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0.5.0: L3 ver1 Updated.

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Jacky Yang
2026-01-24 02:48:56 +08:00
parent 8e59d9497b
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ETL/L3_Builder.py Normal file
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import sqlite3
import logging
import os
import numpy as np
import pandas as pd
from datetime import datetime
# Setup logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
# Constants
L2_DB_PATH = 'database/L2/L2_Main.sqlite'
L3_DB_PATH = 'database/L3/L3_Features.sqlite'
SCHEMA_PATH = 'database/L3/schema.sql'
def init_db():
if not os.path.exists('database/L3'):
os.makedirs('database/L3')
conn = sqlite3.connect(L3_DB_PATH)
with open(SCHEMA_PATH, 'r', encoding='utf-8') as f:
conn.executescript(f.read())
conn.commit()
conn.close()
logger.info("L3 DB Initialized.")
def get_db_connection(db_path):
conn = sqlite3.connect(db_path)
return conn
def safe_div(a, b, default=0.0):
return a / b if b and b != 0 else default
def calculate_basic_features(df):
if df.empty:
return {}
count = len(df)
feats = {
'total_matches': count,
'basic_avg_rating': df['rating'].mean(),
'basic_avg_kd': df['kd_ratio'].mean(),
'basic_avg_kast': df['kast'].mean(),
'basic_avg_rws': df['rws'].mean(),
'basic_avg_headshot_kills': df['headshot_count'].sum() / count,
'basic_headshot_rate': safe_div(df['headshot_count'].sum(), df['kills'].sum()),
'basic_avg_first_kill': df['first_kill'].mean(),
'basic_avg_first_death': df['first_death'].mean(),
'basic_first_kill_rate': safe_div(df['first_kill'].sum(), df['first_kill'].sum() + df['first_death'].sum()),
'basic_first_death_rate': safe_div(df['first_death'].sum(), df['first_kill'].sum() + df['first_death'].sum()),
'basic_avg_kill_2': df['kill_2'].mean(),
'basic_avg_kill_3': df['kill_3'].mean(),
'basic_avg_kill_4': df['kill_4'].mean(),
'basic_avg_kill_5': df['kill_5'].mean(),
'basic_avg_assisted_kill': df['assisted_kill'].mean(),
'basic_avg_perfect_kill': df['perfect_kill'].mean(),
'basic_avg_revenge_kill': df['revenge_kill'].mean(),
'basic_avg_awp_kill': df['awp_kill'].mean(),
'basic_avg_jump_count': df['jump_count'].mean(),
}
return feats
def calculate_sta_features(df):
if df.empty:
return {}
df = df.sort_values('match_time')
last_30 = df.tail(30)
last_10 = df.tail(10)
feats = {
'sta_last_30_rating': last_30['rating'].mean(),
'sta_win_rating': df[df['is_win'] == 1]['rating'].mean() if not df[df['is_win'] == 1].empty else 0.0,
'sta_loss_rating': df[df['is_win'] == 0]['rating'].mean() if not df[df['is_win'] == 0].empty else 0.0,
'sta_rating_volatility': last_10['rating'].std() if len(last_10) > 1 else 0.0,
}
df['date'] = pd.to_datetime(df['match_time'], unit='s').dt.date
day_counts = df.groupby('date').size()
busy_days = day_counts[day_counts >= 4].index
if len(busy_days) > 0:
early_ratings = []
late_ratings = []
for day in busy_days:
day_matches = df[df['date'] == day].sort_values('match_time')
early = day_matches.head(3)
late = day_matches.tail(len(day_matches) - 3)
early_ratings.extend(early['rating'].tolist())
late_ratings.extend(late['rating'].tolist())
feats['sta_fatigue_decay'] = np.mean(early_ratings) - np.mean(late_ratings) if early_ratings and late_ratings else 0.0
else:
feats['sta_fatigue_decay'] = 0.0
df['hour_of_day'] = pd.to_datetime(df['match_time'], unit='s').dt.hour
if len(df) > 5:
corr = df['hour_of_day'].corr(df['rating'])
feats['sta_time_rating_corr'] = corr if not np.isnan(corr) else 0.0
else:
feats['sta_time_rating_corr'] = 0.0
return feats
def calculate_util_features(df):
if df.empty:
return {}
feats = {
'util_avg_nade_dmg': df['throw_harm'].mean() if 'throw_harm' in df.columns else 0.0,
'util_avg_flash_time': df['flash_duration'].mean() if 'flash_duration' in df.columns else 0.0,
'util_avg_flash_enemy': df['flash_enemy'].mean() if 'flash_enemy' in df.columns else 0.0,
'util_avg_flash_team': df['flash_team'].mean() if 'flash_team' in df.columns else 0.0,
'util_usage_rate': (df['flash_enemy'].mean() + df['throw_harm'].mean() / 50.0)
}
return feats
def calculate_side_features(steam_id, l2_conn):
q_ct = f"SELECT * FROM fact_match_players_ct WHERE steam_id_64 = '{steam_id}'"
q_t = f"SELECT * FROM fact_match_players_t WHERE steam_id_64 = '{steam_id}'"
df_ct = pd.read_sql_query(q_ct, l2_conn)
df_t = pd.read_sql_query(q_t, l2_conn)
feats = {}
if not df_ct.empty:
feats['side_rating_ct'] = df_ct['rating'].mean()
feats['side_first_kill_rate_ct'] = safe_div(df_ct['first_kill'].sum(), df_ct['first_kill'].sum() + df_ct['first_death'].sum())
feats['side_hold_success_rate_ct'] = 0.0
feats['side_defused_bomb_count'] = df_ct['defused_bomb'].sum() if 'defused_bomb' in df_ct.columns else 0
else:
feats.update({'side_rating_ct': 0.0, 'side_first_kill_rate_ct': 0.0, 'side_hold_success_rate_ct': 0.0, 'side_defused_bomb_count': 0})
if not df_t.empty:
feats['side_rating_t'] = df_t['rating'].mean()
feats['side_first_kill_rate_t'] = safe_div(df_t['first_kill'].sum(), df_t['first_kill'].sum() + df_t['first_death'].sum())
feats['side_entry_success_rate_t'] = 0.0
feats['side_planted_bomb_count'] = df_t['planted_bomb'].sum() if 'planted_bomb' in df_t.columns else 0
else:
feats.update({'side_rating_t': 0.0, 'side_first_kill_rate_t': 0.0, 'side_entry_success_rate_t': 0.0, 'side_planted_bomb_count': 0})
feats['side_kd_diff_ct_t'] = (df_ct['kd_ratio'].mean() if not df_ct.empty else 0) - (df_t['kd_ratio'].mean() if not df_t.empty else 0)
return feats
def calculate_complex_features(steam_id, match_df, l2_conn):
"""
Calculates BAT, HPS, and PTL features using Round Events and Rounds.
"""
feats = {}
# 1. HPS: Clutch from match stats (easier part)
# clutch_1vX are wins. end_1vX are total attempts (assuming mapping logic).
clutch_wins = match_df[['clutch_1v1', 'clutch_1v2', 'clutch_1v3', 'clutch_1v4', 'clutch_1v5']].sum().sum()
clutch_attempts = match_df[['end_1v1', 'end_1v2', 'end_1v3', 'end_1v4', 'end_1v5']].sum().sum()
# Granular clutch rates
feats['hps_clutch_win_rate_1v1'] = safe_div(match_df['clutch_1v1'].sum(), match_df['end_1v1'].sum())
feats['hps_clutch_win_rate_1v2'] = safe_div(match_df['clutch_1v2'].sum(), match_df['end_1v2'].sum())
feats['hps_clutch_win_rate_1v3_plus'] = safe_div(
match_df[['clutch_1v3', 'clutch_1v4', 'clutch_1v5']].sum().sum(),
match_df[['end_1v3', 'end_1v4', 'end_1v5']].sum().sum()
)
# 2. Heavy Lifting: Round Events
# Fetch all kills involving player
q_events = f"""
SELECT e.*,
p_vic.rank_score as victim_rank,
p_att.rank_score as attacker_rank
FROM fact_round_events e
LEFT JOIN fact_match_players p_vic ON e.match_id = p_vic.match_id AND e.victim_steam_id = p_vic.steam_id_64
LEFT JOIN fact_match_players p_att ON e.match_id = p_att.match_id AND e.attacker_steam_id = p_att.steam_id_64
WHERE (e.attacker_steam_id = '{steam_id}' OR e.victim_steam_id = '{steam_id}')
AND e.event_type = 'kill'
"""
try:
events = pd.read_sql_query(q_events, l2_conn)
except Exception as e:
logger.error(f"Error fetching events for {steam_id}: {e}")
events = pd.DataFrame()
if not events.empty:
# BAT Features
kills = events[events['attacker_steam_id'] == steam_id]
deaths = events[events['victim_steam_id'] == steam_id]
# Determine player rank for each match (approximate using average or self join - wait, p_att is self when attacker)
# We can use the rank from the joined columns.
# When player is attacker, use attacker_rank (self) vs victim_rank (enemy)
kills = kills.copy()
kills['diff'] = kills['victim_rank'] - kills['attacker_rank']
# When player is victim, use victim_rank (self) vs attacker_rank (enemy)
deaths = deaths.copy()
deaths['diff'] = deaths['attacker_rank'] - deaths['victim_rank'] # Enemy rank - My rank
# High Elo: Enemy Rank > My Rank + 100? Or just > My Rank?
# Let's say High Elo = Enemy Rank > My Rank
high_elo_kills = kills[kills['diff'] > 0].shape[0]
high_elo_deaths = deaths[deaths['diff'] > 0].shape[0] # Enemy (Attacker) > Me (Victim)
low_elo_kills = kills[kills['diff'] < 0].shape[0]
low_elo_deaths = deaths[deaths['diff'] < 0].shape[0]
feats['bat_kd_diff_high_elo'] = high_elo_kills - high_elo_deaths
feats['bat_kd_diff_low_elo'] = low_elo_kills - low_elo_deaths
total_duels = len(kills) + len(deaths)
feats['bat_win_rate_vs_all'] = safe_div(len(kills), total_duels)
feats['bat_avg_duel_win_rate'] = feats['bat_win_rate_vs_all'] # Simplifying
feats['bat_avg_duel_freq'] = safe_div(total_duels, len(match_df))
feats['bat_win_rate_close'] = 0.0 # Placeholder for distance logic
feats['bat_win_rate_mid'] = 0.0
feats['bat_win_rate_far'] = 0.0
else:
feats.update({
'bat_kd_diff_high_elo': 0, 'bat_kd_diff_low_elo': 0,
'bat_win_rate_vs_all': 0.0, 'bat_avg_duel_win_rate': 0.0,
'bat_avg_duel_freq': 0.0, 'bat_win_rate_close': 0.0,
'bat_win_rate_mid': 0.0, 'bat_win_rate_far': 0.0
})
# 3. PTL & Match Point (Requires Rounds)
# Fetch rounds for matches played
match_ids = match_df['match_id'].unique().tolist()
if not match_ids:
return feats
match_ids_str = "'" + "','".join(match_ids) + "'"
q_rounds = f"SELECT * FROM fact_rounds WHERE match_id IN ({match_ids_str})"
try:
rounds = pd.read_sql_query(q_rounds, l2_conn)
except:
rounds = pd.DataFrame()
if not rounds.empty and not events.empty:
# PTL: Round 1 and 13 (Assuming MR12)
pistol_rounds = rounds[(rounds['round_num'] == 1) | (rounds['round_num'] == 13)]
# Join kills with pistol rounds
# keys: match_id, round_num
pistol_events = pd.merge(
events[events['attacker_steam_id'] == steam_id],
pistol_rounds[['match_id', 'round_num']],
on=['match_id', 'round_num']
)
feats['ptl_pistol_kills'] = safe_div(len(pistol_events), len(match_df)) # Avg per match
feats['ptl_pistol_multikills'] = 0.0 # Complex to calc without grouping per round
feats['ptl_pistol_win_rate'] = 0.5 # Placeholder (Requires checking winner_team vs player_team)
feats['ptl_pistol_kd'] = 1.0 # Placeholder
feats['ptl_pistol_util_efficiency'] = 0.0
# Match Point (HPS)
# Logic: Score is 12 (MR12) or 15 (MR15).
# We assume MR12 for simplicity or check max score.
match_point_rounds = rounds[(rounds['ct_score'] == 12) | (rounds['t_score'] == 12)]
# This logic is imperfect (OT etc), but okay for v1.
feats['hps_match_point_win_rate'] = 0.5 # Placeholder
else:
feats.update({
'ptl_pistol_kills': 0.0, 'ptl_pistol_multikills': 0.0,
'ptl_pistol_win_rate': 0.0, 'ptl_pistol_kd': 0.0,
'ptl_pistol_util_efficiency': 0.0, 'hps_match_point_win_rate': 0.0
})
# Fill remaining HPS placeholders
feats['hps_undermanned_survival_time'] = 0.0
feats['hps_pressure_entry_rate'] = 0.0
feats['hps_momentum_multikill_rate'] = 0.0
feats['hps_tilt_rating_drop'] = 0.0
feats['hps_clutch_rating_rise'] = 0.0
feats['hps_comeback_kd_diff'] = 0.0
feats['hps_losing_streak_kd_diff'] = 0.0
return feats
def process_players():
l2_conn = get_db_connection(L2_DB_PATH)
l3_conn = get_db_connection(L3_DB_PATH)
logger.info("Fetching player list...")
players = pd.read_sql_query("SELECT DISTINCT steam_id_64 FROM fact_match_players", l2_conn)['steam_id_64'].tolist()
logger.info(f"Found {len(players)} players. Processing...")
for idx, steam_id in enumerate(players):
query = f"SELECT * FROM fact_match_players WHERE steam_id_64 = '{steam_id}' ORDER BY match_time ASC"
df = pd.read_sql_query(query, l2_conn)
if df.empty:
continue
feats = calculate_basic_features(df)
feats.update(calculate_sta_features(df))
feats.update(calculate_side_features(steam_id, l2_conn))
feats.update(calculate_util_features(df))
feats.update(calculate_complex_features(steam_id, df, l2_conn))
# Insert
cols = list(feats.keys())
vals = list(feats.values())
vals = [float(v) if isinstance(v, (np.float32, np.float64)) else v for v in vals]
vals = [int(v) if isinstance(v, (np.int32, np.int64)) else v for v in vals]
col_str = ", ".join(cols)
q_marks = ", ".join(["?"] * len(cols))
sql = f"INSERT OR REPLACE INTO dm_player_features (steam_id_64, {col_str}) VALUES (?, {q_marks})"
l3_conn.execute(sql, [steam_id] + vals)
if idx % 10 == 0:
print(f"Processed {idx}/{len(players)} players...", end='\r')
l3_conn.commit()
l3_conn.commit()
l2_conn.close()
l3_conn.close()
logger.info("\nDone.")
if __name__ == "__main__":
init_db()
process_players()

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ETL/verify/verify_report.txt → ETL/verify/L2_verify_report.txt
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import sqlite3
import pandas as pd
L3_DB_PATH = 'database/L3/L3_Features.sqlite'
def verify():
conn = sqlite3.connect(L3_DB_PATH)
# 1. Row count
cursor = conn.cursor()
cursor.execute("SELECT COUNT(*) FROM dm_player_features")
count = cursor.fetchone()[0]
print(f"Total Players in L3: {count}")
# 2. Sample Data
df = pd.read_sql_query("SELECT * FROM dm_player_features LIMIT 5", conn)
print("\nSample Data (First 5 rows):")
print(df[['steam_id_64', 'total_matches', 'basic_avg_rating', 'sta_last_30_rating', 'bat_kd_diff_high_elo', 'hps_clutch_win_rate_1v1']].to_string())
# 3. Stats Summary
print("\nStats Summary:")
full_df = pd.read_sql_query("SELECT basic_avg_rating, sta_last_30_rating, bat_win_rate_vs_all FROM dm_player_features", conn)
print(full_df.describe())
conn.close()
if __name__ == "__main__":
verify()

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

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

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