  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.

Machine Learning