🗺️ Valorgrids: Tactical Heatmaps for Valorant

Valorgrids is a Python-based tool that creates grid-based heatmaps on Valorant maps, visualizing Kill Differential and Engagements (Kills + Deaths) per zone. This helps coaches, analysts, and players spot tactical patterns across maps like Haven, Icebox, and more.

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✨ Features

• 📊 Kill Differential Heatmap: Visualizes kill vs. death balance on each grid square.
• 🔴 Engagement Heatmap: Highlights zones with the most fights (Kills + Deaths combined).
• 🖼️ Custom map backgrounds (e.g., Haven, Icebox).
• 🔧 Toggle between Kill Diff and Engagements when running.
• 🗂️ CSV-based input for flexibility.
• 📈 High-res PNG output.

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🚀 How It Works

To use Valorgrids, clone the repository and make sure you have Python 3.8+ with the following packages installed:

• pandas
• numpy
• matplotlib

You can install everything via:

pip install -r requirements.txt

Place your map image (for example, Haven.png) inside the maps/ folder. Your CSV file should look like this:

Coordinate	Kills	Deaths	Differential
3,8	4 kills	1 deaths	+3
5,8	2 kills	0 deaths	+2
3,7	1 kills	2 deaths	-1

➡️ Columns:

• Coordinate: grid position as y,x (row,column),
• Kills: number of kills (e.g., 4 kills),
• Deaths: number of deaths (e.g., 1 deaths),
• Differential: (optional, it's recalculated anyway).

Once ready, run the script:

python valorgrids.py

You'll be prompted:

What heatmap do you want to generate? (diff / engage):

• Type diff to generate the Kill Differential Heatmap.
• Type engage to generate the Engagements Heatmap.

The output PNG file will be saved in your working directory as:

• demo_heatmap_diff.png
• demo_heatmap_engage.png

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🖼️ Example Outputs

Kill Differential	Engagements

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📂 Folder Structure

valorgrids/
├── maps/
│   └── Haven.png
├── demo_data.csv
├── valorgrids.py
├── requirements.txt
└── README.md

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✍️ Credits

Built by @Ominousx 🎮

For any feedback, feel free to:

• 🐛 Open an issue here on GitHub,
• 💬 Connect with me on Twitter/X @Ominousx

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💡 Roadmap

• Streamlit web interface.
• Auto-detect map & title from data.
• Support for other stats (e.g., assists, plants).