As a budding data scientist and incoming MS candidate at the University of Chicago studying Applied Data Science, I wanted to engage in a project where I could combine my love of sports with the power of analytics. Specifically, given 20 years of athlete data spanning college, draft, and the National Football League (NFL), I wanted to see if I could develop a machine learning model to predict how wide receivers would perform, and uncover which factors most influence NFL success. I sourced my data from two open-source APIs, which together provided me with three datasets consisting of college football,... […] The post Machine Learning and Sports: Data Science’s Best Example of a Class Imbalance appeared first on DataDrivenInvestor.

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