Making the World’s Fastest Racket Sport even Better: A Systematic Review of Artificial Intelligence-based Objective Player Performance Assessment in Badminton

Artificial intelligence (AI) is increasingly promoted as a means of providing objective player performance assessment in badminton. Compared to other sports, however, the supporting evidence remains fragmented. A systematic review based on 51 studies that satisfied the established quality and eligibility criteria from three major databases (covering the period 2018 to the end of 2025) reveals four dominating methodological schools: computer vision stroke tracking, movement-pattern recognition, spatio-temporal analysis of rally sequences, and multi-modal frameworks that integrate several data streams. Although many studies report high classification or prediction accuracy, only a small proportion of them employ shared validation datasets or evaluate repeatability across testing sessions, which limits the generalisability of their findings. Common shortcomings include small or imbalanced data samples, weak alignment with established sport-science theory, substantial computational requirements, and participant pools drawn largely from elite athletes in a single geographic region. Recent work has begun integrating explainable AI with retrieval-augmented generation (RAG) and large language model (LLM) frameworks to provide grounded, query-responsive feedback that links visual detections and performance metrics to structured match evidence. Future research should focus on larger and more diverse datasets, alignment with skill development models, transparent output formats, and validation across competitive levels and contexts with these state-of-the-art explainable AI-based RAG or LLM frameworks.