This study focuses on using the state-of-the-art deep learning automation technology for object detection in sports with particular attention on player identification using the YOLO (You Only Look Once) method. At the moment, object detection is widely used and is one of the most important algorithms for numerous tasks across various sectors. The YOLO methodology is ideal for sports and other fast-paced activities due to its capability to perform both object detection and localization simultaneously in one forward movement. YOLO uses convolutional deep neural networks for the detection and tracking of players in the field, which provides information required by strategic decision-making, performance analysis, and automatic improvement of television coverage. In this paper, the YOLO solution and the problem of real-time identification of players solved by the system are provided. Experimental results show that YOLO has the potential to become a game changer in sports analytics because it is able to identify subjects in sporting activities successfully and accurately. Experimental results highlight the potential of YOLO in revolutionizing sports analytics through precision and automation for identifying players in sporting activities. In this paper, I engage with the recent deep learning solution in sports object recognition and detection of players termed real-time. YOLO has the potential to be suitable for sports as it can detect multiple objects from a single frame. For solving the player identification and tracking problem, YOLO adopts deep convolutional neural networks, which enhance the quality of performance analysis, broadcasting, and even strategy development. In this paper, the approach, applications, and challenges in real-time player detection are being investigated. The results show how effective and accurate YOLO is in a variety of sports scenarios, which highlights its potential impact on sports analytics and technology.