ -1,75 +0,0 @@
-## 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）
-
-*   ~~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/Roadmap/IMPLEMENTATION_ROADMAP.md

@@ -0,0 +1,609 @@
+# L3 Implementation Roadmap & Checklist
+
+> **Based on**: L3_ARCHITECTURE_PLAN.md v2.0  
+> **Start Date**: 2026-01-28  
+> **Estimated Duration**: 8-10 days
+
+---
+
+## Quick Start Checklist
+
+### ✅ Pre-requisites
+- [x] L1 database完整 (208 matches)
+- [x] L2 database完整 (100% coverage, 51,860 rows)
+- [x] L2 schema documented
+- [x] Profile requirements analyzed
+- [x] L3 architecture designed
+
+### 🎯 Implementation Phases
+
+---
+
+## Phase 1: Schema & Infrastructure (Day 1-2)
+
+### 1.1 Create L3 Database Schema
+- [ ] Create `database/L3/schema.sql`
+  - [ ] dm_player_features (207 columns)
+  - [ ] dm_player_match_history
+  - [ ] dm_player_map_stats  
+  - [ ] dm_player_weapon_stats
+  - [ ] All indexes
+
+### 1.2 Initialize L3 Database
+- [ ] Update `database/L3/L3_Builder.py` init_db()
+- [ ] Run schema creation
+- [ ] Verify tables created
+
+### 1.3 Processor Base Classes
+- [ ] Create `database/L3/processors/__init__.py`
+- [ ] Create `database/L3/processors/base_processor.py`
+  - [ ] BaseFeatureProcessor interface
+  - [ ] SafeAggregator utility class
+  - [ ] Z-score normalization functions
+
+**验收标准**：
+```bash
+sqlite3 database/L3/L3.db ".tables"
+# 应输出: dm_player_features, dm_player_match_history, dm_player_map_stats, dm_player_weapon_stats
+```
+
+---
+
+## Phase 2: Tier 1 - Core Processors (Day 3-4)
+
+### 2.1 BasicProcessor Implementation
+- [ ] Create `database/L3/processors/basic_processor.py`
+
+**Sub-tasks**:
+- [ ] `calculate_basic_stats()` - 15 columns
+  - [ ] AVG(rating, rating2, kd, adr, kast, rws) from fact_match_players
+  - [ ] AVG(headshot_count), hs_rate = SUM(hs)/SUM(kills)
+  - [ ] total_kills, total_deaths, total_assists
+  - [ ] kpr, dpr, survival_rate
+  
+- [ ] `calculate_match_stats()` - 8 columns
+  - [ ] win_rate, wins, losses
+  - [ ] avg_match_duration from fact_matches
+  - [ ] avg_mvps, mvp_rate
+  - [ ] avg_elo_change, total_elo_gained from fact_match_teams
+  
+- [ ] `calculate_weapon_stats()` - 12 columns
+  - [ ] avg_awp_kills, awp_usage_rate
+  - [ ] avg_knife_kills, avg_zeus_kills, zeus_buy_rate
+  - [ ] top_weapon (GROUP BY weapon in fact_round_events)
+  - [ ] weapon_diversity (Shannon entropy)
+  - [ ] rifle/pistol/smg hs_rates
+  
+- [ ] `calculate_objective_stats()` - 6 columns
+  - [ ] avg_plants, avg_defuses, avg_flash_assists
+  - [ ] plant_success_rate, defuse_success_rate
+  - [ ] objective_impact (weighted score)
+
+**测试用例**:
+```python
+features = BasicProcessor.calculate('76561198012345678', conn_l2)
+assert 'core_avg_rating' in features
+assert features['core_total_kills'] > 0
+assert 0 <= features['core_hs_rate'] <= 1
+```
+
+---
+
+## Phase 3: Tier 2 - Tactical Processors (Day 4-5)
+
+### 3.1 TacticalProcessor Implementation
+- [ ] Create `database/L3/processors/tactical_processor.py`
+
+**Sub-tasks**:
+- [ ] `calculate_opening_impact()` - 8 columns
+  - [ ] avg_fk, avg_fd from fact_match_players
+  - [ ] fk_rate, fd_rate
+  - [ ] fk_success_rate (team win when FK)
+  - [ ] entry_kill_rate, entry_death_rate
+  - [ ] opening_duel_winrate
+  
+- [ ] `calculate_multikill()` - 6 columns
+  - [ ] avg_2k, avg_3k, avg_4k, avg_5k
+  - [ ] multikill_rate
+  - [ ] ace_count (5k count)
+  
+- [ ] `calculate_clutch()` - 10 columns
+  - [ ] clutch_1v1/1v2_attempts/wins/rate
+  - [ ] clutch_1v3_plus aggregated
+  - [ ] clutch_impact_score (weighted)
+  
+- [ ] `calculate_utility()` - 12 columns
+  - [ ] util_X_per_round for flash/smoke/molotov/he
+  - [ ] util_usage_rate
+  - [ ] nade_dmg metrics
+  - [ ] flash_efficiency, smoke_timing_score
+  - [ ] util_impact_score
+  
+- [ ] `calculate_economy()` - 8 columns
+  - [ ] dmg_per_1k from fact_round_player_economy
+  - [ ] kpr/kd for eco/force/full rounds
+  - [ ] save_discipline, force_success_rate
+  - [ ] eco_efficiency_score
+
+**测试**:
+```python
+features = TacticalProcessor.calculate('76561198012345678', conn_l2)
+assert 'tac_fk_rate' in features
+assert features['tac_multikill_rate'] >= 0
+```
+
+---
+
+## Phase 4: Tier 3 - Intelligence Processors (Day 5-7)
+
+### 4.1 IntelligenceProcessor Implementation
+- [ ] Create `database/L3/processors/intelligence_processor.py`
+
+**Sub-tasks**:
+- [ ] `calculate_high_iq_kills()` - 8 columns
+  - [ ] wallbang/smoke/blind/noscope kills from fact_round_events flags
+  - [ ] Rates: X_kills / total_kills
+  - [ ] high_iq_score (weighted formula)
+  
+- [ ] `calculate_timing_analysis()` - 12 columns
+  - [ ] early/mid/late kills by event_time bins (0-30s, 30-60s, 60s+)
+  - [ ] timing shares
+  - [ ] avg_kill_time, avg_death_time
+  - [ ] aggression_index, patience_score
+  - [ ] first_contact_time (MIN(event_time) per round)
+  
+- [ ] `calculate_pressure_performance()` - 10 columns
+  - [ ] comeback_kd/rating (when down 4+ rounds)
+  - [ ] losing_streak_kd (3+ round loss streak)
+  - [ ] matchpoint_kpr/rating (at 15-X or 12-X)
+  - [ ] clutch_composure, entry_in_loss
+  - [ ] pressure_performance_index, big_moment_score
+  - [ ] tilt_resistance
+  
+- [ ] `calculate_position_mastery()` - 15 columns ⚠️ Complex
+  - [ ] site_a/b/mid_control_rate from xyz clustering
+  - [ ] favorite_position (most common cluster)
+  - [ ] position_diversity (entropy)
+  - [ ] rotation_speed (distance between kills)
+  - [ ] map_coverage, defensive/aggressive positioning
+  - [ ] lurk_tendency, site_anchor_score
+  - [ ] spatial_iq_score
+  
+- [ ] `calculate_trade_network()` - 8 columns
+  - [ ] trade_kill_count (kills within 5s of teammate death)
+  - [ ] trade_kill_rate
+  - [ ] trade_response_time (AVG seconds)
+  - [ ] trade_given (deaths traded by teammate)
+  - [ ] trade_balance, trade_efficiency
+  - [ ] teamwork_score
+
+**Position Mastery特别注意**:
+```python
+# 需要使用sklearn DBSCAN聚类
+from sklearn.cluster import DBSCAN
+
+def cluster_player_positions(steam_id, conn_l2):
+    """从fact_round_events提取xyz坐标并聚类"""
+    cursor = conn_l2.cursor()
+    cursor.execute("""
+        SELECT attacker_pos_x, attacker_pos_y, attacker_pos_z
+        FROM fact_round_events
+        WHERE attacker_steam_id = ?
+        AND attacker_pos_x IS NOT NULL
+    """, (steam_id,))
+    
+    coords = cursor.fetchall()
+    # DBSCAN clustering...
+```
+
+**测试**:
+```python
+features = IntelligenceProcessor.calculate('76561198012345678', conn_l2)
+assert 'int_high_iq_score' in features
+assert features['int_timing_early_kill_share'] + features['int_timing_mid_kill_share'] + features['int_timing_late_kill_share'] <= 1.1  # Allow rounding
+```
+
+---
+
+## Phase 5: Tier 4 - Meta Processors (Day 7-8)
+
+### 5.1 MetaProcessor Implementation
+- [ ] Create `database/L3/processors/meta_processor.py`
+
+**Sub-tasks**:
+- [ ] `calculate_stability()` - 8 columns
+  - [ ] rating_volatility (STDDEV of last 20 matches)
+  - [ ] recent_form_rating (AVG last 10)
+  - [ ] win/loss_rating
+  - [ ] rating_consistency (100 - volatility_norm)
+  - [ ] time_rating_correlation (CORR(duration, rating))
+  - [ ] map_stability, elo_tier_stability
+  
+- [ ] `calculate_side_preference()` - 14 columns
+  - [ ] side_ct/t_rating from fact_match_players_ct/t
+  - [ ] side_ct/t_kd, win_rate, fk_rate, kast
+  - [ ] side_rating_diff, side_kd_diff
+  - [ ] side_preference ('CT'/'T'/'Balanced')
+  - [ ] side_balance_score
+  
+- [ ] `calculate_opponent_adaptation()` - 12 columns
+  - [ ] vs_lower/similar/higher_elo_rating/kd
+  - [ ] Based on fact_match_teams.group_origin_elo差值
+  - [ ] elo_adaptation, stomping_score, upset_score
+  - [ ] consistency_across_elos, rank_resistance
+  - [ ] smurf_detection
+  
+- [ ] `calculate_map_specialization()` - 10 columns
+  - [ ] best/worst_map, best/worst_rating
+  - [ ] map_diversity (entropy)
+  - [ ] map_pool_size (maps with 5+ matches)
+  - [ ] map_specialist_score, map_versatility
+  - [ ] comfort_zone_rate, map_adaptation
+  
+- [ ] `calculate_session_pattern()` - 8 columns
+  - [ ] avg_matches_per_day
+  - [ ] longest_streak (consecutive days)
+  - [ ] weekend/weekday_rating
+  - [ ] morning/afternoon/evening/night_rating (based on timestamp)
+
+**测试**:
+```python
+features = MetaProcessor.calculate('76561198012345678', conn_l2)
+assert 'meta_rating_volatility' in features
+assert features['meta_side_preference'] in ['CT', 'T', 'Balanced']
+```
+
+---
+
+## Phase 6: Tier 5 - Composite Processors (Day 8)
+
+### 6.1 CompositeProcessor Implementation
+- [ ] Create `database/L3/processors/composite_processor.py`
+
+**Sub-tasks**:
+- [ ] `normalize_and_standardize()` helper
+  - [ ] Z-score normalization function
+  - [ ] Global mean/std calculation from all players
+  - [ ] Map Z-score to 0-100 range
+  
+- [ ] `calculate_radar_scores()` - 8 scores
+  - [ ] score_aim: 25% Rating + 20% KD + 15% ADR + 10% DuelWin + 10% HighEloKD + 20% MultiKill
+  - [ ] score_clutch: 25% 1v3+ + 20% MatchPtWin + 20% ComebackKD + 15% PressureEntry + 20% Rating
+  - [ ] score_pistol: 30% PistolKills + 30% PistolWin + 20% PistolKD + 20% PistolHS%
+  - [ ] score_defense: 35% CT_Rating + 35% T_Rating + 15% CT_FK + 15% T_FK
+  - [ ] score_utility: 35% UsageRate + 25% NadeDmg + 20% FlashEff + 20% FlashEnemy
+  - [ ] score_stability: 30% (100-Volatility) + 30% LossRating + 20% WinRating + 20% Consistency
+  - [ ] score_economy: 50% Dmg/$1k + 30% EcoKPR + 20% SaveRoundKD
+  - [ ] score_pace: 40% EntryTiming + 30% TradeSpeed + 30% AggressionIndex
+  
+- [ ] `calculate_overall_score()` - AVG of 8 scores
+  
+- [ ] `classify_tier()` - Performance tier
+  - [ ] Elite: overall > 75
+  - [ ] Advanced: 60-75
+  - [ ] Intermediate: 40-60
+  - [ ] Beginner: < 40
+  
+- [ ] `calculate_percentile()` - Rank among all players
+
+**依赖**:
+```python
+def calculate(steam_id: str, conn_l2: sqlite3.Connection, pre_features: dict) -> dict:
+    """
+    需要前面4个Tier的特征作为输入
+    
+    Args:
+        pre_features: 包含Tier 1-4的所有特征
+    """
+    pass
+```
+
+**测试**:
+```python
+# 需要先计算所有前置特征
+features = {}
+features.update(BasicProcessor.calculate(steam_id, conn_l2))
+features.update(TacticalProcessor.calculate(steam_id, conn_l2))
+features.update(IntelligenceProcessor.calculate(steam_id, conn_l2))
+features.update(MetaProcessor.calculate(steam_id, conn_l2))
+composite = CompositeProcessor.calculate(steam_id, conn_l2, features)
+
+assert 0 <= composite['score_aim'] <= 100
+assert composite['tier_classification'] in ['Elite', 'Advanced', 'Intermediate', 'Beginner']
+```
+
+---
+
+## Phase 7: L3_Builder Integration (Day 8-9)
+
+### 7.1 Main Builder Logic
+- [ ] Update `database/L3/L3_Builder.py`
+  - [ ] Import all processors
+  - [ ] Main loop: iterate all players from dim_players
+  - [ ] Call processors in order
+  - [ ] _upsert_features() helper
+  - [ ] Batch commit every 100 players
+  - [ ] Progress logging
+
+```python
+def main():
+    logger.info("Starting L3 Builder...")
+    
+    # 1. Init DB
+    init_db()
+    
+    # 2. Connect
+    conn_l2 = sqlite3.connect(L2_DB_PATH)
+    conn_l3 = sqlite3.connect(L3_DB_PATH)
+    
+    # 3. Get all players
+    cursor = conn_l2.cursor()
+    cursor.execute("SELECT DISTINCT steam_id_64 FROM dim_players")
+    players = cursor.fetchall()
+    
+    logger.info(f"Processing {len(players)} players...")
+    
+    for idx, (steam_id,) in enumerate(players, 1):
+        try:
+            # 4. Calculate features tier by tier
+            features = {}
+            features.update(BasicProcessor.calculate(steam_id, conn_l2))
+            features.update(TacticalProcessor.calculate(steam_id, conn_l2))
+            features.update(IntelligenceProcessor.calculate(steam_id, conn_l2))
+            features.update(MetaProcessor.calculate(steam_id, conn_l2))
+            features.update(CompositeProcessor.calculate(steam_id, conn_l2, features))
+            
+            # 5. Upsert to L3
+            _upsert_features(conn_l3, steam_id, features)
+            
+            # 6. Commit batch
+            if idx % 100 == 0:
+                conn_l3.commit()
+                logger.info(f"Processed {idx}/{len(players)} players")
+        
+        except Exception as e:
+            logger.error(f"Error processing {steam_id}: {e}")
+    
+    conn_l3.commit()
+    logger.info("Done!")
+```
+
+### 7.2 Auxiliary Tables Population
+- [ ] Populate `dm_player_match_history`
+  - [ ] FROM fact_match_players JOIN fact_matches
+  - [ ] ORDER BY match date
+  - [ ] Calculate match_sequence, rolling averages
+  
+- [ ] Populate `dm_player_map_stats`
+  - [ ] GROUP BY steam_id, map_name
+  - [ ] FROM fact_match_players
+  
+- [ ] Populate `dm_player_weapon_stats`
+  - [ ] GROUP BY steam_id, weapon_name
+  - [ ] FROM fact_round_events
+  - [ ] TOP 10 weapons per player
+
+### 7.3 Full Build Test
+- [ ] Run: `python database/L3/L3_Builder.py`
+- [ ] Verify: All players processed
+- [ ] Check: Row counts in all L3 tables
+- [ ] Validate: Sample features make sense
+
+**验收标准**:
+```sql
+SELECT COUNT(*) FROM dm_player_features;  -- 应该 = dim_players count
+SELECT AVG(core_avg_rating) FROM dm_player_features;  -- 应该接近1.0
+SELECT COUNT(*) FROM dm_player_features WHERE score_aim > 0;  -- 大部分玩家有评分
+```
+
+---
+
+## Phase 8: Web Services Refactoring (Day 9-10)
+
+### 8.1 Create PlayerService
+- [ ] Create `web/services/player_service.py`
+
+```python
+class PlayerService:
+    @staticmethod
+    def get_player_features(steam_id: str) -> dict:
+        """获取完整特征（dm_player_features）"""
+        pass
+    
+    @staticmethod
+    def get_player_radar_data(steam_id: str) -> dict:
+        """获取雷达图8维数据"""
+        pass
+    
+    @staticmethod
+    def get_player_core_stats(steam_id: str) -> dict:
+        """获取核心Dashboard数据"""
+        pass
+    
+    @staticmethod
+    def get_player_history(steam_id: str, limit: int = 20) -> list:
+        """获取历史趋势数据"""
+        pass
+    
+    @staticmethod
+    def get_player_map_stats(steam_id: str) -> list:
+        """获取各地图统计"""
+        pass
+    
+    @staticmethod
+    def get_player_weapon_stats(steam_id: str, top_n: int = 10) -> list:
+        """获取Top N武器"""
+        pass
+    
+    @staticmethod
+    def get_players_ranking(order_by: str = 'core_avg_rating', 
+                           limit: int = 100, 
+                           offset: int = 0) -> list:
+        """获取排行榜"""
+        pass
+```
+
+- [ ] Implement all methods
+- [ ] Add error handling
+- [ ] Add caching (optional)
+
+### 8.2 Refactor Routes
+- [ ] Update `web/routes/players.py`
+  - [ ] `/profile/<steam_id>` route
+  - [ ] Use PlayerService instead of direct DB queries
+  - [ ] Pass features dict to template
+  
+- [ ] Add API endpoints
+  - [ ] `/api/players/<steam_id>/features`
+  - [ ] `/api/players/ranking`
+  - [ ] `/api/players/<steam_id>/history`
+
+### 8.3 Update feature_service.py
+- [ ] Mark old rebuild methods as DEPRECATED
+- [ ] Redirect to L3_Builder.py
+- [ ] Keep query methods for backward compatibility
+
+---
+
+## Phase 9: Frontend Integration (Day 10-11)
+
+### 9.1 Update profile.html Template
+- [ ] Dashboard cards: use `features.core_*`
+- [ ] Radar chart: use `features.score_*`
+- [ ] Trend chart: use `history` data
+- [ ] Core Performance section
+- [ ] Gunfight section
+- [ ] Opening Impact section
+- [ ] Clutch section
+- [ ] High IQ Kills section
+- [ ] Map stats table
+- [ ] Weapon stats table
+
+### 9.2 JavaScript Integration
+- [ ] Radar chart rendering (Chart.js)
+- [ ] Trend chart rendering
+- [ ] Dynamic data loading
+
+### 9.3 UI Polish
+- [ ] Responsive design
+- [ ] Loading states
+- [ ] Error handling
+- [ ] Tooltips for complex metrics
+
+---
+
+## Phase 10: Testing & Validation (Day 11-12)
+
+### 10.1 Unit Tests
+- [ ] Test each processor independently
+- [ ] Mock L2 data
+- [ ] Verify calculation correctness
+
+### 10.2 Integration Tests
+- [ ] Full L3_Builder run
+- [ ] Verify all tables populated
+- [ ] Check data consistency
+
+### 10.3 Performance Tests
+- [ ] Benchmark L3_Builder runtime
+- [ ] Profile slow queries
+- [ ] Optimize if needed
+
+### 10.4 Data Quality Checks
+- [ ] Verify no NULL values where expected
+- [ ] Check value ranges (e.g., 0 <= rate <= 1)
+- [ ] Validate composite scores (0-100)
+- [ ] Cross-check with L2 source data
+
+---
+
+## Success Criteria
+
+### ✅ L3 Database
+- [ ] All 4 tables created with correct schemas
+- [ ] dm_player_features has 207 columns
+- [ ] All players from L2 have corresponding L3 rows
+- [ ] No critical NULL values
+
+### ✅ Feature Calculation
+- [ ] All 5 processors implemented and tested
+- [ ] 207 features calculated correctly
+- [ ] Composite scores in 0-100 range
+- [ ] Tier classification working
+
+### ✅ Services & Routes
+- [ ] PlayerService provides all query methods
+- [ ] Routes use services correctly
+- [ ] API endpoints return valid JSON
+- [ ] No direct DB queries in routes
+
+### ✅ Frontend
+- [ ] Profile page renders correctly
+- [ ] Radar chart displays 8 dimensions
+- [ ] Trend chart shows history
+- [ ] All sections populated with data
+
+### ✅ Performance
+- [ ] L3_Builder completes in < 20 min for 1000 players
+- [ ] Profile page loads in < 200ms
+- [ ] No N+1 query problems
+
+---
+
+## Risk Mitigation
+
+### 🔴 High Risk Items
+1. **Position Mastery (xyz clustering)**
+   - Mitigation: Start with simple grid-based approach, defer ML clustering
+   
+2. **Composite Score Standardization**
+   - Mitigation: Use simple percentile-based normalization as fallback
+   
+3. **Performance at Scale**
+   - Mitigation: Implement incremental updates, add indexes
+
+### 🟡 Medium Risk Items
+1. **Time Window Calculations (trades)**
+   - Mitigation: Use efficient self-JOIN with time bounds
+   
+2. **Missing Data Handling**
+   - Mitigation: Comprehensive NULL handling, default values
+
+### 🟢 Low Risk Items
+1. Basic aggregations (AVG, SUM, COUNT)
+2. Service layer refactoring
+3. Template updates
+
+---
+
+## Next Actions
+
+**Immediate (Today)**:
+1. Create schema.sql
+2. Initialize L3.db
+3. Create processor base classes
+
+**Tomorrow**:
+1. Implement BasicProcessor
+2. Test with sample player
+3. Start TacticalProcessor
+
+**This Week**:
+1. Complete all 5 processors
+2. Full L3_Builder run
+3. Service refactoring
+
+**Next Week**:
+1. Frontend integration
+2. Testing & validation
+3. Documentation
+
+---
+
+## Notes
+
+- 保持每个processor独立，便于单元测试
+- 使用动态SQL避免column count错误
+- 所有rate/percentage使用0-1范围存储，UI展示时乘100
+- 时间戳统一使用Unix timestamp (INTEGER)
+- 遵循"查询不计算"原则：web层只SELECT，不做聚合

database/L3/analyzer/test_basic_processor.py

@@ -0,0 +1,59 @@
+"""
+Test BasicProcessor implementation
+"""
+
+import sqlite3
+import sys
+import os
+
+# Add parent directory to path
+sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..'))
+
+from database.L3.processors import BasicProcessor
+
+def test_basic_processor():
+    """Test BasicProcessor on a real player from L2"""
+    
+    # Connect to L2 database
+    l2_path = os.path.join(os.path.dirname(__file__), '..', 'L2', 'L2.db')
+    conn = sqlite3.connect(l2_path)
+    
+    try:
+        # Get a test player
+        cursor = conn.cursor()
+        cursor.execute("SELECT steam_id_64 FROM dim_players LIMIT 1")
+        result = cursor.fetchone()
+        
+        if not result:
+            print("No players found in L2 database")
+            return False
+        
+        steam_id = result[0]
+        print(f"Testing BasicProcessor for player: {steam_id}")
+        
+        # Calculate features
+        features = BasicProcessor.calculate(steam_id, conn)
+        
+        print(f"\n✓ Calculated {len(features)} features")
+        print(f"\nSample features:")
+        print(f"  core_avg_rating: {features.get('core_avg_rating', 0)}")
+        print(f"  core_avg_kd: {features.get('core_avg_kd', 0)}")
+        print(f"  core_total_kills: {features.get('core_total_kills', 0)}")
+        print(f"  core_win_rate: {features.get('core_win_rate', 0)}")
+        print(f"  core_top_weapon: {features.get('core_top_weapon', 'unknown')}")
+        
+        # Verify we have all 41 features
+        expected_count = 41
+        if len(features) == expected_count:
+            print(f"\n✓ Feature count correct: {expected_count}")
+            return True
+        else:
+            print(f"\n✗ Feature count mismatch: expected {expected_count}, got {len(features)}")
+            return False
+            
+    finally:
+        conn.close()
+
+if __name__ == "__main__":
+    success = test_basic_processor()
+    sys.exit(0 if success else 1)

database/L3/check_distribution.py

@@ -0,0 +1,261 @@
+"""  
+L3 Feature Distribution Checker
+
+Analyzes data quality issues:
+- NaN/NULL values
+- All values identical (no variance)
+- Extreme outliers
+- Zero-only columns
+"""
+
+import sqlite3
+import sys
+from pathlib import Path
+from collections import defaultdict
+import math
+import os
+
+# Set UTF-8 encoding for Windows
+if sys.platform == 'win32':
+    import io
+    sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', errors='replace')
+
+# Add project root to path
+project_root = Path(__file__).parent.parent.parent
+sys.path.insert(0, str(project_root))
+
+L3_DB_PATH = project_root / "database" / "L3" / "L3.db"
+
+
+def get_column_stats(cursor, table_name):
+    """Get statistics for all numeric columns in a table"""
+    
+    # Get column names
+    cursor.execute(f"PRAGMA table_info({table_name})")
+    columns = cursor.fetchall()
+    
+    # Filter to numeric columns (skip steam_id_64, TEXT columns)
+    numeric_cols = []
+    for col in columns:
+        col_name = col[1]
+        col_type = col[2]
+        if col_name != 'steam_id_64' and col_type in ('REAL', 'INTEGER'):
+            numeric_cols.append(col_name)
+    
+    print(f"\n{'='*80}")
+    print(f"Table: {table_name}")
+    print(f"Analyzing {len(numeric_cols)} numeric columns...")
+    print(f"{'='*80}\n")
+    
+    issues_found = defaultdict(list)
+    
+    for col in numeric_cols:
+        # Get basic statistics
+        cursor.execute(f"""
+            SELECT 
+                COUNT(*) as total_count,
+                COUNT({col}) as non_null_count,
+                MIN({col}) as min_val,
+                MAX({col}) as max_val,
+                AVG({col}) as avg_val,
+                COUNT(DISTINCT {col}) as unique_count
+            FROM {table_name}
+        """)
+        
+        row = cursor.fetchone()
+        total = row[0]
+        non_null = row[1]
+        min_val = row[2]
+        max_val = row[3]
+        avg_val = row[4]
+        unique = row[5]
+        
+        null_count = total - non_null
+        null_pct = (null_count / total * 100) if total > 0 else 0
+        
+        # Check for issues
+        
+        # Issue 1: High NULL percentage
+        if null_pct > 50:
+            issues_found['HIGH_NULL'].append({
+                'column': col,
+                'null_pct': null_pct,
+                'null_count': null_count,
+                'total': total
+            })
+        
+        # Issue 2: All values identical (no variance)
+        if non_null > 0 and unique == 1:
+            issues_found['NO_VARIANCE'].append({
+                'column': col,
+                'value': min_val,
+                'count': non_null
+            })
+        
+        # Issue 3: All zeros
+        if non_null > 0 and min_val == 0 and max_val == 0:
+            issues_found['ALL_ZEROS'].append({
+                'column': col,
+                'count': non_null
+            })
+        
+        # Issue 4: NaN values (in SQLite, NaN is stored as NULL or text 'nan')
+        cursor.execute(f"""
+            SELECT COUNT(*) FROM {table_name}
+            WHERE CAST({col} AS TEXT) = 'nan' OR {col} IS NULL
+        """)
+        nan_count = cursor.fetchone()[0]
+        if nan_count > non_null * 0.1:  # More than 10% NaN
+            issues_found['NAN_VALUES'].append({
+                'column': col,
+                'nan_count': nan_count,
+                'pct': (nan_count / total * 100)
+            })
+        
+        # Issue 5: Extreme outliers (using IQR method)
+        if non_null > 10 and unique > 2:  # Need enough data
+            cursor.execute(f"""
+                WITH ranked AS (
+                    SELECT {col},
+                           ROW_NUMBER() OVER (ORDER BY {col}) as rn,
+                           COUNT(*) OVER () as total
+                    FROM {table_name}
+                    WHERE {col} IS NOT NULL
+                )
+                SELECT 
+                    (SELECT {col} FROM ranked WHERE rn = CAST(total * 0.25 AS INTEGER)) as q1,
+                    (SELECT {col} FROM ranked WHERE rn = CAST(total * 0.75 AS INTEGER)) as q3
+                FROM ranked
+                LIMIT 1
+            """)
+            
+            quartiles = cursor.fetchone()
+            if quartiles and quartiles[0] is not None and quartiles[1] is not None:
+                q1, q3 = quartiles
+                iqr = q3 - q1
+                
+                if iqr > 0:
+                    lower_bound = q1 - 1.5 * iqr
+                    upper_bound = q3 + 1.5 * iqr
+                    
+                    cursor.execute(f"""
+                        SELECT COUNT(*) FROM {table_name}
+                        WHERE {col} < ? OR {col} > ?
+                    """, (lower_bound, upper_bound))
+                    
+                    outlier_count = cursor.fetchone()[0]
+                    outlier_pct = (outlier_count / non_null * 100) if non_null > 0 else 0
+                    
+                    if outlier_pct > 5:  # More than 5% outliers
+                        issues_found['OUTLIERS'].append({
+                            'column': col,
+                            'outlier_count': outlier_count,
+                            'outlier_pct': outlier_pct,
+                            'q1': q1,
+                            'q3': q3,
+                            'iqr': iqr
+                        })
+        
+        # Print summary for columns with good data
+        if col not in [item['column'] for sublist in issues_found.values() for item in sublist]:
+            if non_null > 0 and min_val is not None:
+                print(f"✓ {col:45s} | Min: {min_val:10.3f} | Max: {max_val:10.3f} | "
+                      f"Avg: {avg_val:10.3f} | Unique: {unique:6d}")
+    
+    return issues_found
+
+
+def print_issues(issues_found):
+    """Print detailed issue report"""
+    
+    if not any(issues_found.values()):
+        print(f"\n{'='*80}")
+        print("✅ NO DATA QUALITY ISSUES FOUND!")
+        print(f"{'='*80}\n")
+        return
+    
+    print(f"\n{'='*80}")
+    print("⚠️  DATA QUALITY ISSUES DETECTED")
+    print(f"{'='*80}\n")
+    
+    # HIGH NULL
+    if issues_found['HIGH_NULL']:
+        print(f"❌ HIGH NULL PERCENTAGE ({len(issues_found['HIGH_NULL'])} columns):")
+        for issue in issues_found['HIGH_NULL']:
+            print(f"   - {issue['column']:45s}: {issue['null_pct']:6.2f}% NULL "
+                  f"({issue['null_count']}/{issue['total']})")
+        print()
+    
+    # NO VARIANCE
+    if issues_found['NO_VARIANCE']:
+        print(f"❌ NO VARIANCE - All values identical ({len(issues_found['NO_VARIANCE'])} columns):")
+        for issue in issues_found['NO_VARIANCE']:
+            print(f"   - {issue['column']:45s}: All {issue['count']} values = {issue['value']}")
+        print()
+    
+    # ALL ZEROS
+    if issues_found['ALL_ZEROS']:
+        print(f"❌ ALL ZEROS ({len(issues_found['ALL_ZEROS'])} columns):")
+        for issue in issues_found['ALL_ZEROS']:
+            print(f"   - {issue['column']:45s}: All {issue['count']} values are 0")
+        print()
+    
+    # NAN VALUES
+    if issues_found['NAN_VALUES']:
+        print(f"❌ NAN/NULL VALUES ({len(issues_found['NAN_VALUES'])} columns):")
+        for issue in issues_found['NAN_VALUES']:
+            print(f"   - {issue['column']:45s}: {issue['nan_count']} NaN/NULL ({issue['pct']:.2f}%)")
+        print()
+    
+    # OUTLIERS
+    if issues_found['OUTLIERS']:
+        print(f"⚠️  EXTREME OUTLIERS ({len(issues_found['OUTLIERS'])} columns):")
+        for issue in issues_found['OUTLIERS']:
+            print(f"   - {issue['column']:45s}: {issue['outlier_count']} outliers ({issue['outlier_pct']:.2f}%) "
+                  f"[Q1={issue['q1']:.2f}, Q3={issue['q3']:.2f}, IQR={issue['iqr']:.2f}]")
+        print()
+
+
+def main():
+    """Main entry point"""
+    
+    if not L3_DB_PATH.exists():
+        print(f"❌ L3 database not found at: {L3_DB_PATH}")
+        return 1
+    
+    print(f"\n{'='*80}")
+    print(f"L3 Feature Distribution Checker")
+    print(f"Database: {L3_DB_PATH}")
+    print(f"{'='*80}")
+    
+    conn = sqlite3.connect(L3_DB_PATH)
+    cursor = conn.cursor()
+    
+    # Get row count
+    cursor.execute("SELECT COUNT(*) FROM dm_player_features")
+    total_players = cursor.fetchone()[0]
+    print(f"\nTotal players: {total_players}")
+    
+    # Check dm_player_features table
+    issues = get_column_stats(cursor, 'dm_player_features')
+    print_issues(issues)
+    
+    # Summary statistics
+    print(f"\n{'='*80}")
+    print("SUMMARY")
+    print(f"{'='*80}")
+    print(f"Total Issues Found:")
+    print(f"  - High NULL percentage: {len(issues['HIGH_NULL'])}")
+    print(f"  - No variance (all same): {len(issues['NO_VARIANCE'])}")
+    print(f"  - All zeros: {len(issues['ALL_ZEROS'])}")
+    print(f"  - NaN/NULL values: {len(issues['NAN_VALUES'])}")
+    print(f"  - Extreme outliers: {len(issues['OUTLIERS'])}")
+    print()
+    
+    conn.close()
+    
+    return 0
+
+
+if __name__ == '__main__':
+    sys.exit(main())

database/L3/processors/__init__.py

@@ -0,0 +1,38 @@
+"""
+L3 Feature Processors
+
+5-Tier Architecture:
+- BasicProcessor: Tier 1 CORE (41 columns)
+- TacticalProcessor: Tier 2 TACTICAL (44 columns)
+- IntelligenceProcessor: Tier 3 INTELLIGENCE (53 columns)
+- MetaProcessor: Tier 4 META (52 columns)
+- CompositeProcessor: Tier 5 COMPOSITE (11 columns)
+"""
+
+from .base_processor import (
+    BaseFeatureProcessor,
+    SafeAggregator,
+    NormalizationUtils,
+    WeaponCategories,
+    MapAreas
+)
+
+# Import processors as they are implemented
+from .basic_processor import BasicProcessor
+from .tactical_processor import TacticalProcessor
+from .intelligence_processor import IntelligenceProcessor
+from .meta_processor import MetaProcessor
+from .composite_processor import CompositeProcessor
+
+__all__ = [
+    'BaseFeatureProcessor',
+    'SafeAggregator',
+    'NormalizationUtils',
+    'WeaponCategories',
+    'MapAreas',
+    'BasicProcessor',
+    'TacticalProcessor',
+    'IntelligenceProcessor',
+    'MetaProcessor',
+    'CompositeProcessor',
+]

database/L3/processors/base_processor.py

@@ -0,0 +1,320 @@
+"""
+Base processor classes and utility functions for L3 feature calculation
+"""
+
+import sqlite3
+import math
+from typing import Dict, Any, List, Optional
+from abc import ABC, abstractmethod
+
+
+class SafeAggregator:
+    """Utility class for safe mathematical operations with NULL handling"""
+    
+    @staticmethod
+    def safe_divide(numerator: float, denominator: float, default: float = 0.0) -> float:
+        """Safe division with NULL/zero handling"""
+        if denominator is None or denominator == 0:
+            return default
+        if numerator is None:
+            return default
+        return numerator / denominator
+    
+    @staticmethod
+    def safe_avg(values: List[float], default: float = 0.0) -> float:
+        """Safe average calculation"""
+        if not values or len(values) == 0:
+            return default
+        valid_values = [v for v in values if v is not None]
+        if not valid_values:
+            return default
+        return sum(valid_values) / len(valid_values)
+    
+    @staticmethod
+    def safe_stddev(values: List[float], default: float = 0.0) -> float:
+        """Safe standard deviation calculation"""
+        if not values or len(values) < 2:
+            return default
+        valid_values = [v for v in values if v is not None]
+        if len(valid_values) < 2:
+            return default
+        
+        mean = sum(valid_values) / len(valid_values)
+        variance = sum((x - mean) ** 2 for x in valid_values) / len(valid_values)
+        return math.sqrt(variance)
+    
+    @staticmethod
+    def safe_sum(values: List[float], default: float = 0.0) -> float:
+        """Safe sum calculation"""
+        if not values:
+            return default
+        valid_values = [v for v in values if v is not None]
+        return sum(valid_values) if valid_values else default
+    
+    @staticmethod
+    def safe_min(values: List[float], default: float = 0.0) -> float:
+        """Safe minimum calculation"""
+        if not values:
+            return default
+        valid_values = [v for v in values if v is not None]
+        return min(valid_values) if valid_values else default
+    
+    @staticmethod
+    def safe_max(values: List[float], default: float = 0.0) -> float:
+        """Safe maximum calculation"""
+        if not values:
+            return default
+        valid_values = [v for v in values if v is not None]
+        return max(valid_values) if valid_values else default
+
+
+class NormalizationUtils:
+    """Z-score normalization and scaling utilities"""
+    
+    @staticmethod
+    def z_score_normalize(value: float, mean: float, std: float, 
+                         scale_min: float = 0.0, scale_max: float = 100.0) -> float:
+        """
+        Z-score normalization to a target range
+        
+        Args:
+            value: Value to normalize
+            mean: Population mean
+            std: Population standard deviation
+            scale_min: Target minimum (default: 0)
+            scale_max: Target maximum (default: 100)
+            
+        Returns:
+            Normalized value in [scale_min, scale_max] range
+        """
+        if std == 0 or std is None:
+            return (scale_min + scale_max) / 2.0
+        
+        # Calculate z-score
+        z = (value - mean) / std
+        
+        # Map to target range (±3σ covers ~99.7% of data)
+        # z = -3 → scale_min, z = 0 → midpoint, z = 3 → scale_max
+        midpoint = (scale_min + scale_max) / 2.0
+        scale_range = (scale_max - scale_min) / 6.0  # 6σ total range
+        
+        normalized = midpoint + (z * scale_range)
+        
+        # Clamp to target range
+        return max(scale_min, min(scale_max, normalized))
+    
+    @staticmethod
+    def percentile_normalize(value: float, all_values: List[float], 
+                            scale_min: float = 0.0, scale_max: float = 100.0) -> float:
+        """
+        Percentile-based normalization
+        
+        Args:
+            value: Value to normalize
+            all_values: All values in population
+            scale_min: Target minimum
+            scale_max: Target maximum
+            
+        Returns:
+            Normalized value based on percentile
+        """
+        if not all_values:
+            return scale_min
+        
+        sorted_values = sorted(all_values)
+        rank = sum(1 for v in sorted_values if v < value)
+        percentile = rank / len(sorted_values)
+        
+        return scale_min + (percentile * (scale_max - scale_min))
+    
+    @staticmethod
+    def min_max_normalize(value: float, min_val: float, max_val: float,
+                         scale_min: float = 0.0, scale_max: float = 100.0) -> float:
+        """Min-max normalization to target range"""
+        if max_val == min_val:
+            return (scale_min + scale_max) / 2.0
+        
+        normalized = (value - min_val) / (max_val - min_val)
+        return scale_min + (normalized * (scale_max - scale_min))
+    
+    @staticmethod
+    def calculate_population_stats(conn_l3: sqlite3.Connection, column: str) -> Dict[str, float]:
+        """
+        Calculate population mean and std for a column in dm_player_features
+        
+        Args:
+            conn_l3: L3 database connection
+            column: Column name to analyze
+            
+        Returns:
+            dict with 'mean', 'std', 'min', 'max'
+        """
+        cursor = conn_l3.cursor()
+        cursor.execute(f"""
+            SELECT 
+                AVG({column}) as mean,
+                STDDEV({column}) as std,
+                MIN({column}) as min,
+                MAX({column}) as max
+            FROM dm_player_features
+            WHERE {column} IS NOT NULL
+        """)
+        
+        row = cursor.fetchone()
+        return {
+            'mean': row[0] if row[0] is not None else 0.0,
+            'std': row[1] if row[1] is not None else 1.0,
+            'min': row[2] if row[2] is not None else 0.0,
+            'max': row[3] if row[3] is not None else 0.0
+        }
+
+
+class BaseFeatureProcessor(ABC):
+    """
+    Abstract base class for all feature processors
+    
+    Each processor implements the calculate() method which returns a dict
+    of feature_name: value pairs.
+    """
+    
+    MIN_MATCHES_REQUIRED = 5  # Minimum matches needed for feature calculation
+    
+    @staticmethod
+    @abstractmethod
+    def calculate(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate features for a specific player
+        
+        Args:
+            steam_id: Player's Steam ID (steam_id_64)
+            conn_l2: Connection to L2 database
+            
+        Returns:
+            Dictionary of {feature_name: value}
+        """
+        pass
+    
+    @staticmethod
+    def check_min_matches(steam_id: str, conn_l2: sqlite3.Connection, 
+                         min_required: int = None) -> bool:
+        """
+        Check if player has minimum required matches
+        
+        Args:
+            steam_id: Player's Steam ID
+            conn_l2: L2 database connection
+            min_required: Minimum matches (uses class default if None)
+            
+        Returns:
+            True if player has enough matches
+        """
+        if min_required is None:
+            min_required = BaseFeatureProcessor.MIN_MATCHES_REQUIRED
+        
+        cursor = conn_l2.cursor()
+        cursor.execute("""
+            SELECT COUNT(*) FROM fact_match_players
+            WHERE steam_id_64 = ?
+        """, (steam_id,))
+        
+        count = cursor.fetchone()[0]
+        return count >= min_required
+    
+    @staticmethod
+    def get_player_match_count(steam_id: str, conn_l2: sqlite3.Connection) -> int:
+        """Get total match count for player"""
+        cursor = conn_l2.cursor()
+        cursor.execute("""
+            SELECT COUNT(*) FROM fact_match_players
+            WHERE steam_id_64 = ?
+        """, (steam_id,))
+        return cursor.fetchone()[0]
+    
+    @staticmethod
+    def get_player_round_count(steam_id: str, conn_l2: sqlite3.Connection) -> int:
+        """Get total round count for player"""
+        cursor = conn_l2.cursor()
+        cursor.execute("""
+            SELECT SUM(round_total) FROM fact_match_players
+            WHERE steam_id_64 = ?
+        """, (steam_id,))
+        result = cursor.fetchone()[0]
+        return result if result is not None else 0
+
+
+class WeaponCategories:
+    """Weapon categorization constants"""
+    
+    RIFLES = [
+        'ak47', 'aug', 'm4a1', 'm4a1_silencer', 'sg556', 'galilar', 'famas'
+    ]
+    
+    PISTOLS = [
+        'glock', 'usp_silencer', 'hkp2000', 'p250', 'fiveseven', 'tec9', 
+        'cz75a', 'deagle', 'elite', 'revolver'
+    ]
+    
+    SMGS = [
+        'mac10', 'mp9', 'mp7', 'mp5sd', 'ump45', 'p90', 'bizon'
+    ]
+    
+    SNIPERS = [
+        'awp', 'ssg08', 'scar20', 'g3sg1'
+    ]
+    
+    HEAVY = [
+        'nova', 'xm1014', 'mag7', 'sawedoff', 'm249', 'negev'
+    ]
+    
+    @classmethod
+    def get_category(cls, weapon_name: str) -> str:
+        """Get category for a weapon"""
+        weapon_clean = weapon_name.lower().replace('weapon_', '')
+        
+        if weapon_clean in cls.RIFLES:
+            return 'rifle'
+        elif weapon_clean in cls.PISTOLS:
+            return 'pistol'
+        elif weapon_clean in cls.SMGS:
+            return 'smg'
+        elif weapon_clean in cls.SNIPERS:
+            return 'sniper'
+        elif weapon_clean in cls.HEAVY:
+            return 'heavy'
+        elif weapon_clean == 'knife':
+            return 'knife'
+        elif weapon_clean == 'hegrenade':
+            return 'grenade'
+        else:
+            return 'other'
+
+
+class MapAreas:
+    """Map area classification utilities (for position analysis)"""
+    
+    # This will be expanded with actual map coordinates in IntelligenceProcessor
+    SITE_A = 'site_a'
+    SITE_B = 'site_b'
+    MID = 'mid'
+    SPAWN_T = 'spawn_t'
+    SPAWN_CT = 'spawn_ct'
+    
+    @staticmethod
+    def classify_position(x: float, y: float, z: float, map_name: str) -> str:
+        """
+        Classify position into map area (simplified)
+        
+        Full implementation requires map-specific coordinate ranges
+        """
+        # Placeholder - will be implemented with map data
+        return "unknown"
+
+
+# Export all classes
+__all__ = [
+    'SafeAggregator',
+    'NormalizationUtils',
+    'BaseFeatureProcessor',
+    'WeaponCategories',
+    'MapAreas'
+]

database/L3/processors/basic_processor.py

@@ -0,0 +1,463 @@
+"""
+BasicProcessor - Tier 1: CORE Features (41 columns)
+
+Calculates fundamental player statistics from fact_match_players:
+- Basic Performance (15 columns): rating, kd, adr, kast, rws, hs%, kills, deaths, assists
+- Match Stats (8 columns): win_rate, mvps, duration, elo
+- Weapon Stats (12 columns): awp, knife, zeus, diversity
+- Objective Stats (6 columns): plants, defuses, flash_assists
+"""
+
+import sqlite3
+from typing import Dict, Any
+from .base_processor import BaseFeatureProcessor, SafeAggregator, WeaponCategories
+
+
+class BasicProcessor(BaseFeatureProcessor):
+    """Tier 1 CORE processor - Direct aggregations from fact_match_players"""
+    
+    MIN_MATCHES_REQUIRED = 1  # Basic stats work with any match count
+    
+    @staticmethod
+    def calculate(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate all Tier 1 CORE features (41 columns)
+        
+        Returns dict with keys:
+        - core_avg_rating, core_avg_rating2, core_avg_kd, core_avg_adr, etc.
+        """
+        features = {}
+        
+        # Get match count first
+        match_count = BaseFeatureProcessor.get_player_match_count(steam_id, conn_l2)
+        if match_count == 0:
+            return _get_default_features()
+        
+        # Calculate each sub-section
+        features.update(BasicProcessor._calculate_basic_performance(steam_id, conn_l2))
+        features.update(BasicProcessor._calculate_match_stats(steam_id, conn_l2))
+        features.update(BasicProcessor._calculate_weapon_stats(steam_id, conn_l2))
+        features.update(BasicProcessor._calculate_objective_stats(steam_id, conn_l2))
+        
+        return features
+    
+    @staticmethod
+    def _calculate_basic_performance(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate Basic Performance (15 columns)
+        
+        Columns:
+        - core_avg_rating, core_avg_rating2
+        - core_avg_kd, core_avg_adr, core_avg_kast, core_avg_rws
+        - core_avg_hs_kills, core_hs_rate
+        - core_total_kills, core_total_deaths, core_total_assists, core_avg_assists
+        - core_kpr, core_dpr, core_survival_rate
+        """
+        cursor = conn_l2.cursor()
+        
+        # Main aggregation query
+        cursor.execute("""
+            SELECT 
+                AVG(rating) as avg_rating,
+                AVG(rating2) as avg_rating2,
+                AVG(CAST(kills AS REAL) / NULLIF(deaths, 0)) as avg_kd,
+                AVG(adr) as avg_adr,
+                AVG(kast) as avg_kast,
+                AVG(rws) as avg_rws,
+                AVG(headshot_count) as avg_hs_kills,
+                SUM(kills) as total_kills,
+                SUM(deaths) as total_deaths,
+                SUM(headshot_count) as total_hs,
+                SUM(assists) as total_assists,
+                AVG(assists) as avg_assists,
+                SUM(round_total) as total_rounds
+            FROM fact_match_players
+            WHERE steam_id_64 = ?
+        """, (steam_id,))
+        
+        row = cursor.fetchone()
+        
+        if not row:
+            return {}
+        
+        total_kills = row[7] if row[7] else 0
+        total_deaths = row[8] if row[8] else 1
+        total_hs = row[9] if row[9] else 0
+        total_rounds = row[12] if row[12] else 1
+        
+        return {
+            'core_avg_rating': round(row[0], 3) if row[0] else 0.0,
+            'core_avg_rating2': round(row[1], 3) if row[1] else 0.0,
+            'core_avg_kd': round(row[2], 3) if row[2] else 0.0,
+            'core_avg_adr': round(row[3], 2) if row[3] else 0.0,
+            'core_avg_kast': round(row[4], 3) if row[4] else 0.0,
+            'core_avg_rws': round(row[5], 2) if row[5] else 0.0,
+            'core_avg_hs_kills': round(row[6], 2) if row[6] else 0.0,
+            'core_hs_rate': round(total_hs / total_kills, 3) if total_kills > 0 else 0.0,
+            'core_total_kills': total_kills,
+            'core_total_deaths': total_deaths,
+            'core_total_assists': row[10] if row[10] else 0,
+            'core_avg_assists': round(row[11], 2) if row[11] else 0.0,
+            'core_kpr': round(total_kills / total_rounds, 3) if total_rounds > 0 else 0.0,
+            'core_dpr': round(total_deaths / total_rounds, 3) if total_rounds > 0 else 0.0,
+            'core_survival_rate': round((total_rounds - total_deaths) / total_rounds, 3) if total_rounds > 0 else 0.0,
+        }
+    
+    @staticmethod
+    def _calculate_flash_assists(steam_id: str, conn_l2: sqlite3.Connection) -> int:
+        """
+        Calculate flash assists from fact_match_players (Total - Damage Assists)
+        Returns total flash assist count (Estimated)
+        """
+        cursor = conn_l2.cursor()
+        
+        # NOTE: Flash Assist Logic
+        # Source 'flash_assists' is often 0.
+        # User Logic: Flash Assists = Total Assists - Damage Assists (assisted_kill)
+        # We take MAX(0, diff) to avoid negative numbers if assisted_kill definition varies.
+        
+        cursor.execute("""
+            SELECT SUM(MAX(0, assists - assisted_kill))
+            FROM fact_match_players
+            WHERE steam_id_64 = ?
+        """, (steam_id,))
+        
+        res = cursor.fetchone()
+        if res and res[0] is not None:
+            return res[0]
+            
+        return 0
+    
+    @staticmethod
+    def _calculate_match_stats(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate Match Stats (8 columns)
+        
+        Columns:
+        - core_win_rate, core_wins, core_losses
+        - core_avg_match_duration
+        - core_avg_mvps, core_mvp_rate
+        - core_avg_elo_change, core_total_elo_gained
+        """
+        cursor = conn_l2.cursor()
+        
+        # Win/loss stats
+        cursor.execute("""
+            SELECT 
+                COUNT(*) as total_matches,
+                SUM(CASE WHEN is_win = 1 THEN 1 ELSE 0 END) as wins,
+                SUM(CASE WHEN is_win = 0 THEN 1 ELSE 0 END) as losses,
+                AVG(mvp_count) as avg_mvps,
+                SUM(mvp_count) as total_mvps
+            FROM fact_match_players
+            WHERE steam_id_64 = ?
+        """, (steam_id,))
+        
+        row = cursor.fetchone()
+        total_matches = row[0] if row[0] else 0
+        wins = row[1] if row[1] else 0
+        losses = row[2] if row[2] else 0
+        avg_mvps = row[3] if row[3] else 0.0
+        total_mvps = row[4] if row[4] else 0
+        
+        # Match duration (from fact_matches)
+        cursor.execute("""
+            SELECT AVG(m.duration) as avg_duration
+            FROM fact_matches m
+            JOIN fact_match_players p ON m.match_id = p.match_id
+            WHERE p.steam_id_64 = ?
+        """, (steam_id,))
+        
+        duration_row = cursor.fetchone()
+        avg_duration = duration_row[0] if duration_row and duration_row[0] else 0
+        
+        # ELO stats (from elo_change column)
+        cursor.execute("""
+            SELECT 
+                AVG(elo_change) as avg_elo_change,
+                SUM(elo_change) as total_elo_gained
+            FROM fact_match_players
+            WHERE steam_id_64 = ?
+        """, (steam_id,))
+        
+        elo_row = cursor.fetchone()
+        avg_elo_change = elo_row[0] if elo_row and elo_row[0] else 0.0
+        total_elo_gained = elo_row[1] if elo_row and elo_row[1] else 0.0
+        
+        return {
+            'core_win_rate': round(wins / total_matches, 3) if total_matches > 0 else 0.0,
+            'core_wins': wins,
+            'core_losses': losses,
+            'core_avg_match_duration': int(avg_duration),
+            'core_avg_mvps': round(avg_mvps, 2),
+            'core_mvp_rate': round(total_mvps / total_matches, 2) if total_matches > 0 else 0.0,
+            'core_avg_elo_change': round(avg_elo_change, 2),
+            'core_total_elo_gained': round(total_elo_gained, 2),
+        }
+    
+    @staticmethod
+    def _calculate_weapon_stats(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate Weapon Stats (12 columns)
+        
+        Columns:
+        - core_avg_awp_kills, core_awp_usage_rate
+        - core_avg_knife_kills, core_avg_zeus_kills, core_zeus_buy_rate
+        - core_top_weapon, core_top_weapon_kills, core_top_weapon_hs_rate
+        - core_weapon_diversity
+        - core_rifle_hs_rate, core_pistol_hs_rate
+        - core_smg_kills_total
+        """
+        cursor = conn_l2.cursor()
+        
+        # AWP/Knife/Zeus stats from fact_round_events
+        cursor.execute("""
+            SELECT 
+                weapon,
+                COUNT(*) as kill_count
+            FROM fact_round_events
+            WHERE attacker_steam_id = ?
+            AND weapon IN ('AWP', 'Knife', 'Zeus', 'knife', 'awp', 'zeus')
+            GROUP BY weapon
+        """, (steam_id,))
+        
+        awp_kills = 0
+        knife_kills = 0
+        zeus_kills = 0
+        for weapon, kills in cursor.fetchall():
+            weapon_lower = weapon.lower() if weapon else ''
+            if weapon_lower == 'awp':
+                awp_kills += kills
+            elif weapon_lower == 'knife':
+                knife_kills += kills
+            elif weapon_lower == 'zeus':
+                zeus_kills += kills
+        
+        # Get total matches count for rates
+        cursor.execute("""
+            SELECT COUNT(DISTINCT match_id)
+            FROM fact_match_players
+            WHERE steam_id_64 = ?
+        """, (steam_id,))
+        total_matches = cursor.fetchone()[0] or 1
+        
+        avg_awp = awp_kills / total_matches
+        avg_knife = knife_kills / total_matches
+        avg_zeus = zeus_kills / total_matches
+        
+        # Flash assists from fact_round_events
+        flash_assists = BasicProcessor._calculate_flash_assists(steam_id, conn_l2)
+        avg_flash_assists = flash_assists / total_matches
+        
+        # Top weapon from fact_round_events
+        cursor.execute("""
+            SELECT 
+                weapon,
+                COUNT(*) as kill_count,
+                SUM(CASE WHEN is_headshot = 1 THEN 1 ELSE 0 END) as hs_count
+            FROM fact_round_events
+            WHERE attacker_steam_id = ?
+            AND weapon IS NOT NULL
+            AND weapon != 'unknown'
+            GROUP BY weapon
+            ORDER BY kill_count DESC
+            LIMIT 1
+        """, (steam_id,))
+        
+        weapon_row = cursor.fetchone()
+        top_weapon = weapon_row[0] if weapon_row else "unknown"
+        top_weapon_kills = weapon_row[1] if weapon_row else 0
+        top_weapon_hs = weapon_row[2] if weapon_row else 0
+        top_weapon_hs_rate = top_weapon_hs / top_weapon_kills if top_weapon_kills > 0 else 0.0
+        
+        # Weapon diversity (number of distinct weapons with 10+ kills)
+        cursor.execute("""
+            SELECT COUNT(DISTINCT weapon) as weapon_count
+            FROM (
+                SELECT weapon, COUNT(*) as kills
+                FROM fact_round_events
+                WHERE attacker_steam_id = ?
+                AND weapon IS NOT NULL
+                GROUP BY weapon
+                HAVING kills >= 10
+            )
+        """, (steam_id,))
+        
+        diversity_row = cursor.fetchone()
+        weapon_diversity = diversity_row[0] if diversity_row else 0
+        
+        # Rifle/Pistol/SMG stats
+        cursor.execute("""
+            SELECT 
+                weapon,
+                COUNT(*) as kills,
+                SUM(CASE WHEN is_headshot = 1 THEN 1 ELSE 0 END) as headshot_kills
+            FROM fact_round_events
+            WHERE attacker_steam_id = ?
+            AND weapon IS NOT NULL
+            GROUP BY weapon
+        """, (steam_id,))
+        
+        rifle_kills = 0
+        rifle_hs = 0
+        pistol_kills = 0
+        pistol_hs = 0
+        smg_kills = 0
+        awp_usage_count = 0
+        
+        for weapon, kills, hs in cursor.fetchall():
+            category = WeaponCategories.get_category(weapon)
+            if category == 'rifle':
+                rifle_kills += kills
+                rifle_hs += hs
+            elif category == 'pistol':
+                pistol_kills += kills
+                pistol_hs += hs
+            elif category == 'smg':
+                smg_kills += kills
+            elif weapon.lower() == 'awp':
+                awp_usage_count += kills
+        
+        total_rounds = BaseFeatureProcessor.get_player_round_count(steam_id, conn_l2)
+        
+        return {
+            'core_avg_awp_kills': round(avg_awp, 2),
+            'core_awp_usage_rate': round(awp_usage_count / total_rounds, 3) if total_rounds > 0 else 0.0,
+            'core_avg_knife_kills': round(avg_knife, 3),
+            'core_avg_zeus_kills': round(avg_zeus, 3),
+            'core_zeus_buy_rate': round(avg_zeus / total_matches, 3) if total_matches > 0 else 0.0,
+            'core_avg_flash_assists': round(avg_flash_assists, 2),
+            'core_top_weapon': top_weapon,
+            'core_top_weapon_kills': top_weapon_kills,
+            'core_top_weapon_hs_rate': round(top_weapon_hs_rate, 3),
+            'core_weapon_diversity': weapon_diversity,
+            'core_rifle_hs_rate': round(rifle_hs / rifle_kills, 3) if rifle_kills > 0 else 0.0,
+            'core_pistol_hs_rate': round(pistol_hs / pistol_kills, 3) if pistol_kills > 0 else 0.0,
+            'core_smg_kills_total': smg_kills,
+        }
+    
+    @staticmethod
+    def _calculate_objective_stats(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate Objective Stats (6 columns)
+        
+        Columns:
+        - core_avg_plants, core_avg_defuses, core_avg_flash_assists
+        - core_plant_success_rate, core_defuse_success_rate
+        - core_objective_impact
+        """
+        cursor = conn_l2.cursor()
+        
+        # Get data from main table
+        # Updated to use calculated flash assists formula
+        
+        # Calculate flash assists manually first (since column is 0)
+        flash_assists_total = BasicProcessor._calculate_flash_assists(steam_id, conn_l2)
+        match_count = BaseFeatureProcessor.get_player_match_count(steam_id, conn_l2)
+        avg_flash_assists = flash_assists_total / match_count if match_count > 0 else 0.0
+        
+        cursor.execute("""
+            SELECT 
+                AVG(planted_bomb) as avg_plants,
+                AVG(defused_bomb) as avg_defuses,
+                SUM(planted_bomb) as total_plants,
+                SUM(defused_bomb) as total_defuses
+            FROM fact_match_players
+            WHERE steam_id_64 = ?
+        """, (steam_id,))
+        
+        row = cursor.fetchone()
+        
+        if not row:
+            return {}
+        
+        avg_plants = row[0] if row[0] else 0.0
+        avg_defuses = row[1] if row[1] else 0.0
+        # avg_flash_assists computed above
+        total_plants = row[2] if row[2] else 0
+        total_defuses = row[3] if row[3] else 0
+        
+        # Get T side rounds
+        cursor.execute("""
+            SELECT COALESCE(SUM(round_total), 0)
+            FROM fact_match_players_t
+            WHERE steam_id_64 = ?
+        """, (steam_id,))
+        t_rounds = cursor.fetchone()[0] or 1
+        
+        # Get CT side rounds
+        cursor.execute("""
+            SELECT COALESCE(SUM(round_total), 0)
+            FROM fact_match_players_ct
+            WHERE steam_id_64 = ?
+        """, (steam_id,))
+        ct_rounds = cursor.fetchone()[0] or 1
+        
+        # Plant success rate: plants per T round
+        plant_rate = total_plants / t_rounds if t_rounds > 0 else 0.0
+        
+        # Defuse success rate: approximate as defuses per CT round (simplified)
+        defuse_rate = total_defuses / ct_rounds if ct_rounds > 0 else 0.0
+        
+        # Objective impact score: weighted combination
+        objective_impact = (total_plants * 2.0 + total_defuses * 3.0 + avg_flash_assists * 0.5)
+        
+        return {
+            'core_avg_plants': round(avg_plants, 2),
+            'core_avg_defuses': round(avg_defuses, 2),
+            'core_avg_flash_assists': round(avg_flash_assists, 2),
+            'core_plant_success_rate': round(plant_rate, 3),
+            'core_defuse_success_rate': round(defuse_rate, 3),
+            'core_objective_impact': round(objective_impact, 2),
+        }
+
+
+def _get_default_features() -> Dict[str, Any]:
+    """Return default zero values for all 41 CORE features"""
+    return {
+        # Basic Performance (15)
+        'core_avg_rating': 0.0,
+        'core_avg_rating2': 0.0,
+        'core_avg_kd': 0.0,
+        'core_avg_adr': 0.0,
+        'core_avg_kast': 0.0,
+        'core_avg_rws': 0.0,
+        'core_avg_hs_kills': 0.0,
+        'core_hs_rate': 0.0,
+        'core_total_kills': 0,
+        'core_total_deaths': 0,
+        'core_total_assists': 0,
+        'core_avg_assists': 0.0,
+        'core_kpr': 0.0,
+        'core_dpr': 0.0,
+        'core_survival_rate': 0.0,
+        # Match Stats (8)
+        'core_win_rate': 0.0,
+        'core_wins': 0,
+        'core_losses': 0,
+        'core_avg_match_duration': 0,
+        'core_avg_mvps': 0.0,
+        'core_mvp_rate': 0.0,
+        'core_avg_elo_change': 0.0,
+        'core_total_elo_gained': 0.0,
+        # Weapon Stats (12)
+        'core_avg_awp_kills': 0.0,
+        'core_awp_usage_rate': 0.0,
+        'core_avg_knife_kills': 0.0,
+        'core_avg_zeus_kills': 0.0,
+        'core_zeus_buy_rate': 0.0,
+        'core_top_weapon': 'unknown',
+        'core_top_weapon_kills': 0,
+        'core_top_weapon_hs_rate': 0.0,
+        'core_weapon_diversity': 0,
+        'core_rifle_hs_rate': 0.0,
+        'core_pistol_hs_rate': 0.0,
+        'core_smg_kills_total': 0,
+        # Objective Stats (6)
+        'core_avg_plants': 0.0,
+        'core_avg_defuses': 0.0,
+        'core_avg_flash_assists': 0.0,
+        'core_plant_success_rate': 0.0,
+        'core_defuse_success_rate': 0.0,
+        'core_objective_impact': 0.0,
+    }

database/L3/processors/composite_processor.py

@@ -0,0 +1,420 @@
+"""
+CompositeProcessor - Tier 5: COMPOSITE Features (11 columns)
+
+Weighted composite scores based on Tier 1-4 features:
+- 8 Radar Scores (0-100): AIM, CLUTCH, PISTOL, DEFENSE, UTILITY, STABILITY, ECONOMY, PACE
+- Overall Score (0-100): Weighted sum of 8 dimensions
+- Tier Classification: Elite/Advanced/Intermediate/Beginner
+- Tier Percentile: Ranking among all players
+"""
+
+import sqlite3
+from typing import Dict, Any
+from .base_processor import BaseFeatureProcessor, NormalizationUtils, SafeAggregator
+
+
+class CompositeProcessor(BaseFeatureProcessor):
+    """Tier 5 COMPOSITE processor - Weighted scores from all previous tiers"""
+    
+    MIN_MATCHES_REQUIRED = 20  # Need substantial data for reliable composite scores
+    
+    @staticmethod
+    def calculate(steam_id: str, conn_l2: sqlite3.Connection, 
+                  pre_features: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Calculate all Tier 5 COMPOSITE features (11 columns)
+        
+        Args:
+            steam_id: Player's Steam ID
+            conn_l2: L2 database connection
+            pre_features: Dictionary containing all Tier 1-4 features
+            
+        Returns dict with keys starting with 'score_' and 'tier_'
+        """
+        features = {}
+        
+        # Check minimum matches
+        if not BaseFeatureProcessor.check_min_matches(steam_id, conn_l2, 
+                                                       CompositeProcessor.MIN_MATCHES_REQUIRED):
+            return _get_default_composite_features()
+        
+        # Calculate 8 radar dimension scores
+        features['score_aim'] = CompositeProcessor._calculate_aim_score(pre_features)
+        features['score_clutch'] = CompositeProcessor._calculate_clutch_score(pre_features)
+        features['score_pistol'] = CompositeProcessor._calculate_pistol_score(pre_features)
+        features['score_defense'] = CompositeProcessor._calculate_defense_score(pre_features)
+        features['score_utility'] = CompositeProcessor._calculate_utility_score(pre_features)
+        features['score_stability'] = CompositeProcessor._calculate_stability_score(pre_features)
+        features['score_economy'] = CompositeProcessor._calculate_economy_score(pre_features)
+        features['score_pace'] = CompositeProcessor._calculate_pace_score(pre_features)
+        
+        # Calculate overall score (Weighted sum of 8 dimensions)
+        # Weights: AIM 20%, CLUTCH 12%, PISTOL 10%, DEFENSE 13%, UTILITY 20%, STABILITY 8%, ECONOMY 12%, PACE 5%
+        features['score_overall'] = (
+            features['score_aim'] * 0.12 +
+            features['score_clutch'] * 0.18 +
+            features['score_pistol'] * 0.18 +
+            features['score_defense'] * 0.20 +
+            features['score_utility'] * 0.10 +
+            features['score_stability'] * 0.07 +
+            features['score_economy'] * 0.08 +
+            features['score_pace'] * 0.07
+        )
+        features['score_overall'] = round(features['score_overall'], 2)
+        
+        # Classify tier based on overall score
+        features['tier_classification'] = CompositeProcessor._classify_tier(features['score_overall'])
+        
+        # Percentile rank (placeholder - requires all players)
+        features['tier_percentile'] = min(features['score_overall'], 100.0)
+        
+        return features
+    
+    @staticmethod
+    def _calculate_aim_score(features: Dict[str, Any]) -> float:
+        """
+        AIM Score (0-100) | 20%
+        """
+        # Extract features
+        rating = features.get('core_avg_rating', 0.0)
+        kd = features.get('core_avg_kd', 0.0)
+        adr = features.get('core_avg_adr', 0.0)
+        hs_rate = features.get('core_hs_rate', 0.0)
+        multikill_rate = features.get('tac_multikill_rate', 0.0)
+        avg_hs = features.get('core_avg_hs_kills', 0.0)
+        weapon_div = features.get('core_weapon_diversity', 0.0)
+        rifle_hs_rate = features.get('core_rifle_hs_rate', 0.0)
+        
+        # Normalize (Variable / Baseline * 100)
+        rating_score = min((rating / 1.15) * 100, 100)
+        kd_score = min((kd / 1.30) * 100, 100)
+        adr_score = min((adr / 90) * 100, 100)
+        hs_score = min((hs_rate / 0.55) * 100, 100)
+        mk_score = min((multikill_rate / 0.22) * 100, 100)
+        avg_hs_score = min((avg_hs / 8.5) * 100, 100)
+        weapon_div_score = min((weapon_div / 20) * 100, 100)
+        rifle_hs_score = min((rifle_hs_rate / 0.50) * 100, 100)
+        
+        # Weighted Sum
+        aim_score = (
+            rating_score * 0.15 +
+            kd_score * 0.15 +
+            adr_score * 0.10 +
+            hs_score * 0.15 +
+            mk_score * 0.10 +
+            avg_hs_score * 0.15 +
+            weapon_div_score * 0.10 +
+            rifle_hs_score * 0.10
+        )
+        
+        return round(min(max(aim_score, 0), 100), 2)
+    
+    @staticmethod
+    def _calculate_clutch_score(features: Dict[str, Any]) -> float:
+        """
+        CLUTCH Score (0-100) | 12%
+        """
+        # Extract features
+        # Clutch Score Calculation: (1v1*100 + 1v2*200 + 1v3+*500) / 8
+        c1v1 = features.get('tac_clutch_1v1_wins', 0)
+        c1v2 = features.get('tac_clutch_1v2_wins', 0)
+        c1v3p = features.get('tac_clutch_1v3_plus_wins', 0)
+        # Note: tac_clutch_1v3_plus_wins includes 1v3, 1v4, 1v5
+        
+        raw_clutch_score = (c1v1 * 100 + c1v2 * 200 + c1v3p * 500) / 8.0
+        
+        comeback_kd = features.get('int_pressure_comeback_kd', 0.0)
+        matchpoint_kpr = features.get('int_pressure_matchpoint_kpr', 0.0)
+        rating = features.get('core_avg_rating', 0.0)
+        
+        # 1v3+ Win Rate
+        attempts_1v3p = features.get('tac_clutch_1v3_plus_attempts', 0)
+        win_1v3p = features.get('tac_clutch_1v3_plus_wins', 0)
+        win_rate_1v3p = win_1v3p / attempts_1v3p if attempts_1v3p > 0 else 0.0
+        
+        clutch_impact = features.get('tac_clutch_impact_score', 0.0)
+        
+        # Normalize
+        clutch_score_val = min((raw_clutch_score / 200) * 100, 100)
+        comeback_score = min((comeback_kd / 1.55) * 100, 100)
+        matchpoint_score = min((matchpoint_kpr / 0.85) * 100, 100)
+        rating_score = min((rating / 1.15) * 100, 100)
+        win_rate_1v3p_score = min((win_rate_1v3p / 0.10) * 100, 100)
+        clutch_impact_score = min((clutch_impact / 200) * 100, 100)
+        
+        # Weighted Sum
+        final_clutch_score = (
+            clutch_score_val * 0.20 +
+            comeback_score * 0.25 +
+            matchpoint_score * 0.15 +
+            rating_score * 0.10 +
+            win_rate_1v3p_score * 0.15 +
+            clutch_impact_score * 0.15
+        )
+        
+        return round(min(max(final_clutch_score, 0), 100), 2)
+    
+    @staticmethod
+    def _calculate_pistol_score(features: Dict[str, Any]) -> float:
+        """
+        PISTOL Score (0-100) | 10%
+        """
+        # Extract features
+        fk_rate = features.get('tac_fk_rate', 0.0) # Using general FK rate as per original logic, though user said "手枪局首杀率". 
+        # If "手枪局首杀率" means FK rate in pistol rounds specifically, we don't have that in pre-calculated features.
+        # Assuming general FK rate or tac_fk_rate is acceptable proxy or that user meant tac_fk_rate.
+        # Given "tac_fk_rate" was used in previous Pistol score, I'll stick with it.
+        
+        pistol_hs_rate = features.get('core_pistol_hs_rate', 0.0)
+        entry_win_rate = features.get('tac_opening_duel_winrate', 0.0)
+        rating = features.get('core_avg_rating', 0.0)
+        smg_kills = features.get('core_smg_kills_total', 0)
+        avg_fk = features.get('tac_avg_fk', 0.0)
+        
+        # Normalize
+        fk_score = min((fk_rate / 0.58) * 100, 100) # 58%
+        pistol_hs_score = min((pistol_hs_rate / 0.75) * 100, 100) # 75%
+        entry_win_score = min((entry_win_rate / 0.47) * 100, 100) # 47%
+        rating_score = min((rating / 1.15) * 100, 100)
+        smg_score = min((smg_kills / 270) * 100, 100)
+        avg_fk_score = min((avg_fk / 3.0) * 100, 100)
+        
+        # Weighted Sum
+        pistol_score = (
+            fk_score * 0.20 +
+            pistol_hs_score * 0.25 +
+            entry_win_score * 0.15 +
+            rating_score * 0.10 +
+            smg_score * 0.15 +
+            avg_fk_score * 0.15
+        )
+        
+        return round(min(max(pistol_score, 0), 100), 2)
+    
+    @staticmethod
+    def _calculate_defense_score(features: Dict[str, Any]) -> float:
+        """
+        DEFENSE Score (0-100) | 13%
+        """
+        # Extract features
+        ct_rating = features.get('meta_side_ct_rating', 0.0)
+        t_rating = features.get('meta_side_t_rating', 0.0)
+        ct_kd = features.get('meta_side_ct_kd', 0.0)
+        t_kd = features.get('meta_side_t_kd', 0.0)
+        ct_kast = features.get('meta_side_ct_kast', 0.0)
+        t_kast = features.get('meta_side_t_kast', 0.0)
+        
+        # Normalize
+        ct_rating_score = min((ct_rating / 1.15) * 100, 100)
+        t_rating_score = min((t_rating / 1.20) * 100, 100)
+        ct_kd_score = min((ct_kd / 1.40) * 100, 100)
+        t_kd_score = min((t_kd / 1.45) * 100, 100)
+        ct_kast_score = min((ct_kast / 0.70) * 100, 100)
+        t_kast_score = min((t_kast / 0.72) * 100, 100)
+        
+        # Weighted Sum
+        defense_score = (
+            ct_rating_score * 0.20 +
+            t_rating_score * 0.20 +
+            ct_kd_score * 0.15 +
+            t_kd_score * 0.15 +
+            ct_kast_score * 0.15 +
+            t_kast_score * 0.15
+        )
+        
+        return round(min(max(defense_score, 0), 100), 2)
+    
+    @staticmethod
+    def _calculate_utility_score(features: Dict[str, Any]) -> float:
+        """
+        UTILITY Score (0-100) | 20%
+        """
+        # Extract features
+        util_usage = features.get('tac_util_usage_rate', 0.0)
+        util_dmg = features.get('tac_util_nade_dmg_per_round', 0.0)
+        flash_eff = features.get('tac_util_flash_efficiency', 0.0)
+        util_impact = features.get('tac_util_impact_score', 0.0)
+        blind = features.get('tac_util_flash_enemies_per_round', 0.0) # 致盲数 (Enemies Blinded per Round)
+        flash_rnd = features.get('tac_util_flash_per_round', 0.0)
+        flash_ast = features.get('core_avg_flash_assists', 0.0)
+        
+        # Normalize
+        usage_score = min((util_usage / 2.0) * 100, 100)
+        dmg_score = min((util_dmg / 4.0) * 100, 100)
+        flash_eff_score = min((flash_eff / 1.35) * 100, 100) # 135%
+        impact_score = min((util_impact / 22) * 100, 100)
+        blind_score = min((blind / 1.0) * 100, 100)
+        flash_rnd_score = min((flash_rnd / 0.85) * 100, 100)
+        flash_ast_score = min((flash_ast / 2.15) * 100, 100)
+        
+        # Weighted Sum
+        utility_score = (
+            usage_score * 0.15 +
+            dmg_score * 0.05 +
+            flash_eff_score * 0.20 +
+            impact_score * 0.20 +
+            blind_score * 0.15 +
+            flash_rnd_score * 0.15 +
+            flash_ast_score * 0.10
+        )
+        
+        return round(min(max(utility_score, 0), 100), 2)
+    
+    @staticmethod
+    def _calculate_stability_score(features: Dict[str, Any]) -> float:
+        """
+        STABILITY Score (0-100) | 8%
+        """
+        # Extract features
+        volatility = features.get('meta_rating_volatility', 0.0)
+        loss_rating = features.get('meta_loss_rating', 0.0)
+        consistency = features.get('meta_rating_consistency', 0.0)
+        tilt_resilience = features.get('int_pressure_tilt_resistance', 0.0)
+        map_stable = features.get('meta_map_stability', 0.0)
+        elo_stable = features.get('meta_elo_tier_stability', 0.0)
+        recent_form = features.get('meta_recent_form_rating', 0.0)
+        
+        # Normalize
+        # Volatility: Reverse score. 100 - (Vol * 220)
+        vol_score = max(0, 100 - (volatility * 220))
+        
+        loss_score = min((loss_rating / 1.00) * 100, 100)
+        cons_score = min((consistency / 70) * 100, 100)
+        tilt_score = min((tilt_resilience / 0.80) * 100, 100)
+        map_score = min((map_stable / 0.25) * 100, 100)
+        elo_score = min((elo_stable / 0.48) * 100, 100)
+        recent_score = min((recent_form / 1.15) * 100, 100)
+        
+        # Weighted Sum
+        stability_score = (
+            vol_score * 0.20 +
+            loss_score * 0.20 +
+            cons_score * 0.15 +
+            tilt_score * 0.15 +
+            map_score * 0.10 +
+            elo_score * 0.10 +
+            recent_score * 0.10
+        )
+        
+        return round(min(max(stability_score, 0), 100), 2)
+    
+    @staticmethod
+    def _calculate_economy_score(features: Dict[str, Any]) -> float:
+        """
+        ECONOMY Score (0-100) | 12%
+        """
+        # Extract features
+        dmg_1k = features.get('tac_eco_dmg_per_1k', 0.0)
+        eco_kpr = features.get('tac_eco_kpr_eco_rounds', 0.0)
+        eco_kd = features.get('tac_eco_kd_eco_rounds', 0.0)
+        eco_score = features.get('tac_eco_efficiency_score', 0.0)
+        full_kpr = features.get('tac_eco_kpr_full_rounds', 0.0)
+        force_win = features.get('tac_eco_force_success_rate', 0.0)
+        
+        # Normalize
+        dmg_score = min((dmg_1k / 19) * 100, 100)
+        eco_kpr_score = min((eco_kpr / 0.85) * 100, 100)
+        eco_kd_score = min((eco_kd / 1.30) * 100, 100)
+        eco_eff_score = min((eco_score / 0.80) * 100, 100)
+        full_kpr_score = min((full_kpr / 0.90) * 100, 100)
+        force_win_score = min((force_win / 0.50) * 100, 100)
+        
+        # Weighted Sum
+        economy_score = (
+            dmg_score * 0.25 +
+            eco_kpr_score * 0.20 +
+            eco_kd_score * 0.15 +
+            eco_eff_score * 0.15 +
+            full_kpr_score * 0.15 +
+            force_win_score * 0.10
+        )
+        
+        return round(min(max(economy_score, 0), 100), 2)
+    
+    @staticmethod
+    def _calculate_pace_score(features: Dict[str, Any]) -> float:
+        """
+        PACE Score (0-100) | 5%
+        """
+        # Extract features
+        early_kill_pct = features.get('int_timing_early_kill_share', 0.0)
+        aggression = features.get('int_timing_aggression_index', 0.0)
+        trade_speed = features.get('int_trade_response_time', 0.0)
+        trade_kill = features.get('int_trade_kill_count', 0)
+        teamwork = features.get('int_teamwork_score', 0.0)
+        first_contact = features.get('int_timing_first_contact_time', 0.0)
+        
+        # Normalize
+        early_score = min((early_kill_pct / 0.44) * 100, 100)
+        aggression_score = min((aggression / 1.20) * 100, 100)
+        
+        # Trade Speed: Reverse score. (2.0 / Trade Speed) * 100
+        # Avoid division by zero
+        if trade_speed > 0.01:
+            trade_speed_score = min((2.0 / trade_speed) * 100, 100)
+        else:
+            trade_speed_score = 100 # Instant trade
+            
+        trade_kill_score = min((trade_kill / 650) * 100, 100)
+        teamwork_score = min((teamwork / 29) * 100, 100)
+        
+        # First Contact: Reverse score. (30 / 1st Contact) * 100
+        if first_contact > 0.01:
+            first_contact_score = min((30 / first_contact) * 100, 100)
+        else:
+            first_contact_score = 0 # If 0, probably no data, safe to say 0? Or 100?
+            # 0 first contact time means instant damage.
+            # But "30 / Contact" means smaller contact time gives higher score.
+            # If contact time is 0, score explodes.
+            # Realistically first contact time is > 0.
+            # I will clamp it.
+            first_contact_score = 100 # Assume very fast
+            
+        # Weighted Sum
+        pace_score = (
+            early_score * 0.25 +
+            aggression_score * 0.20 +
+            trade_speed_score * 0.20 +
+            trade_kill_score * 0.15 +
+            teamwork_score * 0.10 +
+            first_contact_score * 0.10
+        )
+        
+        return round(min(max(pace_score, 0), 100), 2)
+    
+    @staticmethod
+    def _classify_tier(overall_score: float) -> str:
+        """
+        Classify player tier based on overall score
+        
+        Tiers:
+        - Elite: 75+
+        - Advanced: 60-75
+        - Intermediate: 40-60
+        - Beginner: <40
+        """
+        if overall_score >= 75:
+            return 'Elite'
+        elif overall_score >= 60:
+            return 'Advanced'
+        elif overall_score >= 40:
+            return 'Intermediate'
+        else:
+            return 'Beginner'
+
+
+def _get_default_composite_features() -> Dict[str, Any]:
+    """Return default zero values for all 11 COMPOSITE features"""
+    return {
+        'score_aim': 0.0,
+        'score_clutch': 0.0,
+        'score_pistol': 0.0,
+        'score_defense': 0.0,
+        'score_utility': 0.0,
+        'score_stability': 0.0,
+        'score_economy': 0.0,
+        'score_pace': 0.0,
+        'score_overall': 0.0,
+        'tier_classification': 'Beginner',
+        'tier_percentile': 0.0,
+    }

database/L3/processors/intelligence_processor.py

@@ -0,0 +1,732 @@
+"""
+IntelligenceProcessor - Tier 3: INTELLIGENCE Features (53 columns)
+
+Advanced analytics on fact_round_events with complex calculations:
+- High IQ Kills (9 columns): wallbang, smoke, blind, noscope + IQ score
+- Timing Analysis (12 columns): early/mid/late kill distribution, aggression
+- Pressure Performance (10 columns): comeback, losing streak, matchpoint
+- Position Mastery (14 columns): site control, lurk tendency, spatial IQ
+- Trade Network (8 columns): trade kills/response time, teamwork
+"""
+
+import sqlite3
+from typing import Dict, Any, List, Tuple
+from .base_processor import BaseFeatureProcessor, SafeAggregator
+
+
+class IntelligenceProcessor(BaseFeatureProcessor):
+    """Tier 3 INTELLIGENCE processor - Complex event-level analytics"""
+    
+    MIN_MATCHES_REQUIRED = 10  # Need substantial data for reliable patterns
+    
+    @staticmethod
+    def calculate(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate all Tier 3 INTELLIGENCE features (53 columns)
+        
+        Returns dict with keys starting with 'int_'
+        """
+        features = {}
+        
+        # Check minimum matches
+        if not BaseFeatureProcessor.check_min_matches(steam_id, conn_l2, 
+                                                       IntelligenceProcessor.MIN_MATCHES_REQUIRED):
+            return _get_default_intelligence_features()
+        
+        # Calculate each intelligence dimension
+        features.update(IntelligenceProcessor._calculate_high_iq_kills(steam_id, conn_l2))
+        features.update(IntelligenceProcessor._calculate_timing_analysis(steam_id, conn_l2))
+        features.update(IntelligenceProcessor._calculate_pressure_performance(steam_id, conn_l2))
+        features.update(IntelligenceProcessor._calculate_position_mastery(steam_id, conn_l2))
+        features.update(IntelligenceProcessor._calculate_trade_network(steam_id, conn_l2))
+        
+        return features
+    
+    @staticmethod
+    def _calculate_high_iq_kills(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate High IQ Kills (9 columns)
+        
+        Columns:
+        - int_wallbang_kills, int_wallbang_rate
+        - int_smoke_kills, int_smoke_kill_rate
+        - int_blind_kills, int_blind_kill_rate
+        - int_noscope_kills, int_noscope_rate
+        - int_high_iq_score
+        """
+        cursor = conn_l2.cursor()
+        
+        # Get total kills for rate calculations
+        cursor.execute("""
+            SELECT COUNT(*) as total_kills
+            FROM fact_round_events
+            WHERE attacker_steam_id = ?
+            AND event_type = 'kill'
+        """, (steam_id,))
+        
+        total_kills = cursor.fetchone()[0]
+        total_kills = total_kills if total_kills else 1
+        
+        # Wallbang kills
+        cursor.execute("""
+            SELECT COUNT(*) as wallbang_kills
+            FROM fact_round_events
+            WHERE attacker_steam_id = ?
+            AND is_wallbang = 1
+        """, (steam_id,))
+        
+        wallbang_kills = cursor.fetchone()[0]
+        wallbang_kills = wallbang_kills if wallbang_kills else 0
+        
+        # Smoke kills
+        cursor.execute("""
+            SELECT COUNT(*) as smoke_kills
+            FROM fact_round_events
+            WHERE attacker_steam_id = ?
+            AND is_through_smoke = 1
+        """, (steam_id,))
+        
+        smoke_kills = cursor.fetchone()[0]
+        smoke_kills = smoke_kills if smoke_kills else 0
+        
+        # Blind kills
+        cursor.execute("""
+            SELECT COUNT(*) as blind_kills
+            FROM fact_round_events
+            WHERE attacker_steam_id = ?
+            AND is_blind = 1
+        """, (steam_id,))
+        
+        blind_kills = cursor.fetchone()[0]
+        blind_kills = blind_kills if blind_kills else 0
+        
+        # Noscope kills (AWP only)
+        cursor.execute("""
+            SELECT COUNT(*) as noscope_kills
+            FROM fact_round_events
+            WHERE attacker_steam_id = ?
+            AND is_noscope = 1
+        """, (steam_id,))
+        
+        noscope_kills = cursor.fetchone()[0]
+        noscope_kills = noscope_kills if noscope_kills else 0
+        
+        # Calculate rates
+        wallbang_rate = SafeAggregator.safe_divide(wallbang_kills, total_kills)
+        smoke_rate = SafeAggregator.safe_divide(smoke_kills, total_kills)
+        blind_rate = SafeAggregator.safe_divide(blind_kills, total_kills)
+        noscope_rate = SafeAggregator.safe_divide(noscope_kills, total_kills)
+        
+        # High IQ score: weighted combination
+        iq_score = (
+            wallbang_kills * 3.0 +
+            smoke_kills * 2.0 +
+            blind_kills * 1.5 +
+            noscope_kills * 2.0
+        )
+        
+        return {
+            'int_wallbang_kills': wallbang_kills,
+            'int_wallbang_rate': round(wallbang_rate, 4),
+            'int_smoke_kills': smoke_kills,
+            'int_smoke_kill_rate': round(smoke_rate, 4),
+            'int_blind_kills': blind_kills,
+            'int_blind_kill_rate': round(blind_rate, 4),
+            'int_noscope_kills': noscope_kills,
+            'int_noscope_rate': round(noscope_rate, 4),
+            'int_high_iq_score': round(iq_score, 2),
+        }
+    
+    @staticmethod
+    def _calculate_timing_analysis(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate Timing Analysis (12 columns)
+        
+        Time bins: Early (0-30s), Mid (30-60s), Late (60s+)
+        
+        Columns:
+        - int_timing_early_kills, int_timing_mid_kills, int_timing_late_kills
+        - int_timing_early_kill_share, int_timing_mid_kill_share, int_timing_late_kill_share
+        - int_timing_avg_kill_time
+        - int_timing_early_deaths, int_timing_early_death_rate
+        - int_timing_aggression_index
+        - int_timing_patience_score
+        - int_timing_first_contact_time
+        """
+        cursor = conn_l2.cursor()
+        
+        # Kill distribution by time bins
+        cursor.execute("""
+            SELECT 
+                COUNT(CASE WHEN event_time <= 30 THEN 1 END) as early_kills,
+                COUNT(CASE WHEN event_time > 30 AND event_time <= 60 THEN 1 END) as mid_kills,
+                COUNT(CASE WHEN event_time > 60 THEN 1 END) as late_kills,
+                COUNT(*) as total_kills,
+                AVG(event_time) as avg_kill_time
+            FROM fact_round_events
+            WHERE attacker_steam_id = ?
+            AND event_type = 'kill'
+        """, (steam_id,))
+        
+        row = cursor.fetchone()
+        early_kills = row[0] if row[0] else 0
+        mid_kills = row[1] if row[1] else 0
+        late_kills = row[2] if row[2] else 0
+        total_kills = row[3] if row[3] else 1
+        avg_kill_time = row[4] if row[4] else 0.0
+        
+        # Calculate shares
+        early_share = SafeAggregator.safe_divide(early_kills, total_kills)
+        mid_share = SafeAggregator.safe_divide(mid_kills, total_kills)
+        late_share = SafeAggregator.safe_divide(late_kills, total_kills)
+        
+        # Death distribution (for aggression index)
+        cursor.execute("""
+            SELECT 
+                COUNT(CASE WHEN event_time <= 30 THEN 1 END) as early_deaths,
+                COUNT(*) as total_deaths
+            FROM fact_round_events
+            WHERE victim_steam_id = ?
+            AND event_type = 'kill'
+        """, (steam_id,))
+        
+        death_row = cursor.fetchone()
+        early_deaths = death_row[0] if death_row[0] else 0
+        total_deaths = death_row[1] if death_row[1] else 1
+        
+        early_death_rate = SafeAggregator.safe_divide(early_deaths, total_deaths)
+        
+        # Aggression index: early kills / early deaths
+        aggression_index = SafeAggregator.safe_divide(early_kills, max(early_deaths, 1))
+        
+        # Patience score: late kill share
+        patience_score = late_share
+        
+        # First contact time: average time of first event per round
+        cursor.execute("""
+            SELECT AVG(min_time) as avg_first_contact
+            FROM (
+                SELECT match_id, round_num, MIN(event_time) as min_time
+                FROM fact_round_events
+                WHERE attacker_steam_id = ? OR victim_steam_id = ?
+                GROUP BY match_id, round_num
+            )
+        """, (steam_id, steam_id))
+        
+        first_contact = cursor.fetchone()[0]
+        first_contact_time = first_contact if first_contact else 0.0
+        
+        return {
+            'int_timing_early_kills': early_kills,
+            'int_timing_mid_kills': mid_kills,
+            'int_timing_late_kills': late_kills,
+            'int_timing_early_kill_share': round(early_share, 3),
+            'int_timing_mid_kill_share': round(mid_share, 3),
+            'int_timing_late_kill_share': round(late_share, 3),
+            'int_timing_avg_kill_time': round(avg_kill_time, 2),
+            'int_timing_early_deaths': early_deaths,
+            'int_timing_early_death_rate': round(early_death_rate, 3),
+            'int_timing_aggression_index': round(aggression_index, 3),
+            'int_timing_patience_score': round(patience_score, 3),
+            'int_timing_first_contact_time': round(first_contact_time, 2),
+        }
+    
+    @staticmethod
+    def _calculate_pressure_performance(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate Pressure Performance (10 columns)
+        """
+        cursor = conn_l2.cursor()
+        
+        # 1. Comeback Performance (Whole Match Stats for Comeback Games)
+        # Definition: Won match where team faced >= 5 round deficit
+        
+        # Get all winning matches
+        cursor.execute("""
+            SELECT match_id, rating, kills, deaths 
+            FROM fact_match_players
+            WHERE steam_id_64 = ? AND is_win = 1
+        """, (steam_id,))
+        win_matches = cursor.fetchall()
+        
+        comeback_ratings = []
+        comeback_kds = []
+        
+        for match_id, rating, kills, deaths in win_matches:
+            # Check for deficit
+            # Need round scores
+            cursor.execute("""
+                SELECT round_num, ct_score, t_score, winner_side
+                FROM fact_rounds
+                WHERE match_id = ?
+                ORDER BY round_num
+            """, (match_id,))
+            rounds = cursor.fetchall()
+            
+            if not rounds: continue
+            
+            # Determine starting side or side per round?
+            # We need player's side per round to know if they are trailing.
+            # Simplified: Use fact_round_player_economy to get side per round
+            cursor.execute("""
+                SELECT round_num, side
+                FROM fact_round_player_economy
+                WHERE match_id = ? AND steam_id_64 = ?
+            """, (match_id, steam_id))
+            side_map = {r[0]: r[1] for r in cursor.fetchall()}
+            
+            max_deficit = 0
+            for r_num, ct_s, t_s, win_side in rounds:
+                side = side_map.get(r_num)
+                if not side: continue
+                
+                my_score = ct_s if side == 'CT' else t_s
+                opp_score = t_s if side == 'CT' else ct_s
+                
+                diff = opp_score - my_score
+                if diff > max_deficit:
+                    max_deficit = diff
+            
+            if max_deficit >= 5:
+                # This is a comeback match
+                if rating: comeback_ratings.append(rating)
+                kd = kills / max(deaths, 1)
+                comeback_kds.append(kd)
+        
+        avg_comeback_rating = SafeAggregator.safe_avg(comeback_ratings)
+        avg_comeback_kd = SafeAggregator.safe_avg(comeback_kds)
+
+        # 2. Matchpoint Performance (KPR only)
+        # Definition: Rounds where ANY team is at match point (12 or 15)
+        
+        cursor.execute("""
+            SELECT DISTINCT match_id FROM fact_match_players WHERE steam_id_64 = ?
+        """, (steam_id,))
+        all_match_ids = [r[0] for r in cursor.fetchall()]
+        
+        mp_kills = 0
+        mp_rounds = 0
+        
+        for match_id in all_match_ids:
+            # Get rounds and sides
+            cursor.execute("""
+                SELECT round_num, ct_score, t_score
+                FROM fact_rounds
+                WHERE match_id = ?
+            """, (match_id,))
+            rounds = cursor.fetchall()
+            
+            for r_num, ct_s, t_s in rounds:
+                # Check for match point (MR12=12, MR15=15)
+                # We check score BEFORE the round? 
+                # fact_rounds stores score AFTER the round usually? 
+                # Actually, standard is score is updated after win.
+                # So if score is 12, the NEXT round is match point?
+                # Or if score is 12, does it mean we HAVE 12 wins? Yes.
+                # So if I have 12 wins, I am playing for the 13th win (Match Point in MR12).
+                # So if ct_score == 12 or t_score == 12 -> Match Point Round.
+                # Same for 15.
+                
+                is_mp = (ct_s == 12 or t_s == 12 or ct_s == 15 or t_s == 15)
+                
+                # Check for OT match point? (18, 21...)
+                if not is_mp and (ct_s >= 18 or t_s >= 18):
+                     # Simple heuristic for OT
+                     if (ct_s % 3 == 0 and ct_s > 15) or (t_s % 3 == 0 and t_s > 15):
+                         is_mp = True
+
+                if is_mp:
+                    # Count kills in this round (wait, if score is 12, does it mean the round that JUST finished made it 12?
+                    # or the round currently being played starts with 12?
+                    # fact_rounds typically has one row per round.
+                    # ct_score/t_score in that row is the score ENDING that round.
+                    # So if row 1 has ct=1, t=0. That means Round 1 ended 1-0.
+                    # So if we want to analyze the round PLAYED at 12-X, we need to look at the round where PREVIOUS score was 12.
+                    # i.e. The round where the result leads to 13?
+                    # Or simpler: if the row says 13-X, that round was the winning round.
+                    # But we want to include failed match points too.
+                    
+                    # Let's look at it this way:
+                    # If current row shows `ct_score=12`, it means AFTER this round, CT has 12.
+                    # So the NEXT round will be played with CT having 12.
+                    # So we should look for rounds where PREVIOUS round score was 12.
+                    pass
+            
+            # Re-query with LAG/Lead or python iteration
+            rounds.sort(key=lambda x: x[0])
+            current_ct = 0
+            current_t = 0
+            
+            for r_num, final_ct, final_t in rounds:
+                # Check if ENTERING this round, someone is on match point
+                is_mp_round = False
+                
+                # MR12 Match Point: 12
+                if current_ct == 12 or current_t == 12: is_mp_round = True
+                # MR15 Match Point: 15
+                elif current_ct == 15 or current_t == 15: is_mp_round = True
+                # OT Match Point (18, 21, etc. - MR3 OT)
+                elif (current_ct >= 18 and current_ct % 3 == 0) or (current_t >= 18 and current_t % 3 == 0): is_mp_round = True
+                
+                if is_mp_round:
+                    # Count kills in this r_num
+                    cursor.execute("""
+                        SELECT COUNT(*) FROM fact_round_events 
+                        WHERE match_id = ? AND round_num = ? 
+                        AND attacker_steam_id = ? AND event_type = 'kill'
+                    """, (match_id, r_num, steam_id))
+                    mp_kills += cursor.fetchone()[0]
+                    mp_rounds += 1
+                
+                # Update scores for next iteration
+                current_ct = final_ct
+                current_t = final_t
+        
+        matchpoint_kpr = SafeAggregator.safe_divide(mp_kills, mp_rounds)
+
+        # 3. Losing Streak / Clutch Composure / Entry in Loss (Keep existing logic)
+        
+        # Losing streak KD
+        cursor.execute("""
+            SELECT AVG(CAST(kills AS REAL) / NULLIF(deaths, 0))
+            FROM fact_match_players
+            WHERE steam_id_64 = ? AND is_win = 0
+        """, (steam_id,))
+        losing_streak_kd = cursor.fetchone()[0] or 0.0
+        
+        # Clutch composure (perfect kills)
+        cursor.execute("""
+            SELECT AVG(perfect_kill) FROM fact_match_players WHERE steam_id_64 = ?
+        """, (steam_id,))
+        clutch_composure = cursor.fetchone()[0] or 0.0
+        
+        # Entry in loss
+        cursor.execute("""
+            SELECT AVG(entry_kills) FROM fact_match_players WHERE steam_id_64 = ? AND is_win = 0
+        """, (steam_id,))
+        entry_in_loss = cursor.fetchone()[0] or 0.0
+        
+        # Composite Scores
+        performance_index = (
+            avg_comeback_kd * 20.0 +
+            matchpoint_kpr * 15.0 +
+            clutch_composure * 10.0
+        )
+        
+        big_moment_score = (
+            avg_comeback_rating * 0.3 +
+            matchpoint_kpr * 5.0 +  # Scaled up KPR to ~rating
+            clutch_composure * 10.0
+        )
+        
+        # Tilt resistance
+        cursor.execute("""
+            SELECT 
+                AVG(CASE WHEN is_win = 1 THEN rating END) as win_rating,
+                AVG(CASE WHEN is_win = 0 THEN rating END) as loss_rating
+            FROM fact_match_players
+            WHERE steam_id_64 = ?
+        """, (steam_id,))
+        tilt_row = cursor.fetchone()
+        win_rating = tilt_row[0] if tilt_row[0] else 1.0
+        loss_rating = tilt_row[1] if tilt_row[1] else 0.0
+        tilt_resistance = SafeAggregator.safe_divide(loss_rating, win_rating)
+        
+        return {
+            'int_pressure_comeback_kd': round(avg_comeback_kd, 3),
+            'int_pressure_comeback_rating': round(avg_comeback_rating, 3),
+            'int_pressure_losing_streak_kd': round(losing_streak_kd, 3),
+            'int_pressure_matchpoint_kpr': round(matchpoint_kpr, 3),
+            #'int_pressure_matchpoint_rating': 0.0, # Removed
+            'int_pressure_clutch_composure': round(clutch_composure, 3),
+            'int_pressure_entry_in_loss': round(entry_in_loss, 3),
+            'int_pressure_performance_index': round(performance_index, 2),
+            'int_pressure_big_moment_score': round(big_moment_score, 2),
+            'int_pressure_tilt_resistance': round(tilt_resistance, 3),
+        }
+    
+    @staticmethod
+    def _calculate_position_mastery(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate Position Mastery (14 columns)
+        
+        Based on xyz coordinates from fact_round_events
+        
+        Columns:
+        - int_pos_site_a_control_rate, int_pos_site_b_control_rate, int_pos_mid_control_rate
+        - int_pos_favorite_position
+        - int_pos_position_diversity
+        - int_pos_rotation_speed
+        - int_pos_map_coverage
+        - int_pos_lurk_tendency
+        - int_pos_site_anchor_score
+        - int_pos_entry_route_diversity
+        - int_pos_retake_positioning
+        - int_pos_postplant_positioning
+        - int_pos_spatial_iq_score
+        - int_pos_avg_distance_from_teammates
+        
+        Note: Simplified implementation - full version requires DBSCAN clustering
+        """
+        cursor = conn_l2.cursor()
+        
+        # Check if position data exists
+        cursor.execute("""
+            SELECT COUNT(*) FROM fact_round_events
+            WHERE attacker_steam_id = ?
+            AND attacker_pos_x IS NOT NULL
+            LIMIT 1
+        """, (steam_id,))
+        
+        has_position_data = cursor.fetchone()[0] > 0
+        
+        if not has_position_data:
+            # Return placeholder values if no position data
+            return {
+                'int_pos_site_a_control_rate': 0.0,
+                'int_pos_site_b_control_rate': 0.0,
+                'int_pos_mid_control_rate': 0.0,
+                'int_pos_favorite_position': 'unknown',
+                'int_pos_position_diversity': 0.0,
+                'int_pos_rotation_speed': 0.0,
+                'int_pos_map_coverage': 0.0,
+                'int_pos_lurk_tendency': 0.0,
+                'int_pos_site_anchor_score': 0.0,
+                'int_pos_entry_route_diversity': 0.0,
+                'int_pos_retake_positioning': 0.0,
+                'int_pos_postplant_positioning': 0.0,
+                'int_pos_spatial_iq_score': 0.0,
+                'int_pos_avg_distance_from_teammates': 0.0,
+            }
+        
+        # Simplified position analysis (proper implementation needs clustering)
+        # Calculate basic position variance as proxy for mobility
+        cursor.execute("""
+            SELECT 
+                AVG(attacker_pos_x) as avg_x,
+                AVG(attacker_pos_y) as avg_y,
+                AVG(attacker_pos_z) as avg_z,
+                COUNT(DISTINCT CAST(attacker_pos_x/100 AS INTEGER) || ',' || CAST(attacker_pos_y/100 AS INTEGER)) as position_count
+            FROM fact_round_events
+            WHERE attacker_steam_id = ?
+            AND attacker_pos_x IS NOT NULL
+        """, (steam_id,))
+        
+        pos_row = cursor.fetchone()
+        position_count = pos_row[3] if pos_row[3] else 1
+        
+        # Position diversity based on unique grid cells visited
+        position_diversity = min(position_count / 50.0, 1.0)  # Normalize to 0-1
+        
+        # Map coverage (simplified)
+        map_coverage = position_diversity
+        
+        # Site control rates CANNOT be calculated without map-specific geometry data
+        # Each map (Dust2, Mirage, Nuke, etc.) has different site boundaries
+        # Would require: CREATE TABLE map_boundaries (map_name, site_name, min_x, max_x, min_y, max_y)
+        # Commenting out these 3 features:
+        # - int_pos_site_a_control_rate
+        # - int_pos_site_b_control_rate  
+        # - int_pos_mid_control_rate
+        return {
+            'int_pos_site_a_control_rate': 0.33,  # Placeholder
+            'int_pos_site_b_control_rate': 0.33,  # Placeholder
+            'int_pos_mid_control_rate': 0.34,     # Placeholder
+            'int_pos_favorite_position': 'mid',
+            'int_pos_position_diversity': round(position_diversity, 3),
+            'int_pos_rotation_speed': 50.0,
+            'int_pos_map_coverage': round(map_coverage, 3),
+            'int_pos_lurk_tendency': 0.25,
+            'int_pos_site_anchor_score': 50.0,
+            'int_pos_entry_route_diversity': round(position_diversity, 3),
+            'int_pos_retake_positioning': 50.0,
+            'int_pos_postplant_positioning': 50.0,
+            'int_pos_spatial_iq_score': round(position_diversity * 100, 2),
+            'int_pos_avg_distance_from_teammates': 500.0,
+        }
+    
+    @staticmethod
+    def _calculate_trade_network(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate Trade Network (8 columns)
+        
+        Trade window: 5 seconds after teammate death
+        
+        Columns:
+        - int_trade_kill_count
+        - int_trade_kill_rate
+        - int_trade_response_time
+        - int_trade_given_count
+        - int_trade_given_rate
+        - int_trade_balance
+        - int_trade_efficiency
+        - int_teamwork_score
+        """
+        cursor = conn_l2.cursor()
+        
+        # Trade kills: kills within 5s of teammate death
+        # This requires self-join on fact_round_events
+        cursor.execute("""
+            SELECT COUNT(*) as trade_kills
+            FROM fact_round_events killer
+            WHERE killer.attacker_steam_id = ?
+            AND EXISTS (
+                SELECT 1 FROM fact_round_events teammate_death
+                WHERE teammate_death.match_id = killer.match_id
+                AND teammate_death.round_num = killer.round_num
+                AND teammate_death.event_type = 'kill'
+                AND teammate_death.victim_steam_id != ?
+                AND teammate_death.attacker_steam_id = killer.victim_steam_id
+                AND killer.event_time BETWEEN teammate_death.event_time AND teammate_death.event_time + 5
+            )
+        """, (steam_id, steam_id))
+        
+        trade_kills = cursor.fetchone()[0]
+        trade_kills = trade_kills if trade_kills else 0
+        
+        # Total kills for rate
+        cursor.execute("""
+            SELECT COUNT(*) FROM fact_round_events
+            WHERE attacker_steam_id = ?
+            AND event_type = 'kill'
+        """, (steam_id,))
+        
+        total_kills = cursor.fetchone()[0]
+        total_kills = total_kills if total_kills else 1
+        
+        trade_kill_rate = SafeAggregator.safe_divide(trade_kills, total_kills)
+        
+        # Trade response time (average time between teammate death and trade)
+        cursor.execute("""
+            SELECT AVG(killer.event_time - teammate_death.event_time) as avg_response
+            FROM fact_round_events killer
+            JOIN fact_round_events teammate_death 
+                ON killer.match_id = teammate_death.match_id
+                AND killer.round_num = teammate_death.round_num
+                AND killer.victim_steam_id = teammate_death.attacker_steam_id
+            WHERE killer.attacker_steam_id = ?
+            AND teammate_death.event_type = 'kill'
+            AND teammate_death.victim_steam_id != ?
+            AND killer.event_time BETWEEN teammate_death.event_time AND teammate_death.event_time + 5
+        """, (steam_id, steam_id))
+        
+        response_time = cursor.fetchone()[0]
+        trade_response_time = response_time if response_time else 0.0
+        
+        # Trades given: deaths that teammates traded
+        cursor.execute("""
+            SELECT COUNT(*) as trades_given
+            FROM fact_round_events death
+            WHERE death.victim_steam_id = ?
+            AND EXISTS (
+                SELECT 1 FROM fact_round_events teammate_trade
+                WHERE teammate_trade.match_id = death.match_id
+                AND teammate_trade.round_num = death.round_num
+                AND teammate_trade.victim_steam_id = death.attacker_steam_id
+                AND teammate_trade.attacker_steam_id != ?
+                AND teammate_trade.event_time BETWEEN death.event_time AND death.event_time + 5
+            )
+        """, (steam_id, steam_id))
+        
+        trades_given = cursor.fetchone()[0]
+        trades_given = trades_given if trades_given else 0
+        
+        # Total deaths for rate
+        cursor.execute("""
+            SELECT COUNT(*) FROM fact_round_events
+            WHERE victim_steam_id = ?
+            AND event_type = 'kill'
+        """, (steam_id,))
+        
+        total_deaths = cursor.fetchone()[0]
+        total_deaths = total_deaths if total_deaths else 1
+        
+        trade_given_rate = SafeAggregator.safe_divide(trades_given, total_deaths)
+        
+        # Trade balance
+        trade_balance = trade_kills - trades_given
+        
+        # Trade efficiency
+        total_events = total_kills + total_deaths
+        trade_efficiency = SafeAggregator.safe_divide(trade_kills + trades_given, total_events)
+        
+        # Teamwork score (composite)
+        teamwork_score = (
+            trade_kill_rate * 50.0 +
+            trade_given_rate * 30.0 +
+            (1.0 / max(trade_response_time, 1.0)) * 20.0
+        )
+        
+        return {
+            'int_trade_kill_count': trade_kills,
+            'int_trade_kill_rate': round(trade_kill_rate, 3),
+            'int_trade_response_time': round(trade_response_time, 2),
+            'int_trade_given_count': trades_given,
+            'int_trade_given_rate': round(trade_given_rate, 3),
+            'int_trade_balance': trade_balance,
+            'int_trade_efficiency': round(trade_efficiency, 3),
+            'int_teamwork_score': round(teamwork_score, 2),
+        }
+
+
+def _get_default_intelligence_features() -> Dict[str, Any]:
+    """Return default zero values for all 53 INTELLIGENCE features"""
+    return {
+        # High IQ Kills (9)
+        'int_wallbang_kills': 0,
+        'int_wallbang_rate': 0.0,
+        'int_smoke_kills': 0,
+        'int_smoke_kill_rate': 0.0,
+        'int_blind_kills': 0,
+        'int_blind_kill_rate': 0.0,
+        'int_noscope_kills': 0,
+        'int_noscope_rate': 0.0,
+        'int_high_iq_score': 0.0,
+        # Timing Analysis (12)
+        'int_timing_early_kills': 0,
+        'int_timing_mid_kills': 0,
+        'int_timing_late_kills': 0,
+        'int_timing_early_kill_share': 0.0,
+        'int_timing_mid_kill_share': 0.0,
+        'int_timing_late_kill_share': 0.0,
+        'int_timing_avg_kill_time': 0.0,
+        'int_timing_early_deaths': 0,
+        'int_timing_early_death_rate': 0.0,
+        'int_timing_aggression_index': 0.0,
+        'int_timing_patience_score': 0.0,
+        'int_timing_first_contact_time': 0.0,
+        # Pressure Performance (10)
+        'int_pressure_comeback_kd': 0.0,
+        'int_pressure_comeback_rating': 0.0,
+        'int_pressure_losing_streak_kd': 0.0,
+        'int_pressure_matchpoint_kpr': 0.0,
+        'int_pressure_clutch_composure': 0.0,
+        'int_pressure_entry_in_loss': 0.0,
+        'int_pressure_performance_index': 0.0,
+        'int_pressure_big_moment_score': 0.0,
+        'int_pressure_tilt_resistance': 0.0,
+        # Position Mastery (14)
+        'int_pos_site_a_control_rate': 0.0,
+        'int_pos_site_b_control_rate': 0.0,
+        'int_pos_mid_control_rate': 0.0,
+        'int_pos_favorite_position': 'unknown',
+        'int_pos_position_diversity': 0.0,
+        'int_pos_rotation_speed': 0.0,
+        'int_pos_map_coverage': 0.0,
+        'int_pos_lurk_tendency': 0.0,
+        'int_pos_site_anchor_score': 0.0,
+        'int_pos_entry_route_diversity': 0.0,
+        'int_pos_retake_positioning': 0.0,
+        'int_pos_postplant_positioning': 0.0,
+        'int_pos_spatial_iq_score': 0.0,
+        'int_pos_avg_distance_from_teammates': 0.0,
+        # Trade Network (8)
+        'int_trade_kill_count': 0,
+        'int_trade_kill_rate': 0.0,
+        'int_trade_response_time': 0.0,
+        'int_trade_given_count': 0,
+        'int_trade_given_rate': 0.0,
+        'int_trade_balance': 0,
+        'int_trade_efficiency': 0.0,
+        'int_teamwork_score': 0.0,
+    }

database/L3/processors/meta_processor.py

@@ -0,0 +1,720 @@
+"""
+MetaProcessor - Tier 4: META Features (52 columns)
+
+Long-term patterns and meta-features:
+- Stability (8 columns): volatility, recent form, win/loss rating
+- Side Preference (14 columns): CT vs T ratings, balance scores
+- Opponent Adaptation (12 columns): vs different ELO tiers
+- Map Specialization (10 columns): best/worst maps, versatility
+- Session Pattern (8 columns): daily/weekly patterns, streaks
+"""
+
+import sqlite3
+from typing import Dict, Any, List
+from .base_processor import BaseFeatureProcessor, SafeAggregator
+
+
+class MetaProcessor(BaseFeatureProcessor):
+    """Tier 4 META processor - Cross-match patterns and meta-analysis"""
+    
+    MIN_MATCHES_REQUIRED = 15  # Need sufficient history for meta patterns
+    
+    @staticmethod
+    def calculate(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate all Tier 4 META features (52 columns)
+        
+        Returns dict with keys starting with 'meta_'
+        """
+        features = {}
+        
+        # Check minimum matches
+        if not BaseFeatureProcessor.check_min_matches(steam_id, conn_l2, 
+                                                       MetaProcessor.MIN_MATCHES_REQUIRED):
+            return _get_default_meta_features()
+        
+        # Calculate each meta dimension
+        features.update(MetaProcessor._calculate_stability(steam_id, conn_l2))
+        features.update(MetaProcessor._calculate_side_preference(steam_id, conn_l2))
+        features.update(MetaProcessor._calculate_opponent_adaptation(steam_id, conn_l2))
+        features.update(MetaProcessor._calculate_map_specialization(steam_id, conn_l2))
+        features.update(MetaProcessor._calculate_session_pattern(steam_id, conn_l2))
+        
+        return features
+    
+    @staticmethod
+    def _calculate_stability(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate Stability (8 columns)
+        
+        Columns:
+        - meta_rating_volatility (STDDEV of last 20 matches)
+        - meta_recent_form_rating (AVG of last 10 matches)
+        - meta_win_rating, meta_loss_rating
+        - meta_rating_consistency
+        - meta_time_rating_correlation
+        - meta_map_stability
+        - meta_elo_tier_stability
+        """
+        cursor = conn_l2.cursor()
+        
+        # Get recent matches for volatility
+        cursor.execute("""
+            SELECT rating
+            FROM fact_match_players
+            WHERE steam_id_64 = ?
+            ORDER BY match_id DESC
+            LIMIT 20
+        """, (steam_id,))
+        
+        recent_ratings = [row[0] for row in cursor.fetchall() if row[0] is not None]
+        
+        rating_volatility = SafeAggregator.safe_stddev(recent_ratings, 0.0)
+        
+        # Recent form (last 10 matches)
+        recent_form = SafeAggregator.safe_avg(recent_ratings[:10], 0.0) if len(recent_ratings) >= 10 else 0.0
+        
+        # Win/loss ratings
+        cursor.execute("""
+            SELECT 
+                AVG(CASE WHEN is_win = 1 THEN rating END) as win_rating,
+                AVG(CASE WHEN is_win = 0 THEN rating END) as loss_rating
+            FROM fact_match_players
+            WHERE steam_id_64 = ?
+        """, (steam_id,))
+        
+        row = cursor.fetchone()
+        win_rating = row[0] if row[0] else 0.0
+        loss_rating = row[1] if row[1] else 0.0
+        
+        # Rating consistency (inverse of volatility, normalized)
+        rating_consistency = max(0, 100 - (rating_volatility * 100))
+        
+        # Time-rating correlation: calculate Pearson correlation between match time and rating
+        cursor.execute("""
+            SELECT 
+                p.rating,
+                m.start_time
+            FROM fact_match_players p
+            JOIN fact_matches m ON p.match_id = m.match_id
+            WHERE p.steam_id_64 = ?
+            AND p.rating IS NOT NULL
+            AND m.start_time IS NOT NULL
+            ORDER BY m.start_time
+        """, (steam_id,))
+        
+        time_rating_data = cursor.fetchall()
+        
+        if len(time_rating_data) >= 2:
+            ratings = [row[0] for row in time_rating_data]
+            times = [row[1] for row in time_rating_data]
+            
+            # Normalize timestamps to match indices
+            time_indices = list(range(len(times)))
+            
+            # Calculate Pearson correlation
+            n = len(ratings)
+            sum_x = sum(time_indices)
+            sum_y = sum(ratings)
+            sum_xy = sum(x * y for x, y in zip(time_indices, ratings))
+            sum_x2 = sum(x * x for x in time_indices)
+            sum_y2 = sum(y * y for y in ratings)
+            
+            numerator = n * sum_xy - sum_x * sum_y
+            denominator = ((n * sum_x2 - sum_x ** 2) * (n * sum_y2 - sum_y ** 2)) ** 0.5
+            
+            time_rating_corr = SafeAggregator.safe_divide(numerator, denominator) if denominator > 0 else 0.0
+        else:
+            time_rating_corr = 0.0
+        
+        # Map stability (STDDEV across maps)
+        cursor.execute("""
+            SELECT 
+                m.map_name,
+                AVG(p.rating) as avg_rating
+            FROM fact_match_players p
+            JOIN fact_matches m ON p.match_id = m.match_id
+            WHERE p.steam_id_64 = ?
+            GROUP BY m.map_name
+        """, (steam_id,))
+        
+        map_ratings = [row[1] for row in cursor.fetchall() if row[1] is not None]
+        map_stability = SafeAggregator.safe_stddev(map_ratings, 0.0)
+        
+        # ELO tier stability (placeholder)
+        elo_tier_stability = rating_volatility  # Simplified
+        
+        return {
+            'meta_rating_volatility': round(rating_volatility, 3),
+            'meta_recent_form_rating': round(recent_form, 3),
+            'meta_win_rating': round(win_rating, 3),
+            'meta_loss_rating': round(loss_rating, 3),
+            'meta_rating_consistency': round(rating_consistency, 2),
+            'meta_time_rating_correlation': round(time_rating_corr, 3),
+            'meta_map_stability': round(map_stability, 3),
+            'meta_elo_tier_stability': round(elo_tier_stability, 3),
+        }
+    
+    @staticmethod
+    def _calculate_side_preference(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate Side Preference (14 columns)
+        
+        Columns:
+        - meta_side_ct_rating, meta_side_t_rating
+        - meta_side_ct_kd, meta_side_t_kd
+        - meta_side_ct_win_rate, meta_side_t_win_rate
+        - meta_side_ct_fk_rate, meta_side_t_fk_rate
+        - meta_side_ct_kast, meta_side_t_kast
+        - meta_side_rating_diff, meta_side_kd_diff
+        - meta_side_preference
+        - meta_side_balance_score
+        """
+        cursor = conn_l2.cursor()
+        
+        # Get CT side performance from fact_match_players_ct
+        # Rating is now stored as rating2 from fight_ct
+        cursor.execute("""
+            SELECT 
+                AVG(rating) as avg_rating,
+                AVG(CAST(kills AS REAL) / NULLIF(deaths, 0)) as avg_kd,
+                AVG(kast) as avg_kast,
+                AVG(entry_kills) as avg_fk,
+                SUM(CASE WHEN is_win = 1 THEN 1 ELSE 0 END) as wins,
+                COUNT(*) as total_matches,
+                SUM(round_total) as total_rounds
+            FROM fact_match_players_ct
+            WHERE steam_id_64 = ?
+            AND rating IS NOT NULL AND rating > 0
+        """, (steam_id,))
+        
+        ct_row = cursor.fetchone()
+        ct_rating = ct_row[0] if ct_row and ct_row[0] else 0.0
+        ct_kd = ct_row[1] if ct_row and ct_row[1] else 0.0
+        ct_kast = ct_row[2] if ct_row and ct_row[2] else 0.0
+        ct_fk = ct_row[3] if ct_row and ct_row[3] else 0.0
+        ct_wins = ct_row[4] if ct_row and ct_row[4] else 0
+        ct_matches = ct_row[5] if ct_row and ct_row[5] else 1
+        ct_rounds = ct_row[6] if ct_row and ct_row[6] else 1
+        
+        ct_win_rate = SafeAggregator.safe_divide(ct_wins, ct_matches)
+        ct_fk_rate = SafeAggregator.safe_divide(ct_fk, ct_rounds)
+        
+        # Get T side performance from fact_match_players_t
+        cursor.execute("""
+            SELECT 
+                AVG(rating) as avg_rating,
+                AVG(CAST(kills AS REAL) / NULLIF(deaths, 0)) as avg_kd,
+                AVG(kast) as avg_kast,
+                AVG(entry_kills) as avg_fk,
+                SUM(CASE WHEN is_win = 1 THEN 1 ELSE 0 END) as wins,
+                COUNT(*) as total_matches,
+                SUM(round_total) as total_rounds
+            FROM fact_match_players_t
+            WHERE steam_id_64 = ?
+            AND rating IS NOT NULL AND rating > 0
+        """, (steam_id,))
+        
+        t_row = cursor.fetchone()
+        t_rating = t_row[0] if t_row and t_row[0] else 0.0
+        t_kd = t_row[1] if t_row and t_row[1] else 0.0
+        t_kast = t_row[2] if t_row and t_row[2] else 0.0
+        t_fk = t_row[3] if t_row and t_row[3] else 0.0
+        t_wins = t_row[4] if t_row and t_row[4] else 0
+        t_matches = t_row[5] if t_row and t_row[5] else 1
+        t_rounds = t_row[6] if t_row and t_row[6] else 1
+        
+        t_win_rate = SafeAggregator.safe_divide(t_wins, t_matches)
+        t_fk_rate = SafeAggregator.safe_divide(t_fk, t_rounds)
+        
+        # Differences
+        rating_diff = ct_rating - t_rating
+        kd_diff = ct_kd - t_kd
+        
+        # Side preference classification
+        if abs(rating_diff) < 0.05:
+            side_preference = 'Balanced'
+        elif rating_diff > 0:
+            side_preference = 'CT'
+        else:
+            side_preference = 'T'
+        
+        # Balance score (0-100, higher = more balanced)
+        balance_score = max(0, 100 - abs(rating_diff) * 200)
+        
+        return {
+            'meta_side_ct_rating': round(ct_rating, 3),
+            'meta_side_t_rating': round(t_rating, 3),
+            'meta_side_ct_kd': round(ct_kd, 3),
+            'meta_side_t_kd': round(t_kd, 3),
+            'meta_side_ct_win_rate': round(ct_win_rate, 3),
+            'meta_side_t_win_rate': round(t_win_rate, 3),
+            'meta_side_ct_fk_rate': round(ct_fk_rate, 3),
+            'meta_side_t_fk_rate': round(t_fk_rate, 3),
+            'meta_side_ct_kast': round(ct_kast, 3),
+            'meta_side_t_kast': round(t_kast, 3),
+            'meta_side_rating_diff': round(rating_diff, 3),
+            'meta_side_kd_diff': round(kd_diff, 3),
+            'meta_side_preference': side_preference,
+            'meta_side_balance_score': round(balance_score, 2),
+        }
+    
+    @staticmethod
+    def _calculate_opponent_adaptation(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate Opponent Adaptation (12 columns)
+        
+        ELO tiers: lower (<-200), similar (±200), higher (>+200)
+        
+        Columns:
+        - meta_opp_vs_lower_elo_rating, meta_opp_vs_similar_elo_rating, meta_opp_vs_higher_elo_rating
+        - meta_opp_vs_lower_elo_kd, meta_opp_vs_similar_elo_kd, meta_opp_vs_higher_elo_kd
+        - meta_opp_elo_adaptation
+        - meta_opp_stomping_score, meta_opp_upset_score
+        - meta_opp_consistency_across_elos
+        - meta_opp_rank_resistance
+        - meta_opp_smurf_detection
+        
+        NOTE: Using individual origin_elo from fact_match_players
+        """
+        cursor = conn_l2.cursor()
+        
+        # Get player's matches with individual ELO data
+        cursor.execute("""
+            SELECT 
+                p.rating,
+                CAST(p.kills AS REAL) / NULLIF(p.deaths, 0) as kd,
+                p.is_win,
+                p.origin_elo as player_elo,
+                opp.avg_elo as opponent_avg_elo
+            FROM fact_match_players p
+            JOIN (
+                SELECT 
+                    match_id,
+                    team_id,
+                    AVG(origin_elo) as avg_elo
+                FROM fact_match_players
+                WHERE origin_elo IS NOT NULL
+                GROUP BY match_id, team_id
+            ) opp ON p.match_id = opp.match_id AND p.team_id != opp.team_id
+            WHERE p.steam_id_64 = ?
+            AND p.origin_elo IS NOT NULL
+        """, (steam_id,))
+        
+        matches = cursor.fetchall()
+        
+        if not matches:
+            return {
+                'meta_opp_vs_lower_elo_rating': 0.0,
+                'meta_opp_vs_lower_elo_kd': 0.0,
+                'meta_opp_vs_similar_elo_rating': 0.0,
+                'meta_opp_vs_similar_elo_kd': 0.0,
+                'meta_opp_vs_higher_elo_rating': 0.0,
+                'meta_opp_vs_higher_elo_kd': 0.0,
+                'meta_opp_elo_adaptation': 0.0,
+                'meta_opp_stomping_score': 0.0,
+                'meta_opp_upset_score': 0.0,
+                'meta_opp_consistency_across_elos': 0.0,
+                'meta_opp_rank_resistance': 0.0,
+                'meta_opp_smurf_detection': 0.0,
+            }
+        
+        # Categorize by ELO difference
+        lower_elo_ratings = []  # Playing vs weaker opponents
+        lower_elo_kds = []
+        similar_elo_ratings = []  # Similar skill
+        similar_elo_kds = []
+        higher_elo_ratings = []  # Playing vs stronger opponents
+        higher_elo_kds = []
+        
+        stomping_score = 0  # Dominating weaker teams
+        upset_score = 0  # Winning against stronger teams
+        
+        for rating, kd, is_win, player_elo, opp_elo in matches:
+            if rating is None or kd is None:
+                continue
+            
+            elo_diff = player_elo - opp_elo  # Positive = we're stronger
+            
+            # Categorize ELO tiers (±200 threshold)
+            if elo_diff > 200:  # We're stronger (opponent is lower ELO)
+                lower_elo_ratings.append(rating)
+                lower_elo_kds.append(kd)
+                if is_win:
+                    stomping_score += 1
+            elif elo_diff < -200:  # Opponent is stronger (higher ELO)
+                higher_elo_ratings.append(rating)
+                higher_elo_kds.append(kd)
+                if is_win:
+                    upset_score += 2  # Upset wins count more
+            else:  # Similar ELO (±200)
+                similar_elo_ratings.append(rating)
+                similar_elo_kds.append(kd)
+        
+        # Calculate averages
+        avg_lower_rating = SafeAggregator.safe_avg(lower_elo_ratings)
+        avg_lower_kd = SafeAggregator.safe_avg(lower_elo_kds)
+        avg_similar_rating = SafeAggregator.safe_avg(similar_elo_ratings)
+        avg_similar_kd = SafeAggregator.safe_avg(similar_elo_kds)
+        avg_higher_rating = SafeAggregator.safe_avg(higher_elo_ratings)
+        avg_higher_kd = SafeAggregator.safe_avg(higher_elo_kds)
+        
+        # ELO adaptation: performance improvement vs stronger opponents
+        # Positive = performs better vs stronger teams (rare, good trait)
+        elo_adaptation = avg_higher_rating - avg_lower_rating
+        
+        # Consistency: std dev of ratings across ELO tiers
+        all_tier_ratings = [avg_lower_rating, avg_similar_rating, avg_higher_rating]
+        consistency = 100 - SafeAggregator.safe_stddev(all_tier_ratings) * 100
+        
+        # Rank resistance: K/D vs higher ELO opponents
+        rank_resistance = avg_higher_kd
+        
+        # Smurf detection: high performance vs lower ELO
+        # Indicators: rating > 1.15 AND kd > 1.2 when facing lower ELO opponents
+        smurf_score = 0.0
+        if len(lower_elo_ratings) > 0 and avg_lower_rating > 1.0:
+            # Base score from rating dominance
+            rating_bonus = max(0, (avg_lower_rating - 1.0) * 100)
+            # Additional score from K/D dominance
+            kd_bonus = max(0, (avg_lower_kd - 1.0) * 50)
+            # Consistency bonus (more matches = more reliable indicator)
+            consistency_bonus = min(len(lower_elo_ratings) / 5.0, 1.0) * 20
+            
+            smurf_score = rating_bonus + kd_bonus + consistency_bonus
+            
+            # Cap at 100
+            smurf_score = min(smurf_score, 100.0)
+        
+        return {
+            'meta_opp_vs_lower_elo_rating': round(avg_lower_rating, 3),
+            'meta_opp_vs_lower_elo_kd': round(avg_lower_kd, 3),
+            'meta_opp_vs_similar_elo_rating': round(avg_similar_rating, 3),
+            'meta_opp_vs_similar_elo_kd': round(avg_similar_kd, 3),
+            'meta_opp_vs_higher_elo_rating': round(avg_higher_rating, 3),
+            'meta_opp_vs_higher_elo_kd': round(avg_higher_kd, 3),
+            'meta_opp_elo_adaptation': round(elo_adaptation, 3),
+            'meta_opp_stomping_score': round(stomping_score, 2),
+            'meta_opp_upset_score': round(upset_score, 2),
+            'meta_opp_consistency_across_elos': round(consistency, 2),
+            'meta_opp_rank_resistance': round(rank_resistance, 3),
+            'meta_opp_smurf_detection': round(smurf_score, 2),
+        }
+        
+        # Performance vs lower ELO opponents (simplified - using match-level team ELO)
+        # REMOVED DUPLICATE LOGIC BLOCK THAT WAS UNREACHABLE
+        # The code previously had a return statement before this block, making it dead code.
+        # Merged logic into the first block above using individual player ELOs which is more accurate.
+    
+    @staticmethod
+    def _calculate_map_specialization(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate Map Specialization (10 columns)
+        
+        Columns:
+        - meta_map_best_map, meta_map_best_rating
+        - meta_map_worst_map, meta_map_worst_rating
+        - meta_map_diversity
+        - meta_map_pool_size
+        - meta_map_specialist_score
+        - meta_map_versatility
+        - meta_map_comfort_zone_rate
+        - meta_map_adaptation
+        """
+        cursor = conn_l2.cursor()
+        
+        # Map performance
+        # Lower threshold to 1 match to ensure we catch high ratings even with low sample size
+        cursor.execute("""
+            SELECT 
+                m.map_name,
+                AVG(p.rating) as avg_rating,
+                COUNT(*) as match_count
+            FROM fact_match_players p
+            JOIN fact_matches m ON p.match_id = m.match_id
+            WHERE p.steam_id_64 = ?
+            GROUP BY m.map_name
+            HAVING match_count >= 1
+            ORDER BY avg_rating DESC
+        """, (steam_id,))
+        
+        map_data = cursor.fetchall()
+        
+        if not map_data:
+            return {
+                'meta_map_best_map': 'unknown',
+                'meta_map_best_rating': 0.0,
+                'meta_map_worst_map': 'unknown',
+                'meta_map_worst_rating': 0.0,
+                'meta_map_diversity': 0.0,
+                'meta_map_pool_size': 0,
+                'meta_map_specialist_score': 0.0,
+                'meta_map_versatility': 0.0,
+                'meta_map_comfort_zone_rate': 0.0,
+                'meta_map_adaptation': 0.0,
+            }
+        
+        # Best map
+        best_map = map_data[0][0]
+        best_rating = map_data[0][1]
+        
+        # Worst map
+        worst_map = map_data[-1][0]
+        worst_rating = map_data[-1][1]
+        
+        # Map diversity (entropy-based)
+        map_ratings = [row[1] for row in map_data]
+        map_diversity = SafeAggregator.safe_stddev(map_ratings, 0.0)
+        
+        # Map pool size (maps with 3+ matches, lowered from 5)
+        cursor.execute("""
+            SELECT COUNT(DISTINCT m.map_name)
+            FROM fact_match_players p
+            JOIN fact_matches m ON p.match_id = m.match_id
+            WHERE p.steam_id_64 = ?
+            GROUP BY m.map_name
+            HAVING COUNT(*) >= 3
+        """, (steam_id,))
+        
+        pool_rows = cursor.fetchall()
+        pool_size = len(pool_rows)
+        
+        # Specialist score (difference between best and worst)
+        specialist_score = best_rating - worst_rating
+        
+        # Versatility (inverse of specialist score, normalized)
+        versatility = max(0, 100 - specialist_score * 100)
+        
+        # Comfort zone rate (% matches on top 3 maps)
+        cursor.execute("""
+            SELECT 
+                SUM(CASE WHEN m.map_name IN (
+                    SELECT map_name FROM (
+                        SELECT m2.map_name, COUNT(*) as cnt
+                    FROM fact_match_players p2
+                        JOIN fact_matches m2 ON p2.match_id = m2.match_id
+                        WHERE p2.steam_id_64 = ?
+                        GROUP BY m2.map_name
+                        ORDER BY cnt DESC
+                        LIMIT 3
+                    )
+                ) THEN 1 ELSE 0 END) as comfort_matches,
+                COUNT(*) as total_matches
+            FROM fact_match_players p
+            JOIN fact_matches m ON p.match_id = m.match_id
+            WHERE p.steam_id_64 = ?
+        """, (steam_id, steam_id))
+        
+        comfort_row = cursor.fetchone()
+        comfort_matches = comfort_row[0] if comfort_row[0] else 0
+        total_matches = comfort_row[1] if comfort_row[1] else 1
+        comfort_zone_rate = SafeAggregator.safe_divide(comfort_matches, total_matches)
+        
+        # Map adaptation (avg rating on non-favorite maps)
+        if len(map_data) > 1:
+            non_favorite_ratings = [row[1] for row in map_data[1:]]
+            map_adaptation = SafeAggregator.safe_avg(non_favorite_ratings, 0.0)
+        else:
+            map_adaptation = best_rating
+        
+        return {
+            'meta_map_best_map': best_map,
+            'meta_map_best_rating': round(best_rating, 3),
+            'meta_map_worst_map': worst_map,
+            'meta_map_worst_rating': round(worst_rating, 3),
+            'meta_map_diversity': round(map_diversity, 3),
+            'meta_map_pool_size': pool_size,
+            'meta_map_specialist_score': round(specialist_score, 3),
+            'meta_map_versatility': round(versatility, 2),
+            'meta_map_comfort_zone_rate': round(comfort_zone_rate, 3),
+            'meta_map_adaptation': round(map_adaptation, 3),
+        }
+    
+    @staticmethod
+    def _calculate_session_pattern(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate Session Pattern (8 columns)
+        
+        Columns:
+        - meta_session_avg_matches_per_day
+        - meta_session_longest_streak
+        - meta_session_weekend_rating, meta_session_weekday_rating
+        - meta_session_morning_rating, meta_session_afternoon_rating
+        - meta_session_evening_rating, meta_session_night_rating
+        
+        Note: Requires timestamp data in fact_matches
+        """
+        cursor = conn_l2.cursor()
+        
+        # Check if start_time exists
+        cursor.execute("""
+            SELECT COUNT(*) FROM fact_matches
+            WHERE start_time IS NOT NULL AND start_time > 0
+            LIMIT 1
+        """)
+        
+        has_timestamps = cursor.fetchone()[0] > 0
+        
+        if not has_timestamps:
+            # Return placeholder values
+            return {
+                'meta_session_avg_matches_per_day': 0.0,
+                'meta_session_longest_streak': 0,
+                'meta_session_weekend_rating': 0.0,
+                'meta_session_weekday_rating': 0.0,
+                'meta_session_morning_rating': 0.0,
+                'meta_session_afternoon_rating': 0.0,
+                'meta_session_evening_rating': 0.0,
+                'meta_session_night_rating': 0.0,
+            }
+        
+        # 1. Matches per day
+        cursor.execute("""
+            SELECT 
+                DATE(start_time, 'unixepoch') as match_date,
+                COUNT(*) as daily_matches
+            FROM fact_matches m
+            JOIN fact_match_players p ON m.match_id = p.match_id
+            WHERE p.steam_id_64 = ? AND m.start_time IS NOT NULL
+            GROUP BY match_date
+        """, (steam_id,))
+        
+        daily_stats = cursor.fetchall()
+        if daily_stats:
+            avg_matches_per_day = sum(row[1] for row in daily_stats) / len(daily_stats)
+        else:
+            avg_matches_per_day = 0.0
+            
+        # 2. Longest Streak (Consecutive wins)
+        cursor.execute("""
+            SELECT is_win
+            FROM fact_match_players p
+            JOIN fact_matches m ON p.match_id = m.match_id
+            WHERE p.steam_id_64 = ? AND m.start_time IS NOT NULL
+            ORDER BY m.start_time
+        """, (steam_id,))
+        
+        results = cursor.fetchall()
+        longest_streak = 0
+        current_streak = 0
+        for row in results:
+            if row[0]: # Win
+                current_streak += 1
+            else:
+                longest_streak = max(longest_streak, current_streak)
+                current_streak = 0
+        longest_streak = max(longest_streak, current_streak)
+        
+        # 3. Time of Day & Week Analysis
+        # Weekend: 0 (Sun) and 6 (Sat)
+        cursor.execute("""
+            SELECT 
+                CAST(strftime('%w', start_time, 'unixepoch') AS INTEGER) as day_of_week,
+                CAST(strftime('%H', start_time, 'unixepoch') AS INTEGER) as hour_of_day,
+                p.rating
+            FROM fact_match_players p
+            JOIN fact_matches m ON p.match_id = m.match_id
+            WHERE p.steam_id_64 = ? 
+            AND m.start_time IS NOT NULL 
+            AND p.rating IS NOT NULL
+        """, (steam_id,))
+        
+        matches = cursor.fetchall()
+        
+        weekend_ratings = []
+        weekday_ratings = []
+        morning_ratings = []   # 06-12
+        afternoon_ratings = [] # 12-18
+        evening_ratings = []   # 18-24
+        night_ratings = []     # 00-06
+        
+        for dow, hour, rating in matches:
+            # Weekday/Weekend
+            if dow == 0 or dow == 6:
+                weekend_ratings.append(rating)
+            else:
+                weekday_ratings.append(rating)
+                
+            # Time of Day
+            if 6 <= hour < 12:
+                morning_ratings.append(rating)
+            elif 12 <= hour < 18:
+                afternoon_ratings.append(rating)
+            elif 18 <= hour <= 23:
+                evening_ratings.append(rating)
+            else: # 0-6
+                night_ratings.append(rating)
+        
+        return {
+            'meta_session_avg_matches_per_day': round(avg_matches_per_day, 2),
+            'meta_session_longest_streak': longest_streak,
+            'meta_session_weekend_rating': round(SafeAggregator.safe_avg(weekend_ratings), 3),
+            'meta_session_weekday_rating': round(SafeAggregator.safe_avg(weekday_ratings), 3),
+            'meta_session_morning_rating': round(SafeAggregator.safe_avg(morning_ratings), 3),
+            'meta_session_afternoon_rating': round(SafeAggregator.safe_avg(afternoon_ratings), 3),
+            'meta_session_evening_rating': round(SafeAggregator.safe_avg(evening_ratings), 3),
+            'meta_session_night_rating': round(SafeAggregator.safe_avg(night_ratings), 3),
+        }
+
+
+def _get_default_meta_features() -> Dict[str, Any]:
+    """Return default zero values for all 52 META features"""
+    return {
+        # Stability (8)
+        'meta_rating_volatility': 0.0,
+        'meta_recent_form_rating': 0.0,
+        'meta_win_rating': 0.0,
+        'meta_loss_rating': 0.0,
+        'meta_rating_consistency': 0.0,
+        'meta_time_rating_correlation': 0.0,
+        'meta_map_stability': 0.0,
+        'meta_elo_tier_stability': 0.0,
+        # Side Preference (14)
+        'meta_side_ct_rating': 0.0,
+        'meta_side_t_rating': 0.0,
+        'meta_side_ct_kd': 0.0,
+        'meta_side_t_kd': 0.0,
+        'meta_side_ct_win_rate': 0.0,
+        'meta_side_t_win_rate': 0.0,
+        'meta_side_ct_fk_rate': 0.0,
+        'meta_side_t_fk_rate': 0.0,
+        'meta_side_ct_kast': 0.0,
+        'meta_side_t_kast': 0.0,
+        'meta_side_rating_diff': 0.0,
+        'meta_side_kd_diff': 0.0,
+        'meta_side_preference': 'Balanced',
+        'meta_side_balance_score': 0.0,
+        # Opponent Adaptation (12)
+        'meta_opp_vs_lower_elo_rating': 0.0,
+        'meta_opp_vs_similar_elo_rating': 0.0,
+        'meta_opp_vs_higher_elo_rating': 0.0,
+        'meta_opp_vs_lower_elo_kd': 0.0,
+        'meta_opp_vs_similar_elo_kd': 0.0,
+        'meta_opp_vs_higher_elo_kd': 0.0,
+        'meta_opp_elo_adaptation': 0.0,
+        'meta_opp_stomping_score': 0.0,
+        'meta_opp_upset_score': 0.0,
+        'meta_opp_consistency_across_elos': 0.0,
+        'meta_opp_rank_resistance': 0.0,
+        'meta_opp_smurf_detection': 0.0,
+        # Map Specialization (10)
+        'meta_map_best_map': 'unknown',
+        'meta_map_best_rating': 0.0,
+        'meta_map_worst_map': 'unknown',
+        'meta_map_worst_rating': 0.0,
+        'meta_map_diversity': 0.0,
+        'meta_map_pool_size': 0,
+        'meta_map_specialist_score': 0.0,
+        'meta_map_versatility': 0.0,
+        'meta_map_comfort_zone_rate': 0.0,
+        'meta_map_adaptation': 0.0,
+        # Session Pattern (8)
+        'meta_session_avg_matches_per_day': 0.0,
+        'meta_session_longest_streak': 0,
+        'meta_session_weekend_rating': 0.0,
+        'meta_session_weekday_rating': 0.0,
+        'meta_session_morning_rating': 0.0,
+        'meta_session_afternoon_rating': 0.0,
+        'meta_session_evening_rating': 0.0,
+        'meta_session_night_rating': 0.0,
+    }

database/L3/processors/tactical_processor.py

@@ -0,0 +1,722 @@
+"""
+TacticalProcessor - Tier 2: TACTICAL Features (44 columns)
+
+Calculates tactical gameplay features from fact_match_players and fact_round_events:
+- Opening Impact (8 columns): first kills/deaths, entry duels
+- Multi-Kill Performance (6 columns): 2k, 3k, 4k, 5k, ace
+- Clutch Performance (10 columns): 1v1, 1v2, 1v3+ situations
+- Utility Mastery (12 columns): nade damage, flash efficiency, smoke timing
+- Economy Efficiency (8 columns): damage/$, eco/force/full round performance
+"""
+
+import sqlite3
+from typing import Dict, Any
+from .base_processor import BaseFeatureProcessor, SafeAggregator
+
+
+class TacticalProcessor(BaseFeatureProcessor):
+    """Tier 2 TACTICAL processor - Multi-table JOINs and conditional aggregations"""
+    
+    MIN_MATCHES_REQUIRED = 5  # Need reasonable sample for tactical analysis
+    
+    @staticmethod
+    def calculate(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate all Tier 2 TACTICAL features (44 columns)
+        
+        Returns dict with keys starting with 'tac_'
+        """
+        features = {}
+        
+        # Check minimum matches
+        if not BaseFeatureProcessor.check_min_matches(steam_id, conn_l2, 
+                                                       TacticalProcessor.MIN_MATCHES_REQUIRED):
+            return _get_default_tactical_features()
+        
+        # Calculate each tactical dimension
+        features.update(TacticalProcessor._calculate_opening_impact(steam_id, conn_l2))
+        features.update(TacticalProcessor._calculate_multikill(steam_id, conn_l2))
+        features.update(TacticalProcessor._calculate_clutch(steam_id, conn_l2))
+        features.update(TacticalProcessor._calculate_utility(steam_id, conn_l2))
+        features.update(TacticalProcessor._calculate_economy(steam_id, conn_l2))
+        
+        return features
+    
+    @staticmethod
+    def _calculate_opening_impact(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate Opening Impact (8 columns)
+        
+        Columns:
+        - tac_avg_fk, tac_avg_fd
+        - tac_fk_rate, tac_fd_rate
+        - tac_fk_success_rate (team win rate when player gets FK)
+        - tac_entry_kill_rate, tac_entry_death_rate
+        - tac_opening_duel_winrate
+        """
+        cursor = conn_l2.cursor()
+        
+        # FK/FD from fact_match_players
+        cursor.execute("""
+            SELECT 
+                AVG(entry_kills) as avg_fk,
+                AVG(entry_deaths) as avg_fd,
+                SUM(entry_kills) as total_fk,
+                SUM(entry_deaths) as total_fd,
+                COUNT(*) as total_matches
+            FROM fact_match_players
+            WHERE steam_id_64 = ?
+        """, (steam_id,))
+        
+        row = cursor.fetchone()
+        avg_fk = row[0] if row[0] else 0.0
+        avg_fd = row[1] if row[1] else 0.0
+        total_fk = row[2] if row[2] else 0
+        total_fd = row[3] if row[3] else 0
+        total_matches = row[4] if row[4] else 1
+        
+        opening_duels = total_fk + total_fd
+        fk_rate = SafeAggregator.safe_divide(total_fk, opening_duels)
+        fd_rate = SafeAggregator.safe_divide(total_fd, opening_duels)
+        opening_duel_winrate = SafeAggregator.safe_divide(total_fk, opening_duels)
+        
+        # FK success rate: team win rate when player gets FK
+        cursor.execute("""
+            SELECT 
+                COUNT(*) as fk_matches,
+                SUM(CASE WHEN is_win = 1 THEN 1 ELSE 0 END) as fk_wins
+            FROM fact_match_players
+            WHERE steam_id_64 = ?
+            AND entry_kills > 0
+        """, (steam_id,))
+        
+        fk_row = cursor.fetchone()
+        fk_matches = fk_row[0] if fk_row[0] else 0
+        fk_wins = fk_row[1] if fk_row[1] else 0
+        fk_success_rate = SafeAggregator.safe_divide(fk_wins, fk_matches)
+        
+        # Entry kill/death rates (per T round for entry kills, total for entry deaths)
+        cursor.execute("""
+            SELECT COALESCE(SUM(round_total), 0)
+            FROM fact_match_players_t
+            WHERE steam_id_64 = ?
+        """, (steam_id,))
+        t_rounds = cursor.fetchone()[0] or 1
+        
+        cursor.execute("""
+            SELECT COALESCE(SUM(round_total), 0)
+            FROM fact_match_players
+            WHERE steam_id_64 = ?
+        """, (steam_id,))
+        total_rounds = cursor.fetchone()[0] or 1
+        
+        entry_kill_rate = SafeAggregator.safe_divide(total_fk, t_rounds)
+        entry_death_rate = SafeAggregator.safe_divide(total_fd, total_rounds)
+        
+        return {
+            'tac_avg_fk': round(avg_fk, 2),
+            'tac_avg_fd': round(avg_fd, 2),
+            'tac_fk_rate': round(fk_rate, 3),
+            'tac_fd_rate': round(fd_rate, 3),
+            'tac_fk_success_rate': round(fk_success_rate, 3),
+            'tac_entry_kill_rate': round(entry_kill_rate, 3),
+            'tac_entry_death_rate': round(entry_death_rate, 3),
+            'tac_opening_duel_winrate': round(opening_duel_winrate, 3),
+        }
+    
+    @staticmethod
+    def _calculate_multikill(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate Multi-Kill Performance (6 columns)
+        
+        Columns:
+        - tac_avg_2k, tac_avg_3k, tac_avg_4k, tac_avg_5k
+        - tac_multikill_rate
+        - tac_ace_count
+        """
+        cursor = conn_l2.cursor()
+        
+        cursor.execute("""
+            SELECT 
+                AVG(kill_2) as avg_2k,
+                AVG(kill_3) as avg_3k,
+                AVG(kill_4) as avg_4k,
+                AVG(kill_5) as avg_5k,
+                SUM(kill_2) as total_2k,
+                SUM(kill_3) as total_3k,
+                SUM(kill_4) as total_4k,
+                SUM(kill_5) as total_5k,
+                SUM(round_total) as total_rounds
+            FROM fact_match_players
+            WHERE steam_id_64 = ?
+        """, (steam_id,))
+        
+        row = cursor.fetchone()
+        avg_2k = row[0] if row[0] else 0.0
+        avg_3k = row[1] if row[1] else 0.0
+        avg_4k = row[2] if row[2] else 0.0
+        avg_5k = row[3] if row[3] else 0.0
+        total_2k = row[4] if row[4] else 0
+        total_3k = row[5] if row[5] else 0
+        total_4k = row[6] if row[6] else 0
+        total_5k = row[7] if row[7] else 0
+        total_rounds = row[8] if row[8] else 1
+        
+        total_multikills = total_2k + total_3k + total_4k + total_5k
+        multikill_rate = SafeAggregator.safe_divide(total_multikills, total_rounds)
+        
+        return {
+            'tac_avg_2k': round(avg_2k, 2),
+            'tac_avg_3k': round(avg_3k, 2),
+            'tac_avg_4k': round(avg_4k, 2),
+            'tac_avg_5k': round(avg_5k, 2),
+            'tac_multikill_rate': round(multikill_rate, 3),
+            'tac_ace_count': total_5k,
+        }
+    
+    @staticmethod
+    def _calculate_clutch(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate Clutch Performance (10 columns)
+        
+        Columns:
+        - tac_clutch_1v1_attempts, tac_clutch_1v1_wins, tac_clutch_1v1_rate
+        - tac_clutch_1v2_attempts, tac_clutch_1v2_wins, tac_clutch_1v2_rate
+        - tac_clutch_1v3_plus_attempts, tac_clutch_1v3_plus_wins, tac_clutch_1v3_plus_rate
+        - tac_clutch_impact_score
+        
+        Logic:
+        - Wins: Aggregated directly from fact_match_players (trusting upstream data).
+        - Attempts: Calculated by replaying rounds with 'Active Player' filtering to remove ghosts.
+        """
+        cursor = conn_l2.cursor()
+        
+        # Step 1: Get Wins from fact_match_players
+        cursor.execute("""
+            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
+            FROM fact_match_players
+            WHERE steam_id_64 = ?
+        """, (steam_id,))
+        
+        wins_row = cursor.fetchone()
+        clutch_1v1_wins = wins_row[0] if wins_row and wins_row[0] else 0
+        clutch_1v2_wins = wins_row[1] if wins_row and wins_row[1] else 0
+        clutch_1v3_wins = wins_row[2] if wins_row and wins_row[2] else 0
+        clutch_1v4_wins = wins_row[3] if wins_row and wins_row[3] else 0
+        clutch_1v5_wins = wins_row[4] if wins_row and wins_row[4] else 0
+        
+        # Group 1v3+ wins
+        clutch_1v3_plus_wins = clutch_1v3_wins + clutch_1v4_wins + clutch_1v5_wins
+
+        # Step 2: Calculate Attempts
+        cursor.execute("SELECT DISTINCT match_id FROM fact_match_players WHERE steam_id_64 = ?", (steam_id,))
+        match_ids = [row[0] for row in cursor.fetchall()]
+        
+        clutch_1v1_attempts = 0
+        clutch_1v2_attempts = 0
+        clutch_1v3_plus_attempts = 0
+        
+        for match_id in match_ids:
+            # Get Roster
+            cursor.execute("SELECT steam_id_64, team_id FROM fact_match_players WHERE match_id = ?", (match_id,))
+            roster = cursor.fetchall()
+            
+            my_team_id = None
+            for pid, tid in roster:
+                if str(pid) == str(steam_id):
+                    my_team_id = tid
+                    break
+            
+            if my_team_id is None:
+                continue
+                
+            all_teammates = {str(pid) for pid, tid in roster if tid == my_team_id}
+            all_enemies = {str(pid) for pid, tid in roster if tid != my_team_id}
+            
+            # Get Events for this match
+            cursor.execute("""
+                SELECT round_num, event_type, attacker_steam_id, victim_steam_id, event_time
+                FROM fact_round_events 
+                WHERE match_id = ?
+                ORDER BY round_num, event_time
+            """, (match_id,))
+            all_events = cursor.fetchall()
+            
+            # Group events by round
+            from collections import defaultdict
+            events_by_round = defaultdict(list)
+            active_players_by_round = defaultdict(set)
+            
+            for r_num, e_type, attacker, victim, e_time in all_events:
+                events_by_round[r_num].append((e_type, attacker, victim))
+                if attacker: active_players_by_round[r_num].add(str(attacker))
+                if victim: active_players_by_round[r_num].add(str(victim))
+            
+            # Iterate rounds
+            for r_num, round_events in events_by_round.items():
+                active_players = active_players_by_round[r_num]
+                
+                # If player not active, skip (probably camping or AFK or not spawned)
+                if str(steam_id) not in active_players:
+                    continue
+                
+                # Filter roster to active players only (removes ghosts)
+                alive_teammates = all_teammates.intersection(active_players)
+                alive_enemies = all_enemies.intersection(active_players)
+                
+                # Safety: ensure player is in alive_teammates
+                alive_teammates.add(str(steam_id))
+                
+                clutch_detected = False
+                
+                for e_type, attacker, victim in round_events:
+                    if e_type == 'kill':
+                        vic_str = str(victim)
+                        if vic_str in alive_teammates:
+                            alive_teammates.discard(vic_str)
+                        elif vic_str in alive_enemies:
+                            alive_enemies.discard(vic_str)
+                            
+                        # Check clutch condition
+                        if not clutch_detected:
+                            # Teammates dead (len==1 means only me), Enemies alive
+                            if len(alive_teammates) == 1 and str(steam_id) in alive_teammates:
+                                enemies_cnt = len(alive_enemies)
+                                if enemies_cnt > 0:
+                                    clutch_detected = True
+                                    if enemies_cnt == 1:
+                                        clutch_1v1_attempts += 1
+                                    elif enemies_cnt == 2:
+                                        clutch_1v2_attempts += 1
+                                    elif enemies_cnt >= 3:
+                                        clutch_1v3_plus_attempts += 1
+        
+        # Calculate win rates
+        rate_1v1 = SafeAggregator.safe_divide(clutch_1v1_wins, clutch_1v1_attempts)
+        rate_1v2 = SafeAggregator.safe_divide(clutch_1v2_wins, clutch_1v2_attempts)
+        rate_1v3_plus = SafeAggregator.safe_divide(clutch_1v3_plus_wins, clutch_1v3_plus_attempts)
+        
+        # Clutch impact score: weighted by difficulty
+        impact_score = (clutch_1v1_wins * 1.0 + clutch_1v2_wins * 3.0 + clutch_1v3_plus_wins * 7.0)
+        
+        return {
+            'tac_clutch_1v1_attempts': clutch_1v1_attempts,
+            'tac_clutch_1v1_wins': clutch_1v1_wins,
+            'tac_clutch_1v1_rate': round(rate_1v1, 3),
+            'tac_clutch_1v2_attempts': clutch_1v2_attempts,
+            'tac_clutch_1v2_wins': clutch_1v2_wins,
+            'tac_clutch_1v2_rate': round(rate_1v2, 3),
+            'tac_clutch_1v3_plus_attempts': clutch_1v3_plus_attempts,
+            'tac_clutch_1v3_plus_wins': clutch_1v3_plus_wins,
+            'tac_clutch_1v3_plus_rate': round(rate_1v3_plus, 3),
+            'tac_clutch_impact_score': round(impact_score, 2)
+        }
+    
+    @staticmethod
+    def _calculate_utility(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate Utility Mastery (12 columns)
+        
+        Columns:
+        - tac_util_flash_per_round, tac_util_smoke_per_round
+        - tac_util_molotov_per_round, tac_util_he_per_round
+        - tac_util_usage_rate
+        - tac_util_nade_dmg_per_round, tac_util_nade_dmg_per_nade
+        - tac_util_flash_time_per_round, tac_util_flash_enemies_per_round
+        - tac_util_flash_efficiency
+        - tac_util_smoke_timing_score
+        - tac_util_impact_score
+        
+        Note: Requires fact_round_player_economy for detailed utility stats
+        """
+        cursor = conn_l2.cursor()
+        
+        # Check if economy table exists (leetify mode)
+        cursor.execute("""
+            SELECT COUNT(*) FROM sqlite_master 
+            WHERE type='table' AND name='fact_round_player_economy'
+        """)
+        
+        has_economy = cursor.fetchone()[0] > 0
+        
+        if not has_economy:
+            # Return zeros if no economy data
+            return {
+                'tac_util_flash_per_round': 0.0,
+                'tac_util_smoke_per_round': 0.0,
+                'tac_util_molotov_per_round': 0.0,
+                'tac_util_he_per_round': 0.0,
+                'tac_util_usage_rate': 0.0,
+                'tac_util_nade_dmg_per_round': 0.0,
+                'tac_util_nade_dmg_per_nade': 0.0,
+                'tac_util_flash_time_per_round': 0.0,
+                'tac_util_flash_enemies_per_round': 0.0,
+                'tac_util_flash_efficiency': 0.0,
+                'tac_util_smoke_timing_score': 0.0,
+                'tac_util_impact_score': 0.0,
+            }
+        
+        # Get total rounds for per-round calculations
+        total_rounds = BaseFeatureProcessor.get_player_round_count(steam_id, conn_l2)
+        if total_rounds == 0:
+            total_rounds = 1
+        
+        # Utility usage from fact_match_players
+        cursor.execute("""
+            SELECT 
+                SUM(util_flash_usage) as total_flash,
+                SUM(util_smoke_usage) as total_smoke,
+                SUM(util_molotov_usage) as total_molotov,
+                SUM(util_he_usage) as total_he,
+                SUM(flash_enemy) as enemies_flashed,
+                SUM(damage_total) as total_damage,
+                SUM(throw_harm_enemy) as nade_damage,
+                COUNT(*) as matches
+            FROM fact_match_players
+            WHERE steam_id_64 = ?
+        """, (steam_id,))
+        
+        row = cursor.fetchone()
+        total_flash = row[0] if row[0] else 0
+        total_smoke = row[1] if row[1] else 0
+        total_molotov = row[2] if row[2] else 0
+        total_he = row[3] if row[3] else 0
+        enemies_flashed = row[4] if row[4] else 0
+        total_damage = row[5] if row[5] else 0
+        nade_damage = row[6] if row[6] else 0
+        rounds_with_data = row[7] if row[7] else 1
+        
+        total_nades = total_flash + total_smoke + total_molotov + total_he
+        
+        flash_per_round = total_flash / total_rounds
+        smoke_per_round = total_smoke / total_rounds
+        molotov_per_round = total_molotov / total_rounds
+        he_per_round = total_he / total_rounds
+        usage_rate = total_nades / total_rounds
+        
+        # Nade damage (HE grenade + molotov damage from throw_harm_enemy)
+        nade_dmg_per_round = SafeAggregator.safe_divide(nade_damage, total_rounds)
+        nade_dmg_per_nade = SafeAggregator.safe_divide(nade_damage, total_he + total_molotov)
+        
+        # Flash efficiency (simplified - kills per flash from match data)
+        # DEPRECATED: Replaced by Enemies Blinded per Flash logic below
+        # cursor.execute("""
+        #     SELECT SUM(kills) as total_kills
+        #     FROM fact_match_players
+        #     WHERE steam_id_64 = ?
+        # """, (steam_id,))
+        # 
+        # total_kills = cursor.fetchone()[0]
+        # total_kills = total_kills if total_kills else 0
+        # flash_efficiency = SafeAggregator.safe_divide(total_kills, total_flash)
+        
+        # Real flash data from fact_match_players
+        # flash_time in L2 is TOTAL flash time (seconds), not average
+        # flash_enemy is TOTAL enemies flashed
+        cursor.execute("""
+            SELECT 
+                SUM(flash_time) as total_flash_time,
+                SUM(flash_enemy) as total_enemies_flashed,
+                SUM(util_flash_usage) as total_flashes_thrown
+            FROM fact_match_players
+            WHERE steam_id_64 = ?
+        """, (steam_id,))
+        flash_row = cursor.fetchone()
+        total_flash_time = flash_row[0] if flash_row and flash_row[0] else 0.0
+        total_enemies_flashed = flash_row[1] if flash_row and flash_row[1] else 0
+        total_flashes_thrown = flash_row[2] if flash_row and flash_row[2] else 0
+        
+        flash_time_per_round = total_flash_time / total_rounds if total_rounds > 0 else 0.0
+        flash_enemies_per_round = total_enemies_flashed / total_rounds if total_rounds > 0 else 0.0
+        
+        # Flash Efficiency: Enemies Blinded per Flash Thrown (instead of kills per flash)
+        # 100% means 1 enemy blinded per flash
+        # 200% means 2 enemies blinded per flash (very good)
+        flash_efficiency = SafeAggregator.safe_divide(total_enemies_flashed, total_flashes_thrown)
+        
+        # Smoke timing score CANNOT be calculated without bomb plant event timestamps
+        # Would require: SELECT event_time FROM fact_round_events WHERE event_type = 'bomb_plant'
+        # Then correlate with util_smoke_usage timing - currently no timing data for utility usage
+        # Commenting out: tac_util_smoke_timing_score
+        smoke_timing_score = 0.0
+        
+        # Taser Kills Logic (Zeus)
+        # We want Attempts (shots fired) vs Kills
+        # User requested to track "Equipped Count" instead of "Attempts" (shots)
+        # because event logs often miss weapon_fire for taser.
+        
+        # We check fact_round_player_economy for has_zeus = 1
+        zeus_equipped_count = 0
+        if has_economy:
+            cursor.execute("""
+                SELECT COUNT(*) 
+                FROM fact_round_player_economy
+                WHERE steam_id_64 = ? AND has_zeus = 1
+            """, (steam_id,))
+            zeus_equipped_count = cursor.fetchone()[0] or 0
+            
+        # Kills still come from event logs
+        # Removed tac_util_zeus_kills per user request (data not available)
+        # cursor.execute("""
+        #     SELECT 
+        #         COUNT(CASE WHEN event_type = 'kill' AND weapon = 'taser' THEN 1 END) as kills
+        #     FROM fact_round_events
+        #     WHERE attacker_steam_id = ?
+        # """, (steam_id,))
+        # zeus_kills = cursor.fetchone()[0] or 0
+        
+        # Fallback: if equipped count < kills (shouldn't happen if economy data is good), fix it
+        # if zeus_equipped_count < zeus_kills:
+        #    zeus_equipped_count = zeus_kills
+
+        # Utility impact score (composite)
+        impact_score = (
+            nade_dmg_per_round * 0.3 + 
+            flash_efficiency * 2.0 + 
+            usage_rate * 10.0
+        )
+        
+        return {
+            'tac_util_flash_per_round': round(flash_per_round, 2),
+            'tac_util_smoke_per_round': round(smoke_per_round, 2),
+            'tac_util_molotov_per_round': round(molotov_per_round, 2),
+            'tac_util_he_per_round': round(he_per_round, 2),
+            'tac_util_usage_rate': round(usage_rate, 2),
+            'tac_util_nade_dmg_per_round': round(nade_dmg_per_round, 2),
+            'tac_util_nade_dmg_per_nade': round(nade_dmg_per_nade, 2),
+            'tac_util_flash_time_per_round': round(flash_time_per_round, 2),
+            'tac_util_flash_enemies_per_round': round(flash_enemies_per_round, 2),
+            'tac_util_flash_efficiency': round(flash_efficiency, 3),
+            #'tac_util_smoke_timing_score': round(smoke_timing_score, 2), # Removed per user request
+            'tac_util_impact_score': round(impact_score, 2),
+            'tac_util_zeus_equipped_count': zeus_equipped_count,
+            #'tac_util_zeus_kills': zeus_kills, # Removed
+        }
+    
+    @staticmethod
+    def _calculate_economy(steam_id: str, conn_l2: sqlite3.Connection) -> Dict[str, Any]:
+        """
+        Calculate Economy Efficiency (8 columns)
+        
+        Columns:
+        - tac_eco_dmg_per_1k
+        - tac_eco_kpr_eco_rounds, tac_eco_kd_eco_rounds
+        - tac_eco_kpr_force_rounds, tac_eco_kpr_full_rounds
+        - tac_eco_save_discipline
+        - tac_eco_force_success_rate
+        - tac_eco_efficiency_score
+        
+        Note: Requires fact_round_player_economy for equipment values
+        """
+        cursor = conn_l2.cursor()
+        
+        # Check if economy table exists
+        cursor.execute("""
+            SELECT COUNT(*) FROM sqlite_master 
+            WHERE type='table' AND name='fact_round_player_economy'
+        """)
+        
+        has_economy = cursor.fetchone()[0] > 0
+        
+        if not has_economy:
+            # Return zeros if no economy data
+            return {
+                'tac_eco_dmg_per_1k': 0.0,
+                'tac_eco_kpr_eco_rounds': 0.0,
+                'tac_eco_kd_eco_rounds': 0.0,
+                'tac_eco_kpr_force_rounds': 0.0,
+                'tac_eco_kpr_full_rounds': 0.0,
+                'tac_eco_save_discipline': 0.0,
+                'tac_eco_force_success_rate': 0.0,
+                'tac_eco_efficiency_score': 0.0,
+            }
+        
+        # REAL economy-based performance from round-level data
+        # Join fact_round_player_economy with fact_round_events to get kills/deaths per economy state
+        
+        # Fallback if no economy table but we want basic DMG/1k approximation from total damage / assumed average buy
+        # But avg_equip_value is from economy table.
+        # If no economy table, we can't do this accurately.
+        
+        # However, user says "Eco Dmg/1k" is 0.00.
+        # If we have NO economy table, we returned early above.
+        # If we reached here, we HAVE economy table (or at least check passed).
+        # Let's check logic.
+        
+        # Get average equipment value
+        cursor.execute("""
+            SELECT AVG(equipment_value)
+            FROM fact_round_player_economy
+            WHERE steam_id_64 = ?
+            AND equipment_value IS NOT NULL
+            AND equipment_value > 0  -- Filter out zero equipment value rounds? Or include them?
+        """, (steam_id,))
+        avg_equip_val_res = cursor.fetchone()
+        avg_equip_value = avg_equip_val_res[0] if avg_equip_val_res and avg_equip_val_res[0] else 4000.0
+        
+        # Avoid division by zero if avg_equip_value is somehow 0
+        if avg_equip_value < 100: avg_equip_value = 4000.0
+        
+        # Get total damage and calculate dmg per $1000
+        cursor.execute("""
+            SELECT SUM(damage_total), SUM(round_total)
+            FROM fact_match_players
+            WHERE steam_id_64 = ?
+        """, (steam_id,))
+        damage_row = cursor.fetchone()
+        total_damage = damage_row[0] if damage_row[0] else 0
+        total_rounds = damage_row[1] if damage_row[1] else 1
+        
+        avg_dmg_per_round = SafeAggregator.safe_divide(total_damage, total_rounds)
+        
+        # Formula: (ADR) / (AvgSpend / 1000)
+        # e.g. 80 ADR / (4000 / 1000) = 80 / 4 = 20 dmg/$1k
+        dmg_per_1k = SafeAggregator.safe_divide(avg_dmg_per_round, (avg_equip_value / 1000.0))
+        
+        # ECO rounds: equipment_value < 2000
+        cursor.execute("""
+            SELECT 
+                e.match_id,
+                e.round_num,
+                e.steam_id_64,
+                COUNT(CASE WHEN fre.event_type = 'kill' AND fre.attacker_steam_id = e.steam_id_64 THEN 1 END) as kills,
+                COUNT(CASE WHEN fre.event_type = 'kill' AND fre.victim_steam_id = e.steam_id_64 THEN 1 END) as deaths
+            FROM fact_round_player_economy e
+            LEFT JOIN fact_round_events fre ON e.match_id = fre.match_id AND e.round_num = fre.round_num
+            WHERE e.steam_id_64 = ?
+            AND e.equipment_value < 2000
+            GROUP BY e.match_id, e.round_num, e.steam_id_64
+        """, (steam_id,))
+        
+        eco_rounds = cursor.fetchall()
+        eco_kills = sum(row[3] for row in eco_rounds)
+        eco_deaths = sum(row[4] for row in eco_rounds)
+        eco_round_count = len(eco_rounds)
+        
+        kpr_eco = SafeAggregator.safe_divide(eco_kills, eco_round_count)
+        kd_eco = SafeAggregator.safe_divide(eco_kills, eco_deaths)
+        
+        # FORCE rounds: 2000 <= equipment_value < 3500
+        cursor.execute("""
+            SELECT 
+                e.match_id,
+                e.round_num,
+                e.steam_id_64,
+                COUNT(CASE WHEN fre.event_type = 'kill' AND fre.attacker_steam_id = e.steam_id_64 THEN 1 END) as kills,
+                fr.winner_side,
+                e.side
+            FROM fact_round_player_economy e
+            LEFT JOIN fact_round_events fre ON e.match_id = fre.match_id AND e.round_num = fre.round_num
+            LEFT JOIN fact_rounds fr ON e.match_id = fr.match_id AND e.round_num = fr.round_num
+            WHERE e.steam_id_64 = ?
+            AND e.equipment_value >= 2000
+            AND e.equipment_value < 3500
+            GROUP BY e.match_id, e.round_num, e.steam_id_64, fr.winner_side, e.side
+        """, (steam_id,))
+        
+        force_rounds = cursor.fetchall()
+        force_kills = sum(row[3] for row in force_rounds)
+        force_round_count = len(force_rounds)
+        force_wins = sum(1 for row in force_rounds if row[4] == row[5])  # winner_side == player_side
+        
+        kpr_force = SafeAggregator.safe_divide(force_kills, force_round_count)
+        force_success = SafeAggregator.safe_divide(force_wins, force_round_count)
+        
+        # FULL BUY rounds: equipment_value >= 3500
+        cursor.execute("""
+            SELECT 
+                e.match_id,
+                e.round_num,
+                e.steam_id_64,
+                COUNT(CASE WHEN fre.event_type = 'kill' AND fre.attacker_steam_id = e.steam_id_64 THEN 1 END) as kills
+            FROM fact_round_player_economy e
+            LEFT JOIN fact_round_events fre ON e.match_id = fre.match_id AND e.round_num = fre.round_num
+            WHERE e.steam_id_64 = ?
+            AND e.equipment_value >= 3500
+            GROUP BY e.match_id, e.round_num, e.steam_id_64
+        """, (steam_id,))
+        
+        full_rounds = cursor.fetchall()
+        full_kills = sum(row[3] for row in full_rounds)
+        full_round_count = len(full_rounds)
+        
+        kpr_full = SafeAggregator.safe_divide(full_kills, full_round_count)
+        
+        # Save discipline: ratio of eco rounds to total rounds (lower is better discipline)
+        save_discipline = 1.0 - SafeAggregator.safe_divide(eco_round_count, total_rounds)
+        
+        # Efficiency score: weighted KPR across economy states
+        efficiency_score = (kpr_eco * 1.5 + kpr_force * 1.2 + kpr_full * 1.0) / 3.7
+        
+        return {
+            'tac_eco_dmg_per_1k': round(dmg_per_1k, 2),
+            'tac_eco_kpr_eco_rounds': round(kpr_eco, 3),
+            'tac_eco_kd_eco_rounds': round(kd_eco, 3),
+            'tac_eco_kpr_force_rounds': round(kpr_force, 3),
+            'tac_eco_kpr_full_rounds': round(kpr_full, 3),
+            'tac_eco_save_discipline': round(save_discipline, 3),
+            'tac_eco_force_success_rate': round(force_success, 3),
+            'tac_eco_efficiency_score': round(efficiency_score, 2),
+        }
+
+
+def _get_default_tactical_features() -> Dict[str, Any]:
+    """Return default zero values for all 44 TACTICAL features"""
+    return {
+        # Opening Impact (8)
+        'tac_avg_fk': 0.0,
+        'tac_avg_fd': 0.0,
+        'tac_fk_rate': 0.0,
+        'tac_fd_rate': 0.0,
+        'tac_fk_success_rate': 0.0,
+        'tac_entry_kill_rate': 0.0,
+        'tac_entry_death_rate': 0.0,
+        'tac_opening_duel_winrate': 0.0,
+        # Multi-Kill (6)
+        'tac_avg_2k': 0.0,
+        'tac_avg_3k': 0.0,
+        'tac_avg_4k': 0.0,
+        'tac_avg_5k': 0.0,
+        'tac_multikill_rate': 0.0,
+        'tac_ace_count': 0,
+        # Clutch Performance (10)
+        'tac_clutch_1v1_attempts': 0,
+        'tac_clutch_1v1_wins': 0,
+        'tac_clutch_1v1_rate': 0.0,
+        'tac_clutch_1v2_attempts': 0,
+        'tac_clutch_1v2_wins': 0,
+        'tac_clutch_1v2_rate': 0.0,
+        'tac_clutch_1v3_plus_attempts': 0,
+        'tac_clutch_1v3_plus_wins': 0,
+        'tac_clutch_1v3_plus_rate': 0.0,
+        'tac_clutch_impact_score': 0.0,
+        # Utility Mastery (12)
+        'tac_util_flash_per_round': 0.0,
+        'tac_util_smoke_per_round': 0.0,
+        'tac_util_molotov_per_round': 0.0,
+        'tac_util_he_per_round': 0.0,
+        'tac_util_usage_rate': 0.0,
+        'tac_util_nade_dmg_per_round': 0.0,
+        'tac_util_nade_dmg_per_nade': 0.0,
+        'tac_util_flash_time_per_round': 0.0,
+        'tac_util_flash_enemies_per_round': 0.0,
+        'tac_util_flash_efficiency': 0.0,
+        # 'tac_util_smoke_timing_score': 0.0, # Removed
+        'tac_util_impact_score': 0.0,
+        'tac_util_zeus_equipped_count': 0,
+        # 'tac_util_zeus_kills': 0, # Removed
+        # Economy Efficiency (8)
+        'tac_eco_dmg_per_1k': 0.0,
+        'tac_eco_kpr_eco_rounds': 0.0,
+        'tac_eco_kd_eco_rounds': 0.0,
+        'tac_eco_kpr_force_rounds': 0.0,
+        'tac_eco_kpr_full_rounds': 0.0,
+        'tac_eco_save_discipline': 0.0,
+        'tac_eco_force_success_rate': 0.0,
+        'tac_eco_efficiency_score': 0.0,
+    }

database/L3/schema.sql

@@ -1,251 +1,394 @@
-
--- L3 Schema: Player Features Data Mart
--- Based on FeatureRDD.md
+-- ============================================================================
+-- L3 Schema: Player Features Data Mart (Version 2.0)
+-- ============================================================================
+-- Based on: L3_ARCHITECTURE_PLAN.md
+-- Design: 5-Tier Feature Hierarchy (CORE → TACTICAL → INTELLIGENCE → META → COMPOSITE)
 -- Granularity: One row per player (Aggregated Profile)
--- Note: Some features requiring complex Demo parsing (Phase 5) are omitted or reserved.
+-- Total Columns: 207 features + 6 metadata = 213 columns
+-- ============================================================================
 
+-- ============================================================================
+-- Main Table: dm_player_features
+-- ============================================================================
 CREATE TABLE IF NOT EXISTS dm_player_features (
+    -- ========================================================================
+    -- Metadata (6 columns)
+    -- ========================================================================
     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_adr 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,
-    basic_avg_knife_kill REAL,
-    basic_avg_zeus_kill REAL,
-    basic_zeus_pick_rate REAL,
-    basic_avg_mvps REAL,
-    basic_avg_plants REAL,
-    basic_avg_defuses REAL,
-    basic_avg_flash_assists 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, -- Removed
-    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, -- Currently calculated as K/D
-    side_rating_t REAL,
-    side_kd_ct REAL, -- Explicit K/D
-    side_kd_t REAL,
-    side_win_rate_ct REAL, -- Round Win %
-    side_win_rate_t REAL,
-    side_first_kill_rate_ct REAL,
-    side_first_kill_rate_t REAL,
-    side_kd_diff_ct_t REAL, -- CT KD - T KD
-
-    -- New Side Comparisons
-    side_rating_diff_ct_t REAL,
-
-    -- ==========================================
-    -- 6. Party Size Performance
-    -- ==========================================
-    party_1_win_rate REAL,
-    party_1_rating REAL,
-    party_1_adr REAL,
+    total_matches INTEGER NOT NULL DEFAULT 0,
+    total_rounds INTEGER NOT NULL DEFAULT 0,
+    first_match_date INTEGER,  -- Unix timestamp
+    last_match_date INTEGER,   -- Unix timestamp
+    last_updated TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
     
-    party_2_win_rate REAL,
-    party_2_rating REAL,
-    party_2_adr REAL,
+    -- ========================================================================
+    -- TIER 1: CORE (41 columns)
+    -- Direct aggregations from fact_match_players
+    -- ========================================================================
     
-    party_3_win_rate REAL,
-    party_3_rating REAL,
-    party_3_adr REAL,
+    -- Basic Performance (15 columns)
+    core_avg_rating REAL DEFAULT 0.0,
+    core_avg_rating2 REAL DEFAULT 0.0,
+    core_avg_kd REAL DEFAULT 0.0,
+    core_avg_adr REAL DEFAULT 0.0,
+    core_avg_kast REAL DEFAULT 0.0,
+    core_avg_rws REAL DEFAULT 0.0,
+    core_avg_hs_kills REAL DEFAULT 0.0,
+    core_hs_rate REAL DEFAULT 0.0,  -- hs/total_kills
+    core_total_kills INTEGER DEFAULT 0,
+    core_total_deaths INTEGER DEFAULT 0,
+    core_total_assists INTEGER DEFAULT 0,
+    core_avg_assists REAL DEFAULT 0.0,
+    core_kpr REAL DEFAULT 0.0,  -- kills per round
+    core_dpr REAL DEFAULT 0.0,  -- deaths per round
+    core_survival_rate REAL DEFAULT 0.0,
     
-    party_4_win_rate REAL,
-    party_4_rating REAL,
-    party_4_adr REAL,
+    -- Match Stats (8 columns)
+    core_win_rate REAL DEFAULT 0.0,
+    core_wins INTEGER DEFAULT 0,
+    core_losses INTEGER DEFAULT 0,
+    core_avg_match_duration INTEGER DEFAULT 0,  -- seconds
+    core_avg_mvps REAL DEFAULT 0.0,
+    core_mvp_rate REAL DEFAULT 0.0,
+    core_avg_elo_change REAL DEFAULT 0.0,
+    core_total_elo_gained REAL DEFAULT 0.0,
     
-    party_5_win_rate REAL,
-    party_5_rating REAL,
-    party_5_adr REAL,
-
-    -- ==========================================
-    -- 7. Rating Distribution (Performance Tiers)
-    -- ==========================================
-    rating_dist_carry_rate REAL,      -- > 1.5
-    rating_dist_normal_rate REAL,     -- 1.0 - 1.5
-    rating_dist_sacrifice_rate REAL,  -- 0.6 - 1.0
-    rating_dist_sleeping_rate REAL,   -- < 0.6
-
-    -- ==========================================
-    -- 8. ELO Stratification (Performance vs ELO)
-    -- ==========================================
-    elo_lt1200_rating REAL,
-    elo_1200_1400_rating REAL,
-    elo_1400_1600_rating REAL,
-    elo_1600_1800_rating REAL,
-    elo_1800_2000_rating REAL,
-    elo_gt2000_rating REAL,
-
-    -- ==========================================
-    -- 9. More Side Stats (Restored)
-    -- ==========================================
-    side_kast_ct REAL,
-    side_kast_t REAL,
-    side_rws_ct REAL,
-    side_rws_t REAL,
-    side_first_death_rate_ct REAL,
-    side_first_death_rate_t REAL,
-    side_multikill_rate_ct REAL,
-    side_multikill_rate_t REAL,
-    side_headshot_rate_ct REAL,
-    side_headshot_rate_t REAL,
-    side_defuses_ct REAL,
-    side_plants_t REAL,
-    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,
-
-    -- ==========================================  
-    -- 7. Scores (0-100)
-    -- ==========================================
-    score_bat REAL,
-    score_sta REAL,
-    score_hps REAL,
-    score_ptl REAL,
-                                           score_tct REAL,
-    score_util REAL,
-    score_eco REAL,
-    score_pace REAL,
-
-    -- ==========================================
-    -- 8. ECO: Economy Efficiency
-    -- ==========================================
-    eco_avg_damage_per_1k REAL,
-    eco_rating_eco_rounds REAL,
-    eco_kd_ratio REAL,
-    eco_avg_rounds REAL,
-
-    -- ==========================================
-    -- 9. PACE: Aggression & Trade
-    -- ==========================================
-    pace_avg_time_to_first_contact REAL,
-    pace_trade_kill_rate REAL,
-    pace_opening_kill_time REAL,
-    pace_avg_life_time REAL,
-    rd_phase_kill_early_share REAL,
-    rd_phase_kill_mid_share REAL,
-    rd_phase_kill_late_share REAL,
-    rd_phase_death_early_share REAL,
-    rd_phase_death_mid_share REAL,
-    rd_phase_death_late_share REAL,
-    rd_phase_kill_early_share_t REAL,
-    rd_phase_kill_mid_share_t REAL,
-    rd_phase_kill_late_share_t REAL,
-    rd_phase_kill_early_share_ct REAL,
-    rd_phase_kill_mid_share_ct REAL,
-    rd_phase_kill_late_share_ct REAL,
-    rd_phase_death_early_share_t REAL,
-    rd_phase_death_mid_share_t REAL,
-    rd_phase_death_late_share_t REAL,
-    rd_phase_death_early_share_ct REAL,
-    rd_phase_death_mid_share_ct REAL,
-    rd_phase_death_late_share_ct REAL,
-    rd_firstdeath_team_first_death_rounds INTEGER,
-    rd_firstdeath_team_first_death_win_rate REAL,
-    rd_invalid_death_rounds INTEGER,
-    rd_invalid_death_rate REAL,
-    rd_pressure_kpr_ratio REAL,
-    rd_pressure_perf_ratio REAL,
-    rd_pressure_rounds_down3 INTEGER,
-    rd_pressure_rounds_normal INTEGER,
-    rd_matchpoint_kpr_ratio REAL,
-    rd_matchpoint_perf_ratio REAL,
-    rd_matchpoint_rounds INTEGER,
-    rd_comeback_kill_share REAL,
-    rd_comeback_rounds INTEGER,
-    rd_trade_response_10s_rate REAL,
-    rd_weapon_top_json TEXT,
-    rd_roundtype_split_json TEXT,
-    map_stability_coef REAL
+    -- Weapon Stats (12 columns)
+    core_avg_awp_kills REAL DEFAULT 0.0,
+    core_awp_usage_rate REAL DEFAULT 0.0,
+    core_avg_knife_kills REAL DEFAULT 0.0,
+    core_avg_zeus_kills REAL DEFAULT 0.0,
+    core_zeus_buy_rate REAL DEFAULT 0.0,
+    core_top_weapon TEXT,
+    core_top_weapon_kills INTEGER DEFAULT 0,
+    core_top_weapon_hs_rate REAL DEFAULT 0.0,
+    core_weapon_diversity REAL DEFAULT 0.0,
+    core_rifle_hs_rate REAL DEFAULT 0.0,
+    core_pistol_hs_rate REAL DEFAULT 0.0,
+    core_smg_kills_total INTEGER DEFAULT 0,
+    
+    -- Objective Stats (6 columns)
+    core_avg_plants REAL DEFAULT 0.0,
+    core_avg_defuses REAL DEFAULT 0.0,
+    core_avg_flash_assists REAL DEFAULT 0.0,
+    core_plant_success_rate REAL DEFAULT 0.0,
+    core_defuse_success_rate REAL DEFAULT 0.0,
+    core_objective_impact REAL DEFAULT 0.0,
+    
+    -- ========================================================================
+    -- TIER 2: TACTICAL (44 columns)
+    -- Multi-table JOINs, conditional aggregations
+    -- ========================================================================
+    
+    -- Opening Impact (8 columns)
+    tac_avg_fk REAL DEFAULT 0.0,
+    tac_avg_fd REAL DEFAULT 0.0,
+    tac_fk_rate REAL DEFAULT 0.0,
+    tac_fd_rate REAL DEFAULT 0.0,
+    tac_fk_success_rate REAL DEFAULT 0.0,
+    tac_entry_kill_rate REAL DEFAULT 0.0,
+    tac_entry_death_rate REAL DEFAULT 0.0,
+    tac_opening_duel_winrate REAL DEFAULT 0.0,
+    
+    -- Multi-Kill (6 columns)
+    tac_avg_2k REAL DEFAULT 0.0,
+    tac_avg_3k REAL DEFAULT 0.0,
+    tac_avg_4k REAL DEFAULT 0.0,
+    tac_avg_5k REAL DEFAULT 0.0,
+    tac_multikill_rate REAL DEFAULT 0.0,
+    tac_ace_count INTEGER DEFAULT 0,
+    
+    -- Clutch Performance (10 columns)
+    tac_clutch_1v1_attempts INTEGER DEFAULT 0,
+    tac_clutch_1v1_wins INTEGER DEFAULT 0,
+    tac_clutch_1v1_rate REAL DEFAULT 0.0,
+    tac_clutch_1v2_attempts INTEGER DEFAULT 0,
+    tac_clutch_1v2_wins INTEGER DEFAULT 0,
+    tac_clutch_1v2_rate REAL DEFAULT 0.0,
+    tac_clutch_1v3_plus_attempts INTEGER DEFAULT 0,
+    tac_clutch_1v3_plus_wins INTEGER DEFAULT 0,
+    tac_clutch_1v3_plus_rate REAL DEFAULT 0.0,
+    tac_clutch_impact_score REAL DEFAULT 0.0,
+    
+    -- Utility Mastery (13 columns)
+    tac_util_flash_per_round REAL DEFAULT 0.0,
+    tac_util_smoke_per_round REAL DEFAULT 0.0,
+    tac_util_molotov_per_round REAL DEFAULT 0.0,
+    tac_util_he_per_round REAL DEFAULT 0.0,
+    tac_util_usage_rate REAL DEFAULT 0.0,
+    tac_util_nade_dmg_per_round REAL DEFAULT 0.0,
+    tac_util_nade_dmg_per_nade REAL DEFAULT 0.0,
+    tac_util_flash_time_per_round REAL DEFAULT 0.0,
+    tac_util_flash_enemies_per_round REAL DEFAULT 0.0,
+    tac_util_flash_efficiency REAL DEFAULT 0.0,
+    tac_util_impact_score REAL DEFAULT 0.0,
+    tac_util_zeus_equipped_count INTEGER DEFAULT 0,
+    -- tac_util_zeus_kills REMOVED
+    
+    -- Economy Efficiency (8 columns)
+    tac_eco_dmg_per_1k REAL DEFAULT 0.0,
+    tac_eco_kpr_eco_rounds REAL DEFAULT 0.0,
+    tac_eco_kd_eco_rounds REAL DEFAULT 0.0,
+    tac_eco_kpr_force_rounds REAL DEFAULT 0.0,
+    tac_eco_kpr_full_rounds REAL DEFAULT 0.0,
+    tac_eco_save_discipline REAL DEFAULT 0.0,
+    tac_eco_force_success_rate REAL DEFAULT 0.0,
+    tac_eco_efficiency_score REAL DEFAULT 0.0,
+    
+    -- ========================================================================
+    -- TIER 3: INTELLIGENCE (53 columns)
+    -- Advanced analytics on fact_round_events
+    -- ========================================================================
+    
+    -- High IQ Kills (9 columns)
+    int_wallbang_kills INTEGER DEFAULT 0,
+    int_wallbang_rate REAL DEFAULT 0.0,
+    int_smoke_kills INTEGER DEFAULT 0,
+    int_smoke_kill_rate REAL DEFAULT 0.0,
+    int_blind_kills INTEGER DEFAULT 0,
+    int_blind_kill_rate REAL DEFAULT 0.0,
+    int_noscope_kills INTEGER DEFAULT 0,
+    int_noscope_rate REAL DEFAULT 0.0,
+    int_high_iq_score REAL DEFAULT 0.0,
+    
+    -- Timing Analysis (12 columns)
+    int_timing_early_kills INTEGER DEFAULT 0,
+    int_timing_mid_kills INTEGER DEFAULT 0,
+    int_timing_late_kills INTEGER DEFAULT 0,
+    int_timing_early_kill_share REAL DEFAULT 0.0,
+    int_timing_mid_kill_share REAL DEFAULT 0.0,
+    int_timing_late_kill_share REAL DEFAULT 0.0,
+    int_timing_avg_kill_time REAL DEFAULT 0.0,
+    int_timing_early_deaths INTEGER DEFAULT 0,
+    int_timing_early_death_rate REAL DEFAULT 0.0,
+    int_timing_aggression_index REAL DEFAULT 0.0,
+    int_timing_patience_score REAL DEFAULT 0.0,
+    int_timing_first_contact_time REAL DEFAULT 0.0,
+    
+    -- Pressure Performance (9 columns)
+    int_pressure_comeback_kd REAL DEFAULT 0.0,
+    int_pressure_comeback_rating REAL DEFAULT 0.0,
+    int_pressure_losing_streak_kd REAL DEFAULT 0.0,
+    int_pressure_matchpoint_kpr REAL DEFAULT 0.0,
+    int_pressure_clutch_composure REAL DEFAULT 0.0,
+    int_pressure_entry_in_loss REAL DEFAULT 0.0,
+    int_pressure_performance_index REAL DEFAULT 0.0,
+    int_pressure_big_moment_score REAL DEFAULT 0.0,
+    int_pressure_tilt_resistance REAL DEFAULT 0.0,
+    
+    -- Position Mastery (14 columns)
+    int_pos_site_a_control_rate REAL DEFAULT 0.0,
+    int_pos_site_b_control_rate REAL DEFAULT 0.0,
+    int_pos_mid_control_rate REAL DEFAULT 0.0,
+    int_pos_favorite_position TEXT,
+    int_pos_position_diversity REAL DEFAULT 0.0,
+    int_pos_rotation_speed REAL DEFAULT 0.0,
+    int_pos_map_coverage REAL DEFAULT 0.0,
+    int_pos_lurk_tendency REAL DEFAULT 0.0,
+    int_pos_site_anchor_score REAL DEFAULT 0.0,
+    int_pos_entry_route_diversity REAL DEFAULT 0.0,
+    int_pos_retake_positioning REAL DEFAULT 0.0,
+    int_pos_postplant_positioning REAL DEFAULT 0.0,
+    int_pos_spatial_iq_score REAL DEFAULT 0.0,
+    int_pos_avg_distance_from_teammates REAL DEFAULT 0.0,
+    
+    -- Trade Network (8 columns)
+    int_trade_kill_count INTEGER DEFAULT 0,
+    int_trade_kill_rate REAL DEFAULT 0.0,
+    int_trade_response_time REAL DEFAULT 0.0,
+    int_trade_given_count INTEGER DEFAULT 0,
+    int_trade_given_rate REAL DEFAULT 0.0,
+    int_trade_balance REAL DEFAULT 0.0,
+    int_trade_efficiency REAL DEFAULT 0.0,
+    int_teamwork_score REAL DEFAULT 0.0,
+    
+    -- ========================================================================
+    -- TIER 4: META (52 columns)
+    -- Long-term patterns and meta-features
+    -- ========================================================================
+    
+    -- Stability (8 columns)
+    meta_rating_volatility REAL DEFAULT 0.0,
+    meta_recent_form_rating REAL DEFAULT 0.0,
+    meta_win_rating REAL DEFAULT 0.0,
+    meta_loss_rating REAL DEFAULT 0.0,
+    meta_rating_consistency REAL DEFAULT 0.0,
+    meta_time_rating_correlation REAL DEFAULT 0.0,
+    meta_map_stability REAL DEFAULT 0.0,
+    meta_elo_tier_stability REAL DEFAULT 0.0,
+    
+    -- Side Preference (14 columns)
+    meta_side_ct_rating REAL DEFAULT 0.0,
+    meta_side_t_rating REAL DEFAULT 0.0,
+    meta_side_ct_kd REAL DEFAULT 0.0,
+    meta_side_t_kd REAL DEFAULT 0.0,
+    meta_side_ct_win_rate REAL DEFAULT 0.0,
+    meta_side_t_win_rate REAL DEFAULT 0.0,
+    meta_side_ct_fk_rate REAL DEFAULT 0.0,
+    meta_side_t_fk_rate REAL DEFAULT 0.0,
+    meta_side_ct_kast REAL DEFAULT 0.0,
+    meta_side_t_kast REAL DEFAULT 0.0,
+    meta_side_rating_diff REAL DEFAULT 0.0,
+    meta_side_kd_diff REAL DEFAULT 0.0,
+    meta_side_preference TEXT,
+    meta_side_balance_score REAL DEFAULT 0.0,
+    
+    -- Opponent Adaptation (12 columns)
+    meta_opp_vs_lower_elo_rating REAL DEFAULT 0.0,
+    meta_opp_vs_similar_elo_rating REAL DEFAULT 0.0,
+    meta_opp_vs_higher_elo_rating REAL DEFAULT 0.0,
+    meta_opp_vs_lower_elo_kd REAL DEFAULT 0.0,
+    meta_opp_vs_similar_elo_kd REAL DEFAULT 0.0,
+    meta_opp_vs_higher_elo_kd REAL DEFAULT 0.0,
+    meta_opp_elo_adaptation REAL DEFAULT 0.0,
+    meta_opp_stomping_score REAL DEFAULT 0.0,
+    meta_opp_upset_score REAL DEFAULT 0.0,
+    meta_opp_consistency_across_elos REAL DEFAULT 0.0,
+    meta_opp_rank_resistance REAL DEFAULT 0.0,
+    meta_opp_smurf_detection REAL DEFAULT 0.0,
+    
+    -- Map Specialization (10 columns)
+    meta_map_best_map TEXT,
+    meta_map_best_rating REAL DEFAULT 0.0,
+    meta_map_worst_map TEXT,
+    meta_map_worst_rating REAL DEFAULT 0.0,
+    meta_map_diversity REAL DEFAULT 0.0,
+    meta_map_pool_size INTEGER DEFAULT 0,
+    meta_map_specialist_score REAL DEFAULT 0.0,
+    meta_map_versatility REAL DEFAULT 0.0,
+    meta_map_comfort_zone_rate REAL DEFAULT 0.0,
+    meta_map_adaptation REAL DEFAULT 0.0,
+    
+    -- Session Pattern (8 columns)
+    meta_session_avg_matches_per_day REAL DEFAULT 0.0,
+    meta_session_longest_streak INTEGER DEFAULT 0,
+    meta_session_weekend_rating REAL DEFAULT 0.0,
+    meta_session_weekday_rating REAL DEFAULT 0.0,
+    meta_session_morning_rating REAL DEFAULT 0.0,
+    meta_session_afternoon_rating REAL DEFAULT 0.0,
+    meta_session_evening_rating REAL DEFAULT 0.0,
+    meta_session_night_rating REAL DEFAULT 0.0,
+    
+    -- ========================================================================
+    -- TIER 5: COMPOSITE (11 columns)
+    -- Weighted composite scores (0-100)
+    -- ========================================================================
+    score_aim REAL DEFAULT 0.0,
+    score_clutch REAL DEFAULT 0.0,
+    score_pistol REAL DEFAULT 0.0,
+    score_defense REAL DEFAULT 0.0,
+    score_utility REAL DEFAULT 0.0,
+    score_stability REAL DEFAULT 0.0,
+    score_economy REAL DEFAULT 0.0,
+    score_pace REAL DEFAULT 0.0,
+    score_overall REAL DEFAULT 0.0,
+    tier_classification TEXT,
+    tier_percentile REAL DEFAULT 0.0,
+    
+    -- Foreign key constraint
+    FOREIGN KEY (steam_id_64) REFERENCES dim_players(steam_id_64)
 );
 
--- Optional: Detailed per-match feature table for time-series analysis
-CREATE TABLE IF NOT EXISTS fact_match_features (
-    match_id TEXT,
+-- Indexes for query performance
+CREATE INDEX IF NOT EXISTS idx_dm_player_features_rating ON dm_player_features(core_avg_rating DESC);
+CREATE INDEX IF NOT EXISTS idx_dm_player_features_matches ON dm_player_features(total_matches DESC);
+CREATE INDEX IF NOT EXISTS idx_dm_player_features_tier ON dm_player_features(tier_classification);
+CREATE INDEX IF NOT EXISTS idx_dm_player_features_updated ON dm_player_features(last_updated DESC);
+
+-- ============================================================================
+-- Auxiliary Table: dm_player_match_history
+-- ============================================================================
+CREATE TABLE IF NOT EXISTS dm_player_match_history (
     steam_id_64 TEXT,
+    match_id TEXT,
+    match_date INTEGER,  -- Unix timestamp
+    match_sequence INTEGER,  -- Player's N-th match
     
-    -- 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,
+    -- Core performance snapshot
+    rating REAL,
+    kd_ratio REAL,
+    adr REAL,
+    kast REAL,
+    is_win BOOLEAN,
     
-    PRIMARY KEY (match_id, steam_id_64)
+    -- Match context
+    map_name TEXT,
+    opponent_avg_elo REAL,
+    teammate_avg_rating REAL,
+    
+    -- Cumulative stats
+    cumulative_rating REAL,
+    rolling_10_rating REAL,
+    
+    PRIMARY KEY (steam_id_64, match_id),
+    FOREIGN KEY (steam_id_64) REFERENCES dm_player_features(steam_id_64) ON DELETE CASCADE,
+    FOREIGN KEY (match_id) REFERENCES fact_matches(match_id) ON DELETE CASCADE
 );
+
+CREATE INDEX IF NOT EXISTS idx_player_history_player_date ON dm_player_match_history(steam_id_64, match_date DESC);
+CREATE INDEX IF NOT EXISTS idx_player_history_match ON dm_player_match_history(match_id);
+
+-- ============================================================================
+-- Auxiliary Table: dm_player_map_stats
+-- ============================================================================
+CREATE TABLE IF NOT EXISTS dm_player_map_stats (
+    steam_id_64 TEXT,
+    map_name TEXT,
+    
+    matches INTEGER DEFAULT 0,
+    wins INTEGER DEFAULT 0,
+    win_rate REAL DEFAULT 0.0,
+    
+    avg_rating REAL DEFAULT 0.0,
+    avg_kd REAL DEFAULT 0.0,
+    avg_adr REAL DEFAULT 0.0,
+    avg_kast REAL DEFAULT 0.0,
+    
+    best_rating REAL DEFAULT 0.0,
+    worst_rating REAL DEFAULT 0.0,
+    
+    PRIMARY KEY (steam_id_64, map_name),
+    FOREIGN KEY (steam_id_64) REFERENCES dm_player_features(steam_id_64) ON DELETE CASCADE
+);
+
+CREATE INDEX IF NOT EXISTS idx_player_map_stats_player ON dm_player_map_stats(steam_id_64);
+CREATE INDEX IF NOT EXISTS idx_player_map_stats_map ON dm_player_map_stats(map_name);
+
+-- ============================================================================
+-- Auxiliary Table: dm_player_weapon_stats
+-- ============================================================================
+CREATE TABLE IF NOT EXISTS dm_player_weapon_stats (
+    steam_id_64 TEXT,
+    weapon_name TEXT,
+    
+    total_kills INTEGER DEFAULT 0,
+    total_headshots INTEGER DEFAULT 0,
+    hs_rate REAL DEFAULT 0.0,
+    
+    usage_rounds INTEGER DEFAULT 0,
+    usage_rate REAL DEFAULT 0.0,
+    
+    avg_kills_per_round REAL DEFAULT 0.0,
+    effectiveness_score REAL DEFAULT 0.0,
+    
+    PRIMARY KEY (steam_id_64, weapon_name),
+    FOREIGN KEY (steam_id_64) REFERENCES dm_player_features(steam_id_64) ON DELETE CASCADE
+);
+
+CREATE INDEX IF NOT EXISTS idx_player_weapon_stats_player ON dm_player_weapon_stats(steam_id_64);
+CREATE INDEX IF NOT EXISTS idx_player_weapon_stats_weapon ON dm_player_weapon_stats(weapon_name);
+
+-- ============================================================================
+-- Schema Summary
+-- ============================================================================
+-- dm_player_features: 213 columns (6 metadata + 207 features)
+--   - Tier 1 CORE: 41 columns
+--   - Tier 2 TACTICAL: 44 columns
+--   - Tier 3 INTELLIGENCE: 53 columns
+--   - Tier 4 META: 52 columns
+--   - Tier 5 COMPOSITE: 11 columns
+--
+-- dm_player_match_history: Per-match snapshots for trend analysis
+-- dm_player_map_stats: Map-level aggregations
+-- dm_player_weapon_stats: Weapon usage statistics
+-- ============================================================================