Features are the different types of input data on which a machine learning model bases its predictions. Next, the team winnowed down the “feature set” for the model. Michael Schaefer, director of product and analytics for the NFL’s Next Gen Stats For instance, defender ghosting can help estimate how a play would have evolved if the quarterback had targeted a different receiver: would the defensive backs have reached the receiver in time to stop a big gain? Defender ghosting can thus help evaluate a quarterback’s decision making. Take, for instance, the problem of defender ghosting, or predicting the trajectories of defensive backs after the ball leaves the quarterback’s hand.ĭefender ghosting is not itself a Next Gen Stat, but it’s an essential component of stats under development. “And where we’ve needed additional ML expertise,” Schaefer adds, “AWS’s data scientists have been an invaluable resource.” Secondary variance “SageMaker makes the development of ML models easy and intuitive - particularly for those who may not have deep familiarity with ML.” “We wouldn’t have been able to make the strides we have as quickly as we have without AWS,” says Michael Schaefer, the director of product and analytics for the NFL’s Next Gen Stats. AWS stores the huge amount of data generated by tracking every player on every play in every NFL game - nearly 300 million data points per season NFL software engineers use Amazon SageMaker to quickly build, train, and deploy the machine learning (ML) models behind their most sophisticated stats and the NFL uses the business intelligence tool Amazon QuickSight to analyze and visualize the resulting statistical data. Since 2017, Amazon Web Services (AWS) has been the NFL’s official technology provider in every phase of the development and deployment of Next Gen Stats. Lin Lee Cheong and her colleagues presented their paper on defender ghosting at the 2021 MIT Sloan School Sports Analytics Conference.
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