Research Alert

Newswise — What determines a team’s home advantage, and why does it change with time? It’s a question that’s been the subject of many studies. But now, 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 made more two-point and free-throw shots. 

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. 

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.

The paper will be published July 31 in the journal PLOS ONE at

Journal Link: PLOS ONE