Advanced VALORANT gameplay intelligence laboratory focused on aim performance, spatial awareness, combat patterns, movement, positioning and competitive match analysis.
The project transforms structured gameplay events into measurable performance indicators and visual intelligence dashboards.
Scope: This repository is an educational gameplay-analysis and visualization concept. It does not implement operational cheats, game-memory manipulation, account bypasses, or anti-cheat evasion.
MATCH DATA
│
▼
┌───────────────────┐
│ Event Processor │
└─────────┬─────────┘
│
┌───────────────┼────────────────┐
▼ ▼ ▼
┌─────────┐ ┌───────────┐ ┌──────────┐
│ AIM │ │ COMBAT │ │ MOVEMENT │
└────┬────┘ └─────┬─────┘ └────┬─────┘
│ │ │
└───────────────┼───────────────┘
▼
┌───────────────────┐
│ Spatial Analytics │
└─────────┬─────────┘
▼
INTELLIGENCE CORE
│
▼
MATCH PERFORMANCE
The aim module evaluates recorded gameplay events and creates a detailed mechanical profile.
- reaction time
- target acquisition
- tracking consistency
- accuracy
- headshot percentage
- first-shot accuracy
- burst efficiency
- engagement distance
- crosshair placement
- close-range performance
- long-range performance
Example:
{
"session": "valorant-ranked-024",
"accuracy": 0.714,
"reaction_ms": 184,
"headshot_rate": 0.29,
"tracking_score": 83,
"crosshair_score": 91
}The spatial analysis layer provides an abstract visualization of gameplay events without interacting with the game client.
+------------------------------------------------+
| SPATIAL INTELLIGENCE |
| |
| ● Encounter |
| \ |
| \ |
| ▲ Player |
| \ |
| ───────► Direction |
| |
| ◎ Objective |
| |
| █████ Engagement Cluster |
| |
+------------------------------------------------+
Visualization layers can include:
- player position
- encounter markers
- objective locations
- movement direction
- rotation paths
- engagement clusters
- historical events
- high-risk areas
Individual fights can be represented as structured events.
ENGAGEMENT
│
├── Start Time
├── Duration
├── Distance
├── Weapon
├── Damage
├── Accuracy
├── Position
├── Movement
└── Result
Example:
{
"engagement": 31,
"distance_m": 27.4,
"damage": 147,
"accuracy": 0.68,
"duration_ms": 3640,
"position_score": 0.88,
"result": "win"
}Crosshair placement can be analyzed independently from the actual game client.
Possible metrics:
- average crosshair height
- pre-aim consistency
- angle preparation
- target acquisition distance
- first-shot alignment
- movement-to-aim synchronization
CROSSHAIR PROFILE
Placement ██████████████████ 91
Pre-Aim ████████████████ 84
Alignment █████████████████ 88
Reaction ███████████████ 81
Consistency ██████████████████ 90
Map analysis identifies patterns in positioning and engagements.
MAP
│
┌─────────────┼─────────────┐
│ │ │
▼ ▼ ▼
ATTACK DEFENSE ROTATION
│ │ │
└─────────────┼─────────────┘
▼
POSITION SCORE
Possible metrics:
| Metric | Example |
|---|---|
| Position Efficiency | 89% |
| Rotation Score | 84% |
| Engagement Density | 72% |
| Objective Control | 91% |
| Survival Efficiency | 86% |
| Map Awareness | 88% |
Movement data can be correlated with combat outcomes.
Tracked patterns:
- strafing
- acceleration
- directional changes
- repositioning
- stopping behavior
- movement during engagements
- rotation timing
- escape paths
STRAFING █████████████████ 87
POSITIONING ██████████████████ 91
REPOSITIONING ████████████████ 82
ROTATION ███████████████ 79
COMBAT MOVEMENT █████████████████ 89
The weapon module compares performance across different engagement types.
Tracked statistics:
- shots fired
- shots connected
- accuracy
- damage
- eliminations
- headshot rate
- engagement distance
- time-to-elimination
- weapon selection
WEAPON PERFORMANCE
Weapon A ██████████████████ 91
Weapon B ████████████████ 84
Weapon C ██████████████ 76
Weapon D ███████████████ 81
VALORANT matches can be analyzed round by round.
ROUND 01 ── Win
ROUND 02 ── Loss
ROUND 03 ── Win
ROUND 04 ── Loss
ROUND 05 ── Win
ROUND 06 ── Win
│
▼
ROUND PATTERN ANALYZER
│
▼
PERFORMANCE REPORT
Each round can contain:
- opening engagement
- utility events
- positioning
- rotations
- eliminations
- deaths
- objective events
- round result
All modules are combined into a single performance score.
+---------------------------------------+
| VALORANT MATCH INTELLIGENCE |
+---------------------------------------+
| Aim Performance 88 / 100 |
| Combat Decisions 84 / 100 |
| Crosshair Placement 91 / 100 |
| Movement 82 / 100 |
| Positioning 89 / 100 |
| Map Awareness 86 / 100 |
+---------------------------------------+
| OVERALL SCORE 87 / 100 |
+---------------------------------------+
Important moments can be displayed chronologically.
00:00 ── Round 01
│
├── 00:48 First Engagement
│
├── 01:31 Rotation
│
├── 02:14 Objective Event
│
├── 03:02 Multi-Target Fight
│
├── 04:26 Defensive Rotation
│
└── 05:01 Round Result
This provides a fast way to identify the most influential events in a match.
1. Import Match Dataset
↓
2. Normalize Events
↓
3. Detect Engagements
↓
4. Calculate Aim Metrics
↓
5. Analyze Crosshair Placement
↓
6. Process Movement
↓
7. Build Spatial Model
↓
8. Evaluate Rounds
↓
9. Generate Match Intelligence
↓
10. Export Performance Report
valorant-hack-aim-intelligence/
│
├── data/
│ ├── matches/
│ ├── rounds/
│ ├── weapons/
│ └── sessions/
│
├── analytics/
│ ├── aim/
│ ├── crosshair/
│ ├── combat/
│ ├── movement/
│ ├── positioning/
│ └── rounds/
│
├── spatial/
│ ├── maps/
│ ├── encounters/
│ └── heatmaps/
│
├── reports/
│ ├── matches/
│ ├── rounds/
│ └── performance/
│
├── examples/
│ └── sample-match.json
│
└── README.md
- Intelligence architecture
- Aim analytics
- Crosshair analysis
- Combat intelligence
- Movement analysis
- Spatial visualization
- Round analysis
- Weapon statistics
- Interactive map dashboard
- Historical session comparison
- Advanced round clustering
- Automated improvement reports
- Long-term performance tracking
Measure reaction, accuracy, tracking and crosshair consistency.
Compare performance across multiple competitive sessions.
Study individual encounters and identify recurring decision patterns.
Visualize rotations, engagements and high-risk areas.
Track movement, crosshair placement and aim consistency over time.
┌──────────────────────────────────────┐
│ VALORANT INTELLIGENCE │
├──────────────────────────────────────┤
│ AIM ████████████████ 88% │
│ CROSSHAIR █████████████████ 91% │
│ COMBAT ███████████████ 84% │
│ MOVEMENT ██████████████ 82% │
│ POSITION █████████████████ 89% │
│ MAP AWARENESS ████████████████ 86% │
├──────────────────────────────────────┤
│ OVERALL PERFORMANCE 87/100 │
└──────────────────────────────────────┘
This project is intended for educational gameplay analysis, visualization and competitive research.
It should not be used to interfere with online services, manipulate game clients, bypass security systems, or gain unauthorized access.
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