Multimodal large language models (MLLMs) excel at general video understanding but struggle with fast, high-frequency sports like tennis, where rally clips are short yet information-dense. To systematically evaluate MLLMs in this challenging domain, we present TennisTV, the first and most comprehensive benchmark for tennis video understanding. TennisTV models each rally as a temporal-ordered sequence of consecutive stroke events, using automated pipelines for filtering and question generation. It covers 8 tasks from the stroke level to the rally level and includes 2527 human-verified questions. Evaluating 17 representative MLLMs, we provide the first systematic assessment of tennis video understanding. Results yield two key insights: (i) frame-sampling density should be tailored and balanced across tasks, and (ii) improving temporal grounding is essential for stronger reasoning.

翻译：多模态大语言模型（MLLMs）在通用视频理解方面表现出色，但在处理网球等快速、高频运动时却面临挑战，因为回合片段虽短却信息密集。为了在这一具有挑战性的领域中系统评估MLLMs，我们提出了TennisTV，这是首个也是最全面的网球视频理解基准。TennisTV将每个回合建模为连续击球事件的时间有序序列，并采用自动化流程进行筛选和问题生成。它涵盖了从击球级别到回合级别的8项任务，包含2527个人工验证的问题。通过对17个代表性MLLMs进行评估，我们首次提供了网球视频理解的系统性评估。结果得出两个关键洞见：（i）帧采样密度应根据任务进行定制并在不同任务间取得平衡；（ii）提升时序定位能力对于增强推理至关重要。