BSERgg: The Competitive Rating Engine

"Context is the difference between a high stat and a high-impact performance."

BSERgg is an advanced, context-aware analytical engine designed to quantify player performance in the Brawl Stars esports scene.

This repository marks the transition of the BSERgg algorithm from a private research environment (Google Colab) into a modular, and open-source Python library.

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The Philosophy

In competitive gaming, flat statistics (like total kills or total damage) fail to tell the whole story. BSERgg moves away from "flat benchmarks" and introduces Dynamic Performance Normalization.

The engine evaluates players not against an arbitrary number, but against the Lobby Intensity.

Key Features (Teaser)

• Dynamic Normalization: Ratings are scaled based on the match's average DPS and Kills. If the game is slow, the bar adjusts. If it's a bloodbath, the requirements for the MVP status rise.
• Role-Based Weighting: A Sniper's impact is measured differently than a Tank's. The engine automatically applies weights based on the Brawler's competitive class.
• Intensity Scaling: The algorithm detects the pace of the match and the series length (e.g., a high-stakes 2-1 set) to adjust performance multipliers.
• Heist-Burner Detection: Specific logic for Heist mode that balances Damage to Safe (DTS) with map control performance.

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Getting Started

Prerequisites

• Python 3.10 or higher.
• NumPy library.

Installation

1. Clone this repository: git clone https://github.com/your-username/bsergg-engine.git cd bsergg-engine

2. Set up a virtual environment: python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate

3. Install dependencies: pip install -r requirements.txt

Quick Usage

from engine import PlayerPerformance, MatchContext, get_match_ratings

players = [
    PlayerPerformance(name="Sitetampo", brawler="Frank", kills=12, dps=450.0, won=True)
]
context = MatchContext(map_name="Super Beach", game_mode="Brawl Ball", series_score="2-0")

results = get_match_ratings(players, context)
print(results)

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Roadmap

We are currently refactoring the core logic to follow Clean Code principles, ensuring the engine is ready for community contributions and professional integration.

• Core Engine: Pure mathematical functions for rating calculations.
• Data Adapters: Seamless integration with official Brawl Stars API JSON outputs.
• Public API Vision: Transforming the engine into a service that websites like Brawlify or competitive hubs can integrate via a public API.

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Rights & Licensing

This project is part of the BSERgg ecosystem. By open-sourcing the engine, we aim to provide the Brawl Stars community with a standardized, transparent, and context-aware method to measure competitive performance.

• Community Use: You are free to use this engine for personal projects, community-run tournaments, and academic research.
• Commercial Integrations: If you intend to integrate this algorithm into a commercial platform, mobile app, or high-traffic website (e.g., global stat trackers), please contact the author for proper attribution and integration support.
• License: This project is distributed under the MIT License. See the LICENSE file for more details.

BSERgg and its logic are developed by Daniel "chimdos" Martins.

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Tech Stack

• Language: Python 3.10+
• Math & Analytics: NumPy (High-performance distribution analysis)
• Architecture: Modular design using Dataclasses and abstractions.

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Developed by Daniel "chimdos" Martins.