This case is reserved for use by Columbia Business School Faculty only.
How can statisticians utilize expected goals data to predict the likelihood of a soccer player's shot resulting in a goal?
How can statisticians utilize expected goals data to predict the likelihood of a soccer player's shot resulting in a goal?
Since 2011 European soccer clubs have turned their attention to data and analytics to empower their teams' coaching strategies. By 2020 a new wave of stats were being utilized, with "expected goals" data, a way of quantifying the likelihood of a shot resulting in a goal, driving this trend. Using tracking data, expected goals-type analysis can lead to a better understanding of player and team's performance, and can predict future outcomes better than traditional stats like goals scored or saved. In this case students are presented with a guided project, using a large set of real-world soccer event data, and are tasked to develop and test their own expected goals model.
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