Analysis of basketball games using neural networks

Data mining is a technology in data analysis with rising application in sports. Basketball is one of most popular sports. Due to its dynamics, a large number of events happen during a game. Basketball statisticians have task to note as many of these events as possible, in order to provide their analysis. In this paper, we used data from the First B basketball league for men in Serbia, for seasons 2005/06, 2006/07, 2007/08, 2008/09 and 2009/2010. During these five seasons, total of 890 games were played. Data were collected for individual players, so it was necessary to adapt these in order to show statistics for a whole team. These data were analyzed using feedforward technique in neural networks, which is the most often used technique in analyzing nonlinear sports data. As a final result, we concluded that the most important elements in basketball are two-point shots under the hoop and defensive rebound, i.e. game “in paint”.