NCAA Bracket Prediction Using Machine Learning and Combinatorial Fusion Analysis

2026-03-11

AI Summary

This study addresses the limitations of single-ranking systems in predicting NCAA tournament outcomes by proposing a novel approach that integrates multiple public rankings. Built upon the Combined Fusion Analysis (CFA) framework, the method synergistically incorporates a Rank-Score Characteristic (RSC) function and a Cognitive Diversity (CD) mechanism, further enhanced by a machine learning classifier to effectively harness the complementary strengths of diverse ranking sources. Experimental results demonstrate that the proposed approach achieves a win-loss prediction accuracy of 74.60%, significantly outperforming the best among ten established ranking systems, which attains 73.02%. This work thus introduces a more robust and generalizable paradigm for sports outcome prediction.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems
• Knowledge Representation and Reasoning: Preferences

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• NCAA Bracket Prediction
• Sports Prediction
• Team Ranking
• Prediction Accuracy

Innovation

• Combinatorial Fusion Analysis
• Rank-Score Characteristic
• Cognitive Diversity
• Sports Prediction
• Team Ranking