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league-of-legends-hack-lab
Advanced League of Legends hack-themed gameplay analysis lab for studying mechanics, decision-making, combat patterns, performance and match statistics
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README 快照
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League of Legends Hack Lab
League of Legends Hack Lab is a research-oriented toolkit for analyzing League of Legends gameplay, combat decisions, champion performance, mechanical consistency and match patterns.
The project organizes gameplay data into structured insights that can be used for performance review, training and competitive analysis.
🧠 Project Overview
LEAGUE OF LEGENDS
│
▼
┌───────────────────┐
│ Match Data │
└─────────┬─────────┘
│
┌────────────────┼────────────────┐
▼ ▼ ▼
Mechanics Combat Decisions
│ │ │
└────────────────┼────────────────┘
▼
┌────────────────────┐
│ Analysis Engine │
└──────────┬─────────┘
│
┌─────────────┼─────────────┐
▼ ▼ ▼
Patterns Metrics Insights
│ │ │
└─────────────┼─────────────┘
▼
PERFORMANCE REPORT⚡ Features
🎯 Mechanics Analysis
- Ability usage tracking
- Skill-shot accuracy metrics
- Combo consistency
- Reaction timing analysis
- Mechanical error detection
- Input pattern analysis
⚔️ Combat Intelligence
- Damage efficiency
- Kill participation
- Fight duration
- Trade efficiency
- First-action analysis
- Teamfight performance
🧭 Decision Analysis
- Positioning patterns
- Rotation decisions
- Objective timing
- Map movement
- Risk/reward evaluation
- Repeated decision patterns
📊 Performance Metrics
| Metric | Purpose |
|---|---|
| KDA | Overall combat performance |
| CS/min | Farming efficiency |
| GPM | Gold generation |
| DPM | Damage output |
| Vision Score | Map information |
| KP | Teamfight participation |
| Death Timing | Mistake analysis |
| Objective Impact | Macro performance |
🔬 Analysis Pipeline
RAW MATCH DATA
│
▼
DATA NORMALIZATION
│
▼
EVENT EXTRACTION
│
├── Champion Events
├── Combat Events
├── Objective Events
├── Movement Events
└── Item Events
│
▼
PATTERN DETECTION
│
▼
PERFORMANCE SCORING
│
▼
INSIGHT GENERATION
│
▼
FINAL REPORT📦 Example Data
{
"match_id": "sample-001",
"champion": "ExampleChampion",
"role": "mid",
"metrics": {
"kills": 9,
"deaths": 4,
"assists": 12,
"cs_per_min": 7.4,
"damage_per_min": 681
},
"analysis": {
"mechanics": 87,
"positioning": 79,
"decision_making": 84,
"teamfight": 91
}
}🧩 Core Modules
Combat Analyzer
Evaluates individual fights and identifies:
- favorable trades
- inefficient engagements
- ability timing
- damage windows
- target selection
- fight outcomes
Mechanics Analyzer
Tracks mechanical consistency across multiple matches.
MATCH 01 ████████████████░░ 82%
MATCH 02 █████████████████░ 87%
MATCH 03 ███████████████░░░ 79%
MATCH 04 ██████████████████ 91%Decision Matrix
Converts gameplay events into decision categories.
LOW RISK HIGH RISK
──────── ────────
GOOD SAFE PLAY HIGH IMPACT
BAD MISSED VALUE THROWPerformance Engine
Combines multiple metrics into a single analytical profile.
Mechanics █████████████████░░ 87
Combat ████████████████░░░ 83
Positioning ███████████████░░░░ 78
Decision Making ████████████████░░░ 84
Teamplay █████████████████░░ 88📁 Project Structure
league-of-legends-hack-lab/
│
├── data/
│ ├── matches/
│ ├── players/
│ └── events/
│
├── analysis/
│ ├── combat/
│ ├── mechanics/
│ ├── positioning/
│ └── decisions/
│
├── metrics/
│ ├── combat_metrics/
│ ├── macro_metrics/
│ └── performance_metrics/
│
├── reports/
│ ├── match_reports/
│ └── performance_reports/
│
├── config/
│ └── analysis.json
│
└── README.md📈 Match Intelligence
The project can build historical performance profiles across multiple games.
MATCH HISTORY
│
├── Game 01 → Strong Laning
├── Game 02 → Weak Mid Game
├── Game 03 → Strong Teamfights
├── Game 04 → Poor Positioning
└── Game 05 → Improved Macro
│
▼
TREND DETECTION
│
▼
PLAYER PROFILE🏆 Performance Score
A sample scoring model:
Performance Score =
Mechanics × 0.20
+ Combat × 0.25
+ Positioning × 0.20
+ Decision × 0.20
+ Teamplay × 0.15The weights can be customized for different analytical goals.
🗺️ Map Analysis
Map events can be grouped into:
- lane states
- rotations
- objectives
- vision events
- jungle interactions
- team movements
- dangerous positions
TOP
│
┌─────┼─────┐
│ │ │
│ OBJECTIVE │
│ │ │
MID ──────┼────── BOT
│ │ │
└─────┼─────┘
│
JUNGLE🔥 Research Goals
- Identify recurring mistakes
- Compare different matches
- Measure mechanical consistency
- Analyze fight efficiency
- Study positioning
- Track improvement over time
- Build competitive performance profiles
🛠️ Roadmap
v0.1
- Project architecture
- Match data model
- Basic performance metrics
v0.2
- Advanced combat analysis
- Mechanics scoring
- Decision matrix
- Historical trend analysis
v0.3
- Champion-specific profiles
- Interactive dashboards
- Match comparison system
- Advanced visualization
v1.0
- Full analytics pipeline
- Automated reports
- Performance history
- Competitive research dashboard
📜 License
This project is provided for research, gameplay analysis and educational purposes.
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Official distribution
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使用前核验
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