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In this paper, we present Mambanet: a hybrid neural network for predictingthe outcomes of Basketball games. Contrary to other studies, which focusprimarily on season games, this study investigates playoff games. MambaNet is ahybrid neural network architecture that processes a time series of teams' andplayers' game statistics and generates the probability of a team winning orlosing an NBA playoff match. In our approach, we utilize Feature ImitatingNetworks to provide latent signal-processing feature representations of gamestatistics to further process with convolutional, recurrent, and dense neurallayers. Three experiments using six different datasets are conducted toevaluate the performance and generalizability of our architecture against awide range of previous studies. Our final method successfully predicted the AUCfrom 0.72 to 0.82, beating the best-performing baseline models by aconsiderable margin.

MambaNet: A Hybrid Neural Network for Predicting the NBA Playoffs