NBA All-Star Lineup Prediction Based on Neural Networks

In this paper we examined the use of Neural Networks as a tool to predict the starting and reserve line up of All-Star game, in the National Basketball Association, from all the candidates. Statistics of data from season 2008-09 to 2012-13 were collected and used to train a verity of Neural Networks such as feed-forward, radial basis and generalized regression Neural Networks. Fusion of the neural networks was also examined by using AdaBoost ensemble learning algorithm. Further, we have explored which features set input to the neural network was the most useful ones for prediction. And an excellent prediction scheme was proposed to improve the forecast accuracy. By using AdaBoost and the proposed scheme, the accuracy of our prediction of the starting line up is up to 91.7%, the reserve line up 73.3%.