Tuesday, January 17,2017
In an article by Jennifer Anzaldi, Wheeler discusses how he uses performance data and other factors such as age, team, and year to produce a salary recommendation. He is quoted in the article as saying that in regards to a real-world application, this type of projection based on data can benefit the team and the players both: “Teams can see players who are over-performing for their salary and poach them. They can look to see which players are likely to be traded. A player may be underperforming for his salary but on a new team, based on certain conditions, he can perform better and the new team can grab him for an appropriate price."
Wheeler is currently a grad student in Data Analytics Engineering.
You can read Anzaldi's article in its entirety here.
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