The application of Natural Language Processing is progressively extending into many domains as time progresses. We are motivated to evaluate language model’s (LMs) capabilities in many real-world domains due to their significant potential. This study examines the use of LMs in sports, explicitly emphasizing their ability to convert data into text and their understanding of cricket. By examining cricket scorecards, a widely played sport on the Indian subcontinent and many other regions, we will evaluate the summaries produced by LMs from several viewpoints. We have collected concise summaries of the scorecards from the ODI World Cup 2023 and assessed them in terms of both factual accuracy and sports-specific significance. We analyze the specific factors that are included in the summaries and those that are excluded. Additionally, it analyzes prevalent mistakes concerning completeness, correctness, and conciseness. We are presenting our findings here and also our dataset and code are available https://github.com/satyawork/ODI-WORLDCUP.git