Marc Kerrest of Whistle Performance discusses how the company synthesizes wearable, global positioning system, heart-rate and camera-derived movement data to deliver real-time insights for professional and collegiate teams. Kerrest explains that they integrate multiple data streams and apply proprietary machine learning models to generate actionable practitioner flags for sports performance analytics and injury prevention.
The segment situates these capabilities within artificial intelligence and machine learning, emphasizing longitudinal analysis and automated flagging across college athletics, the National Football League and international soccer. Analysts from theCUBE Research note platform transferability to first responders, military and insurance verticals with measurable return on investment in risk reduction.
Kerrest describes Whistle Performance as a Switzerland of data that harmonizes disparate sources and produces AI-driven practitioner-ready signals to optimize player load and reduce injury risk. theCUBE hosts Gemma Allen and John Furrier with co-host Dave Vellante moderate the discussion for NYSE Wired's Mixture of Experts series.
Watch the full segment for deeper insights on wearable technology, player health, load management and operational ROI.
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Marc Kerrest, Whistle Performance
Marc Kerrest of Whistle Performance discusses how the company synthesizes wearable, global positioning system, heart-rate and camera-derived movement data to deliver real-time insights for professional and collegiate teams. Kerrest explains that they integrate multiple data streams and apply proprietary machine learning models to generate actionable practitioner flags for sports performance analytics and injury prevention.
The segment situates these capabilities within artificial intelligence and machine learning, emphasizing longitudinal analysis and automated flagging across college athletics, the National Football League and international soccer. Analysts from theCUBE Research note platform transferability to first responders, military and insurance verticals with measurable return on investment in risk reduction.
Kerrest describes Whistle Performance as a Switzerland of data that harmonizes disparate sources and produces AI-driven practitioner-ready signals to optimize player load and reduce injury risk. theCUBE hosts Gemma Allen and John Furrier with co-host Dave Vellante moderate the discussion for NYSE Wired's Mixture of Experts series.
Watch the full segment for deeper insights on wearable technology, player health, load management and operational ROI.
>> Palo Alto studio connections, Silicon Valley and Wall Street. I'm John Furrier, host of theCUBE, here with Dave Vellante, my co-host.
Gemma Allen
>> Welcome back to theCUBE studio here at the New York Stock Exchange. I'm Gemma Allen with NYSE Wired Mixture of Experts and today we are going to talk sports technology. Joining me now is Marc Kerrest, founder and CEO of Whistle Performance. Welcome, Marc.
Marc Kerrest
>> Hi, Gemma. Thanks so much for having me. Really excited to be here.
Gemma Allen
>> Whistle Performance, I feel like it's somewhat of a snappy and self-explanatory title or company name, but give me the lowdown. What is it exactly that you guys do?
Marc Kerrest
>> So we integrate data analytics for sports teams right now, a lot of different types of physical performance data, and analyze that data in seconds to give them insights from what all their data streams are telling.
Gemma Allen
>> Wow. Okay. So bring this to life for me. You are the Knicks.
Marc Kerrest
>> Yep.
Gemma Allen
>> You're on the court last night, winning.
Marc Kerrest
>> Yeah. Well, those heart rates were probably going up late in the game, for sure.
Gemma Allen
>> And you are the manager on the sidelines. You can essentially call this API, request data, live data from a wearable on a player and it will give you some sense of performance optimization and I guess, also some sort of risk prevention?
Marc Kerrest
>> Yeah, that's exactly it, right? Last night they're tracking wearable data, really through cameras actually, during the games. But so they're tracking all their movement patterns, all of that stuff. During the week they're analyzing a lot of different testing and so it gives you a lot of different signals. The gap really is in the analysis of that data. And so that's where we come in. We automate the analysis of the data, pull the data via APIs, run it through our machine learning, and then give them signals, like you said, to help prevent injuries. We have predictive analytics, but also to find optimal performance and what are the surrounding factors around that.
Gemma Allen
>> Wow. So it seems as though the world of sports tech has grown very, very quickly. It's become far more mature, far more data driven in a very, I guess, short space of time really. If you look back, even the Knicks players that took to the court last night that won the last time, right? What has changed in the world of sports and data in that timeframe is phenomenal.
Marc Kerrest
>> No, yeah, you're spot on. So I think what we've seen is a huge development of hardware tracking, right? Whether it's Oura and Whoop bands, whether it's GPS tracking, heart rate tracking. There's so much more hardware right now that sports teams are using to get data on their athletes. And then the gap really is in synthesizing that data to really get the insights out of that data, and that's where we come in. All these teams have a lot of different data sources they're collecting, but it takes them a long time to figure out what that data means. And so we integrate all that data in one place and at a click of a button really, tell them, "Hey, here's what you need to be careful of and here are the people who are reaching peak performance." And then what we do then is take that historical data and use predictive signals. So you can better forecast, hey, what are our loads going to be? The Knicks played last night, now they've got to travel to San Antonio. What is the load from that travel? What are they thinking about in those practice sessions? Whose load from last night is something they have to think about going into Saturday so they can wrap this up?
Gemma Allen
>> So talk about the technology side of this underneath the hood here. You don't have your own wearable. Do you have any desire to?
Marc Kerrest
>> Right now we consider ourselves a Switzerland of data, right? We integrate data from everyone. We want to play nicely in the sandbox. And so for the users, they want to be able to bring their data into one place. For us, we see that as an unnecessary competition. What we bring to the table is the analysis of the data.
So we are analyzing all these different data sources. The more different data streams we bring in, the stronger the signals we're creating. So really, just focused on the technology, the machine learning algorithms, the pattern recognition that basically drives stronger signals for our partners.
Gemma Allen
>> Well, I don't think it's ever been a better time to be Switzerland, quite frankly, right? Because so much is happening so quickly too in this space. So in terms of the partnerships you've developed, talk me through, I guess, the journey of this company. How many years have you guys been around and what sorts of brands, collaborations, have you worked with in that time?
Marc Kerrest
>> Yeah, for sure. So started in the college soccer space. My background is in soccer, European football. So that's where we started. Quickly realized, hey, there's a bigger market out there. So we've grown pretty quickly across college athletics, work with a number of top college football teams, Tennessee football, Auburn football, Penn State football are all recent customers. Grown into the NFL too. We just signed the San Francisco 49ers this week.
Gemma Allen
>> Wow, congrats.
Marc Kerrest
>> So NBA, NHL, WNBA, NWSL. Starting to expand internationally and now we're starting to see that the analysis we're doing for sports teams also applies to different verticals. The analysis we're doing on a $25 million quarterback can also apply to a first responder or someone in the military and there it's about, okay, how can we automate insights that helps prevent a significant injury or significant something bad happening out in the field? So the ROI there, we're talking workers' comp, OSHA, stuff like that, manufacturing. So it's a pretty broad spectrum.
Gemma Allen
>> Wow. Even from an insurance industry perspective, there's huge opportunity there, right? You're transferable.
Marc Kerrest
>> For sure, yeah. For sure. And that's a new vertical for us. Right now the majority of our revenue is from sports and working with elite sports, I think people start to see in other verticals, "Hey, if they can do this for the 49ers, what can they do for us?"
Gemma Allen
>> Well, the 49ers is a great win. You need the Knicks next.
Marc Kerrest
>> Yeah, we're working on that. We work with the New York Rangers, so we're having some conversations with the Knicks and working on that as well.
Gemma Allen
>> So this is all cloud based, I assume? API, on your phone, also a platform coaches, managers, teams log into. What does the UX on this look like?
Marc Kerrest
>> Yeah, so the UX, we have a web-based platform that our users use. Like you said, we also have a native mobile app that we've built, which allows really practitioners to get answers from their data in real time. You're on the field at a coaching session, the coach is asking you a question about your data. You used to have to go back to your laptop and spend a couple hours getting that response. For us, you can voice dictate a question to our AI in real time and get the answer, even create a visual on your phone and show that to the coach and have that conversation in real time. So trying to use all the data that's stored in our databases, bring it to life really quickly.
Gemma Allen
>> And on the AI side of it from the perspective of risk prevention and performance optimization, what sorts of trends are you seeing? What's possible now with data that wasn't possible two, three years ago?
Marc Kerrest
>> Yeah. I mean, obviously generative AI is making huge leaps and bounds on a weekly and monthly basis. I think for us it's beyond the generative AI, right? It's the machine learning longitudinally over big data sets and then having those proprietary flags that our internal AI can reference, that's the difference maker in what we're doing. We have practitioners tell us, "Hey, I can put my spreadsheet in ChatGPT." A great thing about ChatGPT is it'll always give you a response. Is that response always correct? It's sourcing it from everything on the internet, where we've got really hyper-focused models and flags that our internal AI can reference and then give specific responses to those users. But obviously, I think that is what we continue to develop and as those models get stronger and stronger all the time, we try to lean on that and enhance what we're doing.
Gemma Allen
>> And when you're looking for signals from this data, especially around performance, is it typically benchmarking the player themselves past performance, other players, or is it some sort of broad industry benchmark, or all of the above?
Marc Kerrest
>> Yeah.
Gemma Allen
>> What is the core signal?
Marc Kerrest
>> Yeah, I think it's all the above, right? I think there are players who outperform and their baselines will be different than the rest of the group. So obviously, when you're looking at their metrics, you want it contextualized versus their individual baseline, but you also want to see, "Hey, how did this player get here? And I have this player behind them that I'm trying to progress to that level. What are the markers they need to hit as we progress them? How do they look compared to the rest of the team, the rest of the group, the rest of the league?" So it's all those different benchmarks and each team has a different way they want to use the data. I think the most powerful part of our platform is how malleable and customizable it is where you can really dial in your own custom metric, your own custom flag that you've designed through your years of being a practitioner and dial in specifically what you want to see.
Gemma Allen
>> Wow. And Marc, do you have any examples of use cases or events whereby the state actually had some sort of fundamental change in game plan or one player's life?
Marc Kerrest
>> Yeah. Again, all our customer data is confidential, but we have had various times, sometimes on the negative side, where we will flag an athlete for a few days in a row and the practitioner will call us and ask why they were flagged because ultimately they had a big injury. Ultimately, we have some newer flags that we rolled out. Just last year with an NFL team, a player was flagged two days at practice. The practitioners paid attention, called us, we had a conversation and they proactively reduced that athlete's load, used our predictions to kind of model out his next few days and kept him healthy for a playoff game that was coming up. So that was a really positive signal of what we're doing and I think that's really what we're trying to dial in, right? It's easy to look at the data after an injury has occurred, but what can we do to flag the data beforehand and prevent that injury from happening?
Gemma Allen
>> Wow. And you said you're Switzerland on the hardware side. On the software side, are you completely stock agnostic? Are you working with any particular frontier lab? What sorts of tech is running this?
Marc Kerrest
>> So we built everything in-house. As we've developed as a company, we are starting to partner with some people in different aspects. We don't have a full EMR, we do have some injury capabilities. We're starting to partner with Healthy Roster on the EMR side. There's a few different strategic partnerships that we see in terms of how we're growing, but there's a big space out there too. So disrupting the incumbents with our technology and moving forward as best we can.
Gemma Allen
>> Well, we love an underdog on theCUBE, I have to tell you. So last question, what's ahead? What are the goals? You just signed the 49ers, that's pretty big. I hope you're going to market the bejesus out of that. What do you plan to do? What does between now and the end of this year look like for you and the team?
Marc Kerrest
>> Yeah, lifelong 49ers fan, so really excited about that one. We always say we're team agnostic because we work with a lot of teams, so we're not rooting for one over the other. I think for us going forward, we rebranded in the last year. Like you said, Whistle Performance kind of signals what we're doing. I think the next steps for us is we are pretty entrenched in sports. It's looking at those new verticals, right? The firefighters, the military starting to help other use cases. Obviously the market there is huge, but I think a lot of the signals we've developed in sports apply really well to those other markets and I think the impact we can have can really be massive there.
Gemma Allen
>> Wow, certainly so transferable. Last last question and I am going to judge you. What European soccer team do you sport?
Marc Kerrest
>> So I'm a huge Arsenal fan.
Gemma Allen
>> Oh.
Marc Kerrest
>> I'm not shy about it. It's been a good couple months.
Gemma Allen
>> Yeah.
Marc Kerrest
>> And you can ask my kids. They can sing all the songs.
Gemma Allen
>> Oh, wow.
Marc Kerrest
>> I did grow up in France, so with the World Cup coming up, allez les Blues and we'll see what happens.
Gemma Allen
>> Allez les Blues is right. Well, sad end for Arsenal, but hopefully only the beginning for you and this company. Marc, thank you so much for coming on this Cube.
Marc Kerrest
>> Thank you so much for having me, Gemma, really enjoyed it.
Gemma Allen
>> I'm Gemma Allen here at theCUBE Studio at the NYSE. This NYSE Wired's Mixture of Experts. Thanks for watching.
>> Palo Alto studio connections, Silicon Valley and Wall Street. I'm John Furrier, host of theCUBE, here with Dave Vellante, my co-host.
Gemma Allen
>> Welcome back to theCUBE studio here at the New York Stock Exchange. I'm Gemma Allen with NYSE Wired Mixture of Experts and today we are going to talk sports technology. Joining me now is Marc Kerrest, founder and CEO of Whistle Performance. Welcome, Marc.
Marc Kerrest
>> Hi, Gemma. Thanks so much for having me. Really excited to be here.
Gemma Allen
>> Whistle Performance, I feel like it's somewhat of a snappy and self-explanatory title or company name, but give me the lowdown. What is it exactly that you guys do?
Marc Kerrest
>> So we integrate data analytics for sports teams right now, a lot of different types of physical performance data, and analyze that data in seconds to give them insights from what all their data streams are telling.
Gemma Allen
>> Wow. Okay. So bring this to life for me. You are the Knicks.
Marc Kerrest
>> Yep.
Gemma Allen
>> You're on the court last night, winning.
Marc Kerrest
>> Yeah. Well, those heart rates were probably going up late in the game, for sure.
Gemma Allen
>> And you are the manager on the sidelines. You can essentially call this API, request data, live data from a wearable on a player and it will give you some sense of performance optimization and I guess, also some sort of risk prevention?
Marc Kerrest
>> Yeah, that's exactly it, right? Last night they're tracking wearable data, really through cameras actually, during the games. But so they're tracking all their movement patterns, all of that stuff. During the week they're analyzing a lot of different testing and so it gives you a lot of different signals. The gap really is in the analysis of that data. And so that's where we come in. We automate the analysis of the data, pull the data via APIs, run it through our machine learning, and then give them signals, like you said, to help prevent injuries. We have predictive analytics, but also to find optimal performance and what are the surrounding factors around that.
Gemma Allen
>> Wow. So it seems as though the world of sports tech has grown very, very quickly. It's become far more mature, far more data driven in a very, I guess, short space of time really. If you look back, even the Knicks players that took to the court last night that won the last time, right? What has changed in the world of sports and data in that timeframe is phenomenal.
Marc Kerrest
>> No, yeah, you're spot on. So I think what we've seen is a huge development of hardware tracking, right? Whether it's Oura and Whoop bands, whether it's GPS tracking, heart rate tracking. There's so much more hardware right now that sports teams are using to get data on their athletes. And then the gap really is in synthesizing that data to really get the insights out of that data, and that's where we come in. All these teams have a lot of different data sources they're collecting, but it takes them a long time to figure out what that data means. And so we integrate all that data in one place and at a click of a button really, tell them, "Hey, here's what you need to be careful of and here are the people who are reaching peak performance." And then what we do then is take that historical data and use predictive signals. So you can better forecast, hey, what are our loads going to be? The Knicks played last night, now they've got to travel to San Antonio. What is the load from that travel? What are they thinking about in those practice sessions? Whose load from last night is something they have to think about going into Saturday so they can wrap this up?
Gemma Allen
>> So talk about the technology side of this underneath the hood here. You don't have your own wearable. Do you have any desire to?
Marc Kerrest
>> Right now we consider ourselves a Switzerland of data, right? We integrate data from everyone. We want to play nicely in the sandbox. And so for the users, they want to be able to bring their data into one place. For us, we see that as an unnecessary competition. What we bring to the table is the analysis of the data.
So we are analyzing all these different data sources. The more different data streams we bring in, the stronger the signals we're creating. So really, just focused on the technology, the machine learning algorithms, the pattern recognition that basically drives stronger signals for our partners.
Gemma Allen
>> Well, I don't think it's ever been a better time to be Switzerland, quite frankly, right? Because so much is happening so quickly too in this space. So in terms of the partnerships you've developed, talk me through, I guess, the journey of this company. How many years have you guys been around and what sorts of brands, collaborations, have you worked with in that time?
Marc Kerrest
>> Yeah, for sure. So started in the college soccer space. My background is in soccer, European football. So that's where we started. Quickly realized, hey, there's a bigger market out there. So we've grown pretty quickly across college athletics, work with a number of top college football teams, Tennessee football, Auburn football, Penn State football are all recent customers. Grown into the NFL too. We just signed the San Francisco 49ers this week.
Gemma Allen
>> Wow, congrats.
Marc Kerrest
>> So NBA, NHL, WNBA, NWSL. Starting to expand internationally and now we're starting to see that the analysis we're doing for sports teams also applies to different verticals. The analysis we're doing on a $25 million quarterback can also apply to a first responder or someone in the military and there it's about, okay, how can we automate insights that helps prevent a significant injury or significant something bad happening out in the field? So the ROI there, we're talking workers' comp, OSHA, stuff like that, manufacturing. So it's a pretty broad spectrum.
Gemma Allen
>> Wow. Even from an insurance industry perspective, there's huge opportunity there, right? You're transferable.
Marc Kerrest
>> For sure, yeah. For sure. And that's a new vertical for us. Right now the majority of our revenue is from sports and working with elite sports, I think people start to see in other verticals, "Hey, if they can do this for the 49ers, what can they do for us?"
Gemma Allen
>> Well, the 49ers is a great win. You need the Knicks next.
Marc Kerrest
>> Yeah, we're working on that. We work with the New York Rangers, so we're having some conversations with the Knicks and working on that as well.
Gemma Allen
>> So this is all cloud based, I assume? API, on your phone, also a platform coaches, managers, teams log into. What does the UX on this look like?
Marc Kerrest
>> Yeah, so the UX, we have a web-based platform that our users use. Like you said, we also have a native mobile app that we've built, which allows really practitioners to get answers from their data in real time. You're on the field at a coaching session, the coach is asking you a question about your data. You used to have to go back to your laptop and spend a couple hours getting that response. For us, you can voice dictate a question to our AI in real time and get the answer, even create a visual on your phone and show that to the coach and have that conversation in real time. So trying to use all the data that's stored in our databases, bring it to life really quickly.
Gemma Allen
>> And on the AI side of it from the perspective of risk prevention and performance optimization, what sorts of trends are you seeing? What's possible now with data that wasn't possible two, three years ago?
Marc Kerrest
>> Yeah. I mean, obviously generative AI is making huge leaps and bounds on a weekly and monthly basis. I think for us it's beyond the generative AI, right? It's the machine learning longitudinally over big data sets and then having those proprietary flags that our internal AI can reference, that's the difference maker in what we're doing. We have practitioners tell us, "Hey, I can put my spreadsheet in ChatGPT." A great thing about ChatGPT is it'll always give you a response. Is that response always correct? It's sourcing it from everything on the internet, where we've got really hyper-focused models and flags that our internal AI can reference and then give specific responses to those users. But obviously, I think that is what we continue to develop and as those models get stronger and stronger all the time, we try to lean on that and enhance what we're doing.
Gemma Allen
>> And when you're looking for signals from this data, especially around performance, is it typically benchmarking the player themselves past performance, other players, or is it some sort of broad industry benchmark, or all of the above?
Marc Kerrest
>> Yeah.
Gemma Allen
>> What is the core signal?
Marc Kerrest
>> Yeah, I think it's all the above, right? I think there are players who outperform and their baselines will be different than the rest of the group. So obviously, when you're looking at their metrics, you want it contextualized versus their individual baseline, but you also want to see, "Hey, how did this player get here? And I have this player behind them that I'm trying to progress to that level. What are the markers they need to hit as we progress them? How do they look compared to the rest of the team, the rest of the group, the rest of the league?" So it's all those different benchmarks and each team has a different way they want to use the data. I think the most powerful part of our platform is how malleable and customizable it is where you can really dial in your own custom metric, your own custom flag that you've designed through your years of being a practitioner and dial in specifically what you want to see.
Gemma Allen
>> Wow. And Marc, do you have any examples of use cases or events whereby the state actually had some sort of fundamental change in game plan or one player's life?
Marc Kerrest
>> Yeah. Again, all our customer data is confidential, but we have had various times, sometimes on the negative side, where we will flag an athlete for a few days in a row and the practitioner will call us and ask why they were flagged because ultimately they had a big injury. Ultimately, we have some newer flags that we rolled out. Just last year with an NFL team, a player was flagged two days at practice. The practitioners paid attention, called us, we had a conversation and they proactively reduced that athlete's load, used our predictions to kind of model out his next few days and kept him healthy for a playoff game that was coming up. So that was a really positive signal of what we're doing and I think that's really what we're trying to dial in, right? It's easy to look at the data after an injury has occurred, but what can we do to flag the data beforehand and prevent that injury from happening?
Gemma Allen
>> Wow. And you said you're Switzerland on the hardware side. On the software side, are you completely stock agnostic? Are you working with any particular frontier lab? What sorts of tech is running this?
Marc Kerrest
>> So we built everything in-house. As we've developed as a company, we are starting to partner with some people in different aspects. We don't have a full EMR, we do have some injury capabilities. We're starting to partner with Healthy Roster on the EMR side. There's a few different strategic partnerships that we see in terms of how we're growing, but there's a big space out there too. So disrupting the incumbents with our technology and moving forward as best we can.
Gemma Allen
>> Well, we love an underdog on theCUBE, I have to tell you. So last question, what's ahead? What are the goals? You just signed the 49ers, that's pretty big. I hope you're going to market the bejesus out of that. What do you plan to do? What does between now and the end of this year look like for you and the team?
Marc Kerrest
>> Yeah, lifelong 49ers fan, so really excited about that one. We always say we're team agnostic because we work with a lot of teams, so we're not rooting for one over the other. I think for us going forward, we rebranded in the last year. Like you said, Whistle Performance kind of signals what we're doing. I think the next steps for us is we are pretty entrenched in sports. It's looking at those new verticals, right? The firefighters, the military starting to help other use cases. Obviously the market there is huge, but I think a lot of the signals we've developed in sports apply really well to those other markets and I think the impact we can have can really be massive there.
Gemma Allen
>> Wow, certainly so transferable. Last last question and I am going to judge you. What European soccer team do you sport?
Marc Kerrest
>> So I'm a huge Arsenal fan.
Gemma Allen
>> Oh.
Marc Kerrest
>> I'm not shy about it. It's been a good couple months.
Gemma Allen
>> Yeah.
Marc Kerrest
>> And you can ask my kids. They can sing all the songs.
Gemma Allen
>> Oh, wow.
Marc Kerrest
>> I did grow up in France, so with the World Cup coming up, allez les Blues and we'll see what happens.
Gemma Allen
>> Allez les Blues is right. Well, sad end for Arsenal, but hopefully only the beginning for you and this company. Marc, thank you so much for coming on this Cube.
Marc Kerrest
>> Thank you so much for having me, Gemma, really enjoyed it.
Gemma Allen
>> I'm Gemma Allen here at theCUBE Studio at the NYSE. This NYSE Wired's Mixture of Experts. Thanks for watching.
John Furrier
Gemma Allen