Nine Realities Netcode Model

Multi-client state reconciliation in multiplayer game networking: Server-authoritative architecture with client-side prediction and rollback-based reconciliation

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Overview

This repository contains comprehensive research and analysis on advanced netcode architectures for multiplayer games, specifically focusing on the N+1 concurrent simulation model that powers modern competitive titles.

The N+1 Concurrent Simulation Model

In multiplayer networked games, the system maintains N client-local predicted simulations plus one server-authoritative simulation (N+1 total concurrent simulations):

• 1 server-authoritative simulation: The canonical game state that resolves all conflicts and determines final outcomes
• N client-local predicted simulations: Each player runs their own predicted world using local inputs and last known snapshots from the server

For an 8-player Rocket League match, this creates 9 concurrent simulations (8 client predictions + 1 server authority).

Each client continuously reconciles to the server using:

• Client-side prediction: Clients simulate their inputs immediately for responsive gameplay
• Server snapshots: Periodic authoritative state updates from the server
• Rollback and correction: When client prediction diverges from server state, the client rewinds and replays with corrected information
• Interpolation and tolerance-based blending: Smooth visual corrections to mask prediction errors

This architecture explains phenomena like replay divergence, phantom hits, and the competitive advantage of stable, low-entropy input patterns that minimize prediction correction costs.

Repository Structure

/docs          - Interactive HTML documentation (GitHub Pages)
/paper         - Full technical analysis (Word document)
README.md      - This file

Resources

📄 Interactive Documentation

View the full interactive analysis:

📊 Performance Benchmarks

Comprehensive performance analysis and benchmarking data:

• Performance Documentation: PERFORMANCE.md
• Topics: Network latency profiles, prediction accuracy, rollback costs, bandwidth requirements, CPU/memory utilization, real-world case studies
• Testing Methodology: Statistical analysis with 10,000+ gameplay minutes
• GitHub Pages: https://powder-ranger.github.io/nine-realities-netcode/
• Local: Open docs/index.html in your browser

📚 Technical Paper

Comprehensive technical breakdown:

• Location: /paper/Nine-Realities-Netcode-Model_-Technical-Analysis.docx
• Topics: State reconciliation, prediction algorithms, latency compensation, anti-cheat considerations

Key Concepts

State Reconciliation

• Client-side prediction
• Server reconciliation
• Input buffering and replay
• Lag compensation techniques

The N+1 Model

• Why N+1 simulations exist
• Synchronization challenges
• Trade-offs between responsiveness and consistency
• Real-world implementation patterns

Competitive Gaming Implications

• Peeker's advantage
• Hit registration accuracy
• Fair play considerations
• Anti-cheat integration

Research Background

This analysis synthesizes:

• Years of competitive gaming experience (Rocket League, FPS titles)
• 2+ years of networking and systems study
• 4000+ hours of research and development
• 95.2% verification rate across 98 sources

Applications

• Game Development: Implement robust netcode for competitive multiplayer
• Performance Analysis: Understanding latency and prediction artifacts
• Anti-Cheat: Detecting anomalies in client-server state divergence
• Education: Learning advanced networking concepts

Future Work

• Additional diagrams and visualizations
• Code examples in multiple languages
• Performance benchmarks
• Case studies from popular games

Contributing

This is an open research project. Contributions, corrections, and discussions are welcome.

Citation

If you use this research in your work, please cite:

## 🏗️ Architecture

### N+1 Concurrent Simulations

```mermaid
flowchart LR
  subgraph Clients
    C1[Client 1<br/>Predicted Sim]:::client
    C2[Client 2<br/>Predicted Sim]:::client
    Cn[Client N<br/>Predicted Sim]:::client
  end
  S[(Server<br/>Authoritative Sim)]:::server

  C1 -- inputs/acks --> S
  C2 -- inputs/acks --> S
  Cn -- inputs/acks --> S

  S -- snapshots --> C1
  S -- snapshots --> C2
  S -- snapshots --> Cn

  classDef client fill:#1f77b4,stroke:#0d3b66,color:#fff
  classDef server fill:#2ca02c,stroke:#145214,color:#fff

Prediction → Rollback → Blend Pipeline

sequenceDiagram
  participant Input as Local Input
  participant Client as Client Sim
  participant Buffer as Input Buffer
  participant Server as Server
  participant Render as Render

  Input->>Client: Apply input at t
  Client->>Buffer: Store (seq, t, input)
  Client->>Render: Predict state S_pred(t)

  Client->>Server: Send input seq + timestamp
  Server->>Server: Authoritative step (tick)
  Server-->>Client: Snapshot S_auth(Ts), ack last seq

  Client->>Client: Detect divergence Δ = |S_pred - S_auth|
  alt Δ > threshold
    Client->>Client: Rollback to snapshot Ts
    Client->>Client: Replay buffered inputs > Ts
    Client->>Render: Blend S_corr -> S_vis over N frames
  else
    Client->>Render: Continue normal interpolation
  end

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POWDER-RANGER. (2025). Nine Realities Netcode Model: Multi-client state reconciliation in multiplayer game networking. GitHub. https://github.com/POWDER-RANGER/nine-realities-netcode

## License

This project is licensed under the MIT License. See LICENSE..

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## 🏗️ Contributing

We welcome contributions! See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines on:
- Code style and standards
- Testing requirements
- Pull request process
- Community guidelines

## 🔒 Security

See [SECURITY.md](SECURITY.md) for our security policy and how to report vulnerabilities.

## 📊 Performance Benchmarks

## 📚 Case Studies
[**[View Comprehensive Performance Benchmarks →](PERFORMANCE.md)**

Detailed analysis including:
- Real-world latency measurements and impact on gameplay
- Rollback costs and frame performance data  
- Performance comparisons across different network conditions
- Case studies: Rocket League, Valorant, Overwatch 2
- CPU, memory, and bandwidth utilization metrics](url)
In-depth analysis of netcode implementations:
- **Rocket League**: Advanced ball prediction and physics reconciliation
- **Valorant**: 128-tick servers and peeker's advantage mitigation
- **Overwatch 2**: Favor-the-shooter vs hit registration accuracy

*(Detailed case studies in progress)*

If this research has helped your work, please consider [**sponsoring further development**](https://github.com/sponsors/POWDER-RANGER). Every contribution helps fund more detailed analysis, code examples, and community support.

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**Built with**: Deep technical analysis, competitive gaming insight, and years of hands-on experience