NCAA Bracket Prediction Using Machine Learning and Combinatorial Fusion Analysis
This work improves prediction accuracy for sports analytics enthusiasts and bettors, but it is incremental as it builds on existing ranking methods with a novel combination technique.
The paper tackled NCAA bracket prediction by applying Combinatorial Fusion Analysis (CFA) to combine multiple ranking systems, achieving an accuracy of 74.60%, which outperformed the best individual public ranking at 73.02%.
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.