Football analytics at the grassroots level faces challenges due to unstructured play, varied conditions, and limited access to professional tools. This study conducts a systematic review of 142 selected papers from 482, focusing on detection, tracking, and performance analysis. Models like YOLOv4, SSD, R-CNN, and DeepSORT were frequently used, along with K-Means for team categorization. Most studies relied on metrics like precision, recall, and FPS, but faced issues with occlusion and lighting inconsistencies. Clustering struggled with complex jersey patterns. A major gap is the lack of grassroots-specific datasets. The study highlights the need for lightweight, scalable models and improved data diversity. These advancements can bridge the gap between local and professional football analysis.