Roy Krishnan, founder and CEO of Liquid Sports Lab, joins Sunny Ray to explain how a Tommy John surgery that ended his pitching career led him to build a data-driven performance company. After his injury derailed a preferred walk-on path to Dartmouth, Krishnan attended the University of Rochester, where questions about outdated recovery protocols pushed him to start measuring biomechanical stress himself. He incorporated Liquid Sports Lab in 2020, then spent time with the Dallas Mavericks under Mark Cuban and later the Toronto Raptors, where he became the youngest person to hold his position in league history. Krishnan describes Liquid Sports Lab's four C's method, collect, compare, correct, compete, and explains why the final step, competing under real pressure, matters most. He also discusses why the company waited nearly five years to lean into AI, treating early growth as a math and optimization problem rather than a rush to market, and reflects on how ARR growth and a broader analytical shift in sports convinced him to go all in on his own company.
A Tommy John surgery ended his pitching career, so Roy Krishnan built a data lab that turns biomechanics into athlete decisions.
What is Liquid Sports Lab and why does the world need it?
It's a sports performance facility that runs on data analytics instead of conventional gut-feel coaching. Athletes test every day on force plates and central nervous system fatigue measures, giving us a snapshot of how stressed and ready they are. Instead of just saying you're not at a level, we build a regimented, mathematical plan using proven science to actually get you there.
Do you think this science could eventually help everyday people, not just elite athletes?
Yes, that's exactly where we want to go, both for market share and because we believe everyone is an athlete. There are more people running marathons and fewer drinking now, and we want to help the aspiring marathoner or even someone's mom garden longer outside. Right now, being only four or five years into collecting data, we primarily work with professional and college athletes.
Can you take us back to what happened with your injury and how it led to founding Liquid Sports Lab?
I was a high school pitcher who verbally committed to Dartmouth, but I had Tommy John surgery in 2016, which was more of a death sentence back then. Rochester took me hurt and all, and coming back I kept asking my doctors why we don't measure stress or mechanical integrity. The answer was there was no reason, the protocol was written in the 1970s, and that woke me up to start Liquid Sports Lab in 2020.
Before it was a company, what were you actually building?
Before it was a company, it was me buying inertial movement units from Norway and strapping them to my own arm just to see what the data looked like. I learned the hard way, like sampling rates not being high enough for overhead athletes. Eventually I worked with Driveline Baseball, then the Mavericks and Raptors, where everyone was chasing the same question of load management and keeping athletes healthy.
What did your time with the Toronto Raptors teach you about how elite teams make decisions on athlete health?
It showed me there's a spectrum. The Mavericks under Mark Cuban were more family-run with power concentrated in a few hands, while the Raptors, owned by Maple Leaf Sports Entertainment, were much more rooted in an analytical ecosystem. Because MLSE owns so many different teams across sports, I got to see what success looks like across many disciplines and find principles that held true across all of them.
What was the through line that made leaving the NBA for your own company the obvious next move?
It came down to ARR, perspective, and age. We'd passed six figures in revenue while I was still working full-time in the NBA, which proved it wasn't just a side project. I'd also spent years seeing how other ecosystems operated, and at 25 I was still young enough that the risk tolerance was high since I wasn't providing for a family yet.
Of the four C's, collect, compare, correct, compete, which one do people underestimate most?
Compete, which is funny coming from an analytics guy. The real world doesn't work like a lab. You can have a guy who throws 105 miles an hour with perfect data in the lab, but he doesn't get paid unless he can do it on the field with the pressure and crowd. We're basically counting cards at a casino, stacking odds, but that edge can still be beaten.
Why did you wait nearly five years before leaning into AI instead of rushing to build a model?
AI wasn't a buzzword back then, it was just a math and optimization problem for us. Nobody knew the specific path, so the only way to learn was collecting everything over a long period before looking for insights. We had about 30 athletes in 2020 and didn't reach a real sample size for four years, but since I was still working in the NBA, we weren't rushing to monetize.
Building something daring? Sunny talks to founders like this every day. Fifteen minutes to see if your story belongs on the stage.
Claim your pre-interview