论文标题
Mambanet：用于预测NBA季后赛的混合神经网络
MambaNet: A Hybrid Neural Network for Predicting the NBA Playoffs
论文作者
论文摘要
在本文中，我们提出了Mambanet：一种用于预测篮球比赛结果的混合神经网络。与其他主要关注赛季比赛的研究相反，该研究调查了季后赛。 Mambanet是一种混合神经网络体系结构，可以处理一个时间序列的球队和玩家游戏统计数据，并产生球队赢得或输掉NBA季后赛的可能性。在我们的方法中，我们利用模仿网络的功能来提供游戏统计的潜在信号处理特征表示，以进一步处理卷积，经常性和密集的神经层。进行了三个使用六个不同数据集的实验，以评估我们的体系结构的性能和概括性与以前的广泛研究。我们的最终方法成功地预测了AUC从0.72到0.82，以相当大的余量击败了表现最佳的基线模型。
In this paper, we present Mambanet: a hybrid neural network for predicting the outcomes of Basketball games. Contrary to other studies, which focus primarily on season games, this study investigates playoff games. MambaNet is a hybrid neural network architecture that processes a time series of teams' and players' game statistics and generates the probability of a team winning or losing an NBA playoff match. In our approach, we utilize Feature Imitating Networks to provide latent signal-processing feature representations of game statistics to further process with convolutional, recurrent, and dense neural layers. Three experiments using six different datasets are conducted to evaluate the performance and generalizability of our architecture against a wide range of previous studies. Our final method successfully predicted the AUC from 0.72 to 0.82, beating the best-performing baseline models by a considerable margin.
