During a February 2026 spring training session in Port St. Lucie, Florida, Mets pitcher Robert Stock unveiled a new AI-driven pitching analytics tool designed to deepen understanding of pitching mechanics and outcomes. Centered on the primary focus of Robert Stock pitching analytics, this platform combines extensive data and machine learning to help pitchers evaluate strategies like limiting walks without increasing the risk of well-hit balls.
A Question Sparks Data-Driven Insight at Mets Pitchers’ Meeting
At a recent pitchers’ meeting, 24-year-old Mets prospect Nolan McLean posed a thoughtful question about pitching strategy—whether pitchers who aggressively attack the strike zone to reduce walks might also be more likely to give up “barrels,” a term describing balls struck with optimal exit velocity and launch angle. Contrary to expectations, Robert Stock, a 36-year-old veteran reliever with limited MLB success, responded by consulting his AI-powered platform, Stockyard Baseball Co., to explore the data-backed relationship between these stats.
Stock clarified that the two metrics are not inherently linked. He explained that pitchers struggling with command tend to fall into hitter’s counts, which can lead to more damage as often as it results in walks. Stock’s quick access to an extensive database, powered by artificial intelligence, provided an immediate and nuanced answer that traditional coaching discussions might not offer.
Development of Stockyard Baseball Co.: Marrying AI and Pitching Data
Robert Stock built the Stockyard Baseball Co. platform to fulfill a personal need for accessible, detailed pitching data analysis. Utilizing over 8.9 million MLB pitches from public sources like Baseball Savant and AI-driven machine learning algorithms, Stock’s site enables players to manipulate variables such as release points and pitch grades to predict potential outcomes. The platform is currently in a testing phase but aims to bring advanced, transparent analytics directly to players and coaches.
Stock shared,
“Year after year, I watched people on Twitter or wherever put out very interesting analytics and data and I always wished I had a database where I could do that.”
He added,
“For instance, teams can tell you what your grades are when you’re inside an organization, but it’s harder to find out: What if I changed my release point by an inch higher or lower, what would my grade be?… You can ask the pitch model, ‘Does this sound like a good idea?’”
Emerging Trend: Players Taking Control of Their Analytics
Stock is part of a broader movement of MLB players engaging deeply with data to enhance their performance. Retired Mets reliever Trevor May is also developing a data-driven pitching analytics platform, reflecting growing player curiosity and empowerment. Mets starter Clay Holmes emphasized this mindset, noting how analytics-related adjustments rejuvenated his career.
Holmes observed,
“I think as a player, you have to be curious about the game. You have to be open to things. There are principles of the game that’s always going to be there, but there’s always new ways to go about them …You see guys like Stock, it’s cool just to connect and hear about it. And I think that love and joy that it brings is infectious and it pushes other people too.”
Focus on Practical, Transparent Analytics for Pitchers
Both Stock and May emphasize that their tools are meant to complement, not replace, traditional pitching models. Their goal is to democratize pitching data by providing straightforward access to information that might otherwise be tightly controlled within organizations, enabling pitchers especially those on the roster bubble or relievers seeking an edge, to find actionable insights.
Stock’s fascination lies with “stickiness,” a term describing pitching stats that persist across seasons and suggest true underlying skill rather than circumstantial influences. May created a metric called “misfire,” which distinguishes between pitches missed intentionally as part of strategy and those that err due to control failures.
May shared,
“Outside of the truly elite guys baseball is populated with the guys like me where you’re kind of fringe, or you’re a reliever and you need something unlocked.”
He added,
“95% [of us] are like, ‘I don’t want to go back to the minors ever. How do I do that?’ That is what I’ve always been concerned with. And I think if you take ownership of your own relationship with analytics and … if you’re just a little bit curious about the context [of why things happen] you can find little, tiny things that you can slightly adjust that will just completely fix a big problem.”
Balancing AI Insights with Practical Experience and Skepticism
While recognizing AI’s potential, both pitchers understand the technology’s limits, stressing the necessity of applying personal experience and skepticism to interpret results. Stock, with 14 years as a professional pitcher, filters out unreliable analytics, referring to improper data or conclusions as “AI slop.” He also solicits community feedback to improve his platform.
May described his approach as following “the scientific method,” pointing out the importance of validating new metrics by testing against real-world outcomes and seeking input from multiple users.
“You’re trying to prove yourself wrong,”
he said.
“Sometimes, your red flags go off. So you just run tests. [You give it to] 20 different people and see them do it individually, and see if it comes out correctly. If you get 20 out of 20 right, you’re pretty confident the rest was done, right, too.”
Past Examples Inspire Present Data Innovations
May highlighted how his own career was influenced by advanced analytics when, in 2019, Twins pitching coach Wes Johnson used Hawk-Eye 3D technology to identify small tweaks that increased velocity. This success story illustrates the power of biomechanical and pitch-tracking tools to impact careers, many of which are now expanding into player-driven, machine learning-based platforms like those from Stock and May.
May remarked,
“That was the inspiration. It changed my career.”
Looking Ahead: Democratizing Pitching Insights with AI Tools
Robert Stock’s AI-powered Stockyard Baseball Co. reflects a significant shift in how pitchers engage with their craft, blending exhaustive pitch data with intelligent algorithms to answer complex questions in real time. By empowering players with direct access to these insights, this tool could influence training, strategic pitching decisions, and player development throughout the sport.
As AI continues to evolve within baseball analytics, its integration by active players like Stock and forward-thinking veterans like May highlights a transformative movement toward customization, transparency, and player agency in the game’s most intricate aspects.
