Data-driven and algorithm-enabled: The logical evolution, paradigm reconfiguration and practical dimension research of artificial intelligence in sports event prediction

Abstract

With the exponential growth of sensing technology and computing power, artificial intelligence (AI) is increasingly deeply involved in the field of sports prediction, driving a paradigm shift in sports scientific research from "experience-driven" to "data-driven". This article systematically reviews the evolution logic of AI in sports prediction and analyzes the underlying mechanism of the migration from traditional statistical models to deep learning and reinforcement learning algorithms. The study finds that AI, through the deep mining of multi-source heterogeneous sports data, not only demonstrates remarkable accuracy in predicting competitive performance and assessing the risk of sports injuries, but also achieves functional reconfiguration in the dimensions of sports industry decision support and enhanced spectator experience. This article constructs a technical path model for AI sports prediction and conducts a deep examination of its value spillover and technical limitations in practical applications. The research aims to provide theoretical support for the digital transformation of China's sports industry and offer path references for the optimization and ethical governance of intelligent sports prediction algorithms.