More and more videos are part of our daily life. Platforms like Youtube, Facebook and Instagram receive a large amount of hours of videos every day. When we focus on the sports videos category, the growing interest in obtaining statistical data is evident, especially in soccer. This is valuable both for improving the performance of athletes and teams and for platforms that use this information, such as betting platforms. Consequently, interest in solving problems related to Computer Vision has increased. In the case of Supervised Learning, the quality of data annotations is another important point for the success of research. There are several annotation tools available on the market, but few focus on relevant frames and support Artificial Intelligence models. In this sense, this work involves the use of the Transfer Learning technique for Feature Extraction in a Convolutional Neural Network (CNN); the investigation of a classification model based on the Few-Shot Learning approach together with the K-Nearest Neighbors (KNN) algorithm; evaluating results with different approaches to class balancing; the study of 2D graph generation with t-Distributed Stochastic Neighbor Embedding (t-SNE) for annotation analysis and the creation of a tool for annotating important frames in videos, with the aim of assisting research and testing.