The field of sports analytics is moving towards more advanced and fine-grained analysis of player and team performance. This is driven by the increasing availability of large-scale datasets and improvements in computer vision and machine learning algorithms. Recent work has focused on developing new datasets and methods for tracking player movement, estimating player skills, and anticipating opponent actions. These innovations have the potential to revolutionize the way teams and leagues approach player evaluation, strategy development, and game planning. Noteworthy papers include: LATTE-MV, which presents a scalable system for reconstructing monocular video of table tennis matches in 3D and an uncertainty-aware controller that anticipates opponent actions. BASKET, which introduces a large-scale basketball video dataset for fine-grained skill estimation, featuring 20 fine-grained basketball skills and a massive number of skilled participants with unprecedented diversity.