Data-Driven Key Performance Indicators in Individual Sports Performance

The growing integration of wearable technologies, tracking systems, biomechanical tools, and advanced analytical approaches has substantially expanded the possibilities for monitoring and understanding performance in individual sports. As a result, the identification and interpretation of Key Performance Indicators (KPIs) have become essential for supporting evidence-based decision-making in training, competition, talent development, and athlete monitoring.

Despite the increasing availability of performance-related data, important challenges remain regarding the selection, validation, contextualization, and practical application of KPIs across different individual sports disciplines. In many cases, large volumes of data are collected without a clear understanding of which indicators are truly meaningful for performance optimization.

This Research Topic aims to provide a multidisciplinary overview of data-driven approaches for identifying, monitoring, and applying Key Performance Indicators in individual sports performance. Particular emphasis will be placed on ecologically valid, practically applicable, and context-specific approaches capable of supporting coaches, practitioners, and sport scientists in real-world performance environments.

We welcome contributions addressing physiological, biomechanical, technical, tactical, psychological, and contextual dimensions of performance, as well as studies integrating advanced analytics, machine learning, wearable technologies, and longitudinal monitoring strategies.

• Identification and validation of KPIs in individual sports
• Data-driven performance analysis
• Wearable technologies and athlete monitoring
• Biomechanical and physiological performance indicators
• Machine learning and predictive performance models
• Longitudinal monitoring and training adaptation
• Competition analysis in individual sports
• Contextualized and ecologically valid performance assessment
• Applied sport science in elite and developmental settings
• Decision-making supported by performance analytics

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Conceptual Analysis
• Editorial
• FAIR² Data
• General Commentary
• Mini Review
• Opinion
• Original Research
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Conceptual Analysis
• Editorial
• FAIR² Data
• General Commentary
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review

Keywords: Key Performance Indicators, individual sports, wearable technologies, athlete monitoring, data-driven performance analysis, machine learning, biomechanical assessment, ecological validity, longitudinal monitoring, sport science

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.