This study employs a data-driven approach to evaluate player performance in Taiwan’s professional basketball league, P. LEAGUE+, and constructs an AI-integrated performance assessment model. Initially, web scraping techniques are used to extract structured and unstructured data—including basic statistics, advanced metrics, and textual content—from the official P. LEAGUE+ website, covering guards, forwards, and centers. Subsequently, a domain-specific AI vector database is established using Retrieval-Augmented Generation (RAG) for data cleansing. The model then integrates Entropy and TOPSIS decision analysis methods to develop an AI-Driven Comprehensive Performance Index for automated performance scoring. TAIDE-LX-7B is further applied for data inference and decision-making to identify the league’s Annual First Team. Using the 2021-2022 season data from six teams as a case study, the accuracy and validity of the proposed AI-driven model are verified. Results show that the AI-Driven Comprehensive Performance Index aligns with official postseason selections for guards and centers, while discrepancies in forward selection are attributed to league policies favoring domestic players.
Hung et al. (Thu,) studied this question.