Abstract

Data about sports have long been the subject of research and analysis by sports scientists. Machine learning has been applied to many areas of science, health care, and finance industries, such as image detection, cancer detection, stock market prediction, and customer churn prediction. In some areas, such as sports, the effective use of machine learning has still large scope for improvement. The areas of improvement in mainly data collection in improving the accuracy of prediction & sports science/medicine.

The article takes deep dive into the positive impact of ML integration on sports analytics. It is evident that ML can unearth great potential insights with a data-driven approach & decision-making. There are many aspects of ML integration into sports. This paper focuses on deploying the ML into the field of predicting sports injury. ML not only increases the knowledge of sports injury but also assist in proactively taking steps to avoid sports injury by predicting ahead of time. To this end, technological advancements have enabled the collection of multiple points of data for use in analysis and injury prediction. The full breadth of available data has, however, only recently begun to be explored using suitable statistical methods & processing of these large data through ML algorithms.

Paper utilizes the advances in automatic and interactive data analysis with the help of machine learning & establishes the intricacies of the playing surface & injury relationship. Public data shared by NFL for sports analytic competition is analyzed for the relationship between playing surface, NFL player's movements, and their damage, leading to potentially improved performance and minimizing the risk of injury. The article also briefly underlines the importance of critical sports parameters accurate data collection & direct impact on ML accuracy in prediction too.