Agentic AI & Sports Intelligence: Towards Scalable and Autonomous AI Systems for Sports DecisionMaking

This work presents an innovative approach to injury likeliness, performance projection, and athlete evaluation in sports (particularly within the context of the NFL) by integrating agentic artificial intelligence, generative artificial intelligence, and blockchain technology. The objective is to develop a system capable of processing both historical and real-time data to deliver accurate, accessible, and scalable insights, supporting teams with limited resources while enhancing results for those with greater analytical capacity. The methodology integrates three primary datasets: historical yearly player statistics, weekly performance data from previous seasons, and daily biometric metrics from a former player, including heart rate, sleep patterns, and fatigue levels. The proposed system employs Agentic AI to automate and coordinate multiple workflows encompassing data collection, analysis, and cross-referencing, while Generative AI transforms the resulting outputs into natural-language reports tailored for coaches, analysts, and medical teams. Blockchain technology ensures the integrity, traceability, and transparency of athlete records, enabling the creation of an “Athlete Passport” that consolidates player attributes, performance history, and health information. Designed for continuous operation, the framework stores data for longitudinal analysis and computes performance indices that support training-load management, injury prevention, and overall performance optimization. By providing an automated, user-friendly workflow capable of generating alerts and objective comparisons, this research demonstrates how the integration of these technologies can improve the efficiency and accessibility of sports analytics processes in both professional and semi-professional environments, paving the way for broader adoption of predictive analytics in daily team operations.