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NCAA Bracket Prediction Using Machine Learning and Combinatorial Fusion Analysis

arXiv 2603.10916 ↗First posted 11 Mar 2026cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 40 references that are in the map. First posted on arXiv 11 Mar 2026.

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References · 3

  1. 2019When does Diversity Help Generalization in Classification Ensembles?cited by 27
  2. 2019The Application of Machine Learning Techniques for Predicting Results in Team Sport: A Reviewcited by 9
  3. 2019A XGBoost risk model via feature selection and Bayesian hyper-parameter optimizationcited by 5

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