If you want to build a machine learning model to predict the outcome of a soccer match, the algorithm you choose is the least important part of the process. The real challenge is deciding what data to feed it. In this episode, Ari Joury (PhD, particle physics; Founder & CEO of Wangari Global) celebrates the launch of his new book, Soccer Analytics with Machine Learning (O’Reilly Media).
He explains how the lessons learned from analyzing sports data — specifically the art of feature engineering and the necessity of domain expertise — apply directly to the hardest problems in enterprise AI. Whether you are predicting a goal at the World Cup or automating a Solvency II regulatory report, the fundamental challenge is the same: separating the signal from the noise. And yes, he will explain why feeding raw data into a neural network is like handing a toddler a chainsaw and hoping they build a birdhouse.
Topics covered: Soccer analytics, feature engineering, machine learning, enterprise AI, domain expertise, data science, Soccer Analytics with Machine Learning book launch, O’Reilly Media.
Wangari is the newsletter and podcast for practitioners and leaders navigating the real work of enterprise AI. New episodes every Friday.
