If you want to understand the complexities of machine learning, you could study linear algebra and probability theory. Or, you could watch a soccer match.
In this episode, Ari Joury (PhD, particle physics; Founder & CEO of Wangari Global) takes a break from enterprise AI to preview his upcoming O’Reilly book, Soccer Analytics with Python. He explains why the fluid, chaotic nature of soccer is the perfect laboratory for understanding predictive modeling, feature engineering, and the limits of correlational data. From the evolution of Expected Goals (xG) to the bias-variance tradeoff on the pitch, Ari shows how the lessons learned from analyzing sports data translate directly to building robust AI systems for the enterprise. And yes, he will explain why your AI model is basically a confused midfielder passing the ball backward.
Topics covered: Soccer analytics, Expected Goals (xG), feature engineering, bias-variance tradeoff, causal inference, predictive modeling, O’Reilly Media, Python data science, enterprise AI context.
Wangari is the newsletter and podcast for practitioners and leaders navigating the real work of enterprise AI. New episodes every Friday.
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Ari’s book (O’Reilly Media, early release out now and officially out in June): Soccer Analytics with Machine Learning
