Human Performance Analytics

(1.5 Credit Hours) · Undergraduate

This course applies data analytics, machine learning, and AI tools to optimize individual athlete performance and health management using diverse data sources including GPS tracking, wearable sensors, heart rate monitors, and game statistics. Students learn to quantify training load, assess injury risk, monitor recovery, and make data-driven decisions that balance performance maximization with athlete health. Through hands-on work and strategic use of AI for code development and analysis, the curriculum covers calculating performance metrics from GPS data (speed zones, acceleration events, player load), developing advanced indicators that capture movement complexity, and building predictive models for injury risk and performance outcomes. Students also track their own physical performance metrics throughout the semester, applying course concepts to personal data and experiencing firsthand the insights and limitations of performance monitoring systems. These skills, working with time-series sensor data, leveraging AI to accelerate analytical workflows, creating actionable dashboards, balancing competing objectives, and communicating technical findings to non-technical stakeholders, transfer directly to healthcare analytics, wearable technology development, workplace wellness programs, and human factors optimization across industries. The course culminates in a project addressing real performance questions such as injury prediction, load management optimization, or player development strategies, replicating the analytical workflows used by professional sports science teams to drive evidence-based athlete management decisions.