MambaNet: A Hybrid Neural Network for Predicting the NBA Playoffs

Reza Khanmohammadi, Sari Saba-Sadiya, Sina Esfandiarpour, Tuka Alhanai, Mohammad Mahdi Ghassemi

Research output: Contribution to journalArticlepeer-review


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 five 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.

Original languageEnglish (US)
Article number628
JournalSN Computer Science
Issue number5
StatePublished - Jun 2024


  • Feature imitating network
  • Game outcome prediction
  • Hybrid neural network
  • Playoff

ASJC Scopus subject areas

  • General Computer Science
  • Computer Science Applications
  • Computer Networks and Communications
  • Computer Graphics and Computer-Aided Design
  • Computational Theory and Mathematics
  • Artificial Intelligence


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