Advanced data collection technologies, computational tools, and sophisticated algorithms have a revolutionary impact on sports analytics on various aspects of sports, from athletes performance to fan engagement. Computer Vision (CV) and Deep Learning (DL) technologies play a crucial role in predicting players and game states from videos, but their effectiveness depends on the quantity and quality of training data, especially in sports with unique dynamics and camera angles. Each sport comes with its own set of challenges.|This paper introduces DeepSportradar-v2, a multi-sport suite of CV tasks that address the need for high-quality datasets for different sports. Supporting multi-sport allows academic researchers to better understand the dynamics of each sport and their specific challenges. In this paper, we first report the results from the 2022 competition, and provide all resources to replicate each result. Then, we present a newly released Cricket dataset and task, given the global popularity and relevance of this sport for the automated analysis and video understanding.|Similarly to the first edition, a competition has been organized as part of the MMSports(1) workshop, where participants are invited to develop state-of-the-art methods for solving the proposed tasks using the publicly available datasets, development kits, and baselines.