Jay Guild of Musco Corporation

The Umpire Assist project is an AI-driven system designed to improve the consistency and availability of officiating in youth baseball and softball by automating ball and strike calls. Developed in partnership with organizations such as USSSA and Little League Baseball, the system uses camera-based tracking and machine learning to detect pitch location, batter position, and swing activity, delivering real-time decisions with a target accuracy of over 97%. It is designed to reduce reliance on multiple umpires while maintaining the integrity of the game, providing near-instant feedback (under 500 milliseconds) through visual and audio outputs. The system also incorporates a dynamic strike zone that adjusts based on the batter, as well as remote configuration and cloud connectivity for monitoring, updates, and data collection.

Throughout development, the project evolved from a multi-camera, distributed architecture to a more cost-effective stereoscopic camera system housed in a single installation point. Early prototypes validated the concept using separate camera angles and AI inference, but challenges related to installation complexity, cost, and environmental reliability led to a refined design that emphasizes simplified deployment, improved thermal management, and reduced hardware footprint. The final system integrates hardware, software, and cloud services into a cohesive platform capable of operating in diverse field conditions while maintaining high accuracy and reliability. Ongoing efforts focus on reducing costs, improving manufacturability, and preparing the system for broader deployment across youth sports facilities.

Biography:

Jay L. Guild is a computer engineer specializing in embedded systems, AI-driven hardware/software integration, and full product lifecycle development. Currently serving as a Staff Engineer at Musco Sports Lighting, he leads the design of hardware platforms for video-based AI systems, where they combine advanced hardware integration with cloud-based software platforms to deliver intelligent, networked solutions. With a strong foundation in embedded C/C++, communication systems, and system architecture, he has a proven track record of translating complex concepts into scalable, production-ready technologies while collaborating across cross-functional teams.

Over the course of his career, Jay has progressed through roles in embedded systems engineering, software development, and product design, contributing to projects ranging from consumer appliances at Electrolux to advanced lighting control systems at Musco. He holds a ME in Computer and Electrical Engineering from Iowa State University, along with a BS in Electrical and Computer Engineering with minors in Applied Physics, Mathematics, and Computer Science from the University of Iowa. His technical expertise spans multiple programming languages, microcontroller platforms, and system-level design, supported by hands-on experience in testing, troubleshooting, and delivering high-performance solutions from concept through production.