# MambaNet: A Hybrid Neural Network for Predicting the NBA Playoffs

arXiv: https://arxiv.org/abs/2210.17060 · HTML: https://mlsanity.com/paper/2210.17060/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 16 references that are in the map.
- First posted on arXiv 31 Oct 2022.

## Builds on (most cited first, 1 of 1)

- [Feature Imitating Networks](https://mlsanity.com/paper/2110.04831/) (2021)

## Built on by (most cited first, 4 of 4)

- [Applying Tabular Deep Learning Models to Estimate Crash Injury Types of Young Motorcyclists](https://mlsanity.com/paper/2503.10474/) (2025)
- [Crash Severity Analysis of Child Bicyclists using Arm-Net and MambaNet](https://mlsanity.com/paper/2503.11003/) (2025)
- [The Broad Impact of Feature Imitation: Neural Enhancements Across Financial, Speech, and Physiological Domains](https://mlsanity.com/paper/2309.12279/) (2023)
- [Long-Sequence LSTM Modeling for NBA Game Outcome Prediction Using a Novel Multi-Season Dataset](https://mlsanity.com/paper/2512.08591/) (2025)

## Abstract (arXiv)

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.