Predicting NBA Games Using Neural Networks

Abstract

In this paper we examine the use of neural networks as a tool for predicting the success of basketball teams in the National Basketball Association (NBA). Statistics for 620 NBA games were collected and used to train a variety of neural networks such as feed-forward, radial basis, probabilistic and generalized regression neural networks. Fusion of the neural networks is also examined using Bayes belief networks and probabilistic neural network fusion. Further, we investigate which subset of features input to the neural nets are the most salient features for prediction. We explored subsets obtained from signal-to-noise ratios and expert opinions to identify a subset of features input to the neural nets. Results obtained from these networks were compared to predictions made by numerous experts in the field of basketball. The best networks were able to correctly predict the winning team 74.33 percent of the time (on average) as compared to the experts who were correct 68.67 percent of the time.