Balltology AI transforms baseball data through advanced AI processing, uncovering insights that traditional analytics leave on the field.
Balltology AI was founded in August 2026 to apply modern AI techniques to the full breadth of baseball data — pitch-by-pitch tracking, game events, and performance histories — and turn them into genuinely new insight. The company is operated directly by a former professional baseball club data analyst who spent years on the front lines of club-level analytics.
We process baseball data in multiple ways with AI — going beyond traditional dashboards to surface patterns, context, and answers that matter to coaches, analysts, and fans.
A founder-led analytics company. Every product decision is grounded in real experience inside a professional baseball organization, not just abstract data science.
Our goal is not only to open new frontiers in data analysis by combining baseball with AI, but also to pursue a wider range of AI-driven businesses — including real-world education programs that pair baseball with learning, and regional community service.
Continuously developing AI methods that extract insights no traditional statistical model has reached before.
Bringing baseball and AI together in real education and regional volunteer programs — using technology to give back to the game.
Balltology AI combines proprietary data processing pipelines with large language models and machine learning to analyze the game at a level of detail that used to be out of reach.
Core R&D around pitch-tracking data — trajectory, command, and context — as the foundation of our analysis products.
AI pipelines that turn raw tracking and game data into clear, decision-ready insight for baseball professionals.
Patent application in progress. Balltology AI is in the process of filing a patent for technology related to pitch-tracking data.
Broadcast video and pitch-tracking data normally live in different worlds — one zooms, pans and cuts, the other is pure numbers. Our technology — the subject of a patent application now in progress — aligns the two: it continuously locates the catcher's mitt in ordinary broadcast footage, registers the pitch's 3D trajectory onto the video frame, and computes a pitcher's command index from the gap between where the catcher asked for the ball and where it actually arrived.
The result is a metric that neither tracking data nor video alone can produce, from footage that any broadcaster already shoots — no stadium-grade hardware required. The core pipeline has been validated end-to-end in a proof-of-concept on real broadcast game footage.
Patent application in progress. Technical details are intentionally withheld at this stage.
