Sports Analysis Using Machine Learning

Abstract

With the advent of big data and machine learning approaches, sports analysis has experienced a substantial revolution. Large-scale data analysis has been transformed by machine learning, which has also revealed insights that were difficult or impossible to discover with more conventional qualitative methods in the past. This study analyzes machine learning based sports analysis, emphasizing the methodologies and approaches used as well as the applications and difficulties.

Data from numerous sources, such as player monitoring, social media, and video feeds, are analyzed using machine learning models. like neural networks and decision trees. Decisions on player choice, game strategy, injury prevention, and fan engagement may be made using the knowledge gained from these assessments. The need for more thorough and data, the blending of many data and the moral application of data are the difficulties. Despite these difficulties, machine learning is set to become more widely used in sports analysis, offering insightful data and raising the level of competition.