  Understanding sports is crucial for the advancement of Natural Language
Processing (NLP) due to its intricate and dynamic nature. Reasoning over
complex sports scenarios has posed significant challenges to current NLP
technologies which require advanced cognitive capabilities. Toward addressing
the limitations of existing benchmarks on sports understanding in the NLP
field, we extensively evaluated mainstream large language models for various
sports tasks. Our evaluation spans from simple queries on basic rules and
historical facts to complex, context-specific reasoning, leveraging strategies
from zero-shot to few-shot learning, and chain-of-thought techniques. In
addition to unimodal analysis, we further assessed the sports reasoning
capabilities of mainstream video language models to bridge the gap in
multimodal sports understanding benchmarking. Our findings highlighted the
critical challenges of sports understanding for NLP. We proposed a new
benchmark based on a comprehensive overview of existing sports datasets and
provided extensive error analysis which we hope can help identify future
research priorities in this field.
