Winning by Design: Technology-Integrated Learning, Performance, and Recovery in Team Sports

Over the past decade, technological innovation has transformed the landscape of sports science, reshaping how performance is monitored, analyzed, and optimized. Data-driven approaches are now central to evidence-based decision-making, empowering coaches, scientists, and practitioners to design more precise, adaptive, and individualized training and recovery strategies. Advances in wearable technology, computer vision, and artificial intelligence (AI) have generated unprecedented volumes of data, offering deep insights into physiological responses, tactical behaviors, and decision-making under competitive pressure. Yet, the challenge remains: how to meaningfully interpret, integrate, and apply these data streams to improve human performance in real-world sport environments. We particularly emphasize psychological mechanisms—attention, motivation, confidence, decision-making under pressure, and self-regulation—as core outcomes shaped by technology-enabled monitoring and feedback.

To address these challenges, identifying and operationalizing Key Performance Indicators (KPIs) is essential. The validation and standardization of KPIs that represent the physiological, technical, tactical, and psychobehavioral dimensions of performance can establish robust benchmarks across competitive levels and sporting contexts. Such indicators form the foundation for comprehensive and integrative performance analyses that align with the ecological and dynamic nature of team sports. Submissions linking KPIs to validated psychological constructs (e.g., perceived exertion, cognitive workload, stress, resilience, team cohesion) and behavioral markers are strongly encouraged.

Equally critical is understanding workload and adaptation. Both acute and chronic training effects must be examined through advanced modeling approaches, such as alternatives to the Acute:Chronic Workload Ratio (ACWR), individualized thresholds, and readiness indices, to better predict performance trends and mitigate injury risk. Closely linked to this is the study of strength and conditioning, where high-resolution monitoring and adaptive programming can guide evidence-based load progression, recovery interventions, and individualized periodization strategies. We welcome studies that connect workload metrics with psychological readiness, fatigue perception, sleep quality, mood states, and injury risk appraisal, using mixed-methods and validated psychometrics.

Technological tools are also transforming learning and skill acquisition. Representative learning design, constraints-led approaches, and augmented feedback systems—ranging from biofeedback to AI-assisted video analytics—enable the development of perceptual and decision-making skills that transfer effectively to competition. Complementing this, sleep and fatigue management have emerged as crucial determinants of performance sustainability. Objective and subjective sleep assessments, alongside interventions targeting recovery and cognitive function, can improve readiness, decision-making, and overall well-being. We invite research on how AI-informed and biofeedback-based interventions shape attentional focus, perceptual attunement, motivation, learning transfer, and coach–athlete communication.

The integration of multimodal and ecological approaches—combining physiological, biomechanical, tactical, and psychological data streams—represents a frontier in performance science. Leveraging AI, machine learning (ML), and edge computing enables real-time analytics and actionable insights, enhancing coaching decisions both on and off the field. Nonetheless, these advances demand careful attention to implementation science and ethics, including equitable access across resource levels, athlete data governance, algorithmic transparency, and effective coach-athlete communication. We particularly encourage implementation studies addressing behavior change techniques, user experience (athletes/coaches), data governance, and ethical use of AI in applied sport settings.

This Research Topic aims to showcase cutting-edge methodologies and frameworks that advance the monitoring, modeling, and enhancement of sports performance through technology. We particularly encourage contributions that:

• Bridge laboratory and field contexts using ecologically valid designs;
• Compare and validate measurement technologies, promoting data quality and interoperability standards;
• Develop transparent and generalizable AI/ML models validated against gold-standard assessments;
• Provide open data, code, and replicable analytic pipelines to foster reproducibility and collaborative progress.
• Integrate psychophysiological, cognitive, and behavioral outcomes with technology-derived metrics to explain mechanisms of performance and learning.
• Evaluate adoption, adherence, and behavior change in athletes and coaches, including barriers, facilitators, and equity considerations.

By integrating wearable technologies, AI-driven analytics, and simulation tools within holistic frameworks of performance, learning, and recovery, this Collection seeks to translate technological innovation into practical impact—bridging the gap between research and applied performance environments, and shaping the next generation of evidence-based, ethically informed sports science. Submissions spanning youth to elite, women’s and men’s sport, para-sport, and diverse cultural contexts are especially welcome to strengthen generalizability and inclusion.