The Advancements and Applications of Machine Learning in Predicting Football Scores

Abstract

Football stands as one of the world's most popular sports, where match prediction significantly contributes to its development and enhances its economic and cultural value. This calls for a comprehensive review of the technology used to predict football scores. The study first examines three traditional machine learning methods with simple single-layer architectures. While these methods are straightforward to understand, they suffer from limitations such as low accuracy and slow computational speed, rendering them less advantageous for practical applications. Subsequently, this paper explores four deep learning approaches, whose multi-layered structures outperform traditional methods in model capacity, learning efficiency, and computational power. Consequently, deep learning has become a primary direction for both current and future research in football scoring prediction. However, these approaches face challenges including generalization, accuracy, and explainability. To address these challenges, this paper proposes adversarial domain adaptation techniques to tackle generalization challenges. The introduction of high-dimensional dynamic feature data enhances accuracy. By integrating models, their respective strengths are leveraged to resolve interpretability challenges, while presenting optimistic prospects for deep learning applications in football scoring prediction. This article provides a comprehensive review of machine learning in football score prediction, which is conducive to readers' in-depth and comprehensive understanding of this field, and provides readers with reference for subsequent independent exploration and innovation.