  Recent integration of Natural Language Processing (NLP) and multimodal models
has advanced the field of sports analytics. This survey presents a
comprehensive review of the datasets and applications driving these innovations
post-2020. We overviewed and categorized datasets into three primary types:
language-based, multimodal, and convertible datasets. Language-based and
multimodal datasets are for tasks involving text or multimodality (e.g., text,
video, audio), respectively. Convertible datasets, initially single-modal
(video), can be enriched with additional annotations, such as explanations of
actions and video descriptions, to become multimodal, offering future potential
for richer and more diverse applications. Our study highlights the
contributions of these datasets to various applications, from improving fan
experiences to supporting tactical analysis and medical diagnostics. We also
discuss the challenges and future directions in dataset development,
emphasizing the need for diverse, high-quality data to support real-time
processing and personalized user experiences. This survey provides a
foundational resource for researchers and practitioners aiming to leverage NLP
and multimodal models in sports, offering insights into current trends and
future opportunities in the field.
