What determines a team's home advantage, and why does it change with time? Austin Harris, a doctoral student at the University of Wisconsin-Milwaukee, is using data science to find the answer in National Basketball Association games.
Harris collected season performance statistics for all NBA teams across 32 seasons (1983-84 to 2017-18). Data were also obtained for other potential influences identified in the literature, including variables like stadium attendance and team market size.
Using a data science method called an artificial neural network (ANN), a team's home advantage was diagnosed using team performance statistics only. When data from possible influences were applied, the ANN identified only one associated with larger advantages at home: Teams that make more two point and free-throw shots see larger advantages at home.
Given the rise in three-point shooting in recent years, this finding partially explains the gradual decline in home advantage observed across the league over time.
Harris and his co-author, Distinguished Professor Paul Roebber at the University of Wisconsin-Milwaukee, describe their work in "NBA Team Home Advantage: Identifying Key Factors Using an Artificial Neural Network," published in the journal PLOS One.
Neural networks operate in a way that is similar to the human brain with certain data turned "on" or "off," like a neuron firing. The key difference between ANN and similar regression techniques is that ANN can pick up on the nuanced, non-linear connections in the dataset. ANNs are most beneficial when relationships in the data are complex.
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