  Badminton enjoys widespread popularity, and reports on matches generally
include details such as player names, game scores, and ball types, providing
audiences with a comprehensive view of the games. However, writing these
reports can be a time-consuming task. This challenge led us to explore whether
a Large Language Model (LLM) could automate the generation and evaluation of
badminton reports. We introduce a novel framework named BADGE, designed for
this purpose using LLM. Our method consists of two main phases: Report
Generation and Report Evaluation. Initially, badminton-related data is
processed by the LLM, which then generates a detailed report of the match. We
tested different Input Data Types, In-Context Learning (ICL), and LLM, finding
that GPT-4 performs best when using CSV data type and the Chain of Thought
prompting. Following report generation, the LLM evaluates and scores the
reports to assess their quality. Our comparisons between the scores evaluated
by GPT-4 and human judges show a tendency to prefer GPT-4 generated reports.
Since the application of LLM in badminton reporting remains largely unexplored,
our research serves as a foundational step for future advancements in this
area. Moreover, our method can be extended to other sports games, thereby
enhancing sports promotion. For more details, please refer to
https://github.com/AndyChiangSH/BADGE.
