The Silicon Diamond: Integrating Big Tech Engineering in Baseball - Part 1
Written By: Matt Yee
Foreword
In today’s rapidly evolving sports landscape, few areas remain untouched by technology’s transformative power. Having spent over a decade in big tech, specializing in data science and software engineering, I’ve come to understand technology’s immense potential. But my perspective is also grounded in another arena—professional baseball, where I serve as a bullpen catcher with the New York Mets organization. This unique dual experience has allowed me to witness the convergence of two distinct worlds: Silicon Valley’s precision and Major League Baseball’s enduring human dynamics.
What sets my perspective apart is not just my technical expertise, but my understanding of the human element. My time in the bullpen, where every pitch holds meaning and every subtle mechanic affects the game, has taught me that numbers only tell part of the story. Where many might view data as mere statistics, I see an opportunity to bridge the insights within those numbers with the instincts and feel that players and coaches bring to the field. This human-centered approach means leveraging technology in ways that don’t just analyze but enhance the organic rhythms of baseball.
In The Silicon Diamond: Integrating Big Tech Engineering in Baseball, I explore how the precision of data science can be seamlessly woven into the sport without losing its soul. It’s about creating systems that serve the people behind the data—players, coaches, and scouts—and allow them to elevate their game while maintaining the feel and intuition that define it. Whether through real-time analytics, player feedback systems, or development tracking, my mission is to bring data to life in a way that complements the heart of baseball.
Prologue
The crack of the bat still echoes in the same way it did a hundred years ago, but everything around that sound has changed. As I catch bullpen sessions at the Double-A level, I see pitchers reviewing their mechanics on iPads between throws. Coaches analyze spin rates and axis rotations in real-time, while advance scouts thousands of miles away upload their reports to cloud databases. This is baseball in the digital age – a sport balancing its timeless nature with technological revolution.
This revolution isn’t just about adding technology to baseball – it’s about fundamentally rethinking how we develop players, analyze games, and make decisions. With over a decade of experience in software engineering and data science, combined with my time as a bullpen catcher in Double-A baseball, I’ve witnessed both the promise and pitfalls of bringing Silicon Valley’s practices to America’s pastime.
Chapter 1: Baseball’s Digital Crossroads
Minor league baseball provides a unique window into the sport’s technological transformation. While major league clubs employ sophisticated systems that track everything from spin rates to player movement, Double-A teams often operate with a fraction of these resources. This disparity creates both challenges and opportunities that shape how we think about baseball’s future.
During my time behind the plate, I’ve seen pitchers struggling to translate data into actionable adjustments. I’ve watched advance scouts blend traditional evaluation methods with modern analytics. Most importantly, I’ve learned that technology in baseball isn’t about replacing human judgment – it’s about enhancing it.
Consider the standard pre-series advance scouting meeting. In today’s baseball, these meetings have evolved beyond traditional scouting reports and video sessions. Modern advance scouts combine observational data with sophisticated analytics to create comprehensive game plans. However, the key isn’t in the technology itself but in how it’s used to support and enhance baseball decisions.
The same processing power that helps tech companies handle billions of transactions now helps baseball teams process countless data points from every game. But baseball isn’t just about data – it’s about understanding how to use that data to develop players, win games, and advance the sport.
Chapter 2: Building Solutions from the Bullpen
When I first combined my software engineering background with my role as a bullpen catcher, I realized I had a unique opportunity to bridge two worlds. The problems I encountered weren’t abstract technical challenges – they were real issues affecting player development and team performance every day.
My first project emerged from a simple observation: while catching bullpen sessions, I noticed patterns in pitch behavior that weren’t being effectively tracked or analyzed. The expensive systems used in major league stadiums weren’t available to us, but I knew we could create something valuable using basic technology and careful observation.
I started with a fundamental question: what information matters most to pitchers and coaches during a bullpen session? From my position behind the plate, I could feel the late life on a fastball, see the subtle changes in arm slot, and notice patterns in command that might not be obvious from other vantage points. The challenge was turning these observations into actionable data.
My first system was born from necessity. Using just an iPad and some basic motion tracking software, I developed a way to capture key metrics during bullpen sessions. While major league teams were using multiple high-speed cameras and sophisticated biomechanical analysis, I focused on what I call the “catchable metrics” – the characteristics of pitches that you can feel as much as see.
For instance, when working with a pitcher struggling with his breaking ball, I noticed that traditional metrics like spin rate and axis didn’t tell the whole story. By combining video analysis with my direct observations as a catcher, I could track subtle changes in release point and wrist position that affected pitch movement. This became particularly valuable when working with pitchers who were trying to develop new pitches or refine existing ones.
The real breakthrough came in how we used this information. Rather than overwhelming pitchers with data, I created simple visual references that connected what they were feeling to what the numbers showed. A pitcher might not care about the exact degree of his arm angle, but showing him how small changes in his delivery affected his pitch movement made the data meaningful.
Working in Double-A baseball taught me an invaluable lesson: the most sophisticated technology isn’t always the most effective. What matters is creating systems that provide actionable insights. I developed a method I called “Progressive Pitch Mapping” – tracking how pitch characteristics changed throughout a bullpen session and across multiple sessions over time.
This wasn’t just about collecting data; it was about understanding the story behind the numbers. When a pitcher’s slider started losing bite in the latter part of his sessions, was it fatigue? A mechanical issue? A grip problem? By combining traditional baseball knowledge with systematic data collection, we could identify patterns that might otherwise have gone unnoticed.
The system evolved as pitchers and coaches provided feedback. Some wanted more detailed breakdowns of their pitch shapes. Others were more interested in consistency metrics. The key was maintaining flexibility while ensuring the core data remained reliable and useful. This meant creating clear categories for different types of observations:
• Movement Profiles: How pitches behaved from release to plate
• Command Patterns: Where pitches ended up relative to their intended location
• Mechanical Consistency: How well pitchers maintained their delivery
• Recovery Indicators: How pitch quality held up throughout sessions
What made this approach effective wasn’t the technology – it was the way it bridged the gap between traditional baseball development and modern analytics. Coaches could still rely on their expertise and experience, but now they had data to support their observations. Pitchers could maintain their feel-based approach to development while understanding the mechanical principles behind their success.
What started as a way to track bullpen sessions naturally evolved into something much more comprehensive. Pitchers began asking questions that went beyond single sessions: “How does my slider movement compare to last month?” “Are my mechanics more consistent than they were in spring training?” “Why do I lose command in later innings?” These questions demanded a broader approach to player development.
The transition from session tracking to comprehensive development monitoring happened organically. I noticed patterns emerging across different pitchers that suggested common development pathways. A pitcher working on a new changeup would typically go through similar stages of development – from establishing a comfortable grip, to finding consistent arm speed, to finally developing the confidence to throw it in any count. By tracking these patterns, I could help pitchers understand where they were in their development process and what challenges they might face next.
This realization led me to develop what I called “Development Pathways” – structured but flexible frameworks for pitch development and refinement. Instead of treating each bullpen session as an isolated event, we could now see it as part of a larger development story. For example, when working with a pitcher developing a new breaking ball, we could track not just the physical progress of the pitch but also the pitcher’s growing confidence in different situations.
The system became particularly valuable for injury prevention and recovery. By maintaining detailed records of pitch characteristics, we could detect subtle changes that might indicate fatigue or potential arm problems before they became serious issues. A slight drop in fastball movement or inconsistency in release point might not be noticeable in isolation, but when viewed as part of a larger pattern, these changes could be significant indicators.
What made this approach different was its focus on individual pitcher profiles rather than comparison to league-wide standards. While knowing how a pitcher’s metrics compared to league averages was useful, understanding how they compared to their own established patterns was often more valuable. A pitcher whose slider traditionally had less break but was more consistent might be more effective than one with occasionally spectacular but inconsistent movement.
One of the most rewarding aspects of this evolution was seeing how it helped pitchers take ownership of their development. Rather than just following a prescribed program, they could see their progress mapped out over time. The data wasn’t just numbers on a screen – it was a record of their journey and a guide for their continued improvement.
This broader approach also helped bridge the gap between different aspects of player development. Bullpen sessions could be coordinated with weight room work and recovery protocols. If a pitcher’s metrics showed signs of fatigue, we could adjust their training schedule accordingly. This kind of integrated approach to development wasn’t just more effective – it helped prevent the kind of overwork that can lead to injuries.
Perhaps most importantly, this system helped create a common language between pitchers, coaches, and the training staff. When everyone could see the same patterns and track the same progress, it became easier to make coordinated decisions about player development. A pitcher’s success wasn’t just about what happened on the mound – it was about how all aspects of their development worked together.
The development tool I created, which I called “PitchPath,” grew from a simple premise: development isn’t linear, and pitchers need to see their progress in context. What started as my basic pitch tracking evolved into a comprehensive development monitoring system that could identify patterns human observation might miss.
To continue reading about the development and implementation of PitchPath, please look for the next part of this article series.