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Great Lakes Analytics in Sports Conference

Daniel Cunningham

University of Chicago 

Predicting Future Hitting Performance from Statcast Batted Ball Data

Using statistical models and machine learning algorithms, Daniel predicts the value of each batted ball from MLB Statcast data. These predictions can be used to find under- or overachieving players (measured by wOBA) relative to their batted ball profiles and to create player projections.

Daniel Cunningham is a graduate student at the University of Chicago pursuing an MS in statistics. He is interested in statistical modeling and machine learning, especially for applications in the analysis of baseball and other sports. He completed his undergraduate education at the University of Connecticut, where he studied materials science and engineering. Daniel grew up in Massachusetts and is a big Boston sports fan.

Listen to a podcast interview with Daniel and GLASC director Scott Tappa

Watch a video of Daniel's 2018 GLASC presentation ((NOTE: A file error freezes the video at approximately the 0:23:50 mark - it picks up again at the 11:51:40 mark))

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