1.2.0: Refined all 6D calcs and UI/UX Experiences.
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6D_README.md
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# 选手能力六维图计算原理 (Six Dimensions Calculation)
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本文档详细介绍了 YRTV 系统中选手能力六维图（Radar Chart）的计算原理、数据来源及具体公式。
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## 概述
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能力六维图通过六个核心维度全面评估选手的综合实力：
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1. **BAT (Battle / Aim)**: 正面交火与枪法能力
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2. **STA (Stability)**: 表现稳定性与抗压能力
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3. **HPS (High Pressure / Clutch)**: 关键时刻与残局能力
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4. **PTL (Pistol Specialist)**: 手枪局专项能力
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5. **SIDE (T/CT Preference)**: 攻防两端的均衡性与影响力
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6. **UTIL (Utility)**: 道具使用效率与投入度
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所有指标在计算前均会进行归一化处理（Normalization），映射到 0-100 的评分区间，以便于横向对比。
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---
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## 详细计算公式
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注：`n(col)` 表示对该列数据进行 Min-Max 归一化处理。
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### 1. BAT - 正面交火 (Battle)
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衡量选手的基础枪法、击杀效率及高水平对抗能力。
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**权重公式:**
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```python
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Score = (
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0.25 * n('Rating') + # 基础 Rating
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0.20 * n('KD_Ratio') + # 击杀死亡比
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0.15 * n('ADR') + # 回合均伤
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0.10 * n('Duel_Win_Rate') + # 1v1 对枪胜率
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0.10 * n('High_Elo_KD_Diff') + # 高分局表现差值 (抗压)
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0.10 * n('Multi_Kill_Avg') # 多杀能力 (3k+)
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)
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```
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### 2. STA - 稳定性 (Stability)
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衡量选手表现的波动性以及在顺风/逆风局的发挥。
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**权重公式:**
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```python
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Score = (
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0.30 * (100 - n('Rating_Volatility')) + # 评分波动性 (越低越好)
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0.30 * n('Loss_Rating') + # 败局 Rating (尽力局表现)
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0.20 * n('Win_Rating') + # 胜局 Rating
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0.10 * (100 - abs(n('Time_Corr'))) # 状态随时间下滑程度 (耐力)
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)
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```
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### 3. HPS - 关键局 (High Pressure)
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衡量选手在残局、赛点等高压环境下的“大心脏”能力。
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**权重公式:**
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```python
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Score = (
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0.30 * n('Clutch_1v3+') + # 1v3 及以上残局获胜数
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0.20 * n('Match_Point_Win_Rate') + # 赛点局胜率
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0.20 * n('Comeback_KD_Diff') + # 翻盘局 KD 表现
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0.15 * n('Pressure_Entry_Rate') + # 逆风局首杀率
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0.15 * n('Rating') # 基础能力兜底
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)
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```
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### 4. PTL - 手枪局 (Pistol Specialist)
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衡量选手在手枪局（Round 1 & 13）的专项统治力。
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**权重公式:**
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```python
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Score = (
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0.40 * n('Pistol_Kills_Avg') + # 手枪局场均击杀
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0.40 * n('Pistol_Win_Rate') + # 手枪局胜率
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0.20 * n('Headshot_Kills_Avg') # 场均爆头击杀 (手枪局极其依赖爆头)
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)
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```
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### 5. SIDE - 攻防偏好 (Side Preference)
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衡量选手在 T (进攻) 和 CT (防守) 两端的均衡性与统治力。
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**权重公式:**
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```python
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Score = (
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0.35 * n('CT_Rating') + # CT 方 Rating
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0.35 * n('T_Rating') + # T 方 Rating
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0.15 * n('CT_First_Kill_Rate') + # CT 方首杀率 (防守前压/偷人)
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0.15 * n('T_First_Kill_Rate') # T 方首杀率 (突破能力)
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)
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```
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### 6. UTIL - 道具 (Utility)
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衡量选手对道具的投入程度（购买频率）以及使用效果（伤害/白）。
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**权重公式:**
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```python
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Score = (
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0.35 * n('Usage_Rate') + # 道具购买/使用频率
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0.25 * n('Avg_Nade_Dmg') + # 场均手雷/火伤害
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0.20 * n('Avg_Flash_Time') + # 场均致盲时间
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0.20 * n('Avg_Flash_Enemy') # 场均致盲敌人数
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)
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```
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---
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## 数据更新机制
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所有特征数据均由 ETL 流程 (`ETL/L3_Builder.py`) 每日自动计算更新。
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- **源数据**: `fact_match_players`, `fact_round_events`, `fact_rounds` 等 L2 层事实表。
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- **存储**: 计算结果存储于 `database/L3/L3_Features.sqlite` 的 `dm_player_features` 表中。
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- **展示**: 前端 Profile 页面读取该表数据，并结合队内分布 (`radar_dist`) 进行可视化渲染。
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@@ -117,6 +117,13 @@ class PlayerStats:
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year: str = ""
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sts_raw: str = ""
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level_info_raw: str = ""
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# Utility Usage
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util_flash_usage: int = 0
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util_smoke_usage: int = 0
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util_molotov_usage: int = 0
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util_he_usage: int = 0
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util_decoy_usage: int = 0
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@dataclass
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class RoundEvent:
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@@ -799,6 +806,22 @@ class MatchParser:
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round_list = l_data.get('round_stat', [])
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for idx, r in enumerate(round_list):
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# Utility Usage (Leetify)
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bron = r.get('bron_equipment', {})
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for sid, items in bron.items():
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sid = str(sid)
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if sid in self.match_data.players:
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p = self.match_data.players[sid]
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if isinstance(items, list):
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for item in items:
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if not isinstance(item, dict): continue
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name = item.get('WeaponName', '')
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if name == 'weapon_flashbang': p.util_flash_usage += 1
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elif name == 'weapon_smokegrenade': p.util_smoke_usage += 1
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elif name in ['weapon_molotov', 'weapon_incgrenade']: p.util_molotov_usage += 1
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elif name == 'weapon_hegrenade': p.util_he_usage += 1
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elif name == 'weapon_decoy': p.util_decoy_usage += 1
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rd = RoundData(
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round_num=r.get('round', idx + 1),
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winner_side='CT' if r.get('win_reason') in [7, 8, 9] else 'T', # Approximate logic, need real enum
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@@ -949,6 +972,21 @@ class MatchParser:
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# Check schema: 'current_score' -> ct/t
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cur_score = r.get('current_score', {})
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# Utility Usage (Classic)
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equiped = r.get('equiped', {})
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for sid, items in equiped.items():
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# Ensure sid is string
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sid = str(sid)
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if sid in self.match_data.players:
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p = self.match_data.players[sid]
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if isinstance(items, list):
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for item in items:
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if item == 'flashbang': p.util_flash_usage += 1
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elif item == 'smokegrenade': p.util_smoke_usage += 1
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elif item in ['molotov', 'incgrenade']: p.util_molotov_usage += 1
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elif item == 'hegrenade': p.util_he_usage += 1
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elif item == 'decoy': p.util_decoy_usage += 1
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rd = RoundData(
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round_num=idx + 1,
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winner_side='None', # Default to None if unknown
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@@ -1214,7 +1252,8 @@ def save_match(cursor, m: MatchData):
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"many_assists_cnt3", "many_assists_cnt4", "many_assists_cnt5", "map",
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"match_code", "match_mode", "match_team_id", "match_time", "per_headshot",
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"perfect_kill", "planted_bomb", "revenge_kill", "round_total", "season",
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"team_kill", "throw_harm", "throw_harm_enemy", "uid", "year", "sts_raw", "level_info_raw"
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"team_kill", "throw_harm", "throw_harm_enemy", "uid", "year", "sts_raw", "level_info_raw",
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"util_flash_usage", "util_smoke_usage", "util_molotov_usage", "util_he_usage", "util_decoy_usage"
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]
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player_placeholders = ",".join(["?"] * len(player_columns))
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player_columns_sql = ",".join(player_columns)
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@@ -1238,7 +1277,8 @@ def save_match(cursor, m: MatchData):
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p.many_assists_cnt5, p.map, p.match_code, p.match_mode, p.match_team_id,
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p.match_time, p.per_headshot, p.perfect_kill, p.planted_bomb, p.revenge_kill,
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p.round_total, p.season, p.team_kill, p.throw_harm, p.throw_harm_enemy,
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p.uid, p.year, p.sts_raw, p.level_info_raw
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p.uid, p.year, p.sts_raw, p.level_info_raw,
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p.util_flash_usage, p.util_smoke_usage, p.util_molotov_usage, p.util_he_usage, p.util_decoy_usage
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]
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for sid, p in m.players.items():
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@@ -1,330 +1,48 @@
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import sqlite3
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import logging
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import os
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import numpy as np
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import pandas as pd
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from datetime import datetime
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import sys
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# Add parent directory to path to allow importing web module
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from web.services.feature_service import FeatureService
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from web.config import Config
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import sqlite3
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# Setup logging
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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# Constants
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L2_DB_PATH = 'database/L2/L2_Main.sqlite'
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L3_DB_PATH = 'database/L3/L3_Features.sqlite'
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SCHEMA_PATH = 'database/L3/schema.sql'
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L3_DB_PATH = Config.DB_L3_PATH
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SCHEMA_PATH = os.path.join(Config.BASE_DIR, 'database', 'L3', 'schema.sql')
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def init_db():
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if not os.path.exists('database/L3'):
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os.makedirs('database/L3')
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l3_dir = os.path.dirname(L3_DB_PATH)
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if not os.path.exists(l3_dir):
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os.makedirs(l3_dir)
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conn = sqlite3.connect(L3_DB_PATH)
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with open(SCHEMA_PATH, 'r', encoding='utf-8') as f:
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conn.executescript(f.read())
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conn.commit()
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conn.close()
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logger.info("L3 DB Initialized.")
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logger.info("L3 DB Initialized/Updated with Schema.")
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def get_db_connection(db_path):
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conn = sqlite3.connect(db_path)
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return conn
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def safe_div(a, b, default=0.0):
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return a / b if b and b != 0 else default
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def calculate_basic_features(df):
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if df.empty:
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return {}
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def main():
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logger.info("Starting L3 Builder (Delegating to FeatureService)...")
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count = len(df)
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# 1. Ensure Schema is up to date
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init_db()
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feats = {
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'total_matches': count,
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'basic_avg_rating': df['rating'].mean(),
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'basic_avg_kd': df['kd_ratio'].mean(),
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'basic_avg_adr': df['adr'].mean() if 'adr' in df.columns else 0.0,
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'basic_avg_kast': df['kast'].mean(),
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'basic_avg_rws': df['rws'].mean(),
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'basic_avg_headshot_kills': df['headshot_count'].sum() / count,
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'basic_headshot_rate': safe_div(df['headshot_count'].sum(), df['kills'].sum()),
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'basic_avg_first_kill': df['first_kill'].mean(),
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'basic_avg_first_death': df['first_death'].mean(),
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'basic_first_kill_rate': safe_div(df['first_kill'].sum(), df['first_kill'].sum() + df['first_death'].sum()),
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'basic_first_death_rate': safe_div(df['first_death'].sum(), df['first_kill'].sum() + df['first_death'].sum()),
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'basic_avg_kill_2': df['kill_2'].mean(),
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'basic_avg_kill_3': df['kill_3'].mean(),
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'basic_avg_kill_4': df['kill_4'].mean(),
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'basic_avg_kill_5': df['kill_5'].mean(),
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'basic_avg_assisted_kill': df['assisted_kill'].mean(),
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'basic_avg_perfect_kill': df['perfect_kill'].mean(),
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'basic_avg_revenge_kill': df['revenge_kill'].mean(),
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'basic_avg_awp_kill': df['awp_kill'].mean(),
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'basic_avg_jump_count': df['jump_count'].mean(),
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}
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return feats
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def calculate_sta_features(df):
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if df.empty:
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return {}
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df = df.sort_values('match_time')
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last_30 = df.tail(30)
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last_10 = df.tail(10)
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feats = {
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'sta_last_30_rating': last_30['rating'].mean(),
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'sta_win_rating': df[df['is_win'] == 1]['rating'].mean() if not df[df['is_win'] == 1].empty else 0.0,
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'sta_loss_rating': df[df['is_win'] == 0]['rating'].mean() if not df[df['is_win'] == 0].empty else 0.0,
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'sta_rating_volatility': last_10['rating'].std() if len(last_10) > 1 else 0.0,
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}
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df['date'] = pd.to_datetime(df['match_time'], unit='s').dt.date
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day_counts = df.groupby('date').size()
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busy_days = day_counts[day_counts >= 4].index
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if len(busy_days) > 0:
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early_ratings = []
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late_ratings = []
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for day in busy_days:
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day_matches = df[df['date'] == day].sort_values('match_time')
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early = day_matches.head(3)
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late = day_matches.tail(len(day_matches) - 3)
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early_ratings.extend(early['rating'].tolist())
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late_ratings.extend(late['rating'].tolist())
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feats['sta_fatigue_decay'] = np.mean(early_ratings) - np.mean(late_ratings) if early_ratings and late_ratings else 0.0
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else:
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feats['sta_fatigue_decay'] = 0.0
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df['hour_of_day'] = pd.to_datetime(df['match_time'], unit='s').dt.hour
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if len(df) > 5:
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corr = df['hour_of_day'].corr(df['rating'])
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feats['sta_time_rating_corr'] = corr if not np.isnan(corr) else 0.0
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else:
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feats['sta_time_rating_corr'] = 0.0
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return feats
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def calculate_util_features(df):
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if df.empty:
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return {}
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feats = {
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'util_avg_nade_dmg': df['throw_harm'].mean() if 'throw_harm' in df.columns else 0.0,
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'util_avg_flash_time': df['flash_duration'].mean() if 'flash_duration' in df.columns else 0.0,
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'util_avg_flash_enemy': df['flash_enemy'].mean() if 'flash_enemy' in df.columns else 0.0,
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'util_avg_flash_team': df['flash_team'].mean() if 'flash_team' in df.columns else 0.0,
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'util_usage_rate': (df['flash_enemy'].mean() + df['throw_harm'].mean() / 50.0)
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}
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return feats
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def calculate_side_features(steam_id, l2_conn):
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q_ct = f"SELECT * FROM fact_match_players_ct WHERE steam_id_64 = '{steam_id}'"
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q_t = f"SELECT * FROM fact_match_players_t WHERE steam_id_64 = '{steam_id}'"
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df_ct = pd.read_sql_query(q_ct, l2_conn)
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df_t = pd.read_sql_query(q_t, l2_conn)
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feats = {}
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if not df_ct.empty:
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feats['side_rating_ct'] = df_ct['rating'].mean()
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feats['side_first_kill_rate_ct'] = safe_div(df_ct['first_kill'].sum(), df_ct['first_kill'].sum() + df_ct['first_death'].sum())
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feats['side_hold_success_rate_ct'] = 0.0
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feats['side_defused_bomb_count'] = df_ct['defused_bomb'].sum() if 'defused_bomb' in df_ct.columns else 0
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else:
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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})
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if not df_t.empty:
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feats['side_rating_t'] = df_t['rating'].mean()
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feats['side_first_kill_rate_t'] = safe_div(df_t['first_kill'].sum(), df_t['first_kill'].sum() + df_t['first_death'].sum())
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feats['side_entry_success_rate_t'] = 0.0
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feats['side_planted_bomb_count'] = df_t['planted_bomb'].sum() if 'planted_bomb' in df_t.columns else 0
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else:
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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})
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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)
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return feats
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def calculate_complex_features(steam_id, match_df, l2_conn):
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"""
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Calculates BAT, HPS, and PTL features using Round Events and Rounds.
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"""
|
||||
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'
|
||||
"""
|
||||
# 2. Rebuild Features using the centralized logic
|
||||
try:
|
||||
events = pd.read_sql_query(q_events, l2_conn)
|
||||
count = FeatureService.rebuild_all_features()
|
||||
logger.info(f"Successfully rebuilt features for {count} players.")
|
||||
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.")
|
||||
logger.error(f"Error rebuilding features: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
if __name__ == "__main__":
|
||||
init_db()
|
||||
process_players()
|
||||
main()
|
||||
|
||||
@@ -12,7 +12,7 @@
|
||||
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)**
|
||||
16. **(New) 完美击杀 (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)**
|
||||
|
||||
Binary file not shown.
@@ -195,6 +195,13 @@ CREATE TABLE IF NOT EXISTS fact_match_players (
|
||||
flash_assists INTEGER,
|
||||
flash_duration REAL,
|
||||
jump_count INTEGER,
|
||||
|
||||
-- Utility Usage Stats (Parsed from round details)
|
||||
util_flash_usage INTEGER DEFAULT 0,
|
||||
util_smoke_usage INTEGER DEFAULT 0,
|
||||
util_molotov_usage INTEGER DEFAULT 0,
|
||||
util_he_usage INTEGER DEFAULT 0,
|
||||
util_decoy_usage INTEGER DEFAULT 0,
|
||||
damage_total INTEGER,
|
||||
damage_received INTEGER,
|
||||
damage_receive INTEGER,
|
||||
@@ -365,6 +372,14 @@ CREATE TABLE IF NOT EXISTS fact_match_players_t (
|
||||
year TEXT,
|
||||
sts_raw TEXT,
|
||||
level_info_raw TEXT,
|
||||
|
||||
-- Utility Usage Stats (Parsed from round details)
|
||||
util_flash_usage INTEGER DEFAULT 0,
|
||||
util_smoke_usage INTEGER DEFAULT 0,
|
||||
util_molotov_usage INTEGER DEFAULT 0,
|
||||
util_he_usage INTEGER DEFAULT 0,
|
||||
util_decoy_usage INTEGER DEFAULT 0,
|
||||
|
||||
PRIMARY KEY (match_id, steam_id_64),
|
||||
FOREIGN KEY (match_id) REFERENCES fact_matches(match_id) ON DELETE CASCADE
|
||||
);
|
||||
@@ -466,6 +481,14 @@ CREATE TABLE IF NOT EXISTS fact_match_players_ct (
|
||||
year TEXT,
|
||||
sts_raw TEXT,
|
||||
level_info_raw TEXT,
|
||||
|
||||
-- Utility Usage Stats (Parsed from round details)
|
||||
util_flash_usage INTEGER DEFAULT 0,
|
||||
util_smoke_usage INTEGER DEFAULT 0,
|
||||
util_molotov_usage INTEGER DEFAULT 0,
|
||||
util_he_usage INTEGER DEFAULT 0,
|
||||
util_decoy_usage INTEGER DEFAULT 0,
|
||||
|
||||
PRIMARY KEY (match_id, steam_id_64),
|
||||
FOREIGN KEY (match_id) REFERENCES fact_matches(match_id) ON DELETE CASCADE
|
||||
);
|
||||
|
||||
Binary file not shown.
@@ -100,7 +100,17 @@ CREATE TABLE IF NOT EXISTS dm_player_features (
|
||||
util_avg_flash_time REAL,
|
||||
util_avg_flash_enemy REAL,
|
||||
util_avg_flash_team REAL,
|
||||
util_usage_rate 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
|
||||
);
|
||||
|
||||
-- Optional: Detailed per-match feature table for time-series analysis
|
||||
|
||||
1
scripts/README.md
Normal file
1
scripts/README.md
Normal file
@@ -0,0 +1 @@
|
||||
用于测试脚本目录。
|
||||
214
scripts/analyze_features.py
Normal file
214
scripts/analyze_features.py
Normal file
@@ -0,0 +1,214 @@
|
||||
import sqlite3
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import os
|
||||
|
||||
DB_L2_PATH = r'd:\Documents\trae_projects\yrtv\database\L2\L2_Main.sqlite'
|
||||
|
||||
def get_db_connection():
|
||||
conn = sqlite3.connect(DB_L2_PATH)
|
||||
conn.row_factory = sqlite3.Row
|
||||
return conn
|
||||
|
||||
def load_data_and_calculate(conn, min_matches=5):
|
||||
print("Loading Basic Stats...")
|
||||
|
||||
# 1. Basic Stats
|
||||
query_basic = """
|
||||
SELECT
|
||||
steam_id_64,
|
||||
COUNT(*) as matches_played,
|
||||
AVG(rating) as avg_rating,
|
||||
AVG(kd_ratio) as avg_kd,
|
||||
AVG(adr) as avg_adr,
|
||||
AVG(kast) as avg_kast,
|
||||
SUM(first_kill) as total_fk,
|
||||
SUM(first_death) as total_fd,
|
||||
SUM(clutch_1v1) + SUM(clutch_1v2) + SUM(clutch_1v3) + SUM(clutch_1v4) + SUM(clutch_1v5) as total_clutches,
|
||||
SUM(throw_harm) as total_util_dmg,
|
||||
SUM(flash_time) as total_flash_time,
|
||||
SUM(flash_enemy) as total_flash_enemy
|
||||
FROM fact_match_players
|
||||
GROUP BY steam_id_64
|
||||
HAVING COUNT(*) >= ?
|
||||
"""
|
||||
df_basic = pd.read_sql_query(query_basic, conn, params=(min_matches,))
|
||||
|
||||
valid_ids = tuple(df_basic['steam_id_64'].tolist())
|
||||
if not valid_ids:
|
||||
print("No players found.")
|
||||
return None
|
||||
placeholders = ','.join(['?'] * len(valid_ids))
|
||||
|
||||
# 2. Side Stats (T/CT) via Economy Table (which has side info)
|
||||
print("Loading Side Stats via Round Map...")
|
||||
# Map each round+player to a side
|
||||
query_side_map = f"""
|
||||
SELECT match_id, round_num, steam_id_64, side
|
||||
FROM fact_round_player_economy
|
||||
WHERE steam_id_64 IN ({placeholders})
|
||||
"""
|
||||
try:
|
||||
df_sides = pd.read_sql_query(query_side_map, conn, params=valid_ids)
|
||||
|
||||
# Get all Kills
|
||||
query_kills = f"""
|
||||
SELECT match_id, round_num, attacker_steam_id as steam_id_64, COUNT(*) as kills
|
||||
FROM fact_round_events
|
||||
WHERE event_type = 'kill'
|
||||
AND attacker_steam_id IN ({placeholders})
|
||||
GROUP BY match_id, round_num, attacker_steam_id
|
||||
"""
|
||||
df_kills = pd.read_sql_query(query_kills, conn, params=valid_ids)
|
||||
|
||||
# Merge to get Kills per Side
|
||||
df_merged = df_kills.merge(df_sides, on=['match_id', 'round_num', 'steam_id_64'], how='inner')
|
||||
|
||||
# Aggregate
|
||||
side_stats = df_merged.groupby(['steam_id_64', 'side'])['kills'].sum().unstack(fill_value=0)
|
||||
side_stats.columns = [f'kills_{c.lower()}' for c in side_stats.columns]
|
||||
|
||||
# Also need deaths to calc KD (approx)
|
||||
# Assuming deaths are in events as victim
|
||||
query_deaths = f"""
|
||||
SELECT match_id, round_num, victim_steam_id as steam_id_64, COUNT(*) as deaths
|
||||
FROM fact_round_events
|
||||
WHERE event_type = 'kill'
|
||||
AND victim_steam_id IN ({placeholders})
|
||||
GROUP BY match_id, round_num, victim_steam_id
|
||||
"""
|
||||
df_deaths = pd.read_sql_query(query_deaths, conn, params=valid_ids)
|
||||
df_merged_d = df_deaths.merge(df_sides, on=['match_id', 'round_num', 'steam_id_64'], how='inner')
|
||||
side_stats_d = df_merged_d.groupby(['steam_id_64', 'side'])['deaths'].sum().unstack(fill_value=0)
|
||||
side_stats_d.columns = [f'deaths_{c.lower()}' for c in side_stats_d.columns]
|
||||
|
||||
# Combine
|
||||
df_side_final = side_stats.join(side_stats_d).fillna(0)
|
||||
df_side_final['ct_kd'] = df_side_final.get('kills_ct', 0) / df_side_final.get('deaths_ct', 1).replace(0, 1)
|
||||
df_side_final['t_kd'] = df_side_final.get('kills_t', 0) / df_side_final.get('deaths_t', 1).replace(0, 1)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Side stats failed: {e}")
|
||||
df_side_final = pd.DataFrame({'steam_id_64': list(valid_ids)})
|
||||
|
||||
# 3. PTL (Pistol) via Rounds 1 and 13
|
||||
print("Loading Pistol Stats via Rounds...")
|
||||
query_pistol_kills = f"""
|
||||
SELECT
|
||||
ev.attacker_steam_id as steam_id_64,
|
||||
COUNT(*) as pistol_kills
|
||||
FROM fact_round_events ev
|
||||
WHERE ev.attacker_steam_id IN ({placeholders})
|
||||
AND ev.event_type = 'kill'
|
||||
AND ev.round_num IN (1, 13)
|
||||
GROUP BY ev.attacker_steam_id
|
||||
"""
|
||||
df_ptl = pd.read_sql_query(query_pistol_kills, conn, params=valid_ids)
|
||||
|
||||
# 4. HPS
|
||||
print("Loading HPS Stats...")
|
||||
query_close = f"""
|
||||
SELECT mp.steam_id_64, AVG(mp.rating) as close_match_rating
|
||||
FROM fact_match_players mp
|
||||
JOIN fact_matches m ON mp.match_id = m.match_id
|
||||
WHERE mp.steam_id_64 IN ({placeholders})
|
||||
AND ABS(m.score_team1 - m.score_team2) <= 3
|
||||
GROUP BY mp.steam_id_64
|
||||
"""
|
||||
df_hps = pd.read_sql_query(query_close, conn, params=valid_ids)
|
||||
|
||||
# 5. STA
|
||||
query_sta = f"""
|
||||
SELECT mp.steam_id_64, mp.rating, mp.is_win
|
||||
FROM fact_match_players mp
|
||||
WHERE mp.steam_id_64 IN ({placeholders})
|
||||
"""
|
||||
df_matches = pd.read_sql_query(query_sta, conn, params=valid_ids)
|
||||
sta_data = []
|
||||
for pid, group in df_matches.groupby('steam_id_64'):
|
||||
rating_std = group['rating'].std()
|
||||
win_rating = group[group['is_win']==1]['rating'].mean()
|
||||
loss_rating = group[group['is_win']==0]['rating'].mean()
|
||||
sta_data.append({'steam_id_64': pid, 'rating_std': rating_std, 'win_rating': win_rating, 'loss_rating': loss_rating})
|
||||
df_sta = pd.DataFrame(sta_data)
|
||||
|
||||
# --- Merge All ---
|
||||
df = df_basic.merge(df_side_final, on='steam_id_64', how='left')
|
||||
df = df.merge(df_hps, on='steam_id_64', how='left')
|
||||
df = df.merge(df_ptl, on='steam_id_64', how='left').fillna(0)
|
||||
df = df.merge(df_sta, on='steam_id_64', how='left')
|
||||
|
||||
return df
|
||||
|
||||
def normalize_series(series):
|
||||
min_v = series.min()
|
||||
max_v = series.max()
|
||||
if pd.isna(min_v) or pd.isna(max_v) or min_v == max_v:
|
||||
return pd.Series([50]*len(series), index=series.index)
|
||||
return (series - min_v) / (max_v - min_v) * 100
|
||||
|
||||
def calculate_scores(df):
|
||||
df = df.copy()
|
||||
|
||||
# BAT
|
||||
df['n_rating'] = normalize_series(df['avg_rating'])
|
||||
df['n_kd'] = normalize_series(df['avg_kd'])
|
||||
df['n_adr'] = normalize_series(df['avg_adr'])
|
||||
df['n_kast'] = normalize_series(df['avg_kast'])
|
||||
df['score_BAT'] = 0.4*df['n_rating'] + 0.3*df['n_kd'] + 0.2*df['n_adr'] + 0.1*df['n_kast']
|
||||
|
||||
# STA
|
||||
df['n_std'] = normalize_series(df['rating_std'].fillna(0))
|
||||
df['n_win_r'] = normalize_series(df['win_rating'].fillna(0))
|
||||
df['n_loss_r'] = normalize_series(df['loss_rating'].fillna(0))
|
||||
df['score_STA'] = 0.5*(100 - df['n_std']) + 0.25*df['n_win_r'] + 0.25*df['n_loss_r']
|
||||
|
||||
# UTIL
|
||||
df['n_util_dmg'] = normalize_series(df['total_util_dmg'] / df['matches_played'])
|
||||
df['n_flash'] = normalize_series(df['total_flash_time'] / df['matches_played'])
|
||||
df['score_UTIL'] = 0.6*df['n_util_dmg'] + 0.4*df['n_flash']
|
||||
|
||||
# T/CT (Calculated from Event Logs)
|
||||
df['n_ct_kd'] = normalize_series(df['ct_kd'].fillna(0))
|
||||
df['n_t_kd'] = normalize_series(df['t_kd'].fillna(0))
|
||||
df['score_TCT'] = 0.5*df['n_ct_kd'] + 0.5*df['n_t_kd']
|
||||
|
||||
# HPS
|
||||
df['n_clutch'] = normalize_series(df['total_clutches'] / df['matches_played'])
|
||||
df['n_close_r'] = normalize_series(df['close_match_rating'].fillna(0))
|
||||
df['score_HPS'] = 0.5*df['n_clutch'] + 0.5*df['n_close_r']
|
||||
|
||||
# PTL
|
||||
df['n_pistol'] = normalize_series(df['pistol_kills'] / df['matches_played'])
|
||||
df['score_PTL'] = df['n_pistol']
|
||||
|
||||
return df
|
||||
|
||||
def main():
|
||||
conn = get_db_connection()
|
||||
try:
|
||||
df = load_data_and_calculate(conn)
|
||||
if df is None: return
|
||||
|
||||
# Debug: Print raw stats for checking T/CT issue
|
||||
print("\n--- Raw T/CT Stats Sample ---")
|
||||
if 'ct_kd' in df.columns:
|
||||
print(df[['steam_id_64', 'ct_kd', 't_kd']].head())
|
||||
else:
|
||||
print("CT/KD columns missing")
|
||||
|
||||
results = calculate_scores(df)
|
||||
|
||||
print("\n--- Final Dimension Scores (Top 5 by BAT) ---")
|
||||
cols = ['steam_id_64', 'score_BAT', 'score_STA', 'score_UTIL', 'score_TCT', 'score_HPS', 'score_PTL']
|
||||
print(results[cols].sort_values('score_BAT', ascending=False).head(5))
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
304
scripts/analyze_l3_full.py
Normal file
304
scripts/analyze_l3_full.py
Normal file
@@ -0,0 +1,304 @@
|
||||
import sqlite3
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import os
|
||||
|
||||
DB_L2_PATH = r'd:\Documents\trae_projects\yrtv\database\L2\L2_Main.sqlite'
|
||||
|
||||
def get_db_connection():
|
||||
conn = sqlite3.connect(DB_L2_PATH)
|
||||
conn.row_factory = sqlite3.Row
|
||||
return conn
|
||||
|
||||
def load_comprehensive_data(conn, min_matches=5):
|
||||
print("Loading Comprehensive Data...")
|
||||
|
||||
# 1. Base Player List & Basic Stats
|
||||
query_basic = """
|
||||
SELECT
|
||||
steam_id_64,
|
||||
COUNT(*) as total_matches,
|
||||
AVG(rating) as basic_avg_rating,
|
||||
AVG(kd_ratio) as basic_avg_kd,
|
||||
AVG(adr) as basic_avg_adr,
|
||||
AVG(kast) as basic_avg_kast,
|
||||
AVG(rws) as basic_avg_rws,
|
||||
SUM(headshot_count) as sum_headshot,
|
||||
SUM(kills) as sum_kills,
|
||||
SUM(deaths) as sum_deaths,
|
||||
SUM(first_kill) as sum_fk,
|
||||
SUM(first_death) as sum_fd,
|
||||
SUM(kill_2) as sum_2k,
|
||||
SUM(kill_3) as sum_3k,
|
||||
SUM(kill_4) as sum_4k,
|
||||
SUM(kill_5) as sum_5k,
|
||||
SUM(assisted_kill) as sum_assist,
|
||||
SUM(perfect_kill) as sum_perfect,
|
||||
SUM(revenge_kill) as sum_revenge,
|
||||
SUM(awp_kill) as sum_awp,
|
||||
SUM(jump_count) as sum_jump,
|
||||
SUM(clutch_1v1)+SUM(clutch_1v2)+SUM(clutch_1v3)+SUM(clutch_1v4)+SUM(clutch_1v5) as sum_clutches,
|
||||
SUM(throw_harm) as sum_util_dmg,
|
||||
SUM(flash_time) as sum_flash_time,
|
||||
SUM(flash_enemy) as sum_flash_enemy,
|
||||
SUM(flash_team) as sum_flash_team
|
||||
FROM fact_match_players
|
||||
GROUP BY steam_id_64
|
||||
HAVING COUNT(*) >= ?
|
||||
"""
|
||||
df = pd.read_sql_query(query_basic, conn, params=(min_matches,))
|
||||
|
||||
valid_ids = tuple(df['steam_id_64'].tolist())
|
||||
if not valid_ids:
|
||||
print("No players found.")
|
||||
return None
|
||||
placeholders = ','.join(['?'] * len(valid_ids))
|
||||
|
||||
# --- Derived Basic Features ---
|
||||
df['basic_headshot_rate'] = df['sum_headshot'] / df['sum_kills'].replace(0, 1)
|
||||
df['basic_avg_headshot_kills'] = df['sum_headshot'] / df['total_matches']
|
||||
df['basic_avg_first_kill'] = df['sum_fk'] / df['total_matches']
|
||||
df['basic_avg_first_death'] = df['sum_fd'] / df['total_matches']
|
||||
df['basic_first_kill_rate'] = df['sum_fk'] / (df['sum_fk'] + df['sum_fd']).replace(0, 1) # Opening Success
|
||||
df['basic_first_death_rate'] = df['sum_fd'] / (df['sum_fk'] + df['sum_fd']).replace(0, 1)
|
||||
df['basic_avg_kill_2'] = df['sum_2k'] / df['total_matches']
|
||||
df['basic_avg_kill_3'] = df['sum_3k'] / df['total_matches']
|
||||
df['basic_avg_kill_4'] = df['sum_4k'] / df['total_matches']
|
||||
df['basic_avg_kill_5'] = df['sum_5k'] / df['total_matches']
|
||||
df['basic_avg_assisted_kill'] = df['sum_assist'] / df['total_matches']
|
||||
df['basic_avg_perfect_kill'] = df['sum_perfect'] / df['total_matches']
|
||||
df['basic_avg_revenge_kill'] = df['sum_revenge'] / df['total_matches']
|
||||
df['basic_avg_awp_kill'] = df['sum_awp'] / df['total_matches']
|
||||
df['basic_avg_jump_count'] = df['sum_jump'] / df['total_matches']
|
||||
|
||||
# 2. STA (Stability) - Detailed
|
||||
print("Calculating STA...")
|
||||
query_sta = f"""
|
||||
SELECT mp.steam_id_64, mp.rating, mp.is_win, m.start_time
|
||||
FROM fact_match_players mp
|
||||
JOIN fact_matches m ON mp.match_id = m.match_id
|
||||
WHERE mp.steam_id_64 IN ({placeholders})
|
||||
ORDER BY mp.steam_id_64, m.start_time
|
||||
"""
|
||||
df_matches = pd.read_sql_query(query_sta, conn, params=valid_ids)
|
||||
|
||||
sta_list = []
|
||||
for pid, group in df_matches.groupby('steam_id_64'):
|
||||
# Last 30
|
||||
last_30 = group.tail(30)
|
||||
sta_last_30 = last_30['rating'].mean()
|
||||
# Win/Loss
|
||||
sta_win = group[group['is_win']==1]['rating'].mean()
|
||||
sta_loss = group[group['is_win']==0]['rating'].mean()
|
||||
# Volatility (Last 10)
|
||||
sta_vol = group.tail(10)['rating'].std()
|
||||
|
||||
# Time Decay (Simulated): Avg rating of 1st match of day vs >3rd match of day
|
||||
# Need date conversion.
|
||||
group['date'] = pd.to_datetime(group['start_time'], unit='s').dt.date
|
||||
daily_counts = group.groupby('date').cumcount()
|
||||
# Early: index 0, Late: index >= 2
|
||||
early_ratings = group[daily_counts == 0]['rating']
|
||||
late_ratings = group[daily_counts >= 2]['rating']
|
||||
|
||||
if len(late_ratings) > 0:
|
||||
sta_fatigue = early_ratings.mean() - late_ratings.mean() # Positive means fatigue (drop)
|
||||
else:
|
||||
sta_fatigue = 0
|
||||
|
||||
sta_list.append({
|
||||
'steam_id_64': pid,
|
||||
'sta_last_30_rating': sta_last_30,
|
||||
'sta_win_rating': sta_win,
|
||||
'sta_loss_rating': sta_loss,
|
||||
'sta_rating_volatility': sta_vol,
|
||||
'sta_fatigue_decay': sta_fatigue
|
||||
})
|
||||
df_sta = pd.DataFrame(sta_list)
|
||||
df = df.merge(df_sta, on='steam_id_64', how='left')
|
||||
|
||||
# 3. BAT (Battle) - Detailed
|
||||
print("Calculating BAT...")
|
||||
# Need Match ELO
|
||||
query_bat = f"""
|
||||
SELECT mp.steam_id_64, mp.kd_ratio, mp.entry_kills, mp.entry_deaths,
|
||||
(SELECT AVG(group_origin_elo) FROM fact_match_teams fmt WHERE fmt.match_id = mp.match_id AND group_origin_elo > 0) as match_elo
|
||||
FROM fact_match_players mp
|
||||
WHERE mp.steam_id_64 IN ({placeholders})
|
||||
"""
|
||||
df_bat_raw = pd.read_sql_query(query_bat, conn, params=valid_ids)
|
||||
|
||||
bat_list = []
|
||||
for pid, group in df_bat_raw.groupby('steam_id_64'):
|
||||
avg_elo = group['match_elo'].mean()
|
||||
if pd.isna(avg_elo): avg_elo = 1500
|
||||
|
||||
high_elo_kd = group[group['match_elo'] > avg_elo]['kd_ratio'].mean()
|
||||
low_elo_kd = group[group['match_elo'] <= avg_elo]['kd_ratio'].mean()
|
||||
|
||||
sum_entry_k = group['entry_kills'].sum()
|
||||
sum_entry_d = group['entry_deaths'].sum()
|
||||
duel_win_rate = sum_entry_k / (sum_entry_k + sum_entry_d) if (sum_entry_k+sum_entry_d) > 0 else 0
|
||||
|
||||
bat_list.append({
|
||||
'steam_id_64': pid,
|
||||
'bat_kd_diff_high_elo': high_elo_kd, # Higher is better
|
||||
'bat_kd_diff_low_elo': low_elo_kd,
|
||||
'bat_avg_duel_win_rate': duel_win_rate
|
||||
})
|
||||
df_bat = pd.DataFrame(bat_list)
|
||||
df = df.merge(df_bat, on='steam_id_64', how='left')
|
||||
|
||||
# 4. HPS (Pressure) - Detailed
|
||||
print("Calculating HPS...")
|
||||
# Complex query for Match Point and Pressure situations
|
||||
# Logic: Round score diff.
|
||||
# Since we don't have round-by-round player stats in L2 easily (economy table is sparse on stats),
|
||||
# We use Matches for "Close Match" and "Comeback"
|
||||
|
||||
# Comeback/Close Match Logic on MATCH level
|
||||
query_hps_match = f"""
|
||||
SELECT mp.steam_id_64, mp.kd_ratio, mp.rating, m.score_team1, m.score_team2, mp.team_id, m.winner_team
|
||||
FROM fact_match_players mp
|
||||
JOIN fact_matches m ON mp.match_id = m.match_id
|
||||
WHERE mp.steam_id_64 IN ({placeholders})
|
||||
"""
|
||||
df_hps_raw = pd.read_sql_query(query_hps_match, conn, params=valid_ids)
|
||||
|
||||
hps_list = []
|
||||
for pid, group in df_hps_raw.groupby('steam_id_64'):
|
||||
# Close Match: Score diff <= 3
|
||||
group['score_diff'] = abs(group['score_team1'] - group['score_team2'])
|
||||
close_rating = group[group['score_diff'] <= 3]['rating'].mean()
|
||||
|
||||
# Comeback: Won match where score was close?
|
||||
# Actually without round history, we can't define "Comeback" (was behind then won).
|
||||
# We can define "Underdog Win": Won when ELO was lower? Or just Close Win.
|
||||
# Let's use Close Match Rating as primary HPS metric from matches.
|
||||
|
||||
hps_list.append({
|
||||
'steam_id_64': pid,
|
||||
'hps_close_match_rating': close_rating
|
||||
})
|
||||
df_hps = pd.DataFrame(hps_list)
|
||||
|
||||
# HPS Clutch (from Basic)
|
||||
df['hps_clutch_rate'] = df['sum_clutches'] / df['total_matches']
|
||||
|
||||
df = df.merge(df_hps, on='steam_id_64', how='left')
|
||||
|
||||
# 5. PTL (Pistol)
|
||||
print("Calculating PTL...")
|
||||
# R1/R13 Kills
|
||||
query_ptl = f"""
|
||||
SELECT ev.attacker_steam_id as steam_id_64, COUNT(*) as pistol_kills
|
||||
FROM fact_round_events ev
|
||||
WHERE ev.event_type = 'kill' AND ev.round_num IN (1, 13)
|
||||
AND ev.attacker_steam_id IN ({placeholders})
|
||||
GROUP BY ev.attacker_steam_id
|
||||
"""
|
||||
df_ptl = pd.read_sql_query(query_ptl, conn, params=valid_ids)
|
||||
# Pistol Win Rate (Team)
|
||||
# Need to join rounds. Too slow?
|
||||
# Simplify: Just use Pistol Kills per Match (normalized)
|
||||
|
||||
df = df.merge(df_ptl, on='steam_id_64', how='left')
|
||||
df['ptl_pistol_kills_per_match'] = df['pistol_kills'] / df['total_matches']
|
||||
|
||||
# 6. T/CT
|
||||
print("Calculating T/CT...")
|
||||
query_ct = f"SELECT steam_id_64, AVG(rating) as ct_rating, AVG(kd_ratio) as ct_kd FROM fact_match_players_ct WHERE steam_id_64 IN ({placeholders}) GROUP BY steam_id_64"
|
||||
query_t = f"SELECT steam_id_64, AVG(rating) as t_rating, AVG(kd_ratio) as t_kd FROM fact_match_players_t WHERE steam_id_64 IN ({placeholders}) GROUP BY steam_id_64"
|
||||
df_ct = pd.read_sql_query(query_ct, conn, params=valid_ids)
|
||||
df_t = pd.read_sql_query(query_t, conn, params=valid_ids)
|
||||
df = df.merge(df_ct, on='steam_id_64', how='left').merge(df_t, on='steam_id_64', how='left')
|
||||
|
||||
# 7. UTIL
|
||||
print("Calculating UTIL...")
|
||||
df['util_avg_dmg'] = df['sum_util_dmg'] / df['total_matches']
|
||||
df['util_avg_flash_time'] = df['sum_flash_time'] / df['total_matches']
|
||||
|
||||
return df
|
||||
|
||||
def normalize(series):
|
||||
s = series.fillna(series.mean())
|
||||
if s.max() == s.min(): return pd.Series([50]*len(s), index=s.index)
|
||||
return (s - s.min()) / (s.max() - s.min()) * 100
|
||||
|
||||
def calculate_full_scores(df):
|
||||
df = df.copy()
|
||||
|
||||
# --- BAT Calculation ---
|
||||
# Components: Rating, KD, ADR, KAST, Duel Win Rate, High ELO KD
|
||||
# Weights: Rating(30), KD(20), ADR(15), KAST(10), Duel(15), HighELO(10)
|
||||
df['n_bat_rating'] = normalize(df['basic_avg_rating'])
|
||||
df['n_bat_kd'] = normalize(df['basic_avg_kd'])
|
||||
df['n_bat_adr'] = normalize(df['basic_avg_adr'])
|
||||
df['n_bat_kast'] = normalize(df['basic_avg_kast'])
|
||||
df['n_bat_duel'] = normalize(df['bat_avg_duel_win_rate'])
|
||||
df['n_bat_high'] = normalize(df['bat_kd_diff_high_elo'])
|
||||
|
||||
df['score_BAT'] = (0.3*df['n_bat_rating'] + 0.2*df['n_bat_kd'] + 0.15*df['n_bat_adr'] +
|
||||
0.1*df['n_bat_kast'] + 0.15*df['n_bat_duel'] + 0.1*df['n_bat_high'])
|
||||
|
||||
# --- STA Calculation ---
|
||||
# Components: Volatility (Neg), Win Rating, Loss Rating, Fatigue (Neg)
|
||||
# Weights: Consistency(40), WinPerf(20), LossPerf(30), Fatigue(10)
|
||||
df['n_sta_vol'] = normalize(df['sta_rating_volatility']) # Lower is better -> 100 - X
|
||||
df['n_sta_win'] = normalize(df['sta_win_rating'])
|
||||
df['n_sta_loss'] = normalize(df['sta_loss_rating'])
|
||||
df['n_sta_fat'] = normalize(df['sta_fatigue_decay']) # Lower (less drop) is better -> 100 - X
|
||||
|
||||
df['score_STA'] = (0.4*(100-df['n_sta_vol']) + 0.2*df['n_sta_win'] +
|
||||
0.3*df['n_sta_loss'] + 0.1*(100-df['n_sta_fat']))
|
||||
|
||||
# --- HPS Calculation ---
|
||||
# Components: Clutch Rate, Close Match Rating
|
||||
df['n_hps_clutch'] = normalize(df['hps_clutch_rate'])
|
||||
df['n_hps_close'] = normalize(df['hps_close_match_rating'])
|
||||
|
||||
df['score_HPS'] = 0.5*df['n_hps_clutch'] + 0.5*df['n_hps_close']
|
||||
|
||||
# --- PTL Calculation ---
|
||||
# Components: Pistol Kills/Match
|
||||
df['score_PTL'] = normalize(df['ptl_pistol_kills_per_match'])
|
||||
|
||||
# --- T/CT Calculation ---
|
||||
# Components: CT Rating, T Rating
|
||||
df['n_ct'] = normalize(df['ct_rating'])
|
||||
df['n_t'] = normalize(df['t_rating'])
|
||||
df['score_TCT'] = 0.5*df['n_ct'] + 0.5*df['n_t']
|
||||
|
||||
# --- UTIL Calculation ---
|
||||
# Components: Dmg, Flash Time
|
||||
df['n_util_dmg'] = normalize(df['util_avg_dmg'])
|
||||
df['n_util_flash'] = normalize(df['util_avg_flash_time'])
|
||||
df['score_UTIL'] = 0.6*df['n_util_dmg'] + 0.4*df['n_util_flash']
|
||||
|
||||
return df
|
||||
|
||||
def main():
|
||||
conn = get_db_connection()
|
||||
try:
|
||||
df = load_comprehensive_data(conn)
|
||||
if df is None: return
|
||||
|
||||
results = calculate_full_scores(df)
|
||||
|
||||
print("\n--- Final Full Scores ---")
|
||||
cols = ['steam_id_64', 'score_BAT', 'score_STA', 'score_UTIL', 'score_TCT', 'score_HPS', 'score_PTL']
|
||||
print(results[cols].sort_values('score_BAT', ascending=False).head(5))
|
||||
|
||||
print("\n--- Available Features Used ---")
|
||||
print("BAT: Rating, KD, ADR, KAST, Duel Win Rate, High ELO Performance")
|
||||
print("STA: Volatility, Win Rating, Loss Rating, Fatigue Decay")
|
||||
print("HPS: Clutch Rate, Close Match Rating")
|
||||
print("PTL: Pistol Kills per Match")
|
||||
print("T/CT: CT Rating, T Rating")
|
||||
print("UTIL: Util Dmg, Flash Duration")
|
||||
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
499
scripts/analyze_l3_ultimate.py
Normal file
499
scripts/analyze_l3_ultimate.py
Normal file
@@ -0,0 +1,499 @@
|
||||
import sqlite3
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import os
|
||||
|
||||
DB_L2_PATH = r'd:\Documents\trae_projects\yrtv\database\L2\L2_Main.sqlite'
|
||||
|
||||
def get_db_connection():
|
||||
conn = sqlite3.connect(DB_L2_PATH)
|
||||
conn.row_factory = sqlite3.Row
|
||||
return conn
|
||||
|
||||
def safe_div(a, b):
|
||||
if b == 0: return 0
|
||||
return a / b
|
||||
|
||||
def load_and_calculate_ultimate(conn, min_matches=5):
|
||||
print("Loading Ultimate Data Set...")
|
||||
|
||||
# 1. Basic Stats (Already have)
|
||||
query_basic = """
|
||||
SELECT
|
||||
steam_id_64,
|
||||
COUNT(*) as matches_played,
|
||||
SUM(round_total) as rounds_played,
|
||||
AVG(rating) as basic_avg_rating,
|
||||
AVG(kd_ratio) as basic_avg_kd,
|
||||
AVG(adr) as basic_avg_adr,
|
||||
AVG(kast) as basic_avg_kast,
|
||||
AVG(rws) as basic_avg_rws,
|
||||
SUM(headshot_count) as sum_hs,
|
||||
SUM(kills) as sum_kills,
|
||||
SUM(deaths) as sum_deaths,
|
||||
SUM(first_kill) as sum_fk,
|
||||
SUM(first_death) as sum_fd,
|
||||
SUM(clutch_1v1) as sum_1v1,
|
||||
SUM(clutch_1v2) as sum_1v2,
|
||||
SUM(clutch_1v3) + SUM(clutch_1v4) + SUM(clutch_1v5) as sum_1v3p,
|
||||
SUM(kill_2) as sum_2k,
|
||||
SUM(kill_3) as sum_3k,
|
||||
SUM(kill_4) as sum_4k,
|
||||
SUM(kill_5) as sum_5k,
|
||||
SUM(assisted_kill) as sum_assist,
|
||||
SUM(perfect_kill) as sum_perfect,
|
||||
SUM(revenge_kill) as sum_revenge,
|
||||
SUM(awp_kill) as sum_awp,
|
||||
SUM(jump_count) as sum_jump,
|
||||
SUM(throw_harm) as sum_util_dmg,
|
||||
SUM(flash_time) as sum_flash_time,
|
||||
SUM(flash_enemy) as sum_flash_enemy,
|
||||
SUM(flash_team) as sum_flash_team
|
||||
FROM fact_match_players
|
||||
GROUP BY steam_id_64
|
||||
HAVING COUNT(*) >= ?
|
||||
"""
|
||||
df = pd.read_sql_query(query_basic, conn, params=(min_matches,))
|
||||
valid_ids = tuple(df['steam_id_64'].tolist())
|
||||
if not valid_ids: return None
|
||||
placeholders = ','.join(['?'] * len(valid_ids))
|
||||
|
||||
# --- Basic Derived ---
|
||||
df['basic_headshot_rate'] = df['sum_hs'] / df['sum_kills'].replace(0, 1)
|
||||
df['basic_avg_headshot_kills'] = df['sum_hs'] / df['matches_played']
|
||||
df['basic_avg_first_kill'] = df['sum_fk'] / df['matches_played']
|
||||
df['basic_avg_first_death'] = df['sum_fd'] / df['matches_played']
|
||||
df['basic_first_kill_rate'] = df['sum_fk'] / (df['sum_fk'] + df['sum_fd']).replace(0, 1)
|
||||
df['basic_first_death_rate'] = df['sum_fd'] / (df['sum_fk'] + df['sum_fd']).replace(0, 1)
|
||||
df['basic_avg_kill_2'] = df['sum_2k'] / df['matches_played']
|
||||
df['basic_avg_kill_3'] = df['sum_3k'] / df['matches_played']
|
||||
df['basic_avg_kill_4'] = df['sum_4k'] / df['matches_played']
|
||||
df['basic_avg_kill_5'] = df['sum_5k'] / df['matches_played']
|
||||
df['basic_avg_assisted_kill'] = df['sum_assist'] / df['matches_played']
|
||||
df['basic_avg_perfect_kill'] = df['sum_perfect'] / df['matches_played']
|
||||
df['basic_avg_revenge_kill'] = df['sum_revenge'] / df['matches_played']
|
||||
df['basic_avg_awp_kill'] = df['sum_awp'] / df['matches_played']
|
||||
df['basic_avg_jump_count'] = df['sum_jump'] / df['matches_played']
|
||||
|
||||
# 2. STA - Detailed Time Series
|
||||
print("Calculating STA (Detailed)...")
|
||||
query_sta = f"""
|
||||
SELECT mp.steam_id_64, mp.rating, mp.is_win, m.start_time, m.duration
|
||||
FROM fact_match_players mp
|
||||
JOIN fact_matches m ON mp.match_id = m.match_id
|
||||
WHERE mp.steam_id_64 IN ({placeholders})
|
||||
ORDER BY mp.steam_id_64, m.start_time
|
||||
"""
|
||||
df_matches = pd.read_sql_query(query_sta, conn, params=valid_ids)
|
||||
|
||||
sta_list = []
|
||||
for pid, group in df_matches.groupby('steam_id_64'):
|
||||
group = group.sort_values('start_time')
|
||||
# Last 30
|
||||
last_30 = group.tail(30)
|
||||
sta_last_30 = last_30['rating'].mean()
|
||||
# Win/Loss
|
||||
sta_win = group[group['is_win']==1]['rating'].mean()
|
||||
sta_loss = group[group['is_win']==0]['rating'].mean()
|
||||
# Volatility
|
||||
sta_vol = group.tail(10)['rating'].std()
|
||||
# Time Correlation (Duration vs Rating)
|
||||
sta_time_corr = group['duration'].corr(group['rating']) if len(group) > 2 else 0
|
||||
# Fatigue
|
||||
group['date'] = pd.to_datetime(group['start_time'], unit='s').dt.date
|
||||
daily = group.groupby('date')['rating'].agg(['first', 'last', 'count'])
|
||||
daily_fatigue = daily[daily['count'] >= 3]
|
||||
if len(daily_fatigue) > 0:
|
||||
fatigue_decay = (daily_fatigue['first'] - daily_fatigue['last']).mean()
|
||||
else:
|
||||
fatigue_decay = 0
|
||||
|
||||
sta_list.append({
|
||||
'steam_id_64': pid,
|
||||
'sta_last_30_rating': sta_last_30,
|
||||
'sta_win_rating': sta_win,
|
||||
'sta_loss_rating': sta_loss,
|
||||
'sta_rating_volatility': sta_vol,
|
||||
'sta_time_rating_corr': sta_time_corr,
|
||||
'sta_fatigue_decay': fatigue_decay
|
||||
})
|
||||
df = df.merge(pd.DataFrame(sta_list), on='steam_id_64', how='left')
|
||||
|
||||
# 3. BAT - Distance & Advanced
|
||||
print("Calculating BAT (Distance & Context)...")
|
||||
# Distance Logic: Get all kills with positions
|
||||
# We need to map positions.
|
||||
query_dist = f"""
|
||||
SELECT attacker_steam_id as steam_id_64,
|
||||
attacker_pos_x, attacker_pos_y, attacker_pos_z,
|
||||
victim_pos_x, victim_pos_y, victim_pos_z
|
||||
FROM fact_round_events
|
||||
WHERE event_type = 'kill'
|
||||
AND attacker_steam_id IN ({placeholders})
|
||||
AND attacker_pos_x IS NOT NULL AND victim_pos_x IS NOT NULL
|
||||
"""
|
||||
# Note: This might be heavy. If memory issue, sample or chunk.
|
||||
try:
|
||||
df_dist = pd.read_sql_query(query_dist, conn, params=valid_ids)
|
||||
if not df_dist.empty:
|
||||
# Calc Euclidian Distance
|
||||
df_dist['dist'] = np.sqrt(
|
||||
(df_dist['attacker_pos_x'] - df_dist['victim_pos_x'])**2 +
|
||||
(df_dist['attacker_pos_y'] - df_dist['victim_pos_y'])**2 +
|
||||
(df_dist['attacker_pos_z'] - df_dist['victim_pos_z'])**2
|
||||
)
|
||||
# Units: 1 unit ~ 1 inch.
|
||||
# Close: < 500 (~12m)
|
||||
# Mid: 500 - 1500 (~12m - 38m)
|
||||
# Far: > 1500
|
||||
df_dist['is_close'] = df_dist['dist'] < 500
|
||||
df_dist['is_mid'] = (df_dist['dist'] >= 500) & (df_dist['dist'] <= 1500)
|
||||
df_dist['is_far'] = df_dist['dist'] > 1500
|
||||
|
||||
bat_dist = df_dist.groupby('steam_id_64').agg({
|
||||
'is_close': 'mean', # % of kills that are close
|
||||
'is_mid': 'mean',
|
||||
'is_far': 'mean'
|
||||
}).reset_index()
|
||||
bat_dist.columns = ['steam_id_64', 'bat_kill_share_close', 'bat_kill_share_mid', 'bat_kill_share_far']
|
||||
|
||||
# Note: "Win Rate" by distance requires Deaths by distance.
|
||||
# We can try to get deaths too, but for now Share of Kills is a good proxy for "Preference/Style"
|
||||
# To get "Win Rate", we need to know how many duels occurred at that distance.
|
||||
# Approximation: Win Rate = Kills_at_dist / (Kills_at_dist + Deaths_at_dist)
|
||||
|
||||
# Fetch Deaths
|
||||
query_dist_d = f"""
|
||||
SELECT victim_steam_id as steam_id_64,
|
||||
attacker_pos_x, attacker_pos_y, attacker_pos_z,
|
||||
victim_pos_x, victim_pos_y, victim_pos_z
|
||||
FROM fact_round_events
|
||||
WHERE event_type = 'kill'
|
||||
AND victim_steam_id IN ({placeholders})
|
||||
AND attacker_pos_x IS NOT NULL AND victim_pos_x IS NOT NULL
|
||||
"""
|
||||
df_dist_d = pd.read_sql_query(query_dist_d, conn, params=valid_ids)
|
||||
df_dist_d['dist'] = np.sqrt(
|
||||
(df_dist_d['attacker_pos_x'] - df_dist_d['victim_pos_x'])**2 +
|
||||
(df_dist_d['attacker_pos_y'] - df_dist_d['victim_pos_y'])**2 +
|
||||
(df_dist_d['attacker_pos_z'] - df_dist_d['victim_pos_z'])**2
|
||||
)
|
||||
|
||||
# Aggregate Kills Counts
|
||||
k_counts = df_dist.groupby('steam_id_64').agg(
|
||||
k_close=('is_close', 'sum'),
|
||||
k_mid=('is_mid', 'sum'),
|
||||
k_far=('is_far', 'sum')
|
||||
)
|
||||
# Aggregate Deaths Counts
|
||||
df_dist_d['is_close'] = df_dist_d['dist'] < 500
|
||||
df_dist_d['is_mid'] = (df_dist_d['dist'] >= 500) & (df_dist_d['dist'] <= 1500)
|
||||
df_dist_d['is_far'] = df_dist_d['dist'] > 1500
|
||||
d_counts = df_dist_d.groupby('steam_id_64').agg(
|
||||
d_close=('is_close', 'sum'),
|
||||
d_mid=('is_mid', 'sum'),
|
||||
d_far=('is_far', 'sum')
|
||||
)
|
||||
|
||||
# Merge
|
||||
bat_rates = k_counts.join(d_counts, how='outer').fillna(0)
|
||||
bat_rates['bat_win_rate_close'] = bat_rates['k_close'] / (bat_rates['k_close'] + bat_rates['d_close']).replace(0, 1)
|
||||
bat_rates['bat_win_rate_mid'] = bat_rates['k_mid'] / (bat_rates['k_mid'] + bat_rates['d_mid']).replace(0, 1)
|
||||
bat_rates['bat_win_rate_far'] = bat_rates['k_far'] / (bat_rates['k_far'] + bat_rates['d_far']).replace(0, 1)
|
||||
bat_rates['bat_win_rate_vs_all'] = (bat_rates['k_close']+bat_rates['k_mid']+bat_rates['k_far']) / (bat_rates['k_close']+bat_rates['d_close']+bat_rates['k_mid']+bat_rates['d_mid']+bat_rates['k_far']+bat_rates['d_far']).replace(0, 1)
|
||||
|
||||
df = df.merge(bat_rates[['bat_win_rate_close', 'bat_win_rate_mid', 'bat_win_rate_far', 'bat_win_rate_vs_all']], on='steam_id_64', how='left')
|
||||
else:
|
||||
print("No position data found.")
|
||||
except Exception as e:
|
||||
print(f"Dist calculation error: {e}")
|
||||
|
||||
# High/Low ELO KD
|
||||
query_elo = f"""
|
||||
SELECT mp.steam_id_64, mp.kd_ratio,
|
||||
(SELECT AVG(group_origin_elo) FROM fact_match_teams fmt WHERE fmt.match_id = mp.match_id AND group_origin_elo > 0) as elo
|
||||
FROM fact_match_players mp
|
||||
WHERE mp.steam_id_64 IN ({placeholders})
|
||||
"""
|
||||
df_elo = pd.read_sql_query(query_elo, conn, params=valid_ids)
|
||||
elo_list = []
|
||||
for pid, group in df_elo.groupby('steam_id_64'):
|
||||
avg = group['elo'].mean()
|
||||
if pd.isna(avg): avg = 1000
|
||||
elo_list.append({
|
||||
'steam_id_64': pid,
|
||||
'bat_kd_diff_high_elo': group[group['elo'] > avg]['kd_ratio'].mean(),
|
||||
'bat_kd_diff_low_elo': group[group['elo'] <= avg]['kd_ratio'].mean()
|
||||
})
|
||||
df = df.merge(pd.DataFrame(elo_list), on='steam_id_64', how='left')
|
||||
|
||||
# Avg Duel Freq
|
||||
df['bat_avg_duel_freq'] = (df['sum_fk'] + df['sum_fd']) / df['rounds_played']
|
||||
|
||||
# 4. HPS - High Pressure Contexts
|
||||
print("Calculating HPS (Contexts)...")
|
||||
# We need round-by-round score evolution.
|
||||
# Join rounds and economy(side) and matches
|
||||
query_hps_ctx = f"""
|
||||
SELECT r.match_id, r.round_num, r.ct_score, r.t_score, r.winner_side,
|
||||
m.score_team1, m.score_team2, m.winner_team,
|
||||
e.steam_id_64, e.side as player_side,
|
||||
(SELECT COUNT(*) FROM fact_round_events ev WHERE ev.match_id=r.match_id AND ev.round_num=r.round_num AND ev.attacker_steam_id=e.steam_id_64 AND ev.event_type='kill') as kills,
|
||||
(SELECT COUNT(*) FROM fact_round_events ev WHERE ev.match_id=r.match_id AND ev.round_num=r.round_num AND ev.victim_steam_id=e.steam_id_64 AND ev.event_type='kill') as deaths
|
||||
FROM fact_rounds r
|
||||
JOIN fact_matches m ON r.match_id = m.match_id
|
||||
JOIN fact_round_player_economy e ON r.match_id = e.match_id AND r.round_num = e.round_num
|
||||
WHERE e.steam_id_64 IN ({placeholders})
|
||||
"""
|
||||
# This is heavy.
|
||||
try:
|
||||
# Optimization: Process per match or use SQL aggregation?
|
||||
# SQL aggregation for specific conditions is better.
|
||||
|
||||
# 4.1 Match Point Win Rate
|
||||
# Condition: (player_side='CT' AND ct_score >= 12) OR (player_side='T' AND t_score >= 12) (Assuming MR12)
|
||||
# Or just max score of match?
|
||||
# Let's approximate: Rounds where total_score >= 23 (MR12) or 29 (MR15)
|
||||
# Actually, let's use: round_num >= match.round_total - 1? No.
|
||||
# Use: Rounds where One Team Score = Match Win Score - 1.
|
||||
# Since we don't know MR12/MR15 per match easily (some are short), check `game_mode`.
|
||||
# Fallback: Rounds where `ct_score` or `t_score` >= 12.
|
||||
|
||||
# 4.2 Pressure Entry Rate (Losing Streak)
|
||||
# Condition: Team score < Enemy score - 3.
|
||||
|
||||
# 4.3 Momentum Multi-kill (Winning Streak)
|
||||
# Condition: Team score > Enemy score + 3.
|
||||
|
||||
# Let's load a simplified dataframe of rounds
|
||||
df_rounds = pd.read_sql_query(query_hps_ctx, conn, params=valid_ids)
|
||||
|
||||
hps_stats = []
|
||||
for pid, group in df_rounds.groupby('steam_id_64'):
|
||||
# Determine Player Team Score and Enemy Team Score
|
||||
# If player_side == 'CT', player_score = ct_score
|
||||
group['my_score'] = np.where(group['player_side'] == 'CT', group['ct_score'], group['t_score'])
|
||||
group['enemy_score'] = np.where(group['player_side'] == 'CT', group['t_score'], group['ct_score'])
|
||||
|
||||
# Match Point (My team or Enemy team at match point)
|
||||
# Simple heuristic: Score >= 12
|
||||
is_match_point = (group['my_score'] >= 12) | (group['enemy_score'] >= 12)
|
||||
mp_rounds = group[is_match_point]
|
||||
# Did we win?
|
||||
# winner_side matches player_side
|
||||
mp_wins = mp_rounds[mp_rounds['winner_side'] == mp_rounds['player_side']]
|
||||
mp_win_rate = len(mp_wins) / len(mp_rounds) if len(mp_rounds) > 0 else 0.5
|
||||
|
||||
# Pressure (Losing by 3+)
|
||||
is_pressure = (group['enemy_score'] - group['my_score']) >= 3
|
||||
# Entry Rate in pressure? Need FK data.
|
||||
# We only loaded kills. Let's use Kills per round in pressure.
|
||||
pressure_kpr = group[is_pressure]['kills'].mean() if len(group[is_pressure]) > 0 else 0
|
||||
|
||||
# Momentum (Winning by 3+)
|
||||
is_momentum = (group['my_score'] - group['enemy_score']) >= 3
|
||||
# Multi-kill rate (>=2 kills)
|
||||
momentum_rounds = group[is_momentum]
|
||||
momentum_multikills = len(momentum_rounds[momentum_rounds['kills'] >= 2])
|
||||
momentum_mk_rate = momentum_multikills / len(momentum_rounds) if len(momentum_rounds) > 0 else 0
|
||||
|
||||
# Comeback KD Diff
|
||||
# Avg KD in Pressure rounds vs Avg KD overall
|
||||
pressure_deaths = group[is_pressure]['deaths'].sum()
|
||||
pressure_kills = group[is_pressure]['kills'].sum()
|
||||
pressure_kd = pressure_kills / pressure_deaths if pressure_deaths > 0 else pressure_kills
|
||||
|
||||
overall_deaths = group['deaths'].sum()
|
||||
overall_kills = group['kills'].sum()
|
||||
overall_kd = overall_kills / overall_deaths if overall_deaths > 0 else overall_kills
|
||||
|
||||
comeback_diff = pressure_kd - overall_kd
|
||||
|
||||
hps_stats.append({
|
||||
'steam_id_64': pid,
|
||||
'hps_match_point_win_rate': mp_win_rate,
|
||||
'hps_pressure_entry_rate': pressure_kpr, # Proxy
|
||||
'hps_momentum_multikill_rate': momentum_mk_rate,
|
||||
'hps_comeback_kd_diff': comeback_diff,
|
||||
'hps_losing_streak_kd_diff': comeback_diff # Same metric
|
||||
})
|
||||
|
||||
df = df.merge(pd.DataFrame(hps_stats), on='steam_id_64', how='left')
|
||||
|
||||
# 4.4 Clutch Win Rates (Detailed)
|
||||
df['hps_clutch_win_rate_1v1'] = df['sum_1v1'] / df['matches_played'] # Normalizing by match for now, ideal is by 1v1 opportunities
|
||||
df['hps_clutch_win_rate_1v2'] = df['sum_1v2'] / df['matches_played']
|
||||
df['hps_clutch_win_rate_1v3_plus'] = df['sum_1v3p'] / df['matches_played']
|
||||
|
||||
# 4.5 Close Match Rating (from previous)
|
||||
# ... (Already have logic in previous script, reusing)
|
||||
|
||||
except Exception as e:
|
||||
print(f"HPS Error: {e}")
|
||||
|
||||
# 5. PTL - Pistol Detailed
|
||||
print("Calculating PTL...")
|
||||
# Filter Round 1, 13 (and 16 for MR15?)
|
||||
# Just use 1 and 13 (common for MR12)
|
||||
query_ptl = f"""
|
||||
SELECT
|
||||
e.steam_id_64,
|
||||
(SELECT COUNT(*) FROM fact_round_events ev WHERE ev.match_id=e.match_id AND ev.round_num=e.round_num AND ev.attacker_steam_id=e.steam_id_64 AND ev.event_type='kill') as kills,
|
||||
(SELECT COUNT(*) FROM fact_round_events ev WHERE ev.match_id=e.match_id AND ev.round_num=e.round_num AND ev.victim_steam_id=e.steam_id_64 AND ev.event_type='kill') as deaths,
|
||||
r.winner_side, e.side as player_side,
|
||||
e.equipment_value
|
||||
FROM fact_round_player_economy e
|
||||
JOIN fact_rounds r ON e.match_id = r.match_id AND e.round_num = r.round_num
|
||||
WHERE e.steam_id_64 IN ({placeholders})
|
||||
AND e.round_num IN (1, 13)
|
||||
"""
|
||||
try:
|
||||
df_ptl_raw = pd.read_sql_query(query_ptl, conn, params=valid_ids)
|
||||
ptl_stats = []
|
||||
for pid, group in df_ptl_raw.groupby('steam_id_64'):
|
||||
kills = group['kills'].sum()
|
||||
deaths = group['deaths'].sum()
|
||||
kd = kills / deaths if deaths > 0 else kills
|
||||
|
||||
wins = len(group[group['winner_side'] == group['player_side']])
|
||||
win_rate = wins / len(group)
|
||||
|
||||
multikills = len(group[group['kills'] >= 2])
|
||||
|
||||
# Util Efficiency: Not easy here.
|
||||
|
||||
ptl_stats.append({
|
||||
'steam_id_64': pid,
|
||||
'ptl_pistol_kills': kills, # Total? Or Avg? Schema says REAL. Let's use Avg per Match later.
|
||||
'ptl_pistol_kd': kd,
|
||||
'ptl_pistol_win_rate': win_rate,
|
||||
'ptl_pistol_multikills': multikills
|
||||
})
|
||||
|
||||
df_ptl = pd.DataFrame(ptl_stats)
|
||||
df_ptl['ptl_pistol_kills'] = df_ptl['ptl_pistol_kills'] / df['matches_played'].mean() # Approximate
|
||||
df = df.merge(df_ptl, on='steam_id_64', how='left')
|
||||
|
||||
except Exception as e:
|
||||
print(f"PTL Error: {e}")
|
||||
|
||||
# 6. T/CT & UTIL (Straightforward)
|
||||
print("Calculating T/CT & UTIL...")
|
||||
# T/CT Side Stats
|
||||
query_side = f"""
|
||||
SELECT steam_id_64,
|
||||
SUM(CASE WHEN side='CT' THEN 1 ELSE 0 END) as ct_rounds,
|
||||
SUM(CASE WHEN side='T' THEN 1 ELSE 0 END) as t_rounds
|
||||
FROM fact_round_player_economy
|
||||
WHERE steam_id_64 IN ({placeholders})
|
||||
GROUP BY steam_id_64
|
||||
"""
|
||||
# Combine with aggregated ratings from fact_match_players_ct/t
|
||||
query_side_r = f"""
|
||||
SELECT steam_id_64, AVG(rating) as ct_rating, AVG(kd_ratio) as ct_kd, SUM(first_kill) as ct_fk
|
||||
FROM fact_match_players_ct WHERE steam_id_64 IN ({placeholders}) GROUP BY steam_id_64
|
||||
"""
|
||||
df_ct = pd.read_sql_query(query_side_r, conn, params=valid_ids)
|
||||
# Similar for T...
|
||||
|
||||
# Merge...
|
||||
|
||||
# UTIL
|
||||
df['util_avg_nade_dmg'] = df['sum_util_dmg'] / df['matches_played']
|
||||
df['util_avg_flash_time'] = df['sum_flash_time'] / df['matches_played']
|
||||
df['util_avg_flash_enemy'] = df['sum_flash_enemy'] / df['matches_played']
|
||||
|
||||
# Fill NaN
|
||||
df = df.fillna(0)
|
||||
|
||||
return df
|
||||
|
||||
def calculate_ultimate_scores(df):
|
||||
# Normalize Helper
|
||||
def n(col):
|
||||
if col not in df.columns: return 50
|
||||
s = df[col]
|
||||
if s.max() == s.min(): return 50
|
||||
return (s - s.min()) / (s.max() - s.min()) * 100
|
||||
|
||||
df = df.copy()
|
||||
|
||||
# 1. BAT: Battle (30%)
|
||||
# Weights: Rating(25), KD(20), ADR(15), Duel(10), HighELO(10), CloseRange(10), MultiKill(10)
|
||||
df['score_BAT'] = (
|
||||
0.25 * n('basic_avg_rating') +
|
||||
0.20 * n('basic_avg_kd') +
|
||||
0.15 * n('basic_avg_adr') +
|
||||
0.10 * n('bat_avg_duel_win_rate') + # Need to ensure col exists
|
||||
0.10 * n('bat_kd_diff_high_elo') +
|
||||
0.10 * n('bat_win_rate_close') +
|
||||
0.10 * n('basic_avg_kill_3') # Multi-kill proxy
|
||||
)
|
||||
|
||||
# 2. STA: Stability (15%)
|
||||
# Weights: Volatility(30), LossRating(30), WinRating(20), TimeCorr(10), Fatigue(10)
|
||||
df['score_STA'] = (
|
||||
0.30 * (100 - n('sta_rating_volatility')) +
|
||||
0.30 * n('sta_loss_rating') +
|
||||
0.20 * n('sta_win_rating') +
|
||||
0.10 * (100 - n('sta_time_rating_corr').abs()) + # Closer to 0 is better (independent of duration)
|
||||
0.10 * (100 - n('sta_fatigue_decay'))
|
||||
)
|
||||
|
||||
# 3. HPS: Pressure (20%)
|
||||
# Weights: Clutch(30), MatchPoint(20), Comeback(20), PressureEntry(15), CloseMatch(15)
|
||||
df['score_HPS'] = (
|
||||
0.30 * n('sum_1v3p') + # Using high tier clutches
|
||||
0.20 * n('hps_match_point_win_rate') +
|
||||
0.20 * n('hps_comeback_kd_diff') +
|
||||
0.15 * n('hps_pressure_entry_rate') +
|
||||
0.15 * n('basic_avg_rating') # Fallback if close match rating missing
|
||||
)
|
||||
|
||||
# 4. PTL: Pistol (10%)
|
||||
# Weights: Kills(40), WinRate(30), KD(30)
|
||||
df['score_PTL'] = (
|
||||
0.40 * n('ptl_pistol_kills') +
|
||||
0.30 * n('ptl_pistol_win_rate') +
|
||||
0.30 * n('ptl_pistol_kd')
|
||||
)
|
||||
|
||||
# 5. T/CT (15%)
|
||||
# Weights: CT(50), T(50)
|
||||
# Need to load CT/T ratings properly, using basic rating as placeholder if missing
|
||||
df['score_TCT'] = 0.5 * n('basic_avg_rating') + 0.5 * n('basic_avg_rating')
|
||||
|
||||
# 6. UTIL (10%)
|
||||
# Weights: Dmg(50), Flash(30), EnemiesFlashed(20)
|
||||
df['score_UTIL'] = (
|
||||
0.50 * n('util_avg_nade_dmg') +
|
||||
0.30 * n('util_avg_flash_time') +
|
||||
0.20 * n('util_avg_flash_enemy')
|
||||
)
|
||||
|
||||
return df
|
||||
|
||||
def main():
|
||||
conn = get_db_connection()
|
||||
try:
|
||||
df = load_and_calculate_ultimate(conn)
|
||||
if df is None: return
|
||||
|
||||
results = calculate_ultimate_scores(df)
|
||||
|
||||
print("\n--- Ultimate Scores (Top 5 BAT) ---")
|
||||
cols = ['steam_id_64', 'score_BAT', 'score_STA', 'score_HPS', 'score_PTL', 'score_UTIL']
|
||||
print(results[cols].sort_values('score_BAT', ascending=False).head(5))
|
||||
|
||||
# Verify coverage
|
||||
print("\n--- Feature Coverage ---")
|
||||
print(f"Total Columns: {len(results.columns)}")
|
||||
print("BAT Distances:", 'bat_win_rate_close' in results.columns)
|
||||
print("HPS Contexts:", 'hps_match_point_win_rate' in results.columns)
|
||||
print("PTL Detailed:", 'ptl_pistol_kd' in results.columns)
|
||||
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
22
scripts/check_l1a.py
Normal file
22
scripts/check_l1a.py
Normal file
@@ -0,0 +1,22 @@
|
||||
import sqlite3
|
||||
import os
|
||||
|
||||
L1A_DB_PATH = r'd:\Documents\trae_projects\yrtv\database\L1A\L1A.sqlite'
|
||||
|
||||
print("Checking L1A...")
|
||||
if os.path.exists(L1A_DB_PATH):
|
||||
try:
|
||||
conn = sqlite3.connect(L1A_DB_PATH)
|
||||
cursor = conn.cursor()
|
||||
cursor.execute("SELECT name FROM sqlite_master WHERE type='table'")
|
||||
tables = cursor.fetchall()
|
||||
print(f"Tables: {tables}")
|
||||
|
||||
cursor.execute("SELECT COUNT(*) FROM raw_iframe_network")
|
||||
count = cursor.fetchone()[0]
|
||||
print(f"L1A Records: {count}")
|
||||
conn.close()
|
||||
except Exception as e:
|
||||
print(f"Error checking L1A: {e}")
|
||||
else:
|
||||
print(f"L1A DB not found at {L1A_DB_PATH}")
|
||||
55
scripts/check_l3_variance.py
Normal file
55
scripts/check_l3_variance.py
Normal file
@@ -0,0 +1,55 @@
|
||||
import sqlite3
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import os
|
||||
|
||||
# Config to match your project structure
|
||||
class Config:
|
||||
DB_L3_PATH = r'd:\Documents\trae_projects\yrtv\database\L3\L3_Features.sqlite'
|
||||
|
||||
def check_variance():
|
||||
db_path = Config.DB_L3_PATH
|
||||
if not os.path.exists(db_path):
|
||||
print(f"L3 DB not found at {db_path}")
|
||||
return
|
||||
|
||||
conn = sqlite3.connect(db_path)
|
||||
try:
|
||||
# Read all features
|
||||
df = pd.read_sql_query("SELECT * FROM dm_player_features", conn)
|
||||
|
||||
print(f"Total rows: {len(df)}")
|
||||
if len(df) == 0:
|
||||
print("Table is empty.")
|
||||
return
|
||||
|
||||
numeric_cols = df.select_dtypes(include=['number']).columns
|
||||
|
||||
print("\n--- Variance Analysis ---")
|
||||
for col in numeric_cols:
|
||||
if col in ['steam_id_64']: continue # Skip ID
|
||||
|
||||
# Check for all zeros
|
||||
if (df[col] == 0).all():
|
||||
print(f"[ALL ZERO] {col}")
|
||||
continue
|
||||
|
||||
# Check for single value (variance = 0)
|
||||
if df[col].nunique() <= 1:
|
||||
val = df[col].iloc[0]
|
||||
print(f"[SINGLE VAL] {col} = {val}")
|
||||
continue
|
||||
|
||||
# Check for mostly zeros
|
||||
zero_pct = (df[col] == 0).mean()
|
||||
if zero_pct > 0.9:
|
||||
print(f"[MOSTLY ZERO] {col} ({zero_pct:.1%} zeros)")
|
||||
|
||||
# Basic stats for valid ones
|
||||
# print(f"{col}: min={df[col].min():.2f}, max={df[col].max():.2f}, mean={df[col].mean():.2f}")
|
||||
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
if __name__ == "__main__":
|
||||
check_variance()
|
||||
63
scripts/check_side_mapping.py
Normal file
63
scripts/check_side_mapping.py
Normal file
@@ -0,0 +1,63 @@
|
||||
import sqlite3
|
||||
import pandas as pd
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
|
||||
# Add parent directory
|
||||
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
from web.config import Config
|
||||
|
||||
def check_mapping():
|
||||
conn = sqlite3.connect(Config.DB_L2_PATH)
|
||||
|
||||
# Join economy and teams via match_id
|
||||
# We need to match steam_id (in eco) to group_uids (in teams)
|
||||
|
||||
# 1. Get Economy R1 samples
|
||||
query_eco = """
|
||||
SELECT match_id, steam_id_64, side
|
||||
FROM fact_round_player_economy
|
||||
WHERE round_num = 1
|
||||
LIMIT 10
|
||||
"""
|
||||
eco_rows = pd.read_sql_query(query_eco, conn)
|
||||
|
||||
if eco_rows.empty:
|
||||
print("No Economy R1 data found.")
|
||||
conn.close()
|
||||
return
|
||||
|
||||
print("Checking Mapping...")
|
||||
for _, row in eco_rows.iterrows():
|
||||
mid = row['match_id']
|
||||
sid = row['steam_id_64']
|
||||
side = row['side']
|
||||
|
||||
# Get Teams for this match
|
||||
query_teams = "SELECT group_id, group_fh_role, group_uids FROM fact_match_teams WHERE match_id = ?"
|
||||
team_rows = pd.read_sql_query(query_teams, conn, params=(mid,))
|
||||
|
||||
for _, t_row in team_rows.iterrows():
|
||||
# Check if sid is in group_uids (which contains UIDs, not SteamIDs!)
|
||||
# We need to map SteamID -> UID
|
||||
# Use dim_players or fact_match_players
|
||||
q_uid = "SELECT uid FROM fact_match_players WHERE match_id = ? AND steam_id_64 = ?"
|
||||
uid_res = conn.execute(q_uid, (mid, sid)).fetchone()
|
||||
if not uid_res:
|
||||
continue
|
||||
|
||||
uid = str(uid_res[0])
|
||||
group_uids = str(t_row['group_uids']).split(',')
|
||||
|
||||
if uid in group_uids:
|
||||
role = t_row['group_fh_role']
|
||||
print(f"Match {mid}: Steam {sid} (UID {uid}) is on Side {side} in R1.")
|
||||
print(f" Found in Group {t_row['group_id']} with FH Role {role}.")
|
||||
print(f" MAPPING: Role {role} = {side}")
|
||||
break
|
||||
|
||||
conn.close()
|
||||
|
||||
if __name__ == "__main__":
|
||||
check_mapping()
|
||||
43
scripts/check_tables.py
Normal file
43
scripts/check_tables.py
Normal file
@@ -0,0 +1,43 @@
|
||||
import sqlite3
|
||||
import os
|
||||
|
||||
DB_PATH = r'd:\Documents\trae_projects\yrtv\database\L2\L2_Main.sqlite'
|
||||
|
||||
def check_tables():
|
||||
if not os.path.exists(DB_PATH):
|
||||
print(f"DB not found: {DB_PATH}")
|
||||
return
|
||||
|
||||
conn = sqlite3.connect(DB_PATH)
|
||||
cursor = conn.cursor()
|
||||
|
||||
tables = [
|
||||
'dim_players', 'dim_maps',
|
||||
'fact_matches', 'fact_match_teams',
|
||||
'fact_match_players', 'fact_match_players_ct', 'fact_match_players_t',
|
||||
'fact_rounds', 'fact_round_events', 'fact_round_player_economy'
|
||||
]
|
||||
|
||||
print(f"--- L2 Database Check: {DB_PATH} ---")
|
||||
for table in tables:
|
||||
try:
|
||||
cursor.execute(f"SELECT COUNT(*) FROM {table}")
|
||||
count = cursor.fetchone()[0]
|
||||
print(f"{table:<25}: {count:>6} rows")
|
||||
|
||||
# Simple column check for recently added columns
|
||||
if table == 'fact_match_players':
|
||||
cursor.execute(f"PRAGMA table_info({table})")
|
||||
cols = [info[1] for info in cursor.fetchall()]
|
||||
if 'util_flash_usage' in cols:
|
||||
print(f" [OK] util_flash_usage exists")
|
||||
else:
|
||||
print(f" [ERR] util_flash_usage MISSING")
|
||||
|
||||
except Exception as e:
|
||||
print(f"{table:<25}: [ERROR] {e}")
|
||||
|
||||
conn.close()
|
||||
|
||||
if __name__ == "__main__":
|
||||
check_tables()
|
||||
@@ -1,65 +1,63 @@
|
||||
import sqlite3
|
||||
import pandas as pd
|
||||
import os
|
||||
|
||||
# Define database paths
|
||||
BASE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
|
||||
L2_PATH = os.path.join(BASE_DIR, 'database', 'L2', 'L2_Main.sqlite')
|
||||
|
||||
def check_l2_tables():
|
||||
print(f"Checking L2 database at: {L2_PATH}")
|
||||
if not os.path.exists(L2_PATH):
|
||||
print("Error: L2 database not found!")
|
||||
return
|
||||
L2_PATH = r'd:\Documents\trae_projects\yrtv\database\L2\L2_Main.sqlite'
|
||||
WEB_PATH = r'd:\Documents\trae_projects\yrtv\database\Web\Web_App.sqlite'
|
||||
|
||||
def debug_db():
|
||||
# --- L2 Checks ---
|
||||
conn = sqlite3.connect(L2_PATH)
|
||||
cursor = conn.cursor()
|
||||
|
||||
cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
|
||||
tables = cursor.fetchall()
|
||||
print("Tables in L2 Database:")
|
||||
for table in tables:
|
||||
print(f" - {table[0]}")
|
||||
print("--- Data Source Type Distribution ---")
|
||||
try:
|
||||
df = pd.read_sql_query("SELECT data_source_type, COUNT(*) as cnt FROM fact_matches GROUP BY data_source_type", conn)
|
||||
print(df)
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
|
||||
print("\n--- Economy Table Count ---")
|
||||
try:
|
||||
count = conn.execute("SELECT COUNT(*) FROM fact_round_player_economy").fetchone()[0]
|
||||
print(f"Rows: {count}")
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
|
||||
print("\n--- Check util_flash_usage in fact_match_players ---")
|
||||
try:
|
||||
cursor = conn.cursor()
|
||||
cursor.execute("PRAGMA table_info(fact_match_players)")
|
||||
cols = [info[1] for info in cursor.fetchall()]
|
||||
if 'util_flash_usage' in cols:
|
||||
print("Column 'util_flash_usage' EXISTS.")
|
||||
nz = conn.execute("SELECT COUNT(*) FROM fact_match_players WHERE util_flash_usage > 0").fetchone()[0]
|
||||
print(f"Rows with util_flash_usage > 0: {nz}")
|
||||
else:
|
||||
print("Column 'util_flash_usage' MISSING.")
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
|
||||
conn.close()
|
||||
|
||||
def debug_player_query(player_name_query=None):
|
||||
print(f"\nDebugging Player Query (L2)...")
|
||||
conn = sqlite3.connect(L2_PATH)
|
||||
cursor = conn.cursor()
|
||||
|
||||
# --- Web DB Checks ---
|
||||
print("\n--- Web DB Check ---")
|
||||
if not os.path.exists(WEB_PATH):
|
||||
print(f"Web DB not found at {WEB_PATH}")
|
||||
return
|
||||
|
||||
try:
|
||||
# Check if 'dim_players' exists
|
||||
cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='dim_players';")
|
||||
if not cursor.fetchone():
|
||||
print("Error: 'dim_players' table not found!")
|
||||
return
|
||||
|
||||
# Check schema of dim_players
|
||||
print("\nChecking dim_players schema:")
|
||||
cursor.execute("PRAGMA table_info(dim_players)")
|
||||
for col in cursor.fetchall():
|
||||
print(col)
|
||||
|
||||
# Check sample data
|
||||
print("\nSampling dim_players (first 5):")
|
||||
cursor.execute("SELECT * FROM dim_players LIMIT 5")
|
||||
for row in cursor.fetchall():
|
||||
print(row)
|
||||
|
||||
# Test Search
|
||||
search_term = 'zy'
|
||||
print(f"\nTesting search for '{search_term}':")
|
||||
cursor.execute("SELECT * FROM dim_players WHERE name LIKE ?", (f'%{search_term}%',))
|
||||
results = cursor.fetchall()
|
||||
print(f"Found {len(results)} matches.")
|
||||
for r in results:
|
||||
print(r)
|
||||
|
||||
conn_web = sqlite3.connect(WEB_PATH)
|
||||
cursor = conn_web.cursor()
|
||||
cursor.execute("SELECT name FROM sqlite_master WHERE type='table'")
|
||||
tables = cursor.fetchall()
|
||||
print(f"Tables: {[t[0] for t in tables]}")
|
||||
|
||||
if 'player_metadata' in [t[0] for t in tables]:
|
||||
count = conn_web.execute("SELECT COUNT(*) FROM player_metadata").fetchone()[0]
|
||||
print(f"player_metadata rows: {count}")
|
||||
conn_web.close()
|
||||
except Exception as e:
|
||||
print(f"Error querying L2: {e}")
|
||||
finally:
|
||||
conn.close()
|
||||
print(f"Error checking Web DB: {e}")
|
||||
|
||||
if __name__ == '__main__':
|
||||
check_l2_tables()
|
||||
debug_player_query()
|
||||
if __name__ == "__main__":
|
||||
debug_db()
|
||||
|
||||
18
scripts/run_rebuild.py
Normal file
18
scripts/run_rebuild.py
Normal file
@@ -0,0 +1,18 @@
|
||||
import sys
|
||||
import os
|
||||
|
||||
# Add project root to path
|
||||
current_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
project_root = os.path.dirname(current_dir)
|
||||
sys.path.append(project_root)
|
||||
|
||||
from web.services.feature_service import FeatureService
|
||||
|
||||
print("Starting Rebuild...")
|
||||
try:
|
||||
count = FeatureService.rebuild_all_features(min_matches=1)
|
||||
print(f"Rebuild Complete. Processed {count} players.")
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
30
scripts/update_l2_schema_utility.py
Normal file
30
scripts/update_l2_schema_utility.py
Normal file
@@ -0,0 +1,30 @@
|
||||
import sys
|
||||
import os
|
||||
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
|
||||
|
||||
import sqlite3
|
||||
from web.config import Config
|
||||
|
||||
conn = sqlite3.connect(Config.DB_L2_PATH)
|
||||
cursor = conn.cursor()
|
||||
|
||||
columns = [
|
||||
'util_flash_usage',
|
||||
'util_smoke_usage',
|
||||
'util_molotov_usage',
|
||||
'util_he_usage',
|
||||
'util_decoy_usage'
|
||||
]
|
||||
|
||||
for col in columns:
|
||||
try:
|
||||
cursor.execute(f"ALTER TABLE fact_match_players ADD COLUMN {col} INTEGER DEFAULT 0")
|
||||
print(f"Added column {col}")
|
||||
except sqlite3.OperationalError as e:
|
||||
if "duplicate column name" in str(e):
|
||||
print(f"Column {col} already exists.")
|
||||
else:
|
||||
print(f"Error adding {col}: {e}")
|
||||
|
||||
conn.commit()
|
||||
conn.close()
|
||||
39
scripts/update_l3_schema.py
Normal file
39
scripts/update_l3_schema.py
Normal file
@@ -0,0 +1,39 @@
|
||||
import sqlite3
|
||||
import os
|
||||
|
||||
DB_PATH = r'd:\Documents\trae_projects\yrtv\database\L3\L3_Features.sqlite'
|
||||
|
||||
def add_columns():
|
||||
conn = sqlite3.connect(DB_PATH)
|
||||
cursor = conn.cursor()
|
||||
|
||||
# Check existing columns
|
||||
cursor.execute("PRAGMA table_info(dm_player_features)")
|
||||
columns = [row[1] for row in cursor.fetchall()]
|
||||
|
||||
new_columns = [
|
||||
'score_bat', 'score_sta', 'score_hps', 'score_ptl', 'score_tct', 'score_util',
|
||||
'bat_avg_duel_win_rate', 'bat_kd_diff_high_elo', 'bat_win_rate_close',
|
||||
'sta_time_rating_corr', 'sta_fatigue_decay',
|
||||
'hps_match_point_win_rate', 'hps_comeback_kd_diff', 'hps_pressure_entry_rate',
|
||||
'ptl_pistol_win_rate', 'ptl_pistol_kd',
|
||||
'util_avg_flash_enemy'
|
||||
]
|
||||
|
||||
for col in new_columns:
|
||||
if col not in columns:
|
||||
print(f"Adding column: {col}")
|
||||
try:
|
||||
cursor.execute(f"ALTER TABLE dm_player_features ADD COLUMN {col} REAL")
|
||||
except Exception as e:
|
||||
print(f"Error adding {col}: {e}")
|
||||
|
||||
conn.commit()
|
||||
conn.close()
|
||||
print("Schema update complete.")
|
||||
|
||||
if __name__ == "__main__":
|
||||
if not os.path.exists(DB_PATH):
|
||||
print("L3 DB not found, skipping schema update (will be created by build script).")
|
||||
else:
|
||||
add_columns()
|
||||
@@ -141,15 +141,24 @@ def charts_data(steam_id):
|
||||
# Radar Data (Construct from features)
|
||||
features = FeatureService.get_player_features(steam_id)
|
||||
radar_data = {}
|
||||
radar_dist = FeatureService.get_roster_features_distribution(steam_id)
|
||||
|
||||
if features:
|
||||
# Dimensions: STA, BAT, HPS, PTL, T/CT, UTIL
|
||||
# Use calculated scores (0-100 scale)
|
||||
|
||||
# Helper to get score safely
|
||||
def get_score(key):
|
||||
val = features[key] if key in features.keys() else 0
|
||||
return float(val) if val else 0
|
||||
|
||||
radar_data = {
|
||||
'STA': features['basic_avg_rating'] or 0,
|
||||
'BAT': features['bat_avg_duel_win_rate'] or 0,
|
||||
'HPS': features['hps_clutch_win_rate_1v1'] or 0,
|
||||
'PTL': features['ptl_pistol_win_rate'] or 0,
|
||||
'SIDE': features['side_rating_ct'] or 0,
|
||||
'UTIL': features['util_usage_rate'] or 0
|
||||
'STA': get_score('score_sta'),
|
||||
'BAT': get_score('score_bat'),
|
||||
'HPS': get_score('score_hps'),
|
||||
'PTL': get_score('score_ptl'),
|
||||
'SIDE': get_score('score_tct'),
|
||||
'UTIL': get_score('score_util')
|
||||
}
|
||||
|
||||
trend_labels = []
|
||||
@@ -166,7 +175,8 @@ def charts_data(steam_id):
|
||||
|
||||
return jsonify({
|
||||
'trend': {'labels': trend_labels, 'values': trend_values},
|
||||
'radar': radar_data
|
||||
'radar': radar_data,
|
||||
'radar_dist': radar_dist
|
||||
})
|
||||
|
||||
# --- API for Comparison ---
|
||||
|
||||
@@ -1,4 +1,7 @@
|
||||
from web.database import query_db
|
||||
from web.database import query_db, get_db, execute_db
|
||||
import sqlite3
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
|
||||
class FeatureService:
|
||||
@staticmethod
|
||||
@@ -40,15 +43,11 @@ class FeatureService:
|
||||
p['matches_played'] = cnt_dict.get(p['steam_id_64'], 0)
|
||||
|
||||
if search:
|
||||
# ... existing search logic ...
|
||||
# Get all matching players
|
||||
l2_players, _ = StatsService.get_players(page=1, per_page=100, search=search)
|
||||
if not l2_players:
|
||||
return [], 0
|
||||
|
||||
# ... (Merge logic) ...
|
||||
# I need to insert the match count logic inside the merge loop or after
|
||||
|
||||
steam_ids = [p['steam_id_64'] for p in l2_players]
|
||||
placeholders = ','.join('?' for _ in steam_ids)
|
||||
sql = f"SELECT * FROM dm_player_features WHERE steam_id_64 IN ({placeholders})"
|
||||
@@ -76,7 +75,7 @@ class FeatureService:
|
||||
else:
|
||||
m['basic_avg_rating'] = 0
|
||||
m['basic_avg_kd'] = 0
|
||||
m['basic_avg_kast'] = 0 # Ensure kast exists
|
||||
m['basic_avg_kast'] = 0
|
||||
|
||||
m['matches_played'] = cnt_dict.get(p['steam_id_64'], 0)
|
||||
merged.append(m)
|
||||
@@ -90,20 +89,10 @@ class FeatureService:
|
||||
|
||||
else:
|
||||
# Browse mode
|
||||
# Check L3
|
||||
l3_count = query_db('l3', "SELECT COUNT(*) as cnt FROM dm_player_features", one=True)['cnt']
|
||||
|
||||
if l3_count == 0 or sort_by == 'matches':
|
||||
# If sorting by matches, we MUST use L2 counts because L3 might not have it or we want dynamic.
|
||||
# OR if L3 is empty.
|
||||
# Since L3 schema is unknown regarding 'matches_played', let's assume we fallback to L2 logic
|
||||
# but paginated in memory if dataset is small, or just fetch all L2 players?
|
||||
# Fetching all L2 players is bad if many.
|
||||
# But for 'matches' sort, we need to know counts for ALL to sort correctly.
|
||||
# Solution: Query L2 for top N players by match count.
|
||||
|
||||
if sort_by == 'matches':
|
||||
# Query L2 for IDs ordered by count
|
||||
sql = """
|
||||
SELECT steam_id_64, COUNT(*) as cnt
|
||||
FROM fact_match_players
|
||||
@@ -118,24 +107,18 @@ class FeatureService:
|
||||
total = query_db('l2', "SELECT COUNT(DISTINCT steam_id_64) as cnt FROM fact_match_players", one=True)['cnt']
|
||||
|
||||
ids = [r['steam_id_64'] for r in top_ids]
|
||||
# Fetch details for these IDs
|
||||
l2_players = StatsService.get_players_by_ids(ids)
|
||||
|
||||
# Merge logic (reuse)
|
||||
# Merge logic
|
||||
merged = []
|
||||
# Fetch L3 features for these IDs to show stats
|
||||
p_ph = ','.join('?' for _ in ids)
|
||||
f_sql = f"SELECT * FROM dm_player_features WHERE steam_id_64 IN ({p_ph})"
|
||||
features = query_db('l3', f_sql, ids)
|
||||
f_dict = {f['steam_id_64']: f for f in features}
|
||||
|
||||
cnt_dict = {r['steam_id_64']: r['cnt'] for r in top_ids}
|
||||
|
||||
# Map L2 players to dict for easy access (though list order matters for sort?)
|
||||
# Actually top_ids is sorted.
|
||||
p_dict = {p['steam_id_64']: p for p in l2_players}
|
||||
|
||||
for r in top_ids: # Preserve order
|
||||
for r in top_ids:
|
||||
sid = r['steam_id_64']
|
||||
p = p_dict.get(sid)
|
||||
if not p: continue
|
||||
@@ -160,10 +143,10 @@ class FeatureService:
|
||||
|
||||
return merged, total
|
||||
|
||||
# L3 empty fallback (existing logic)
|
||||
# L3 empty fallback
|
||||
l2_players, total = StatsService.get_players(page, per_page, sort_by=None)
|
||||
merged = []
|
||||
attach_match_counts(l2_players) # Helper
|
||||
attach_match_counts(l2_players)
|
||||
|
||||
for p in l2_players:
|
||||
m = dict(p)
|
||||
@@ -184,7 +167,7 @@ class FeatureService:
|
||||
|
||||
return merged, total
|
||||
|
||||
# Normal L3 browse (sort by rating/kd/kast)
|
||||
# Normal L3 browse
|
||||
sql = f"SELECT * FROM dm_player_features ORDER BY {order_col} DESC LIMIT ? OFFSET ?"
|
||||
features = query_db('l3', sql, [per_page, offset])
|
||||
|
||||
@@ -204,53 +187,711 @@ class FeatureService:
|
||||
if p:
|
||||
m.update(dict(p))
|
||||
else:
|
||||
m['username'] = f['steam_id_64'] # Fallback
|
||||
m['username'] = f['steam_id_64']
|
||||
m['avatar_url'] = None
|
||||
merged.append(m)
|
||||
|
||||
return merged, total
|
||||
|
||||
@staticmethod
|
||||
def get_top_players(limit=20, sort_by='basic_avg_rating'):
|
||||
# Safety check for sort_by to prevent injection
|
||||
allowed_sorts = ['basic_avg_rating', 'basic_avg_kd', 'basic_avg_kast', 'basic_avg_rws']
|
||||
if sort_by not in allowed_sorts:
|
||||
sort_by = 'basic_avg_rating'
|
||||
|
||||
sql = f"""
|
||||
SELECT f.*, p.username, p.avatar_url
|
||||
FROM dm_player_features f
|
||||
LEFT JOIN l2.dim_players p ON f.steam_id_64 = p.steam_id_64
|
||||
ORDER BY {sort_by} DESC
|
||||
LIMIT ?
|
||||
def rebuild_all_features(min_matches=5):
|
||||
"""
|
||||
# Note: Cross-database join (l2.dim_players) works in SQLite if attached.
|
||||
# But `query_db` connects to one DB.
|
||||
# Strategy: Fetch features, then fetch player infos from L2. Or attach DB.
|
||||
# Simple strategy: Fetch features, then extract steam_ids and batch fetch from L2 in StatsService.
|
||||
# Or simpler: Just return features and let the controller/template handle the name/avatar via another call or pre-fetching.
|
||||
Refreshes the L3 Data Mart with full feature calculations.
|
||||
"""
|
||||
from web.config import Config
|
||||
l3_db_path = Config.DB_L3_PATH
|
||||
l2_db_path = Config.DB_L2_PATH
|
||||
|
||||
# Actually, for "Player List" view, we really want L3 data joined with L2 names.
|
||||
# I will change this to just return features for now, and handle joining in the route handler or via a helper that attaches databases.
|
||||
# Attaching is better.
|
||||
conn_l2 = sqlite3.connect(l2_db_path)
|
||||
conn_l2.row_factory = sqlite3.Row
|
||||
|
||||
return query_db('l3', f"SELECT * FROM dm_player_features ORDER BY {sort_by} DESC LIMIT ?", [limit])
|
||||
try:
|
||||
print("Loading L2 data...")
|
||||
df = FeatureService._load_and_calculate_dataframe(conn_l2, min_matches)
|
||||
|
||||
if df is None or df.empty:
|
||||
print("No data to process.")
|
||||
return 0
|
||||
|
||||
print("Calculating Scores...")
|
||||
df = FeatureService._calculate_ultimate_scores(df)
|
||||
|
||||
print("Saving to L3...")
|
||||
conn_l3 = sqlite3.connect(l3_db_path)
|
||||
|
||||
cursor = conn_l3.cursor()
|
||||
|
||||
# Ensure columns exist in DataFrame match DB columns
|
||||
cursor.execute("PRAGMA table_info(dm_player_features)")
|
||||
valid_cols = [r[1] for r in cursor.fetchall()]
|
||||
|
||||
# Filter DF columns
|
||||
df_cols = [c for c in df.columns if c in valid_cols]
|
||||
df_to_save = df[df_cols].copy()
|
||||
df_to_save['updated_at'] = pd.Timestamp.now().strftime('%Y-%m-%d %H:%M:%S')
|
||||
|
||||
# Generate Insert SQL
|
||||
placeholders = ','.join(['?'] * len(df_to_save.columns))
|
||||
cols_str = ','.join(df_to_save.columns)
|
||||
sql = f"INSERT OR REPLACE INTO dm_player_features ({cols_str}) VALUES ({placeholders})"
|
||||
|
||||
data = df_to_save.values.tolist()
|
||||
cursor.executemany(sql, data)
|
||||
conn_l3.commit()
|
||||
conn_l3.close()
|
||||
|
||||
return len(df)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Rebuild Error: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return 0
|
||||
finally:
|
||||
conn_l2.close()
|
||||
|
||||
@staticmethod
|
||||
def get_player_trend(steam_id, limit=30):
|
||||
# This requires `fact_match_features` or querying L2 matches for historical data.
|
||||
# WebRDD says: "Trend graph: Recent 10/20 matches Rating trend (Chart.js)."
|
||||
# We can get this from L2 fact_match_players.
|
||||
sql = """
|
||||
SELECT m.start_time, mp.rating, mp.kd_ratio, mp.adr, m.match_id
|
||||
FROM fact_match_players mp
|
||||
JOIN fact_matches m ON mp.match_id = m.match_id
|
||||
WHERE mp.steam_id_64 = ?
|
||||
ORDER BY m.start_time DESC
|
||||
LIMIT ?
|
||||
def _load_and_calculate_dataframe(conn, min_matches):
|
||||
# 1. Basic Stats
|
||||
query_basic = """
|
||||
SELECT
|
||||
steam_id_64,
|
||||
COUNT(*) as matches_played,
|
||||
SUM(round_total) as rounds_played,
|
||||
AVG(rating) as basic_avg_rating,
|
||||
AVG(kd_ratio) as basic_avg_kd,
|
||||
AVG(adr) as basic_avg_adr,
|
||||
AVG(kast) as basic_avg_kast,
|
||||
AVG(rws) as basic_avg_rws,
|
||||
SUM(headshot_count) as sum_hs,
|
||||
SUM(kills) as sum_kills,
|
||||
SUM(deaths) as sum_deaths,
|
||||
SUM(first_kill) as sum_fk,
|
||||
SUM(first_death) as sum_fd,
|
||||
SUM(clutch_1v1) as sum_1v1,
|
||||
SUM(clutch_1v2) as sum_1v2,
|
||||
SUM(clutch_1v3) + SUM(clutch_1v4) + SUM(clutch_1v5) as sum_1v3p,
|
||||
SUM(kill_2) as sum_2k,
|
||||
SUM(kill_3) as sum_3k,
|
||||
SUM(kill_4) as sum_4k,
|
||||
SUM(kill_5) as sum_5k,
|
||||
SUM(assisted_kill) as sum_assist,
|
||||
SUM(perfect_kill) as sum_perfect,
|
||||
SUM(revenge_kill) as sum_revenge,
|
||||
SUM(awp_kill) as sum_awp,
|
||||
SUM(jump_count) as sum_jump,
|
||||
SUM(throw_harm) as sum_util_dmg,
|
||||
SUM(flash_time) as sum_flash_time,
|
||||
SUM(flash_enemy) as sum_flash_enemy,
|
||||
SUM(flash_team) as sum_flash_team,
|
||||
SUM(util_flash_usage) as sum_util_flash,
|
||||
SUM(util_smoke_usage) as sum_util_smoke,
|
||||
SUM(util_molotov_usage) as sum_util_molotov,
|
||||
SUM(util_he_usage) as sum_util_he,
|
||||
SUM(util_decoy_usage) as sum_util_decoy
|
||||
FROM fact_match_players
|
||||
GROUP BY steam_id_64
|
||||
HAVING COUNT(*) >= ?
|
||||
"""
|
||||
# This query needs to run against L2.
|
||||
# So this method should actually be in StatsService or FeatureService connecting to L2.
|
||||
# I will put it here but note it uses L2. Actually, better to put in StatsService if it uses L2 tables.
|
||||
# But FeatureService conceptualizes "Trends". I'll move it to StatsService for implementation correctness (DB context).
|
||||
pass
|
||||
df = pd.read_sql_query(query_basic, conn, params=(min_matches,))
|
||||
if df.empty: return None
|
||||
|
||||
# Basic Derived
|
||||
df['basic_headshot_rate'] = df['sum_hs'] / df['sum_kills'].replace(0, 1)
|
||||
df['basic_avg_headshot_kills'] = df['sum_hs'] / df['matches_played']
|
||||
df['basic_avg_first_kill'] = df['sum_fk'] / df['matches_played']
|
||||
df['basic_avg_first_death'] = df['sum_fd'] / df['matches_played']
|
||||
df['basic_first_kill_rate'] = df['sum_fk'] / (df['sum_fk'] + df['sum_fd']).replace(0, 1)
|
||||
df['basic_first_death_rate'] = df['sum_fd'] / (df['sum_fk'] + df['sum_fd']).replace(0, 1)
|
||||
df['basic_avg_kill_2'] = df['sum_2k'] / df['matches_played']
|
||||
df['basic_avg_kill_3'] = df['sum_3k'] / df['matches_played']
|
||||
df['basic_avg_kill_4'] = df['sum_4k'] / df['matches_played']
|
||||
df['basic_avg_kill_5'] = df['sum_5k'] / df['matches_played']
|
||||
df['basic_avg_assisted_kill'] = df['sum_assist'] / df['matches_played']
|
||||
df['basic_avg_perfect_kill'] = df['sum_perfect'] / df['matches_played']
|
||||
df['basic_avg_revenge_kill'] = df['sum_revenge'] / df['matches_played']
|
||||
df['basic_avg_awp_kill'] = df['sum_awp'] / df['matches_played']
|
||||
df['basic_avg_jump_count'] = df['sum_jump'] / df['matches_played']
|
||||
|
||||
# UTIL Basic
|
||||
df['util_avg_nade_dmg'] = df['sum_util_dmg'] / df['matches_played']
|
||||
df['util_avg_flash_time'] = df['sum_flash_time'] / df['matches_played']
|
||||
df['util_avg_flash_enemy'] = df['sum_flash_enemy'] / df['matches_played']
|
||||
|
||||
valid_ids = tuple(df['steam_id_64'].tolist())
|
||||
placeholders = ','.join(['?'] * len(valid_ids))
|
||||
|
||||
# 2. STA (Detailed)
|
||||
query_sta = f"""
|
||||
SELECT mp.steam_id_64, mp.rating, mp.is_win, m.start_time, m.duration
|
||||
FROM fact_match_players mp
|
||||
JOIN fact_matches m ON mp.match_id = m.match_id
|
||||
WHERE mp.steam_id_64 IN ({placeholders})
|
||||
ORDER BY mp.steam_id_64, m.start_time
|
||||
"""
|
||||
df_matches = pd.read_sql_query(query_sta, conn, params=valid_ids)
|
||||
sta_list = []
|
||||
for pid, group in df_matches.groupby('steam_id_64'):
|
||||
group = group.sort_values('start_time')
|
||||
last_30 = group.tail(30)
|
||||
|
||||
# Fatigue Calc
|
||||
# Simple heuristic: split matches by day, compare early (first 3) vs late (rest)
|
||||
group['date'] = pd.to_datetime(group['start_time'], unit='s').dt.date
|
||||
day_counts = group.groupby('date').size()
|
||||
busy_days = day_counts[day_counts >= 4].index # Days with 4+ matches
|
||||
|
||||
fatigue_decays = []
|
||||
for day in busy_days:
|
||||
day_matches = group[group['date'] == day]
|
||||
if len(day_matches) >= 4:
|
||||
early_rating = day_matches.head(3)['rating'].mean()
|
||||
late_rating = day_matches.tail(len(day_matches) - 3)['rating'].mean()
|
||||
fatigue_decays.append(early_rating - late_rating)
|
||||
|
||||
avg_fatigue = np.mean(fatigue_decays) if fatigue_decays else 0
|
||||
|
||||
sta_list.append({
|
||||
'steam_id_64': pid,
|
||||
'sta_last_30_rating': last_30['rating'].mean(),
|
||||
'sta_win_rating': group[group['is_win']==1]['rating'].mean(),
|
||||
'sta_loss_rating': group[group['is_win']==0]['rating'].mean(),
|
||||
'sta_rating_volatility': group.tail(10)['rating'].std() if len(group) > 1 else 0,
|
||||
'sta_time_rating_corr': group['duration'].corr(group['rating']) if len(group)>2 and group['rating'].std() > 0 else 0,
|
||||
'sta_fatigue_decay': avg_fatigue
|
||||
})
|
||||
df = df.merge(pd.DataFrame(sta_list), on='steam_id_64', how='left')
|
||||
|
||||
# 3. BAT (High ELO)
|
||||
query_elo = f"""
|
||||
SELECT mp.steam_id_64, mp.kd_ratio,
|
||||
(SELECT AVG(group_origin_elo) FROM fact_match_teams fmt WHERE fmt.match_id = mp.match_id AND group_origin_elo > 0) as elo
|
||||
FROM fact_match_players mp
|
||||
WHERE mp.steam_id_64 IN ({placeholders})
|
||||
"""
|
||||
df_elo = pd.read_sql_query(query_elo, conn, params=valid_ids)
|
||||
elo_list = []
|
||||
for pid, group in df_elo.groupby('steam_id_64'):
|
||||
avg = group['elo'].mean() or 1000
|
||||
elo_list.append({
|
||||
'steam_id_64': pid,
|
||||
'bat_kd_diff_high_elo': group[group['elo'] > avg]['kd_ratio'].mean(),
|
||||
'bat_kd_diff_low_elo': group[group['elo'] <= avg]['kd_ratio'].mean()
|
||||
})
|
||||
df = df.merge(pd.DataFrame(elo_list), on='steam_id_64', how='left')
|
||||
|
||||
# Duel Win Rate
|
||||
query_duel = f"""
|
||||
SELECT steam_id_64, SUM(entry_kills) as ek, SUM(entry_deaths) as ed
|
||||
FROM fact_match_players WHERE steam_id_64 IN ({placeholders}) GROUP BY steam_id_64
|
||||
"""
|
||||
df_duel = pd.read_sql_query(query_duel, conn, params=valid_ids)
|
||||
df_duel['bat_avg_duel_win_rate'] = df_duel['ek'] / (df_duel['ek'] + df_duel['ed']).replace(0, 1)
|
||||
df = df.merge(df_duel[['steam_id_64', 'bat_avg_duel_win_rate']], on='steam_id_64', how='left')
|
||||
|
||||
# 4. HPS
|
||||
# Clutch Rate
|
||||
df['hps_clutch_win_rate_1v1'] = df['sum_1v1'] / df['matches_played']
|
||||
df['hps_clutch_win_rate_1v3_plus'] = df['sum_1v3p'] / df['matches_played']
|
||||
|
||||
# Prepare Detailed Event Data for HPS (Comeback), PTL (KD), and T/CT
|
||||
|
||||
# A. Determine Side Info using fact_match_teams
|
||||
# 1. Get Match Teams
|
||||
query_teams = f"""
|
||||
SELECT match_id, group_fh_role, group_uids
|
||||
FROM fact_match_teams
|
||||
WHERE match_id IN (SELECT match_id FROM fact_match_players WHERE steam_id_64 IN ({placeholders}))
|
||||
"""
|
||||
df_teams = pd.read_sql_query(query_teams, conn, params=valid_ids)
|
||||
|
||||
# 2. Get Player UIDs
|
||||
query_uids = f"SELECT match_id, steam_id_64, uid FROM fact_match_players WHERE steam_id_64 IN ({placeholders})"
|
||||
df_uids = pd.read_sql_query(query_uids, conn, params=valid_ids)
|
||||
|
||||
# 3. Get Match Meta (Start Time for MR12/MR15)
|
||||
query_meta = f"SELECT match_id, start_time FROM fact_matches WHERE match_id IN (SELECT match_id FROM fact_match_players WHERE steam_id_64 IN ({placeholders}))"
|
||||
df_meta = pd.read_sql_query(query_meta, conn, params=valid_ids)
|
||||
df_meta['halftime_round'] = np.where(df_meta['start_time'] > 1695772800, 12, 15) # CS2 Release Date approx
|
||||
|
||||
# 4. Build FH Side DataFrame
|
||||
fh_rows = []
|
||||
if not df_teams.empty and not df_uids.empty:
|
||||
match_teams = {} # match_id -> [(role, [uids])]
|
||||
for _, row in df_teams.iterrows():
|
||||
mid = row['match_id']
|
||||
role = row['group_fh_role'] # 1=CT, 0=T
|
||||
try:
|
||||
uids = str(row['group_uids']).split(',')
|
||||
uids = [u.strip() for u in uids if u.strip()]
|
||||
except:
|
||||
uids = []
|
||||
if mid not in match_teams: match_teams[mid] = []
|
||||
match_teams[mid].append((role, uids))
|
||||
|
||||
for _, row in df_uids.iterrows():
|
||||
mid = row['match_id']
|
||||
sid = row['steam_id_64']
|
||||
uid = str(row['uid'])
|
||||
if mid in match_teams:
|
||||
for role, uids in match_teams[mid]:
|
||||
if uid in uids:
|
||||
fh_rows.append({
|
||||
'match_id': mid,
|
||||
'steam_id_64': sid,
|
||||
'fh_side': 'CT' if role == 1 else 'T'
|
||||
})
|
||||
break
|
||||
|
||||
df_fh_sides = pd.DataFrame(fh_rows)
|
||||
if not df_fh_sides.empty:
|
||||
df_fh_sides = df_fh_sides.merge(df_meta[['match_id', 'halftime_round']], on='match_id', how='left')
|
||||
|
||||
# B. Get Kill Events
|
||||
query_events = f"""
|
||||
SELECT match_id, round_num, attacker_steam_id, victim_steam_id, event_type, is_headshot, event_time
|
||||
FROM fact_round_events
|
||||
WHERE event_type='kill'
|
||||
AND (attacker_steam_id IN ({placeholders}) OR victim_steam_id IN ({placeholders}))
|
||||
"""
|
||||
df_events = pd.read_sql_query(query_events, conn, params=valid_ids + valid_ids)
|
||||
|
||||
# C. Get Round Scores
|
||||
query_rounds = f"""
|
||||
SELECT match_id, round_num, ct_score, t_score, winner_side
|
||||
FROM fact_rounds
|
||||
WHERE match_id IN (SELECT match_id FROM fact_match_players WHERE steam_id_64 IN ({placeholders}))
|
||||
"""
|
||||
df_rounds = pd.read_sql_query(query_rounds, conn, params=valid_ids)
|
||||
|
||||
# Fix missing winner_side by calculating from score changes
|
||||
if not df_rounds.empty:
|
||||
df_rounds = df_rounds.sort_values(['match_id', 'round_num']).reset_index(drop=True)
|
||||
df_rounds['prev_ct'] = df_rounds.groupby('match_id')['ct_score'].shift(1).fillna(0)
|
||||
df_rounds['prev_t'] = df_rounds.groupby('match_id')['t_score'].shift(1).fillna(0)
|
||||
|
||||
# Determine winner based on score increment
|
||||
df_rounds['ct_win'] = (df_rounds['ct_score'] > df_rounds['prev_ct'])
|
||||
df_rounds['t_win'] = (df_rounds['t_score'] > df_rounds['prev_t'])
|
||||
|
||||
df_rounds['calculated_winner'] = np.where(df_rounds['ct_win'], 'CT',
|
||||
np.where(df_rounds['t_win'], 'T', None))
|
||||
|
||||
# Force overwrite winner_side with calculated winner since DB data is unreliable (mostly NULL)
|
||||
df_rounds['winner_side'] = df_rounds['calculated_winner']
|
||||
|
||||
# Fallback for Round 1 if still None (e.g. if prev is 0 and score is 1)
|
||||
# Logic above handles Round 1 correctly (prev is 0).
|
||||
|
||||
# --- Process Logic ---
|
||||
# Logic above handles Round 1 correctly (prev is 0).
|
||||
|
||||
# --- Process Logic ---
|
||||
has_events = not df_events.empty
|
||||
has_sides = not df_fh_sides.empty
|
||||
|
||||
if has_events and has_sides:
|
||||
# 1. Attacker Side
|
||||
df_events = df_events.merge(df_fh_sides, left_on=['match_id', 'attacker_steam_id'], right_on=['match_id', 'steam_id_64'], how='left')
|
||||
df_events.rename(columns={'fh_side': 'att_fh_side'}, inplace=True)
|
||||
df_events.drop(columns=['steam_id_64'], inplace=True)
|
||||
|
||||
# 2. Victim Side
|
||||
df_events = df_events.merge(df_fh_sides, left_on=['match_id', 'victim_steam_id'], right_on=['match_id', 'steam_id_64'], how='left', suffixes=('', '_vic'))
|
||||
df_events.rename(columns={'fh_side': 'vic_fh_side'}, inplace=True)
|
||||
df_events.drop(columns=['steam_id_64'], inplace=True)
|
||||
|
||||
# 3. Determine Actual Side (CT/T)
|
||||
# Logic: If round <= halftime -> FH Side. Else -> Opposite.
|
||||
def calc_side(fh_side, round_num, halftime):
|
||||
if pd.isna(fh_side): return None
|
||||
if round_num <= halftime: return fh_side
|
||||
return 'T' if fh_side == 'CT' else 'CT'
|
||||
|
||||
# Vectorized approach
|
||||
# Attacker
|
||||
mask_fh_att = df_events['round_num'] <= df_events['halftime_round']
|
||||
df_events['attacker_side'] = np.where(mask_fh_att, df_events['att_fh_side'],
|
||||
np.where(df_events['att_fh_side'] == 'CT', 'T', 'CT'))
|
||||
# Victim
|
||||
mask_fh_vic = df_events['round_num'] <= df_events['halftime_round']
|
||||
df_events['victim_side'] = np.where(mask_fh_vic, df_events['vic_fh_side'],
|
||||
np.where(df_events['vic_fh_side'] == 'CT', 'T', 'CT'))
|
||||
|
||||
# Merge Scores
|
||||
df_events = df_events.merge(df_rounds, on=['match_id', 'round_num'], how='left')
|
||||
|
||||
# --- HPS: Match Point & Comeback ---
|
||||
# Match Point Win Rate
|
||||
mp_rounds = df_rounds[((df_rounds['ct_score'] == 12) | (df_rounds['t_score'] == 12) |
|
||||
(df_rounds['ct_score'] == 15) | (df_rounds['t_score'] == 15))]
|
||||
|
||||
if not mp_rounds.empty and has_sides:
|
||||
# Need player side for these rounds
|
||||
# Expand sides for all rounds
|
||||
q_all_rounds = f"SELECT match_id, round_num FROM fact_rounds WHERE match_id IN (SELECT match_id FROM fact_match_players WHERE steam_id_64 IN ({placeholders}))"
|
||||
df_all_rounds = pd.read_sql_query(q_all_rounds, conn, params=valid_ids)
|
||||
|
||||
df_player_rounds = df_all_rounds.merge(df_fh_sides, on='match_id')
|
||||
mask_fh = df_player_rounds['round_num'] <= df_player_rounds['halftime_round']
|
||||
df_player_rounds['side'] = np.where(mask_fh, df_player_rounds['fh_side'],
|
||||
np.where(df_player_rounds['fh_side'] == 'CT', 'T', 'CT'))
|
||||
|
||||
# Filter for MP rounds
|
||||
# Join mp_rounds with df_player_rounds
|
||||
mp_player = df_player_rounds.merge(mp_rounds[['match_id', 'round_num', 'winner_side']], on=['match_id', 'round_num'])
|
||||
mp_player['is_win'] = (mp_player['side'] == mp_player['winner_side']).astype(int)
|
||||
|
||||
hps_mp = mp_player.groupby('steam_id_64')['is_win'].mean().reset_index()
|
||||
hps_mp.rename(columns={'is_win': 'hps_match_point_win_rate'}, inplace=True)
|
||||
df = df.merge(hps_mp, on='steam_id_64', how='left')
|
||||
else:
|
||||
df['hps_match_point_win_rate'] = 0.5
|
||||
|
||||
# Comeback KD Diff
|
||||
# Attacker Context
|
||||
df_events['att_team_score'] = np.where(df_events['attacker_side'] == 'CT', df_events['ct_score'], df_events['t_score'])
|
||||
df_events['att_opp_score'] = np.where(df_events['attacker_side'] == 'CT', df_events['t_score'], df_events['ct_score'])
|
||||
df_events['is_comeback_att'] = (df_events['att_team_score'] + 4 <= df_events['att_opp_score'])
|
||||
|
||||
# Victim Context
|
||||
df_events['vic_team_score'] = np.where(df_events['victim_side'] == 'CT', df_events['ct_score'], df_events['t_score'])
|
||||
df_events['vic_opp_score'] = np.where(df_events['victim_side'] == 'CT', df_events['t_score'], df_events['ct_score'])
|
||||
df_events['is_comeback_vic'] = (df_events['vic_team_score'] + 4 <= df_events['vic_opp_score'])
|
||||
|
||||
att_k = df_events.groupby('attacker_steam_id').size()
|
||||
vic_d = df_events.groupby('victim_steam_id').size()
|
||||
|
||||
cb_k = df_events[df_events['is_comeback_att']].groupby('attacker_steam_id').size()
|
||||
cb_d = df_events[df_events['is_comeback_vic']].groupby('victim_steam_id').size()
|
||||
|
||||
kd_stats = pd.DataFrame({'k': att_k, 'd': vic_d, 'cb_k': cb_k, 'cb_d': cb_d}).fillna(0)
|
||||
kd_stats['kd'] = kd_stats['k'] / kd_stats['d'].replace(0, 1)
|
||||
kd_stats['cb_kd'] = kd_stats['cb_k'] / kd_stats['cb_d'].replace(0, 1)
|
||||
kd_stats['hps_comeback_kd_diff'] = kd_stats['cb_kd'] - kd_stats['kd']
|
||||
|
||||
kd_stats.index.name = 'steam_id_64'
|
||||
df = df.merge(kd_stats[['hps_comeback_kd_diff']], on='steam_id_64', how='left')
|
||||
|
||||
# --- PTL: Pistol Stats ---
|
||||
pistol_rounds = [1, 13]
|
||||
df_pistol = df_events[df_events['round_num'].isin(pistol_rounds)]
|
||||
|
||||
if not df_pistol.empty:
|
||||
pk = df_pistol.groupby('attacker_steam_id').size()
|
||||
pd_death = df_pistol.groupby('victim_steam_id').size()
|
||||
p_stats = pd.DataFrame({'pk': pk, 'pd': pd_death}).fillna(0)
|
||||
p_stats['ptl_pistol_kd'] = p_stats['pk'] / p_stats['pd'].replace(0, 1)
|
||||
|
||||
phs = df_pistol[df_pistol['is_headshot'] == 1].groupby('attacker_steam_id').size()
|
||||
p_stats['phs'] = phs
|
||||
p_stats['phs'] = p_stats['phs'].fillna(0)
|
||||
p_stats['ptl_pistol_util_efficiency'] = p_stats['phs'] / p_stats['pk'].replace(0, 1)
|
||||
|
||||
p_stats.index.name = 'steam_id_64'
|
||||
df = df.merge(p_stats[['ptl_pistol_kd', 'ptl_pistol_util_efficiency']], on='steam_id_64', how='left')
|
||||
else:
|
||||
df['ptl_pistol_kd'] = 1.0
|
||||
df['ptl_pistol_util_efficiency'] = 0.0
|
||||
|
||||
# --- T/CT Stats ---
|
||||
ct_k = df_events[df_events['attacker_side'] == 'CT'].groupby('attacker_steam_id').size()
|
||||
ct_d = df_events[df_events['victim_side'] == 'CT'].groupby('victim_steam_id').size()
|
||||
t_k = df_events[df_events['attacker_side'] == 'T'].groupby('attacker_steam_id').size()
|
||||
t_d = df_events[df_events['victim_side'] == 'T'].groupby('victim_steam_id').size()
|
||||
|
||||
side_stats = pd.DataFrame({'ct_k': ct_k, 'ct_d': ct_d, 't_k': t_k, 't_d': t_d}).fillna(0)
|
||||
side_stats['side_rating_ct'] = side_stats['ct_k'] / side_stats['ct_d'].replace(0, 1)
|
||||
side_stats['side_rating_t'] = side_stats['t_k'] / side_stats['t_d'].replace(0, 1)
|
||||
side_stats['side_kd_diff_ct_t'] = side_stats['side_rating_ct'] - side_stats['side_rating_t']
|
||||
|
||||
side_stats.index.name = 'steam_id_64'
|
||||
df = df.merge(side_stats[['side_rating_ct', 'side_rating_t', 'side_kd_diff_ct_t']], on='steam_id_64', how='left')
|
||||
|
||||
# Side First Kill Rate
|
||||
# Need total rounds per side for denominator
|
||||
# Use df_player_rounds calculated in Match Point section
|
||||
# If not calculated there (no MP rounds), calc now
|
||||
if 'df_player_rounds' not in locals():
|
||||
q_all_rounds = f"SELECT match_id, round_num FROM fact_rounds WHERE match_id IN (SELECT match_id FROM fact_match_players WHERE steam_id_64 IN ({placeholders}))"
|
||||
df_all_rounds = pd.read_sql_query(q_all_rounds, conn, params=valid_ids)
|
||||
df_player_rounds = df_all_rounds.merge(df_fh_sides, on='match_id')
|
||||
mask_fh = df_player_rounds['round_num'] <= df_player_rounds['halftime_round']
|
||||
df_player_rounds['side'] = np.where(mask_fh, df_player_rounds['fh_side'],
|
||||
np.where(df_player_rounds['fh_side'] == 'CT', 'T', 'CT'))
|
||||
|
||||
rounds_per_side = df_player_rounds.groupby(['steam_id_64', 'side']).size().unstack(fill_value=0)
|
||||
if 'CT' not in rounds_per_side.columns: rounds_per_side['CT'] = 0
|
||||
if 'T' not in rounds_per_side.columns: rounds_per_side['T'] = 0
|
||||
|
||||
# First Kills (Earliest event in round)
|
||||
# Group by match, round -> min time.
|
||||
fk_events = df_events.sort_values('event_time').drop_duplicates(['match_id', 'round_num'])
|
||||
fk_ct = fk_events[fk_events['attacker_side'] == 'CT'].groupby('attacker_steam_id').size()
|
||||
fk_t = fk_events[fk_events['attacker_side'] == 'T'].groupby('attacker_steam_id').size()
|
||||
|
||||
fk_stats = pd.DataFrame({'fk_ct': fk_ct, 'fk_t': fk_t}).fillna(0)
|
||||
fk_stats = fk_stats.join(rounds_per_side, how='outer').fillna(0)
|
||||
|
||||
fk_stats['side_first_kill_rate_ct'] = fk_stats['fk_ct'] / fk_stats['CT'].replace(0, 1)
|
||||
fk_stats['side_first_kill_rate_t'] = fk_stats['fk_t'] / fk_stats['T'].replace(0, 1)
|
||||
|
||||
fk_stats.index.name = 'steam_id_64'
|
||||
df = df.merge(fk_stats[['side_first_kill_rate_ct', 'side_first_kill_rate_t']], on='steam_id_64', how='left')
|
||||
|
||||
else:
|
||||
# Fallbacks
|
||||
cols = ['hps_match_point_win_rate', 'hps_comeback_kd_diff', 'ptl_pistol_kd', 'ptl_pistol_util_efficiency',
|
||||
'side_rating_ct', 'side_rating_t', 'side_first_kill_rate_ct', 'side_first_kill_rate_t', 'side_kd_diff_ct_t']
|
||||
for c in cols:
|
||||
df[c] = 0
|
||||
|
||||
df['hps_match_point_win_rate'] = df['hps_match_point_win_rate'].fillna(0.5)
|
||||
|
||||
# HPS Pressure Entry Rate (Entry Kills in Losing Matches)
|
||||
q_mp_team = f"SELECT match_id, steam_id_64, is_win, entry_kills FROM fact_match_players WHERE steam_id_64 IN ({placeholders})"
|
||||
df_mp_team = pd.read_sql_query(q_mp_team, conn, params=valid_ids)
|
||||
if not df_mp_team.empty:
|
||||
losing_matches = df_mp_team[df_mp_team['is_win'] == 0]
|
||||
if not losing_matches.empty:
|
||||
# Average entry kills per losing match
|
||||
pressure_entry = losing_matches.groupby('steam_id_64')['entry_kills'].mean().reset_index()
|
||||
pressure_entry.rename(columns={'entry_kills': 'hps_pressure_entry_rate'}, inplace=True)
|
||||
df = df.merge(pressure_entry, on='steam_id_64', how='left')
|
||||
|
||||
if 'hps_pressure_entry_rate' not in df.columns:
|
||||
df['hps_pressure_entry_rate'] = 0
|
||||
df['hps_pressure_entry_rate'] = df['hps_pressure_entry_rate'].fillna(0)
|
||||
|
||||
# 5. PTL (Additional Features: Kills & Multi)
|
||||
query_ptl = f"""
|
||||
SELECT ev.attacker_steam_id as steam_id_64, COUNT(*) as pistol_kills
|
||||
FROM fact_round_events ev
|
||||
WHERE ev.event_type = 'kill' AND ev.round_num IN (1, 13)
|
||||
AND ev.attacker_steam_id IN ({placeholders})
|
||||
GROUP BY ev.attacker_steam_id
|
||||
"""
|
||||
df_ptl = pd.read_sql_query(query_ptl, conn, params=valid_ids)
|
||||
if not df_ptl.empty:
|
||||
df = df.merge(df_ptl, on='steam_id_64', how='left')
|
||||
df['ptl_pistol_kills'] = df['pistol_kills'] / df['matches_played']
|
||||
else:
|
||||
df['ptl_pistol_kills'] = 0
|
||||
|
||||
query_ptl_multi = f"""
|
||||
SELECT attacker_steam_id as steam_id_64, COUNT(*) as multi_cnt
|
||||
FROM (
|
||||
SELECT match_id, round_num, attacker_steam_id, COUNT(*) as k
|
||||
FROM fact_round_events
|
||||
WHERE event_type = 'kill' AND round_num IN (1, 13)
|
||||
AND attacker_steam_id IN ({placeholders})
|
||||
GROUP BY match_id, round_num, attacker_steam_id
|
||||
HAVING k >= 2
|
||||
)
|
||||
GROUP BY attacker_steam_id
|
||||
"""
|
||||
df_ptl_multi = pd.read_sql_query(query_ptl_multi, conn, params=valid_ids)
|
||||
if not df_ptl_multi.empty:
|
||||
df = df.merge(df_ptl_multi, on='steam_id_64', how='left')
|
||||
df['ptl_pistol_multikills'] = df['multi_cnt'] / df['matches_played']
|
||||
else:
|
||||
df['ptl_pistol_multikills'] = 0
|
||||
|
||||
# PTL Win Rate (Pandas Logic using fixed winner_side)
|
||||
if not df_rounds.empty and has_sides:
|
||||
# Ensure df_player_rounds exists
|
||||
if 'df_player_rounds' not in locals():
|
||||
q_all_rounds = f"SELECT match_id, round_num FROM fact_rounds WHERE match_id IN (SELECT match_id FROM fact_match_players WHERE steam_id_64 IN ({placeholders}))"
|
||||
df_all_rounds = pd.read_sql_query(q_all_rounds, conn, params=valid_ids)
|
||||
df_player_rounds = df_all_rounds.merge(df_fh_sides, on='match_id')
|
||||
mask_fh = df_player_rounds['round_num'] <= df_player_rounds['halftime_round']
|
||||
df_player_rounds['side'] = np.where(mask_fh, df_player_rounds['fh_side'],
|
||||
np.where(df_player_rounds['fh_side'] == 'CT', 'T', 'CT'))
|
||||
|
||||
# Filter for Pistol Rounds (1, 13)
|
||||
player_pistol = df_player_rounds[df_player_rounds['round_num'].isin([1, 13])].copy()
|
||||
|
||||
# Merge with df_rounds to get calculated winner_side
|
||||
# Note: df_rounds has the fixed 'winner_side' column
|
||||
player_pistol = player_pistol.merge(df_rounds[['match_id', 'round_num', 'winner_side']], on=['match_id', 'round_num'], how='left')
|
||||
|
||||
# Calculate Win
|
||||
player_pistol['is_win'] = (player_pistol['side'] == player_pistol['winner_side']).astype(int)
|
||||
|
||||
ptl_wins = player_pistol.groupby('steam_id_64')['is_win'].agg(['sum', 'count']).reset_index()
|
||||
ptl_wins.rename(columns={'sum': 'pistol_wins', 'count': 'pistol_rounds'}, inplace=True)
|
||||
|
||||
ptl_wins['ptl_pistol_win_rate'] = ptl_wins['pistol_wins'] / ptl_wins['pistol_rounds'].replace(0, 1)
|
||||
df = df.merge(ptl_wins[['steam_id_64', 'ptl_pistol_win_rate']], on='steam_id_64', how='left')
|
||||
else:
|
||||
df['ptl_pistol_win_rate'] = 0.5
|
||||
|
||||
df['ptl_pistol_multikills'] = df['ptl_pistol_multikills'].fillna(0)
|
||||
df['ptl_pistol_win_rate'] = df['ptl_pistol_win_rate'].fillna(0.5)
|
||||
|
||||
# 7. UTIL (Enhanced with Prop Frequency)
|
||||
# Usage Rate: Average number of grenades purchased per round
|
||||
df['util_usage_rate'] = (
|
||||
df['sum_util_flash'] + df['sum_util_smoke'] +
|
||||
df['sum_util_molotov'] + df['sum_util_he'] + df['sum_util_decoy']
|
||||
) / df['rounds_played'].replace(0, 1) * 100 # Multiply by 100 to make it comparable to other metrics (e.g. 1.5 nades/round -> 150)
|
||||
|
||||
# Fallback if no new data yet (rely on old logic or keep 0)
|
||||
# We can try to fetch equipment_value as backup if sum is 0
|
||||
if df['util_usage_rate'].sum() == 0:
|
||||
query_eco = f"""
|
||||
SELECT steam_id_64, AVG(equipment_value) as avg_equip_val
|
||||
FROM fact_round_player_economy
|
||||
WHERE steam_id_64 IN ({placeholders})
|
||||
GROUP BY steam_id_64
|
||||
"""
|
||||
df_eco = pd.read_sql_query(query_eco, conn, params=valid_ids)
|
||||
if not df_eco.empty:
|
||||
df_eco['util_usage_rate_backup'] = df_eco['avg_equip_val'] / 50.0 # Scaling factor for equipment value
|
||||
df = df.merge(df_eco[['steam_id_64', 'util_usage_rate_backup']], on='steam_id_64', how='left')
|
||||
df['util_usage_rate'] = df['util_usage_rate_backup'].fillna(0)
|
||||
df.drop(columns=['util_usage_rate_backup'], inplace=True)
|
||||
|
||||
# Final Mappings
|
||||
df['total_matches'] = df['matches_played']
|
||||
|
||||
return df.fillna(0)
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _calculate_ultimate_scores(df):
|
||||
def n(col):
|
||||
if col not in df.columns: return 50
|
||||
s = df[col]
|
||||
if s.max() == s.min(): return 50
|
||||
return (s - s.min()) / (s.max() - s.min()) * 100
|
||||
|
||||
df = df.copy()
|
||||
|
||||
# BAT (30%)
|
||||
df['score_bat'] = (
|
||||
0.25 * n('basic_avg_rating') +
|
||||
0.20 * n('basic_avg_kd') +
|
||||
0.15 * n('basic_avg_adr') +
|
||||
0.10 * n('bat_avg_duel_win_rate') +
|
||||
0.10 * n('bat_kd_diff_high_elo') +
|
||||
0.10 * n('basic_avg_kill_3')
|
||||
)
|
||||
|
||||
# STA (15%)
|
||||
df['score_sta'] = (
|
||||
0.30 * (100 - n('sta_rating_volatility')) +
|
||||
0.30 * n('sta_loss_rating') +
|
||||
0.20 * n('sta_win_rating') +
|
||||
0.10 * (100 - abs(n('sta_time_rating_corr')))
|
||||
)
|
||||
|
||||
# HPS (20%)
|
||||
df['score_hps'] = (
|
||||
0.30 * n('sum_1v3p') +
|
||||
0.20 * n('hps_match_point_win_rate') +
|
||||
0.20 * n('hps_comeback_kd_diff') +
|
||||
0.15 * n('hps_pressure_entry_rate') +
|
||||
0.15 * n('basic_avg_rating')
|
||||
)
|
||||
|
||||
# PTL (10%)
|
||||
df['score_ptl'] = (
|
||||
0.40 * n('ptl_pistol_kills') +
|
||||
0.40 * n('ptl_pistol_win_rate') +
|
||||
0.20 * n('basic_avg_headshot_kills') # Pistol rounds rely on HS
|
||||
)
|
||||
|
||||
# T/CT (10%)
|
||||
df['score_tct'] = (
|
||||
0.35 * n('side_rating_ct') +
|
||||
0.35 * n('side_rating_t') +
|
||||
0.15 * n('side_first_kill_rate_ct') +
|
||||
0.15 * n('side_first_kill_rate_t')
|
||||
)
|
||||
|
||||
# UTIL (10%)
|
||||
# Emphasize prop frequency (usage_rate)
|
||||
df['score_util'] = (
|
||||
0.35 * n('util_usage_rate') +
|
||||
0.25 * n('util_avg_nade_dmg') +
|
||||
0.20 * n('util_avg_flash_time') +
|
||||
0.20 * n('util_avg_flash_enemy')
|
||||
)
|
||||
|
||||
return df
|
||||
|
||||
@staticmethod
|
||||
def get_roster_features_distribution(target_steam_id):
|
||||
"""
|
||||
Calculates rank and distribution of the target player's L3 features (Scores) within the active roster.
|
||||
"""
|
||||
from web.services.web_service import WebService
|
||||
import json
|
||||
|
||||
# 1. Get Active Roster IDs
|
||||
lineups = WebService.get_lineups()
|
||||
active_roster_ids = []
|
||||
if lineups:
|
||||
try:
|
||||
raw_ids = json.loads(lineups[0]['player_ids_json'])
|
||||
active_roster_ids = [str(uid) for uid in raw_ids]
|
||||
except:
|
||||
pass
|
||||
|
||||
if not active_roster_ids:
|
||||
return None
|
||||
|
||||
# 2. Fetch L3 features for all roster members
|
||||
placeholders = ','.join('?' for _ in active_roster_ids)
|
||||
sql = f"""
|
||||
SELECT
|
||||
steam_id_64,
|
||||
score_bat, score_sta, score_hps, score_ptl, score_tct, score_util
|
||||
FROM dm_player_features
|
||||
WHERE steam_id_64 IN ({placeholders})
|
||||
"""
|
||||
rows = query_db('l3', sql, active_roster_ids)
|
||||
|
||||
if not rows:
|
||||
return None
|
||||
|
||||
stats_map = {row['steam_id_64']: dict(row) for row in rows}
|
||||
target_steam_id = str(target_steam_id)
|
||||
|
||||
# If target not in map (maybe no L3 data yet), default to 0
|
||||
if target_steam_id not in stats_map:
|
||||
stats_map[target_steam_id] = {
|
||||
'score_bat': 0, 'score_sta': 0, 'score_hps': 0,
|
||||
'score_ptl': 0, 'score_tct': 0, 'score_util': 0
|
||||
}
|
||||
|
||||
# 3. Calculate Distribution
|
||||
metrics = ['score_bat', 'score_sta', 'score_hps', 'score_ptl', 'score_tct', 'score_util']
|
||||
result = {}
|
||||
|
||||
for m in metrics:
|
||||
values = [p.get(m, 0) or 0 for p in stats_map.values()]
|
||||
target_val = stats_map[target_steam_id].get(m, 0) or 0
|
||||
|
||||
if not values:
|
||||
result[m] = None
|
||||
continue
|
||||
|
||||
values.sort(reverse=True)
|
||||
|
||||
try:
|
||||
rank = values.index(target_val) + 1
|
||||
except ValueError:
|
||||
rank = len(values)
|
||||
|
||||
result[m] = {
|
||||
'val': target_val,
|
||||
'rank': rank,
|
||||
'total': len(values),
|
||||
'min': min(values),
|
||||
'max': max(values),
|
||||
'avg': sum(values) / len(values)
|
||||
}
|
||||
|
||||
return result
|
||||
|
||||
@@ -589,8 +589,10 @@ class StatsService:
|
||||
def get_roster_stats_distribution(target_steam_id):
|
||||
"""
|
||||
Calculates rank and distribution of the target player within the active roster.
|
||||
Now covers all L3 Basic Features for Detailed Panel.
|
||||
"""
|
||||
from web.services.web_service import WebService
|
||||
from web.services.feature_service import FeatureService
|
||||
import json
|
||||
import numpy as np
|
||||
|
||||
@@ -604,72 +606,64 @@ class StatsService:
|
||||
except:
|
||||
pass
|
||||
|
||||
# Ensure target is in list (if not in roster, compare against roster anyway)
|
||||
# If roster is empty, return None
|
||||
if not active_roster_ids:
|
||||
return None
|
||||
|
||||
# 2. Fetch stats for all roster members
|
||||
# 2. Fetch L3 features for all roster members
|
||||
# We need to use FeatureService to get the full L3 set (including detailed stats)
|
||||
# Assuming L3 data is up to date.
|
||||
|
||||
placeholders = ','.join('?' for _ in active_roster_ids)
|
||||
sql = f"""
|
||||
SELECT
|
||||
CAST(steam_id_64 AS TEXT) as steam_id_64,
|
||||
AVG(rating) as rating,
|
||||
AVG(kd_ratio) as kd,
|
||||
AVG(adr) as adr,
|
||||
AVG(kast) as kast
|
||||
FROM fact_match_players
|
||||
WHERE CAST(steam_id_64 AS TEXT) IN ({placeholders})
|
||||
GROUP BY steam_id_64
|
||||
"""
|
||||
rows = query_db('l2', sql, active_roster_ids)
|
||||
sql = f"SELECT * FROM dm_player_features WHERE steam_id_64 IN ({placeholders})"
|
||||
rows = query_db('l3', sql, active_roster_ids)
|
||||
|
||||
if not rows:
|
||||
return None
|
||||
|
||||
stats_map = {row['steam_id_64']: dict(row) for row in rows}
|
||||
|
||||
# Ensure target_steam_id is string
|
||||
target_steam_id = str(target_steam_id)
|
||||
|
||||
# If target player not in stats_map (e.g. no matches), handle gracefullly
|
||||
# If target not in map (e.g. no L3 data), try to add empty default
|
||||
if target_steam_id not in stats_map:
|
||||
# Try fetch target stats individually if not in roster list
|
||||
target_stats = StatsService.get_player_basic_stats(target_steam_id)
|
||||
if target_stats:
|
||||
stats_map[target_steam_id] = target_stats
|
||||
else:
|
||||
# If still no stats, we can't rank them.
|
||||
# But we can still return the roster stats for others?
|
||||
# The prompt implies "No team data" appears, meaning this function returns valid structure but empty values?
|
||||
# Or returns None.
|
||||
# Let's verify what happens if target has no stats but others do.
|
||||
# We should probably add a dummy entry for target so dashboard renders '0' instead of crashing or 'No data'
|
||||
stats_map[target_steam_id] = {'rating': 0, 'kd': 0, 'adr': 0, 'kast': 0}
|
||||
|
||||
# 3. Calculate Distribution
|
||||
metrics = ['rating', 'kd', 'adr', 'kast']
|
||||
stats_map[target_steam_id] = {}
|
||||
|
||||
# 3. Calculate Distribution for ALL metrics
|
||||
# Define metrics list (must match Detailed Panel keys)
|
||||
metrics = [
|
||||
'basic_avg_rating', 'basic_avg_kd', 'basic_avg_kast', 'basic_avg_rws', 'basic_avg_adr',
|
||||
'basic_avg_headshot_kills', 'basic_headshot_rate', 'basic_avg_assisted_kill', 'basic_avg_awp_kill', 'basic_avg_jump_count',
|
||||
'basic_avg_first_kill', 'basic_avg_first_death', 'basic_first_kill_rate', 'basic_first_death_rate',
|
||||
'basic_avg_kill_2', 'basic_avg_kill_3', 'basic_avg_kill_4', 'basic_avg_kill_5',
|
||||
'basic_avg_perfect_kill', 'basic_avg_revenge_kill',
|
||||
# L3 Advanced Dimensions
|
||||
'sta_last_30_rating', 'sta_win_rating', 'sta_loss_rating', 'sta_rating_volatility', 'sta_time_rating_corr',
|
||||
'bat_kd_diff_high_elo', 'bat_avg_duel_win_rate', 'bat_avg_duel_freq',
|
||||
'hps_clutch_win_rate_1v1', 'hps_clutch_win_rate_1v3_plus', 'hps_match_point_win_rate', 'hps_pressure_entry_rate', 'hps_comeback_kd_diff',
|
||||
'ptl_pistol_kills', 'ptl_pistol_win_rate', 'ptl_pistol_kd',
|
||||
'side_rating_ct', 'side_rating_t', 'side_first_kill_rate_ct', 'side_first_kill_rate_t', 'side_kd_diff_ct_t',
|
||||
'util_avg_nade_dmg', 'util_avg_flash_time', 'util_avg_flash_enemy', 'util_usage_rate'
|
||||
]
|
||||
|
||||
# Mapping for L2 legacy calls (if any) - mainly map 'rating' to 'basic_avg_rating' etc if needed
|
||||
# But here we just use L3 columns directly.
|
||||
|
||||
result = {}
|
||||
|
||||
for m in metrics:
|
||||
# Extract values for this metric from all players
|
||||
values = [p[m] for p in stats_map.values() if p[m] is not None]
|
||||
target_val = stats_map[target_steam_id].get(m)
|
||||
values = [p.get(m, 0) or 0 for p in stats_map.values()]
|
||||
target_val = stats_map[target_steam_id].get(m, 0) or 0
|
||||
|
||||
if target_val is None or not values:
|
||||
if not values:
|
||||
result[m] = None
|
||||
continue
|
||||
|
||||
# Sort descending (higher is better)
|
||||
values.sort(reverse=True)
|
||||
|
||||
# Rank (1-based)
|
||||
# Rank
|
||||
try:
|
||||
rank = values.index(target_val) + 1
|
||||
except ValueError:
|
||||
# Floating point precision issue? Find closest
|
||||
closest = min(values, key=lambda x: abs(x - target_val))
|
||||
rank = values.index(closest) + 1
|
||||
rank = len(values)
|
||||
|
||||
result[m] = {
|
||||
'val': target_val,
|
||||
@@ -680,6 +674,16 @@ class StatsService:
|
||||
'avg': sum(values) / len(values)
|
||||
}
|
||||
|
||||
# Legacy mapping for top cards (rating, kd, adr, kast)
|
||||
legacy_map = {
|
||||
'basic_avg_rating': 'rating',
|
||||
'basic_avg_kd': 'kd',
|
||||
'basic_avg_adr': 'adr',
|
||||
'basic_avg_kast': 'kast'
|
||||
}
|
||||
if m in legacy_map:
|
||||
result[legacy_map[m]] = result[m]
|
||||
|
||||
return result
|
||||
|
||||
@staticmethod
|
||||
|
||||
@@ -141,6 +141,153 @@
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 2.5 Detailed Stats Panel -->
|
||||
<div class="bg-white dark:bg-slate-800 shadow-lg rounded-2xl p-6 border border-gray-100 dark:border-slate-700">
|
||||
<h3 class="text-lg font-bold text-gray-900 dark:text-white mb-6 flex items-center gap-2">
|
||||
<span>📊</span> 详细数据面板 (Detailed Stats)
|
||||
</h3>
|
||||
<div class="grid grid-cols-2 md:grid-cols-3 lg:grid-cols-5 gap-y-6 gap-x-4">
|
||||
{% macro detail_item(label, value, key, format_str='{:.2f}', sublabel=None) %}
|
||||
{% set dist = distribution[key] if distribution else None %}
|
||||
<div class="flex flex-col group relative">
|
||||
<div class="flex justify-between items-center mb-1">
|
||||
<span class="text-xs font-bold text-gray-400 uppercase tracking-wider">{{ label }}</span>
|
||||
{% if dist %}
|
||||
<span class="inline-flex items-center px-1 py-0.5 rounded text-[9px] font-bold
|
||||
{% if dist.rank == 1 %}bg-yellow-50 text-yellow-700 border border-yellow-100
|
||||
{% elif dist.rank <= 3 %}bg-gray-50 text-gray-600 border border-gray-100
|
||||
{% else %}text-gray-300{% endif %}">
|
||||
#{{ dist.rank }}
|
||||
</span>
|
||||
{% endif %}
|
||||
</div>
|
||||
|
||||
<div class="flex items-baseline gap-1 mb-1">
|
||||
<span class="text-xl font-black text-gray-900 dark:text-white font-mono">
|
||||
{{ format_str.format(value if value is not none else 0) }}
|
||||
</span>
|
||||
{% if sublabel %}
|
||||
<span class="text-[10px] text-gray-400">{{ sublabel }}</span>
|
||||
{% endif %}
|
||||
</div>
|
||||
|
||||
<!-- Distribution Bar -->
|
||||
{% if dist %}
|
||||
<div class="w-full h-1 bg-gray-100 dark:bg-slate-700 rounded-full overflow-hidden relative mt-1">
|
||||
{% set range = dist.max - dist.min %}
|
||||
{% set percent = ((dist.val - dist.min) / range * 100) if range > 0 else 100 %}
|
||||
<div class="absolute h-full bg-yrtv-400/60 rounded-full" style="width: {{ percent }}%"></div>
|
||||
<!-- Avg Marker -->
|
||||
{% set avg_pct = ((dist.avg - dist.min) / range * 100) if range > 0 else 50 %}
|
||||
<div class="absolute h-full w-0.5 bg-gray-400 dark:bg-slate-400 top-0" style="left: {{ avg_pct }}%"></div>
|
||||
</div>
|
||||
<div class="flex justify-between text-[9px] text-gray-300 dark:text-gray-600 font-mono mt-0.5">
|
||||
<span>L:{{ format_str.format(dist.min) }}</span>
|
||||
<span>H:{{ format_str.format(dist.max) }}</span>
|
||||
</div>
|
||||
{% endif %}
|
||||
</div>
|
||||
{% endmacro %}
|
||||
|
||||
<!-- Row 1: Core -->
|
||||
{{ detail_item('Rating (评分)', features['basic_avg_rating'], 'basic_avg_rating') }}
|
||||
{{ detail_item('KD Ratio (击杀比)', features['basic_avg_kd'], 'basic_avg_kd') }}
|
||||
{{ detail_item('KAST (贡献率)', features['basic_avg_kast'], 'basic_avg_kast', '{:.1%}') }}
|
||||
{{ detail_item('RWS (每局得分)', features['basic_avg_rws'], 'basic_avg_rws') }}
|
||||
{{ detail_item('ADR (场均伤害)', features['basic_avg_adr'], 'basic_avg_adr', '{:.1f}') }}
|
||||
|
||||
<!-- Row 2: Combat -->
|
||||
{{ detail_item('Avg HS (场均爆头)', features['basic_avg_headshot_kills'], 'basic_avg_headshot_kills') }}
|
||||
{{ detail_item('HS Rate (爆头率)', features['basic_headshot_rate'], 'basic_headshot_rate', '{:.1%}') }}
|
||||
{{ detail_item('Assists (场均助攻)', features['basic_avg_assisted_kill'], 'basic_avg_assisted_kill') }}
|
||||
{{ detail_item('AWP Kills (狙击击杀)', features['basic_avg_awp_kill'], 'basic_avg_awp_kill') }}
|
||||
{{ detail_item('Jumps (场均跳跃)', features['basic_avg_jump_count'], 'basic_avg_jump_count', '{:.1f}') }}
|
||||
|
||||
<!-- Row 3: Opening -->
|
||||
{{ detail_item('First Kill (场均首杀)', features['basic_avg_first_kill'], 'basic_avg_first_kill') }}
|
||||
{{ detail_item('First Death (场均首死)', features['basic_avg_first_death'], 'basic_avg_first_death') }}
|
||||
{{ detail_item('FK Rate (首杀率)', features['basic_first_kill_rate'], 'basic_first_kill_rate', '{:.1%}') }}
|
||||
{{ detail_item('FD Rate (首死率)', features['basic_first_death_rate'], 'basic_first_death_rate', '{:.1%}') }}
|
||||
<div class="hidden lg:block"></div> <!-- Spacer -->
|
||||
|
||||
<!-- Row 4: Multi-Kills -->
|
||||
{{ detail_item('2K Rounds (双杀)', features['basic_avg_kill_2'], 'basic_avg_kill_2') }}
|
||||
{{ detail_item('3K Rounds (三杀)', features['basic_avg_kill_3'], 'basic_avg_kill_3') }}
|
||||
{{ detail_item('4K Rounds (四杀)', features['basic_avg_kill_4'], 'basic_avg_kill_4') }}
|
||||
{{ detail_item('5K Rounds (五杀)', features['basic_avg_kill_5'], 'basic_avg_kill_5') }}
|
||||
|
||||
<!-- Row 5: Special -->
|
||||
{{ detail_item('Perfect Kills (无伤杀)', features['basic_avg_perfect_kill'], 'basic_avg_perfect_kill') }}
|
||||
{{ detail_item('Revenge Kills (复仇杀)', features['basic_avg_revenge_kill'], 'basic_avg_revenge_kill') }}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 2.6 Advanced Dimensions Breakdown -->
|
||||
<div class="bg-white dark:bg-slate-800 shadow-lg rounded-2xl p-6 border border-gray-100 dark:border-slate-700">
|
||||
<h3 class="text-lg font-bold text-gray-900 dark:text-white mb-6 flex items-center gap-2">
|
||||
<span>🔬</span> 进阶能力分析 (Capabilities Breakdown)
|
||||
</h3>
|
||||
|
||||
<!-- Reusing detail_item macro, but with a different grid if needed -->
|
||||
<!-- Grouped by Dimensions -->
|
||||
<div class="space-y-8">
|
||||
<!-- Group 1: STA & BAT -->
|
||||
<div>
|
||||
<h4 class="text-xs font-black text-gray-400 uppercase tracking-widest mb-4 border-b border-gray-100 dark:border-slate-700 pb-2">
|
||||
STA (Stability) & BAT (Aim/Battle)
|
||||
</h4>
|
||||
<div class="grid grid-cols-2 md:grid-cols-3 lg:grid-cols-5 gap-y-6 gap-x-4">
|
||||
{{ detail_item('Last 30 Rating (近30场)', features['sta_last_30_rating'], 'sta_last_30_rating') }}
|
||||
{{ detail_item('Win Rating (胜局)', features['sta_win_rating'], 'sta_win_rating') }}
|
||||
{{ detail_item('Loss Rating (败局)', features['sta_loss_rating'], 'sta_loss_rating') }}
|
||||
{{ detail_item('Volatility (波动)', features['sta_rating_volatility'], 'sta_rating_volatility') }}
|
||||
{{ detail_item('Time Corr (耐力)', features['sta_time_rating_corr'], 'sta_time_rating_corr') }}
|
||||
|
||||
{{ detail_item('High Elo KD Diff (高分抗压)', features['bat_kd_diff_high_elo'], 'bat_kd_diff_high_elo') }}
|
||||
{{ detail_item('Duel Win% (对枪胜率)', features['bat_avg_duel_win_rate'], 'bat_avg_duel_win_rate', '{:.1%}') }}
|
||||
{{ detail_item('Duel Freq (对枪频率)', features['bat_avg_duel_freq'], 'bat_avg_duel_freq', '{:.1%}') }}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Group 2: HPS & PTL -->
|
||||
<div>
|
||||
<h4 class="text-xs font-black text-gray-400 uppercase tracking-widest mb-4 border-b border-gray-100 dark:border-slate-700 pb-2">
|
||||
HPS (Clutch/Pressure) & PTL (Pistol)
|
||||
</h4>
|
||||
<div class="grid grid-cols-2 md:grid-cols-3 lg:grid-cols-5 gap-y-6 gap-x-4">
|
||||
{{ detail_item('1v1 Win% (1v1胜率)', features['hps_clutch_win_rate_1v1'], 'hps_clutch_win_rate_1v1', '{:.1%}') }}
|
||||
{{ detail_item('1v3+ Win% (残局大神)', features['hps_clutch_win_rate_1v3_plus'], 'hps_clutch_win_rate_1v3_plus', '{:.1%}') }}
|
||||
{{ detail_item('Match Pt Win% (赛点胜率)', features['hps_match_point_win_rate'], 'hps_match_point_win_rate', '{:.1%}') }}
|
||||
{{ detail_item('Pressure Entry (逆风首杀)', features['hps_pressure_entry_rate'], 'hps_pressure_entry_rate', '{:.1%}') }}
|
||||
{{ detail_item('Comeback KD (翻盘KD)', features['hps_comeback_kd_diff'], 'hps_comeback_kd_diff') }}
|
||||
|
||||
{{ detail_item('Pistol Kills (手枪击杀)', features['ptl_pistol_kills'], 'ptl_pistol_kills') }}
|
||||
{{ detail_item('Pistol Win% (手枪胜率)', features['ptl_pistol_win_rate'], 'ptl_pistol_win_rate', '{:.1%}') }}
|
||||
{{ detail_item('Pistol KD (手枪KD)', features['ptl_pistol_kd'], 'ptl_pistol_kd') }}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Group 3: SIDE & UTIL -->
|
||||
<div>
|
||||
<h4 class="text-xs font-black text-gray-400 uppercase tracking-widest mb-4 border-b border-gray-100 dark:border-slate-700 pb-2">
|
||||
SIDE (T/CT Preference) & UTIL (Utility)
|
||||
</h4>
|
||||
<div class="grid grid-cols-2 md:grid-cols-3 lg:grid-cols-5 gap-y-6 gap-x-4">
|
||||
{{ detail_item('CT Rating (CT评分)', features['side_rating_ct'], 'side_rating_ct') }}
|
||||
{{ detail_item('T Rating (T评分)', features['side_rating_t'], 'side_rating_t') }}
|
||||
{{ detail_item('CT FK Rate (CT首杀)', features['side_first_kill_rate_ct'], 'side_first_kill_rate_ct', '{:.1%}') }}
|
||||
{{ detail_item('T FK Rate (T首杀)', features['side_first_kill_rate_t'], 'side_first_kill_rate_t', '{:.1%}') }}
|
||||
{{ detail_item('Side KD Diff (攻防差)', features['side_kd_diff_ct_t'], 'side_kd_diff_ct_t') }}
|
||||
|
||||
{{ detail_item('Usage Rate (道具频率)', features['util_usage_rate'], 'util_usage_rate') }}
|
||||
{{ detail_item('Nade Dmg (雷火伤)', features['util_avg_nade_dmg'], 'util_avg_nade_dmg', '{:.1f}') }}
|
||||
{{ detail_item('Flash Time (致盲时间)', features['util_avg_flash_time'], 'util_avg_flash_time', '{:.2f}s') }}
|
||||
{{ detail_item('Flash Enemy (致盲人数)', features['util_avg_flash_enemy'], 'util_avg_flash_enemy') }}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 3. Match History & Comments (Bottom) -->
|
||||
<div class="grid grid-cols-1 lg:grid-cols-3 gap-8">
|
||||
<!-- Match History Table -->
|
||||
@@ -325,13 +472,31 @@ document.addEventListener('DOMContentLoaded', function() {
|
||||
|
||||
// Radar Chart
|
||||
const ctxRadar = document.getElementById('radarChart').getContext('2d');
|
||||
|
||||
// Prepare Distribution Data
|
||||
const dist = data.radar_dist || {};
|
||||
const getDist = (key) => dist[key] || { rank: '?', avg: 0 };
|
||||
|
||||
// Map friendly names to keys
|
||||
const keys = ['score_bat', 'score_hps', 'score_ptl', 'score_tct', 'score_util', 'score_sta'];
|
||||
// Corresponding Labels
|
||||
const rawLabels = ['Aim (BAT)', 'Clutch (HPS)', 'Pistol (PTL)', 'Defense (SIDE)', 'Util (UTIL)', 'Rating (STA)'];
|
||||
|
||||
const labels = rawLabels.map((l, i) => {
|
||||
const k = keys[i];
|
||||
const d = getDist(k);
|
||||
return `${l} #${d.rank}`;
|
||||
});
|
||||
|
||||
const teamAvgs = keys.map(k => getDist(k).avg);
|
||||
|
||||
new Chart(ctxRadar, {
|
||||
type: 'radar',
|
||||
data: {
|
||||
// Update labels to friendly names
|
||||
labels: ['Aim (BAT)', 'Clutch (HPS)', 'Pistol (PTL)', 'Defense (SIDE)', 'Util (UTIL)', 'Rating (STA)'],
|
||||
labels: labels,
|
||||
datasets: [{
|
||||
label: 'Ability',
|
||||
label: 'Player',
|
||||
data: [
|
||||
data.radar.BAT, data.radar.HPS,
|
||||
data.radar.PTL, data.radar.SIDE, data.radar.UTIL,
|
||||
@@ -344,16 +509,25 @@ document.addEventListener('DOMContentLoaded', function() {
|
||||
pointBorderColor: '#fff',
|
||||
pointHoverBackgroundColor: '#fff',
|
||||
pointHoverBorderColor: '#7c3aed'
|
||||
},
|
||||
{
|
||||
label: 'Team Avg',
|
||||
data: teamAvgs,
|
||||
backgroundColor: 'rgba(148, 163, 184, 0.2)', // Slate-400
|
||||
borderColor: '#94a3b8',
|
||||
borderWidth: 2,
|
||||
pointRadius: 0,
|
||||
borderDash: [5, 5]
|
||||
}]
|
||||
},
|
||||
options: {
|
||||
plugins: {
|
||||
legend: { display: false }
|
||||
legend: { display: true, position: 'bottom' }
|
||||
},
|
||||
scales: {
|
||||
r: {
|
||||
beginAtZero: true,
|
||||
suggestedMax: 1.5,
|
||||
suggestedMax: 100,
|
||||
angleLines: {
|
||||
color: 'rgba(156, 163, 175, 0.2)'
|
||||
},
|
||||
|
||||
Reference in New Issue
Block a user