In this article, I want to share my (possibly unconventional) visualization approach.
Pass network analysis has long been a popular visualization method amongst football analysts. So why yet another article about it? Well, one rainy Sunday I thought it would be a great idea to boost my science ego by running someone else’s comprehensive Python script on my computer, patting myself on the back for what a great data scientist I am. Not surprisingly, the script executed successfully at first attempt — obviously not because of my programming genius, but because of the excellent quality of the script I used, namely Sergio Llala’s GitHub repository for building customizable passing networks with Matplotlib. So after a single button press [Run Script], I’m proudly staring at my first “self-programmed” pass network:
2D pass network. Image by author. Cody by Sergio Llana.
But then a thought comes to mind: “_Wouldn’t it be nice if I could immerse myself into the network to see how many low or high passes were played?”. _That’s when I began experimenting with Cinema4D’s built-in Python module. To my own surprise, I was able to realize my idea with minimal effort. In this article, I want to share my (possibly unconventional) visualization approach.
Disclaimer: If you’re here for analytical insights, I’m afraid you’ll be disappointed. This article is solely about how to transform traditional 2D passing networks into 3D.
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