Principal component analysis to understand basketball performance: A systematic review of key performance indicators, workload monitoring and physical fitness

Basketball performance analysis now generates large datasets containing multiple physical and technical-tactical variables from wearable sensors and tracking systems. However, variable intercorrelation often obscures meaningful patterns, complicating decision-making in athlete development, training prescription, and tactical planning. Principal Component Analysis (PCA) has emerged as a widely applied dimensionality reduction technique, though its specific application within basketball research remains to be systematically examined. Therefore, this systematic review aimed to: (a) identify and synthesize PCA studies across three basketball performance domains (workload monitoring, key performance indicators, and physical fitness); (b) evaluate methodological quality and reporting standards; (c) characterize component structures; and (d) provide methodological recommendations. Following PRISMA guidelines, a comprehensive search of Web of Science, PubMed, SPORTDiscus, and Scopus was conducted, yielding 32 eligible studies. Overall study design quality was predominantly excellent (87.5% scoring >75% on MINORS). However, substantial heterogeneity existed in PCA-specific methodological practices: 68.8% of studies failed to report factorability assessment procedures and only 40.6% conducted sample adequacy testing. Consistent component structures emerged across domains. In workload monitoring, PC1 captured composite load profiles (accelerations, decelerations, distance) and PC2 reflected speed or well-being. In performance indicators, PC1 reflected offensive production and PC2 represented rebounding and defense. In physical fitness, PC1 captured aerobic or neuromuscular time-dependent qualities and PC2 reflected force-power attributes. These findings provide frameworks for identifying essential performance variables and reveal substantial methodological heterogeneity requiring standardized reporting of factorability testing, component retention, rotation procedures, and structural validation.