Stroke frequency and decisiveness in elite competitive badminton (2020–2026): a systematic review of match analysis studies - Melky Pangemanan; Fredrik Alfrets Makadada; Fadli Ihsan (IICET (Indonesian Institute for Counseling, Education and Therapy), 2026)
In accordance with the PRISMA 2020 guidelines, a systematic search was conducted in the Scopus database, and in the first phase, 639 documents were identified that were published from 2020 to 2026. When 15 duplicates were removed, 624 records left were screened by title and abstract. Records that did not fall under English-language peer-reviewed articles or reviews deal explicitly with badminton stroke or match analysis were discarded, so in the end, 158 full-text articles were obtained for the eligibility check. The final thematic screening found 55 papers that were synthesized to prepare this review. The four main findings of the analysis are: (1) the smash stroke was recognized as the shot most used for attacking and also the shot most responsible for playing the winning points in elite singles and doubles; regardless of the level, (2) net shots, along with net-kills, were also the second most frequently used weapon, mainly in short-rally scenarios enabled by the rally-point scoring system; (3) gender and format-based differences are so marked that in male singles, more rear-court to front-court transitions occur than in female players; (4) there is a growing use of artificial intelligence and machine learning techniques to accurately quantify stroke patterns instead of mere notational analysis. In terms of numbers, the smash and net shot combined made up just about 73% of the winning shots in elite singles as per the gathered research, whereas net-kills represented more than 40% of the points won in elite women's doubles. These results lead directly to areas of coaching pedagogy, talent identification, and the design of training programs. The aim of future research should be to longitudinally monitor changes in stroke distribution as scoring systems change and to utilize wearable sensor technology for real-time stroke classification in natural match conditions.