2023
DOI: 10.21203/rs.3.rs-3727963/v1
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Advancing NCAA March Madness Forecasts Through Deep Learning and Combinatorial Fusion Analysis

Ali Alfatemi,
Mohamed Rahouti,
Frank Hsu
et al.

Abstract: This paper presents a novel methodology for accurately predicting game outcomes in the NCAA Men's Basketball Tournament, known as March Madness. The high variance inherent to this tournament poses challenges that often exceed traditional forecasting methods. We implement a rigorous data preprocessing and feature engineering pipeline tailored to this problem using a historical NCAA Men's Tournament dataset. Four diverse neural network architectures, including convolutional, recurrent, feedforward, and residual … Show more

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