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NCAA Bracket Prediction Using Machine Learning and Combinatorial Fusion Analysis

arXiv cs.LG / 3/12/2026

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Key Points

  • The paper introduces Combinatorial Fusion Analysis (CFA) for combining multiple scoring systems to predict NCAA tournament outcomes, using the rank-score characteristic (RSC) and cognitive diversity (CD).
  • On the 2024 dataset, CFA-based rank fusion achieves 74.60% accuracy, higher than the best of ten public ranking systems at 73.02%.
  • The approach treats ranking fusion as a new paradigm for sports data analysis, aiming to enhance prediction precision by integrating diverse ranking perspectives.
  • The work highlights CFA's potential impact on sports analytics and ensemble methods in ML-driven forecasting.

Abstract

Machine learning models have demonstrated remarkable success in sports prediction in the past years, often treating sports prediction as a classification task within the field. This paper introduces new perspectives for analyzing sports data to predict outcomes more accurately. We leverage rankings to generate team rankings for the 2024 dataset using Combinatorial Fusion Analysis (CFA), a new paradigm for combining multiple scoring systems through the rank-score characteristic (RSC) function and cognitive diversity (CD). Our result based on rank combination with respect to team ranking has an accuracy rate of 74.60\%, which is higher than the best of the ten popular public ranking systems (73.02\%). This exhibits the efficacy of CFA in enhancing the precision of sports prediction through different lens.

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