06-07, 11:50–12:35 (Europe/London), Hardwick Hub
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.
Football analytics has become an essential part of the modern game, influencing everything from tactical decisions to player recruitment. However, much of the cutting-edge research remains locked behind club training grounds, making it difficult for those outside the professional sphere to explore advanced analytical techniques. Fortunately, open-source tools have lowered the barrier to entry, enabling analysts, researchers, and enthusiasts to develop sophisticated models using publicly available data.
This talk will provide a hands-on introduction to building football analytics models with Polars, Keras, and Spektral. We will start by exploring specific open-source football analytics Python libraries (kloppy and mplsoccer) followed by a brief introduction of basic Polars functionality, to efficiently process millions of player and ball coordinates from high-frequency positional tracking data. Next, we will introduce Keras and Spektral for Deep Learning and Graph Neural Networks (GNNs), demonstrating how these tools can be used to develop in-game prediction models and extract advanced football metrics.
Attendees will gain insights into how open-source machine learning techniques can be applied to football analytics, from raw data processing to model deployment. The session is suitable for those with a basic understanding of Python and machine learning concepts, but no prior experience with Polars or GNNs is required. Whether you're a data scientist, football analyst, or simply curious about the intersection of AI and sports, this talk will provide an overview of some of the most prominent open-source resources for cutting-edge football research.
Previous knowledge expected
I'm Joris Bekkers, a self-employed football analytics consultant with over 8 years of experience, specializing in research, development and implementation of cutting-edge tools, models and data visualizations. I'm a co-founder of PySport, a non-profit that aims to grow open-source sports analytics. You can find more information about me at www.unravelsports.github.io