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πŸš€ DOWNLOAD NOW

ARC Raiders Hack Intelligence Lab

ARC Raiders Hack Intelligence Lab is a research-oriented gameplay analysis toolkit for studying combat encounters, aim consistency, loot decisions, positioning, movement, extraction routes and overall match performance.

The project transforms recorded gameplay events into structured intelligence that can be reviewed across individual raids and longer play sessions.


🎯 Project Overview

ARC Raiders matches combine exploration, looting, PvE threats, PvP encounters and extraction decisions.

This project focuses on analyzing those elements through structured session data.

                    ARC RAIDERS SESSION
                           β”‚
                           β–Ό
                 β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
                 β”‚   Session Tracker  β”‚
                 β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                           β”‚
          β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
          β–Ό                β–Ό                β–Ό
       Combat            Loot          Positioning
          β”‚                β”‚                β”‚
          β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                           β–Ό
                  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
                  β”‚ Intelligence    β”‚
                  β”‚ Analysis Engine β”‚
                  β””β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                           β”‚
             β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
             β–Ό             β–Ό             β–Ό
           Aim          Routes        Decisions
             β”‚             β”‚             β”‚
             β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                           β–Ό
                    RAID REPORT

⚑ Core Features

🎯 Aim Analysis

Track combat performance through metrics such as:

  • Accuracy
  • First-shot timing
  • Target switching
  • Tracking consistency
  • Engagement duration
  • Damage efficiency
{
  "aim": {
    "accuracy": 0.78,
    "first_shot_ms": 286,
    "target_switches": 14,
    "tracking_score": 82,
    "damage_efficiency": 0.74
  }
}

πŸ’₯ Combat Intelligence

Analyze individual encounters and identify how fights developed.

CONTACT
   β”‚
   β–Ό
INITIAL POSITION
   β”‚
   β–Ό
FIRST SHOT
   β”‚
   β”œβ”€β”€ Advantage
   β”‚
   └── Disadvantage
          β”‚
          β–Ό
     REPOSITION
          β”‚
          β–Ό
      ENGAGEMENT
          β”‚
          β–Ό
     FIGHT RESULT

Useful metrics include:

Metric Example
Encounters 37
Successful Fights 24
Failed Fights 13
Avg. Damage 412
Avg. Fight Time 18.6s
Survival After Fight 68%

πŸŽ’ Loot Intelligence

Record loot decisions and identify inefficient inventory patterns.

LOCATION
    β”‚
    β–Ό
LOOT FOUND
    β”‚
    β”œβ”€β”€ Keep
    β”œβ”€β”€ Replace
    β”œβ”€β”€ Drop
    └── Ignore
         β”‚
         β–Ό
   INVENTORY VALUE

Possible analysis fields:

  • Item category
  • Rarity
  • Carry value
  • Weight
  • Loot location
  • Time spent looting
  • Inventory changes
  • Extraction value

πŸ—ΊοΈ Route Analysis

Analyze movement through a raid and compare successful extraction routes.

Spawn
  β”‚
  β–Ό
Loot Area ────────► POI
  β”‚                  β”‚
  β”‚                  β–Ό
  └────────────► Encounter
                     β”‚
                     β–Ό
                  Rotation
                     β”‚
                     β–Ό
                  Extract

Route Metrics

  • Distance traveled
  • Time between locations
  • Number of encounters
  • Loot collected
  • Damage received
  • Extraction success
  • Risk level

πŸ‘οΈ Positioning Intelligence

Positioning can be represented as a sequence of tactical states:

SAFE
 β”‚
 β–Ό
UNKNOWN CONTACT
 β”‚
 β–Ό
COVER
 β”‚
 β”œβ”€β”€ HOLD
 β”‚
 β”œβ”€β”€ REPOSITION
 β”‚
 └── DISENGAGE
       β”‚
       β–Ό
    EXTRACT

The analyzer can compare positioning choices with the final outcome of each encounter.


πŸ“‘ Encounter Timeline

Every raid can be represented as a chronological event stream.

00:00  Spawn
01:12  First loot interaction
02:47  ARC activity detected
04:03  Player contact
04:11  Combat begins
04:29  Reposition
05:17  Combat ends
06:42  High-value loot found
08:14  Extraction route selected
10:03  Extraction completed

πŸ“Š Raid Performance Score

The project can calculate a composite performance score.

Combat Efficiency       β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‘β–‘β–‘ 84%
Aim Consistency         β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‘β–‘β–‘β–‘ 81%
Loot Efficiency         β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‘β–‘ 89%
Route Efficiency        β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‘β–‘β–‘β–‘β–‘ 76%
Positioning             β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‘β–‘β–‘ 85%
Extraction Decisions    β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‘β–‘ 90%

🧠 Decision Matrix

Major decisions can be categorized and compared against outcomes.

{
  "decision": {
    "type": "extraction",
    "risk": "medium",
    "resources": 0.68,
    "distance": 420,
    "expected_value": 0.81,
    "outcome": "successful"
  }
}

This allows researchers to identify recurring decision patterns across multiple raids.


πŸ”₯ High-Risk Encounter Detection

The analysis engine can flag situations where several risk factors appear together.

LOW RESOURCES
      +
MULTIPLE CONTACTS
      +
LONG EXTRACTION ROUTE
      +
LOW POSITIONAL ADVANTAGE
      β”‚
      β–Ό
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚   HIGH RISK EVENT    β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

πŸ“ˆ Session Comparison

Multiple raids can be compared to identify long-term trends.

Category Session 1 Session 10 Session 25
Accuracy 64% 72% 78%
Extraction 52% 67% 76%
Loot Efficiency 61% 75% 84%
Combat Score 58 71 82
Decision Score 63 76 88

πŸ§ͺ Analysis Pipeline

RAW SESSION DATA
       β”‚
       β–Ό
EVENT NORMALIZATION
       β”‚
       β–Ό
COMBAT CLASSIFICATION
       β”‚
       β”œβ”€β”€β”€β”€ AIM
       β”œβ”€β”€β”€β”€ POSITION
       β”œβ”€β”€β”€β”€ LOOT
       └──── EXTRACTION
       β”‚
       β–Ό
PATTERN DETECTION
       β”‚
       β–Ό
PERFORMANCE SCORING
       β”‚
       β–Ό
SESSION COMPARISON
       β”‚
       β–Ό
INTELLIGENCE REPORT

πŸ“ Project Structure

arc-raiders-hack-intelligence-lab/
β”‚
β”œβ”€β”€ data/
β”‚   β”œβ”€β”€ raids/
β”‚   β”œβ”€β”€ encounters/
β”‚   β”œβ”€β”€ loot/
β”‚   └── routes/
β”‚
β”œβ”€β”€ analysis/
β”‚   β”œβ”€β”€ aim/
β”‚   β”œβ”€β”€ combat/
β”‚   β”œβ”€β”€ positioning/
β”‚   β”œβ”€β”€ loot/
β”‚   └── extraction/
β”‚
β”œβ”€β”€ reports/
β”‚   β”œβ”€β”€ raids/
β”‚   β”œβ”€β”€ encounters/
β”‚   └── performance/
β”‚
β”œβ”€β”€ schemas/
β”‚   β”œβ”€β”€ raid.json
β”‚   β”œβ”€β”€ encounter.json
β”‚   └── event.json
β”‚
β”œβ”€β”€ docs/
β”‚   β”œβ”€β”€ methodology.md
β”‚   └── architecture.md
β”‚
└── README.md

🧩 Example Raid Record

{
  "raid": {
    "duration": 603,
    "encounters": 4,
    "loot_events": 21,
    "distance": 1840,
    "damage_dealt": 1260,
    "damage_received": 740,
    "extracted": true,
    "performance_score": 86
  }
}

πŸš€ Roadmap

  • Raid session model
  • Combat event tracking
  • Loot analysis
  • Route analysis
  • Extraction statistics
  • Performance scoring
  • Interactive raid map
  • Encounter heatmaps
  • Advanced aim consistency metrics
  • Squad performance analysis
  • Long-term player trends
  • Exportable raid reports

πŸ” Project Scope

This repository is intended for gameplay research, statistics, visualization and review of recorded or manually collected session data.

It does not provide operational aimbots, wallhacks, triggerbots, memory manipulation or anti-cheat bypass functionality.


πŸ“œ License

MIT License.

See LICENSE for details.


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Advanced ARC Raiders hack-themed gameplay analysis lab for studying combat patterns, aim consistency, loot routes, positioning, extraction decisions and match performance

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