A Hybrid Model of Hadoop and Large Language Models for Football Analysis, Prediction and Insight Generation

Abstract

The world of football is undergoing a transformation, driven by the power of data. Traditional analysis methods are struggling to keep pace with the vast amount of information generated during matches.The advent of big data has caused catastrophic success in a new era of football analytics, providing unprecedented insights into player performance and team strategies. However, the sheer volume and complexity of football data present significant challenges for traditional analytical methods. This paper proposes a novel approach utilizing the Hadoop framework to revolutionize football analysis. By leveraging Hadoop's distributed processing capabilities, the model can efficiently handle massive datasets, including player statistics, match events, and video footage.It demonstrates the effectiveness of our approach through case studies, such as predicting match outcomes, identifying player strengths and weaknesses, and optimizing team formations. The findings highlight the potential of Hadoop to unlock valuable insights from football data, empowering coaches and analysts to make data-driven decisions and gain a competitive edge over other teams.