MambaNet: A Hybrid Neural Network for Predicting the NBA Playoffs

In this work, we develop MambaNet: a large hybrid neural network for predicting the outcome of a basketball match In this paper, we present Mambanet: a hybrid neural network during the playoffs. There are five main differences between for predicting the outcomes of Basketball games. Contrary our work and previous studies: (1) we use a combination of to other studies, which focus primarily on season games, both player and team statistics;(2) we account for the evolution this study investigates playoff games. MambaNet is a hybrid in player and team statistics over time using a signal neural network architecture that processes a time series of processing approach; (3) we utilize Feature Imitating Networks teams' and players' game statistics and generates the probability (FINs) [1] to embed feature representations into the of a team winning or losing an NBA playoff match. In network; (4) we predict the outcome of playoff results, as opposed our approach, we utilize Feature Imitating Networks to provide to season games; and (5) we test the generalizability latent signal-processing feature representations of game of our model across two distinct national basketball leagues.