Paper Contents
Abstract
The advent of big data analytics in sports has fundamentally changed how teams, coaches, and athletes approach performance enhancement. This paper explains how big data impacts athlete development, strategic planning, and decision-making in a variety of sports and examines how it is transforming sports performance analytics. Big data analytics in sports refers to the collection, processing, and analysis of massive amounts of data generated by numerous sources, including wearables, sensors, video, and statistical records. Using advanced algorithms and machine learning techniques, this data is transformed into insightful knowledge that can be used to enhance tactical plans, maximize training schedules, and lower the risk of injury. The main way that big data contributes to sports performance analytics is by provide a comprehensive understanding of the dynamics of player performance and behaviour. Coaches and sports scientists can analyse complicated performance data, such as movement patterns, biomechanics, and physiological markers, to identify strengths, weaknesses, and areas for improvement with unprecedented detail. Additionally, big data enables the development of personalized training regimens according to the needs and CNNs-Convolution neural Network characteristics of every athlete. Using realtime monitoring and predictive modelling, coaches can optimize training loads, prevent overtraining, and enhance performance outcomes while reducing the risk of injury. In addition to examining individual players, big data analytics assists teams in gaining strategic insights. By looking at opponent trends and team performance data, coaches may develop game plans, adjust formations, and make more precise and useful in-game decisions.
Copyright
Copyright © 2024 DHRUV SINGH. This is an open access article distributed under the Creative Commons Attribution License.
