Suppose you want to write a data science tool to do feature engineering. Your experience may go like this: - Expectation: you can focus on state-of-the art techniques for feature engineering. - Reality: you keep having to make you codebase more complex because a new dataframe library has come out and users are demanding support for it.

Or rather, it might have gone like that in the pre-Narwhals era. Because now, you can focus on solving the problems which your tool set out to do, and let Narwhals handle the subtle differences between different kinds of dataframe inputs!

Football analytics has rapidly evolved over the past five years, becoming a crucial part of professional and fan discourse. While much of the cutting-edge research remains hidden behind the fences of club training grounds, a growing ecosystem of open-source tools now enables anyone to develop advanced football analytics models.

In this talk, I'll showcase key open-source libraries—Polars for high-performance data processing, Keras for deep learning, and Spektral for Graph Neural Networks (GNNs)—to analyze millions of player coordinates from publicly available high-frequency positional tracking data. I'll demonstrate how these tools can be used to build in-game prediction models and extract advanced football metrics that only the most advanced football clubs currently use.