In this episode of The Next Next, host Jason Jacobs interviews Dr. Stephen McGregor, a professor of exercise science at Eastern Michigan University and an expert in using sensors to monitor and manage sports performance. Dr. McGregor shares insights from his 25-year career advising world-class athletes and teams across various sports, including NFL, NCAA basketball, and hockey. He discusses his company, Sport Performance Technologies, and its flagship product, Achieve, which helps teams manage athlete load and optimize performance. The conversation covers the practical applications of sensor technology in sports, the complexities of load management, and integrating heart rate and external load metrics to enhance athlete training and readiness. They also delve into the potential future use of AI to improve performance metrics and individual development, especially in hockey.
Optimizing Athlete Development Through Load Management with Dr. Stephen McGregor
In this episode of 'The Next Next', host Jason Jacobs engages in a detailed discussion with Dr. Stephen McGregor, a professor of exercise science at Eastern Michigan University and an expert in sensor technology for sports performance. Dr. McGregor shares insights from his 25+ years of experience in using sensors for monitoring athlete performance, particularly in hockey. The conversation covers a range of topics including load management, the application of sensor data for both team and individual athlete development, and the unique challenges of measuring physical exertion in sports settings. Dr. McGregor also discusses how his company, Sport Performance Technologies, under the brand Achieve, uses data to help teams optimize training loads and performance metrics. The episode delves into how different sensors and metrics can be used to tailor training regimens to individual athletes’ needs, ensuring optimal performance without overtraining.
00:00 Introduction to The Next Next
00:17 Meet Dr. Stephen McGregor: Expert in Sports Performance
01:26 The Role of Sensors in Sports
04:02 Steve's Journey from Soccer to Cycling
06:49 Innovations in Hockey Performance Tracking
12:12 Balancing Customization and Standardization
15:07 Achieve's Unique Approach to Load Management
20:22 Comparing Achieve with Other Wearables
25:57 Practical Applications and Success Stories
31:50 Balancing Rest and Performance
32:12 The Importance of Not Being Too Fresh
32:23 Case Study: Collegiate Team Experience
33:48 Stanley Cup Playoffs: Rest vs. Sharpness
35:05 Modeling and Metrics for Optimal Performance
36:30 Contextual Load Management
37:15 Team Characterization and Individual Needs
37:46 Practical Applications of Load Management
38:45 High Capacity Players and Workload
41:19 International Tournaments and Player Workload
43:24 Technology in Hockey: Adoption and Challenges
46:31 Comparing Sensor Systems in Hockey
50:57 Achieve's Role in Player Development
58:41 Integrating AI and Big Data in Hockey
01:04:58 Final Thoughts and Future Directions
[00:00:00]
Jason Jacobs: Welcome to The Next Next. I'm the host, Jason Jacobs. This show sorts through the nuances of athlete development through the lenses of a dad who's actively sorting through those nuances myself with my own kids. And also as an entrepreneur who's in the earliest stages of building my next company in the space.
Today's guest is Dr. Steven McGregor. Steve is a professor of exercise science at Eastern Michigan University and is a leading expert in the area of the use of sensors to monitor and manage sports performance. He's been a leading researcher and practitioner in the use of sensors and their application with athletes for over 25 years and has advised.
And coach numerous world champions and Olympians, including multiple Olympic medalists in team sports. Steve has advised teams in the NFL NCAA basketball and NCAA hockey specific to hockey. He's accumulated over 20,000 individual sessions, including accelerometer and heart rate data for teams ranging from squirt tier two up [00:01:00] to national team development program, U 18 teams outside of his research.
With his software product Achieve. Steve advises and manages sensor analytics for teams at the USHL national team and college levels. We have a great discussion and I hope you enjoy it.
Okay, Dr. Steve McGregor, welcome to the show.
Stephen McGregor: Thanks, Jason.
Jason Jacobs: Well, thanks for coming. I did a show recently with Nathan Bowen over at total Package Hockey. And and in that episode we were talking about some of the things that I've been thinking about in terms of taking high end training and making it accessible to more players coming up in the game.
And he said that you were doing really interesting work at more of the high end of the sport around sensors and load management and stuff like that, and that and that'd be a great discussion. So I'm grateful that he connected us, and I'm grateful for you making the time to come on the show.
Stephen McGregor: Sure, no [00:02:00] problem. Thanks for making the time to have me on the show. Appreciate it.
Jason Jacobs: Of course. Well for, well, for starters, for listeners, maybe just talk a bit about about what you're up to in hockey and and about what you're up to generally. Just to frame the discussion to.
Stephen McGregor: Yeah, so I guess, the big picture right now is. Have a company the company's called Support Performance Technologies. We don't use that very much. The brand is achieve, and that's what most people in hockey that are familiar with us are familiar with. And we work primarily at the high end.
Yes. So, USA hockey national teams all the men's national teams men's world's world Juniors right now, the World Junior Summer, summer showcases going on. The NTDP teams, both seventeens and eighteens. And then as well as that some professional teams and half of the USHL approximately half the USHL.
And so that's in hockey, that's the high end of the sport, right? And at the end of the day I think I think as Nathan and I talk [00:03:00] a lot, and most people that know him, that development is a big, piece of my, my DNA and my framework. And so development to me means that you've gotta, trickle stuff down for the most part, right?
The things we do at the high end, ultimately, we want to get to the, to, to the lower end, or at least the broader base to help build hockey and players and the sport as a whole. So anyway that's really what we do for the most part. And I also am a professor at the university in my free time, so, I shouldn't say that's my main job.
So, but but the nice thing I tell people all the time is that. My, my university job dovetails nicely with what I do on a day in and day out basis with the company, right? Is that I'll go and talk about via two max or lactate threshold or periodization in a class, and then I'll go work on that and talk about it with a team and wanna get outta class.
So, so everything everything I do for the most part, just dovetails and overlaps. So I'm pretty buried in, in hockey performance management, load [00:04:00] management and development, I guess.
Jason Jacobs: Huh And I've I've read and listened to some of the things you've put out historically, and it sounds like you, your interest in performance tracking started. By tracking yourself. And it actually started in I think it was cycling, right? Or was it running?
Stephen McGregor: Yeah, well, no, actually, honestly, it was really, it was cycling, but it was cycling as a as a cross training or an adjunct to soccer. So I played, I was old school, everybody played, everybody athletes played everything, right? So soccer, football, wasn't a basketball player, but pretty much everything else.
And I played soccer in college and. I always had issues with shin splints and those types of things, overuse injuries. And so as an adjunct I took up cycling and it was one of those things where some guy said, oh yeah, I knew this guy and he played soccer and he started riding a bike and he got faster and just better and all that stuff.
Oh, well I gotta do that, right? So [00:05:00] before we had the internet, it's just word of mouth and stupid recommendations from friends, right? So I picked up cycling and. And then at the same time was doing it quite a bit because I could cycle a lot more than I could run. I wanted to train.
I'm a, I'm kind, I'm one of those people that's wired to train, right? So, but I just couldn't train enough just because of injuries from overuse. And so all of a sudden I had a bike and I could do pretty much any, I could train as much as I wanted, which is actually a bad thing, right? So anyway over the years started getting into trying to understand what was puritization and puritization for various sports. And and that's how I got into load management. And that was in the eighties. And then in the nineties, I really got plugged into cycling more directly because then it became less of a, an adjunct and more my main.
Activity. And it just so happened that there's things going on in the nineties and cycling, aside from the doping of course, but from a load management standpoint, the advent of power meters and [00:06:00] our ability to illuminate elements of a sport that was really a black box before that.
Right? So, I don't wanna go in depth on that, but it's, cycling really was a black box until we had power meters. Now in contrast, it's one of the most easily characterizable sports because we have this power meter, this thing on a bike, and it really captures everything you do in a very exact way.
So the power meters are really, very effective tool for cycling and measuring things in cycling, right? So, cut my teeth there. From a academic end practical. Perspective working with other athletes and because one of one, I was one of the early adopters, I started be, became a defacto coach just because of helping people.
And then that led to more coaching and more stuff and working with national bodies and doing my PhD. And so over the years then that moved from cycling to running. And then we did a bunch of things in running with Acceleron and GPS and then have always been a hockey, person my whole life.
And [00:07:00] so we started doing stuff in hockey with, and we started with USA hockey as a research project really. And did some things that nobody else was doing at the time. And then that grew and then because we wanted to do it more for not. Under the restrictions of research where you can't do things if people don't wanna do it, right?
So a team wants to use our approach and they throw a sensor on a player and he goes, I don't wanna do this. Well, you gotta let 'em out, right? Whereas the coach says, you're gonna do this. And then and so research and that was really why we started the company. At the end of the day.
It was so that we could do stuff, not that we're torturing people, but just that, a lot of times players don't wanna wear a sensor, right? But the coach wants 'em to wear a sensor and research. You can't make 'em wear it. A coach can make 'em wear it on a team, right? So that's really what boils down to.
So, since then we've been, as I said, working with the national team and then it's grown. And then we've again gone back to things we've done before. So now at this point, over the past couple years, we've had. A skate [00:08:00] sensor, which is again, illuminating things in hockey that were previously a black box.
And that's gaining traction now. And we've got a new version of it that's more sensitive and is giving us more information from that sensor. And so it's I think gonna be a pretty big thing in, in hockey going forward. But it's giving us information that we can't get any other way.
So there's a lot of things going on that we do that are all informed by things I've been doing since the eighties, really, what it boils down to. And it's that's scary to say.
Jason Jacobs: And when you determine what to do and how to prioritize what you could do, how much of that is figuring out what's possible and then looking at where to apply it versus figuring out where the pain is and then trying to see if you can make it possible. Like, is there a rhythm to it and does one lead the other?
Stephen McGregor: So I guess
Jason Jacobs: how does that go?
Stephen McGregor: is the question there like load management?
Jason Jacobs: Well, [00:09:00] well, the question is, I mean, when you think about all the different things you could track and then all the different things you could do with the data, do you first figure out what you can track and what you can do with the data and then figure out where it might be useful?
Or do you go and lock in on a problem first and then go see if you can solve it?
Stephen McGregor: Yeah. No, I'm by virtue of the way I'm wired, I don't like collecting. Stuff just for the sake of collecting it, right? Like we do that to some extent when there's information available and you think, well, you never know. We might be able to use it in the future, but at the end of the day, I I think the reason I was, I gravitated towards science was to answer questions and solve problems.
And that's really what we've been doing over the years, right? So, and things I did in cycling, a lot of piggybacked of off other people that were doing kind of the pointy end of the spear type work. And I was learning from them. And then when we got into running, we actually, we were doing some stuff in the putting into the spear and running.
And then I think also same thing as now in hockey, I'm old enough. I [00:10:00] clearly at this point say, yeah, I know a lot and I've forgotten a lot. And I still like solving problems. And so there are things that are interesting and again, the thing that's interesting about hockey is it's a black box you can play, compare it to like soccer's running is the mode of locomotion.
Running's pretty straightforward. You measure distance and speed and you got things figured out for the most part, right? Hockey's much more complicated just because of skating. And skating is interesting because what makes a, there's a load management aspect of it, right? Like how do we quantify what players are doing?
And we've got different tools that we use to do that from a load management standpoint, but also how do you characterize a good and a bad skater? And to me, I think, what we get a lot in sports, again, going back to my example of why I started riding a bike back in the day, is like somebody told me, ah this guy know.
And that's what we've done in sports for centuries, right? Is like, you had a coach and the coach told you to do this stuff, and so you did that stuff and then that's what you knew, right? And you [00:11:00] kept doing that. And at the end of the day that's not always best. And I think at this point we have tools available to sort out what's good and bad, what are good approaches, what are bad approaches, what are effective, what are ineffective, but also in skating, what's really interesting is what makes a good skater, right?
And I think there's a lot of different theories out there and you've got skating coaches that will, try and help with skating and they have expertise they've learned over the years. But we can also use sensors and. Tools to identify what constitutes or what contributes to effective skating.
Right? And it may not look the best, but it could still be effective, right? So that's, those are examples of things we look at. And at the end of the day I'm more curious than a businessman. So we do a lot of stuff that not isn't necessarily great from a business standpoint, but is answering questions and helping illuminate problems and solve problems at the end of the day.
So I think that's why the teams we work with, I think have learned [00:12:00] and accepted that we like to solve problems. And if there's a problem, we're gonna help try and solve it. And we're more practical minded than just doing something for the sake of doing it at the end of the day.
Jason Jacobs: And when you think about that problem solving capacity as a firm, do you try to solve it in a way that you take input from each of the different teams and coaches that you work with, and then try to have an offering that kind of blends and addresses a good amount of the needs across teams? Or is it a very custom and personalized approach in terms of what you're providing from team to team and coach to coach?
Stephen McGregor: Yeah. I think, and that again from a business standpoint advisors are always, well, you gotta scale and you have to normalize and everything has to be standardized. Right? And I personally just, I have difficulty with that. I just, it's, again, I've been working with, and it probably comes from my, my [00:13:00] individual coaching background over the years, right?
Like coaching, either, whether it's individuals or teams. They're, even the team is an individual, right? The characteristic of, and I'll just use a couple of our Michigan State is a different team than Boston College, right? We work with both of them. They're just different animals. It's, they're like athletes.
They're like people, they're just different. The characteristics are different. And so using one approach to apply it to the other is not necessarily gonna work. 'cause the components of the team are different. The coach is different. So the brain is different. The, so at the end of the day, what we do with all the teams is individualized, which is a lot of work and is not great from a, from a ultimate profit standpoint.
Right. But yeah I think our customers are happy. That's my main thing is making customers happy and and solving their individual problems and addressing things for them that way. Now that being said. For example with the skate center, I think that is a, that's a unique thing we do, right?
The skate sensor. We [00:14:00] do lots of things that are unique, but the thing that's really obviously unique is a skate center. The no has that and at the end of the day, I really think can help a lot of people. And so the trick there is gonna be as we go forward we're going to have to standardize some things and say, okay, well, as we offer this to a larger we can't help thousands or hundreds of thousands of people, like, like hundreds of thousands of people are gonna buy skates.
But at the end of the day, like the more people you have, the less individualized you can be, right? So we, we have a mix of customized and standardized approaches that the teams work with are all pretty much customized. But, they're standard things that are used across all teams because they're standard benchmarks that, that we are familiar with and comfortable with and that users are familiar and comfortable with.
And what'll happen a lot of times is somebody will work with us with at one team, and they'll leave and go to another team and they want to use us at the other team and the things they're familiar with, the first team. So there's a lot of stuff that is standardized that, that carries over. But again, at the end of the [00:15:00] day, I always talk about each team as an individual.
It's almost like a person, if you will. And that's the way we treat it. So.
Jason Jacobs: And you mentioned when we talked in our initial discussion a couple weeks ago, I think it was a couple weeks ago you said that you don't have a Salesforce and that it's a really small team. So, is it all word of mouth in terms of getting new customers and when you get a call from a customer what is it that they're looking for when they call?
What is that pain?
Stephen McGregor: Well, that's actually
Jason Jacobs: and how do they think about, what, how do they think about what Achieve will provide them?
Stephen McGregor: yeah, that's actually a really good question. So again, we, yeah, we don't have a sales force and everything is word of mouth like so, to me, which is I think is flattering to me because we don't go out, we don't. Push sales at all. And at the end of the day we're growing.
And we're growing simply because people hear about us or see us or work with us and then want to do that. And it just [00:16:00] expands organically, if you will, like that. So it, it depends when somebody reaches out. Like, so the first one will be somebody that has worked with us somewhere else and they know exactly what they did there.
That happens actually pretty regularly. The main example I'll use there was the first customer we had as a company, as a kind of business customer was the Chicago Steel and the USHL. And that was because Greg Moore who was an assistant coach at NTDP, we were working, doing research with at NTDP had been there for.
I think maybe only a year. It might've been two years. And was leaving to get his first head coaching job in the USHL. And when he went there, he'd been used to seeing what we were doing at N TT P and said, he called us up and said, Hey, can you do that here? Right? Can we do that
Jason Jacobs: and do what? What was he looking for?
Stephen McGregor: well, so again the it's load management really is what most people think of in that context, right?
Is that how [00:17:00] much are your players doing, and how much is enough? And how much is too much, is really what it boils down to. And so all the players, the NTDP. Wear a sensor for everything they do that's activity related. So strength off ice skills and practices and games. And then we put that in a system that kind of quantifies everything and puts it in some metrics that are common and comparable apples to apples comparisons off ice and honest, which is tricky.
And so we'd been doing that for a couple years with them and he'd seen it and saw the value in it. And so when he went to the USHL and essentially had this team and the owner said, well, you go buy a bunch of stuff because we want to be the best team in the league. And so he calls and says, yeah, can we do that here?
And so, we set them up and we started them as our first non-research customer. And we've been with the member since, and that's eight years ago, I think. I believe, yeah, eight years ago. And since then they won the league championship. They won, they were the best team in [00:18:00] the league from a record standpoint, a couple years in a row, and then COVID killed one year when they were gonna make.
The playoffs, but they've been in the finals a bunch of times. But then also they haven't been the best team in the league because there's a cyclical nature of teams in that, you'll have some mature players and then you turn over and then all of a sudden you've got a group of young players and you've got a bunch of 16 and 17 year olds playing against 20 year olds.
It's that's a tough nut to crack. Right? That's the same thing that the N TDP seventeens have when they play, when they start in the playing NALL and USHL teams is they're a bunch of 16-year-old kids, and a lot of 'em haven't really hit puberty full on yet, and they're playing. 17, 18, 19 year olds who are good hockey players in the os HL.
Right. And so they, they don't fare very well in the first year usually. And then they mature, they hit puberty, and they do a lot at the NTDP, which builds 'em up physically, not only from a hockey skill and IQ standpoint, but also physically to build them up. And then the next year, the 18 year, they're usually a pretty dominant team when they play USHL teams.
And they're only one year older, but there's, that year has had a lot crammed into it. So they're competing then at that [00:19:00] point. They're generally winning USHL games and then competing against NCD one teams and in some cases dominating NCD one teams, depending on the year. Right. So, so, yeah I guess so that to get back to your original question you asked what are people looking for when they call and, if somebody's been using the system, they know exactly what they want and what they're gonna get. Right. And then other hand, another case, somebody will call, and we've, we had this quite a lot now, is that they'll talk to a user and they'll say, oh, you gotta use Achieve. And then they'll call me and they won't really even know what Achieve is.
Like, yeah, somebody told me they use this and it's great and we know everything we do and what is it? So then I have to do an education a discovery meeting with them and show them what the system is, which is the guts of it. Is a analytics, visual visualization platform, which takes sensor data and converts it into metrics that are again, apples to apples comparison.
[00:20:00] And then tells a story about what's going on with your players, right? Are they doing a lot? Not doing very much. They're working hard, not very hard. And then. That's the core of achieve with or without a skate sensor, for example. Then some other things like the skate sensor will add another layer of illumination, if you will, or information.
So I hope that answers the question.
Jason Jacobs: Yeah, so I mean, for, I mean, some people are only listening and not video, but I'm holding up my whoop band. I assume, whoop, I'm in Boston. It's a Boston company. And for whoop, they have a bunch of different activities that they track, but it's meant to be holistic all day for everybody.
Right. Is achieve, it, how do you think about it? Is it, are you only doing hockey and if not, how much of what you do is consistent from hockey to other sports? And if so, same question. If you went into other sports, would it matter that it was another sport? But and I guess I'm also just trying to get my brain around [00:21:00] what is the same about Whoop, and then what is fundamentally different with Achieve.
Stephen McGregor: Okay, well, not to compare against whoop, pretty different animal but whoop essentially is a heart rate slash HRV sensor.
And in the load management world that's what the way you're thinking about things. A lot of people, I don't like this term, but a lot of people refer to heart rate and HRV as internal load.
So your body's, physiological load that you're experiencing. So again, heart rate, everybody knows heart rate for the most part, right? So if you go out and you do an exercise you run the treadmill and your heart rate's, average heart rate is 120 beats per minute for that session, and you go, come back the next day and your average heart rate is one 40.
Well, you worked harder, right? Presumably. That's the argument is that the internal load metric is a way to measure how much work you're doing and how hard you're doing it, right? There's a lot of caveats and hand waving and things that go on. 'cause heart rate is a very in, in, in finicky metric, I guess is the thing we'll [00:22:00] say.
That being said. And she, we use heart rate and we use HRV with teams that have the tools for that. We think they're very valuable but they're, we consider them secondary metrics. So what we primarily measure is the external load, which again is a term I'm, I don't know if I'm, but the physical work the person's doing, whether that's in running, running a certain distance and speed using GPS or an accelerometer.
In hockey it's just essentially body movement generally. Up to this point, what we've done is body movement. So you wear a sensor on your body and how much does your body move while you're skating? Nowadays we get the skate sensor which can get a little bit more detailed and granular in that.
But, so the physical things we're measuring are the primary metrics, and so the system is really not. I don't think of it necessarily as a quote unquote holistic system like, like you might with Whoop or some of the other 24 7 apps and systems. I'm primarily interested in what's happening when people are working [00:23:00] and yeah, we can bring in data from outside of that and we can get average, we can pull data from pretty much any sensor system.
So whether it's whoop or a Polar or first whatever, some whatever the vendor is, we can pull the data in. If you're capturing it, then we can represent it. But at the end of the day we primarily focus on the stuff that's happening when players are working practicing skills activity looking at the primary physical metrics for what.
People call external load. And then also looking at heart rate and HRV if it's available, it's not always available. Sometimes it is. And that can tell a really good story. So I guess the reason I equivocate on HRV and the holistic things is they are very, again, heart rate and HRV are finicky in that they're influenced by a lot of things.
Right. And the way I put it is the signal and noise ratio is not great, right? There's a, it fluctuates a lot and there's a lot of, so, so putting a lot of stock in one day versus another day is [00:24:00] challenging with heart rate and H rv, not that they're bad, it's just there's a lot of things that influence that, right?
So, so, on the other hand, if we look at HR heart rate and HRV while the person's doing the work. That's a different animal that signal the noise ratio is much better. Better. Just like when you go running the treadmill and your heart rate's 120, the next day you come back, it's 140 for whatever reason, your heart rate's higher the second day, presumably 'cause you worked harder or you were tired or whatever.
Sick, whatever. Like, so the information we get in those circumstances to me, is much more reliable than the kind of the 24 7 stuff right. Now. That being said, if somebody is sick and we measure their HRV while they're working and their work says that the HRV is off and they're, they probably are gonna agree.
And I work with athletes that wear 24 7 devices on a, on an individual level and their HRV is bad for a couple days and say, eh, maybe take a look at this and, [00:25:00] but, and generally it's more of a confirmation a lot of times the things we already know rather than the leading indicator of things.
Now if that makes sense. So, so, I guess that would be the kind of, the main way to distinguish, achieve, and what she does, what we look at versus something like a whoop or any other device that's a 24 7 wearable.
Jason Jacobs: Okay, so it sounds like, and correct me if I'm misspeaking in any way, but that focusing on the external load is more about what you do which is more around load management and keeping track of what you do. Over time. How is that actionable and to, to what end? So I guess some general themes of how coaches and teams are using that data and even some specific examples of when it was applied in ways that do cause the coaches, when they switch teams to make you one of their first calls when they get to the new spot,
Stephen McGregor: Yeah, I think so thi [00:26:00] tools like. HRV, whether it's whoop or there, there's been other brands and companies that have done HRV stuff over the years that have been marketed and load management. The way that, what I'll say is they've been primarily used over the years as maybe a protective mechanism, if you will.
Right, okay. In the professional setting or NCAD one setting that, like a big concern is making sure people don't too mu do too much injury mitigation, those types of things. And they've been used as cautionary devices as protective devices, governors, if you will. Right. And I think to a large extent over the years, and I'll just use HRV as an example, HR v's.
Been around, like I've taught HRV in. 25 years I think is, but it's only really recently been a useful tool on a regular basis. But it's been around, it's been used at the elite level in sports to diagnose and mitigate overuse, if you will for a while. But [00:27:00] it's, again, it's been really finicky.
And I think a lot of people have put stock in it and then left stuff on the table, right? Like, you can't train today 'cause their HRV is bad. Right? And I
Jason Jacobs: I ignore it. It tells me to recover and I don't listen.
Stephen McGregor: Well that, well, that's just it. So then people start to ignore it. And then if ignore it and things go well, then, oh, wait a second.
Well, what am I, so I think over the years, HRV has gotten a bad rap because it was oversold in that context, right? It became, oh, there's this magic thing that, that you can use and it'll prevent stuff. And I think it just was oversold. So people got away from it, like high level people got away from it because it didn't work like that.
And they got in the, into the jam because they took too many days off or took it too easy. And it was drive, it was driving the ship. And coaches, at the end of the day, coaches want to drive the ship, right? So, so I think that was a challenge. That was a problem. Again, we use it as a secondary indicator. As a secondary indicator and it, and if it's really off for a couple days, they're like, oh wait, there's something going on. But that I think is the main thing is that [00:28:00] load management and some of the tools have been used as protective mechanisms over the years because, and part of that is because people have just started using them and are very unfamiliar with them.
Again, I tell people I've been doing it for 30 years, right. Like I and I don't want to use it and just to prevent people from doing stuff.
Jason Jacobs: so, so is Achi is achieved more about playing offense
Stephen McGregor: I would, the way I put it's, you thread the needle, right? Like, and I'll go back and use the example of Greg Moore and I hope I'm not talking at a school but he's, ironically, things have come around. 'cause he went to the AHL with Toronto for a while and then came back to NTDP and he's a user at NTDP again.
And he's a, he's a. I don't wanna say a believer because that connotates but he, there, I think he he understands and uses what we do pretty consistently. And one of the, but to that point, like when he went to the, still, when he started, again, he's first head coaching job.
You wanna, you want to have an impact, right? And you've got a, an owner who says, here, you can have all these tools. You can, you've got ice whenever [00:29:00] you want it. They were doing a lot, right? They were doing a lot. And we modeled it out for 'em and said in October, November, they were losing a lot of games in October.
And I said you're doing too much, right? Like, and that was the case where I said, you gotta pull back. You gotta pull back. And, but as opposed to, and here's one of the things that's different is one of our. When rules of thumb that people that I work with is you don't take the day off before a game that matters like that is a recipe for disaster.
And that's a pretty common theme in hockey, right? Over the years. Like, if you're, you want to be arrested for the game, that's important, right? Well take the day off before and that's when you don't know the details. When you can't really get a handle on things and have confidence in what people are doing, then well, it's better to be rested than too tired.
Right. Well, for us I don't wanna be either one. I wanna be just perfect just where you want to be. Right. So one of the things I think honestly, is in most cases it's actually probably better to be a [00:30:00] little bit tired as opposed to taking the day off before an important game. And it happens a lot just because again, when coaches see that they're tired.
And one of the examples I use here is that was a high profile one is last year in world Juniors. We were working with team USA right. And Team Canada was one of the favorites, if not the favorite in the tournament. 'cause they had a stacked team. Right. And they took like, I think two or three days off the ice in the middle of the tournament.
And the argument was the guys were tired. And I'm like, man, these are all number one players on their teams. They're playing 20 minutes if not more, a game in major juniors. They are not tired in a tournament. They are not, I can promise you they are not tired in a tournament 'cause they're not, probably not getting enough work.
Right. So, but that's as the coach doesn't know the details of what's and have a handle on on, on load if you will for, is that's. The default thing. Oh, let's take a look, take it easy, [00:31:00] right. To make sure we're rested. And they were horrible that first period of the game. They came back and I told people when people were texting, 'cause people know, I, that's my rule of thumb, don't take the day off before it bake the game.
Right. And I said, well, he is either gonna look like a genius if it works out, but it's probably not gonna work out. 'cause they're gonna be so bad in the first that they're gonna get behind. And sure enough, they were horrible in the first, they started coming around the second, they were bad at hell on the third, but it just was too little, too late.
Right. So, that's just one example. And that's an anecdote if you will but to me, I think when you, we don't want to just take a day off and teams that we work with, I don't want them to say, Hey, we gotta take some time off because we've done too much. Right. I want them to say strategically, let's take a day off and place that day off in a place where it's not gonna affect performance.
Right? 'cause you're not gonna be stale if you take a day off three days before a game, right? That's, that matters. Or two days before a game that matters. You've got a couple of practices in there to sharpen [00:32:00] up. And we can plot that out and model that out and say, what are you gonna look like so that you can hit this game that's really important and just be firing all on all cylinders, not be, and it is Goldilocks, right?
You're not too tired, you're not too fresh because, and this is one of the things that, that a lot of people have a hard time wrapping their head around is being too fresh. Right? And I tell the story about one of the collegiate teams we work with. We've been working with the coach, both coaches, head coach and strength coach for. Five or six years, I think at this point. 'cause they started NTDP. And when the strength coach was getting familiar with how to interpret what we were doing, and I, some of the things you have to show them quantitatively first and then say this is what's gonna happen. And when they see it happen, like I always say, it's proving water's wet, right?
When you show water's wet, like then, oh wait, that makes sense, right? So I, when you do this, guys are too fresh. And when you're too fresh, you play [00:33:00] horrible. You can have great jumps on the force plate, which is one of the people that, things that people use for load management all the time.
How are they doing the force plate? They're gonna have a lot of, they're gonna have great strength, right? You're gonna feel fresh and you're gonna feel great, but execution and IQ is just not there, right? It's just not there. And if you are not. There necessarily, and that you're playing a team that is and is a game that matters and the teams are equally matched, you're in trouble.
So you wanna be perfect. Right? So again, so when that co, when that strength coach then told his hockey coach, the guys are gonna be too fresh, the coach looked at him like he's from another planet. Like, what are you talking about Too fresh? Well, then he saw it, right? Then we showed him, and now they're believers.
And when I talk to people without load managers say, okay, let's think about the Stanley Cup playoffs. Okay? Everybody's watched the Stanley Cup playoffs. And what typically will happen is you'll have one series that a [00:34:00] team runs the table and they're done early and another series that runs long, right?
And people are worried about that season. That series run ran long and the team went seven games and they gotta play next. Really short turnaround from the one team long turnaround for the team that was finished early, right? And. People are worried about that team being tired coming outta their seven game stretch and all that stuff.
And I'm gonna bet on the team that's played more recently than the team's been sitting around for a week, right? And that first game that matters and the series may be a different matter. 'cause cumulative fatigue in that series could be play a factor over several games. But that first game, invariably the team that's been sitting around for a week plays worse than the team that just finished their series and is still game sharp even though they're probably tired, right?
They're probably more tired. And if we use some of our metrics that, like force plates and those types of things, like they would probably look tired as well, [00:35:00] but they're sharp because they've been playing games. And so being too fresh. Getting, so I'm getting back to the point like is that what we do is we model things out and we show, and at this point we know how metrics align and this is a standardized thing across all teams is, and it's actually across all sports as well.
To your original point is that how the different metrics line up on a given day will determine how close you are to being optimal and the farther away from you are the less likely it's now you could still play pretty well. Right. And there's been times when a team has looked.
On paper looks really bad and then they play pretty well on or on paper. They look like they should play pretty well and they don't. Right? But for the most part people that use the system are pretty consistently convinced that when we model things out and manage load in a way that gets to them where they're optimal and in their game, that matters if you will.
And sometimes it's more than one game that [00:36:00] matters. And that's the tricky one. You right, you've got seven games you need to play, you have to be good for all of 'em. How do you manage that? And you can, you work out different scenarios, right? So we have a modeling system built into achieve that kind of games out, different scenarios quantitatively to say, what will your load look like?
And how will you perform presumably on, on this day, two weeks in the future, or three weeks in the future or four months in the future based on the loads that are accumulating up to that point, right? And at the end of the day, there's really nothing else that does that. Anywhere. And the other point of it is, as I say, like back to the earlier question is load management used as a protective mechanism over the years, right?
And partly because people really didn't know what to expect. And I thought it was a lot. Oh, we did a lot where we should back off, right? And again, my point is I've been around a very long time and seen all sorts of manifestations of load management and I have a pretty good handle on what's a lot and what's not a lot.
And it's all contextual really, is what it boils down [00:37:00] to. If your team is used to doing a lot, you gotta do a lot to be able to perform well. 'cause your team's used to it, right? If you've got a team that's not used to doing very much, you do too much, well then you're gonna look bad. That's all there is to it.
So, everything's contextual for the most part. And so you have to know the team. Yeah, that's why we accumulated data over a lot of over and we try and capture as much as possible to get a handle on everything that team's doing. And then gives us the characterization of the team. And then, so you'll have some teams that do a lot and other teams that don't do much at the end of the day, you can get 'em all, both at the same rough place on the game day where they want to perform optimally.
But how they get there will be totally different between the two teams. 'cause the characteristics of teams are different.
Jason Jacobs: So is it more about driving the practice plan? Is it more about driving the lines? Is it more about figuring out which players are should take the night off? Is it different with different coaches and teams? Like how are how are these [00:38:00] teams putting this data into action?
Stephen McGregor: Yeah. So, it, it depends on the team, right? So I think we've got a couple of teams which are power users and they're the ones that are most familiar with the way things work. And so in that case, if you have, a first line player who plays 20 minutes a game he may need. Some, a lighter load than some other players. They don't have as much, right? But the counterintuitive thing there is that if you look at the metrics, the way they appear in our system is that, again, the more you do generally, the more you can do, right? Like, like I'll use an example that's more nebulous, but is more apparent.
Like if you're a first pairing or a first a number 1D in the NHL, you're gonna play a lot and you're gonna have to play a lot. So if you can't play a lot, then you can't play a lot, [00:39:00] right? You have to have a high capacity. That high capacity is built through a lot of work. And if you don't do that work, then you can't do the work, right?
So you can't take a bottom pairing d and make him a for even if the exact same skill set and the exact same hockey iq, if you're used to playing 12 minutes a game and all that, that goes along with that. You can't play 20 minutes a game. It's just, it's not gonna happen. Right. So to that point, high minute guys can generally handle more because they've been doing more of their entire career 'cause they've been relied on and along their entire career.
Right? So, so oftentimes I think our system will show data to a team that will say, you know what, this player is fine. Whereas other people would look at it and go, this guy's doing a lot, right? Yeah. He is doing a lot, but he's been doing a lot for 20, for 10, 15 years. Right? His body's used to doing a lot, and if he doesn't do a lot, then he feels off again. Too [00:40:00] fresh. Right? And I'll use it there in the NHL, like there's players that will do a morning skate that. That's a full on practice before they go play a game and play 20 minutes in that game that night and they go into overtime. Right? That's insane when you look at it on paper.
But they've been doing it for 10, 15 years and if they don't do that, then they don't feel like they're getting enough work to stay sharp. And it just, it depends on individuals, right? So in that regard, all the players in the team are expressed relative to the player's own benchmarks, whether it's fitness level or loads that are typical, right?
And if you got a relatively low minute guy who's all of a sudden playing lots more minutes, he'll flag as this load's high for this guy relative to what he's used to. And you may need to back off a bit, ironically. Because he's had this kind of unusual load compared to what he's used to.
Whereas another guy who's playing, the same amount of minutes, [00:41:00] but he's accustomed to it because he's been doing it for two years you don't have to back off because he's fine. Right. So, so, it's actionable in that each player in the system is judged based on their own characteristics and yes, you may give some guys less work, you may give some guys more work.
And we've gotten to that situation where for example, in an international tournament, I won't name the tournament or the player, but in an international tournament players where the system has said the players, because a lot of them we know because they'll, they've been in the national program for years before they go to international tournaments.
Right. And a player needs more work. Then the coach is comfortable giving them because the coach isn't familiar with them. Right. So generally international tournaments like World Juniors or men's Worlds you've got coaches that come in and they may know the player from their league or seeing them, but they don't know the player, what the player's accustomed to.
[00:42:00] Right. And the player may be a guy that just has a really high capacity to do work and needs that work to be able to perform well. And we've done that where we give recommendations and then the strength coaches who are familiar with using the system will say to the coaches who are not as familiar with the player, and they'll advocate and argue that the player needs more work than the coach wanted to give the player because they feel the player will play better that way in the tournament scenario.
Right. Because a lot of times in tournaments, again, getting back to the whole. With, for lack of better information, you want to be fresher, so you're not gonna train that much in a tournament. Right. And a lot of the kids that say come from the A TDP are used to doing a lot. They train more probably than any other hockey players in North America.
And they go to a tournament and all of a sudden the coach pulls back because they're not accustomed to that amount of work. The player's actually too fresh in the tournament. They're not tired for the most part. Right. So they can handle more. So [00:43:00] it ismal in that we quantify stuff, everything they do and then on an individual basis show whether players are fresh or not fresh based on their individual characteristics.
Which is again, I think more illuminative for last lack of a better term than other approaches, if you will. Does that make sense?
Jason Jacobs: It does. Yeah. And I mean, and from what I've seen, hockey has a reputation of having people involved even at the highest levels of the sport that are not that into technology. And so I'm curious it sounds like the system has some self-service capabilities. Do the coaches tend to be in there looking at the data themselves or are you like the load management whisperer that where they just go to you and your head's in the data and you tell them what they need to know?
Stephen McGregor: That all depends. There'll be some coaches that are just gimme the facts and they'll just, they'll want one or two metrics, right? Like, so one of the metrics that we provide is an intensity metric, which is based on, [00:44:00] again, it's unique in that it's based on individual capacities, right? So every time data comes in the data for that session is judged against the individual player's capacities, and he gets a percentage.
85% effort or 90% effort or 50% effort, right? The coaches a lot, most coaches like that. And a lot of 'em just want that metric. They don't really care about anything else, right? And I just wanna know if the guys were working hard or not. And then you'll have others who are really elbow deep in it and looking at everything and thinking it would be, because again, you're right in that hockey may have a reputation of being a low tech sport in general.
But what I tell people all the time, if you're a good hockey coach, there's no such thing as an unintelligent, good hockey coach. They're all immensely intelligent and a lot of them are very curious, right? So it just depends on the individual. So [00:45:00] we've got some coaches that know all the metrics and they're in there doing some of the digging themselves, and they'll generally. Call up and say, Hey or contact us and say, Hey, what do you think about this? Right? Because they still want the backstop of the experience and the kind of the interpretation. But at the end of the day, they get a handle on their team. They get, they know their team and then to that extent, like Greg Moore and then Brock Sheen who followed him at the steel they knew their team and they would look at the load management metrics and say, yeah, my team's where I wanted to be, or No, I want to do more.
I wanted do less. Right. So, it just depends really. So I there's a lot of really smart and technologically savvy coaches out there. I think the main thing when you get to the head coach at certain levels is it just the bandwidth. You just don't have it. Right. So it's not like if you're an NHL coach, you don't have time to go poking around at all the details.
Right. Or even a D one [00:46:00] coach at the lower levels, although we have had, we've had some D one coaches who are really digging and trying to find and understand things. But as you get down the lower levels, like USHL and juniors that, there's smaller operations for the most part, and the head coaches have to play a role just 'cause that's just the way it is.
Everybody else, everybody in the organization does more than the D one coaches or the pro coaches.
Jason Jacobs: And we talked about whoop and how this is similar or different than whoop. What about some of the other sensors that are in the market that are hockey specific? I've talked a little bit with the Helios guys. They haven't come on the show yet. I there's Catapult, there's, I think Zephyr is another one that I've heard of.
I, I haven't made the rounds and really gotten to know each of these different devices and what they're good at, what they're not, where they sit, who they're for. What's your assessment of the landscape and and where do you sit relative to to the other primary players.
Stephen McGregor: Well, [00:47:00] there's two things. I have to be somewhat. Diplomatic because again, achieve the platform. The analytics and visualization platform itself is essentially brand diagnostic, right? So we have teams that are catapult teams. We have teams that are first beat teams. We have teams that are Zephyr teams.
We have had teams that are polar pro team teams, which is another kind of team sensor system. So they all have their pluses and minuses and strengths and weaknesses. But we, I understand all of them very well because we take their metrics and convert it into our standardized metrics, which are then in comparable.
And to that point, like we've got a team that, that's used three different vendor systems at this point. And the metrics. Pardon me. The metrics have been the same across all three different systems for them. And they've been comfortable with changing systems because they're using Achieve to interpret all the data that comes from the system.
So, the only of the ones you listed, the only one that's really [00:48:00] hockey specific Catapult is probably, I would say the, it's the world leader in external load measurement from team sports standpoint. And then first beat is probably at this point Polar used to be, but I think at this point, first Speech's probably taken over for them as kinda the world leader in team sport heart rate measurement what some people will call internal load, but I'll just call it heart rate measurement.
And neither, neither one of those are hockey specific, per se. Right. Heus is the only one outta that bunch that's really. Quote, unquote, hockey specific. And then our skate sensor, which is hockey specific, right? So, at the end of the day, most of 'em from, so if we distinguish what some people call external load or the physical exertion aspect of it, right?
Is they'll measure the same thing. So, in hockey so, so catapult. In other systems, in outdoor sports, we'll use GPS and I. [00:49:00] And interestingly, over the years, I have talked to people that thought that you could use GPS in hockey and you cannot. They've tried it, didn't work very well. But so, some of them use GPS outdoors and some of 'em use accelerometers outdoors in indoors, generally accelerometer based.
There's also what others that are LPS based, which is positional system, which is like GPS for indoors. And that has some systems run off of those as well. But Acceleron is probably the prime. Type of measuring things from an exertional standpoint in hockey. And Catapult is exo on our base, er is Helios.
And they're measuring body movement is related to balls down trunk movement and then interpreting stuff from that. The skate sensor is measuring stuff at the foot skating and that's a different animal, right? So you can you can infer that from a trunk mounted sensor, but there's a lot of stuff that's missing because between a sensor that's worn in the back of the neck and the foot there's a lot of [00:50:00] stuff that's lost in the filter of the human body and joint mechanics and all that stuff.
So, I would say that, the skate sensor all is ours and is unique. And then the others I still, catapult's great first beat's great, zephyr's great that at what they do. And we work with all of them. But so that, I guess that would be the main thing is most of 'em are all based accelerometer and trunk mounted accelerometer devices.
Jason Jacobs: And I've heard you mentioned several times in this discussion, coaches teams, coaches, teams increasingly, I dunno if it's increasingly, but a lot of players, especially at the higher end, they, they also work on their own player development and maybe have a player development coach that's not affiliated.
With the team to help them specifically in their journey. And then increasingly now, the agents are having, beefing up their own player development capabilities so that they're not just helping with relationships and finding the right spot and advocacy and stuff, but they're also helping with your development.
Is achieve primarily meant for [00:51:00] coaches and teams, or does it have application for individual development journeys as well?
Stephen McGregor: It. So it is primarily team oriented. Right. And the story I tell all the time about how we got to where we are is when I actually came from individual sports, right? Like cycling is an individual sport, it's team based, but it's an individual sport training standpoint, then running and triathlon and endurance sports.
So I know that world very well. I still work with individual athletes and from various disciplines and. When I came to doing stuff in team sports first in, in the NFL and then in hockey I was like, man, the, these are horrible tools for teams. Like, like Cat, not to speak ill of anybody but Catapult Systems and Zephyr Systems and it didn't matter what it was, like you, if you, compared to what we were using in cycling 10 years before that, it was stone age.
And like this stuff's horrible. You can't use it for anything. Right. And [00:52:00] so, and the joke I tell, not the joke is the anecdotes is when I was working with NFL teams, like we would do a retro analysis of their data from the year before, three quarters of players didn't have data.
They didn't even know it. They had no way of knowing it. There's no way to keep on track of large that large amount of data anyway. So we built Achieve essentially to be able to use the things we'd been doing in. It's individual supports for years in the team setting and give teams useful information and actionable information.
So now there's other, catapults in or their own system is useful and can give you good information. Did all sensor systems have some utility now as opposed in the old days where they had no utility at all? That we still have advantages over those systems, but still but I think at this point.
I didn't think there was much value in a catapult type s sensor or those kind of trunk mounted sensors for individual development. [00:53:00] We've tried it with some people and it just doesn't provide much information. Honestly, at the end of the skate sensor's a different animal, right? So at the end of the day we will certainly get to the point where we're going to be doing some individual we'll have an individual app that's gonna be available for the skate sensor to do development on an individual level at whatever level.
The right now we have pros that are using 'em with our development coaches in a modified team-based system as opposed to individual app and, going forward, at some point in the near future, we're gonna have an individual app where players at any level will be able to have a skate sensor and have an app that we interact with to get that information to help with their development.
And there's a lot of interest and I hear about it a lot and there's only so much we can do and our main focus is high end teams. And, but at the end of the day, we do want to help with development at lower levels and that actually at all levels and the skate center just gonna be able to do that.
So.
Jason Jacobs: So I, I wanna ask an achieve specific question [00:54:00] and then a more general player development question. So, is there any compatibility or data sharing or or mutual touch points? Do you end up overlapping with the Sport Logics 49 s huddle ins stat? Like these companies that are collecting the data around like, puck possession, where the shots are coming from?
Turnovers, more team-based stats around gameplay. Like, does that touch at all your world and vice versa. And then same question just about player development more generally. Is it just about the team and how the team's performing on the ice? Or do you see those platforms moving more in the player development direction over time.
Stephen McGregor: So there's currently not a lot of interaction with what we do. And no necessarily like, overlay type of things, right? So we do some video overlay. Again, getting back to the skate sensor thing we do some video overlay with the skate sensor. Other than that, I've used it for more instructional purposes in the past.[00:55:00]
But and for example there is some value, but the value I think is the return on investment for the invest investment is hard to really really make a case for at this point. But we used to, we worked with a national team years ago. We had heart rate and accelerometer data.
And I the coach that was one of the co teams that I argued, don't take the day off before a game that matters. And they did. They lost. And I said, here's why. And it was a really short term engagement, so it didn't really have a chance to go into, but also I had video that we synced the sensor data with and I said here's where the value is that this player who's a pretty high profile player had been on the ice for 20 seconds and draws a penalty, and we'd been tracking both their.
Their acceleration metrics and their heart rate and showing how after about 20 seconds, those things started going up and they drew the penalty and well, they're on the first power play [00:56:00] unit, so they stay out there, right? So the coach thinks they're getting arrest while they go to the faceoff dot, and they stand around, but you don't, you're still out there.
You don't really rest that much. And that's why when a team is trapped in their zone on the PK and they ice the puck, they're still in trouble while they're out there, right? Because until you go sit on the bench, you don't really recover. So, the players on the ice for, for 20 seconds, building up some physiological stress then stands there on the face off dot for another 10 or 20 seconds and then they drop the puck, then the team gets possession of the puck and has to work.
And so, by the time they. Move the puck into the zone. It's been almost another 20 or 30 seconds. So now you're at a minute, right? So a minute's a long shift for a forward, and especially for this forward, who's known for their explosiveness and not necessarily their stamina. Right? And I showed an example where previously in that same game, the player was coming down the left wing and took the puck and carried it with speed into the zone and beat the defender and went to the net, right?
[00:57:00] The same exact scenario played out in the power play. And they dumped it in because they were essentially gassed after a minute, right? And you could see the metrics show that they were gassed, right? Like their, the heart rate was maxed out. And the other metrics that we show from the accelerometer maxed out, that kind of matched it, and they've been out there too long.
So they, so I said, here's something you could think about for the future. I'm not, wasn't making a. Coaching criticism. I'm just saying that you don't really necessarily see this, but this is what happens, right? When your player on the first line power play draws the penalty, you might wanna put the second power play out there to start to give that player a rest and then put the first player play out against the second pk and you've got a stronger situation.
And those are some things you can see that takes a fair bit of work to really, again, drill down on those types of scenarios. So I think from big picture statistics statistics are all about large sample sizes, right? And whether it's Corsi, Fenwick, whatever the case may be, right?
Like those types [00:58:00] of statistics are not, they don't necessarily match up with the physiological data all that. Well. And we've actually done some studies in the past where we said, okay, how do these load metrics match up with. Say standardized stats, like plus minus shots and those types of things.
And then there, there was a relationship, but it's relatively weak. So, it, because your stats depend on your competition there's a whole number of things that, that depend on stats. So I think that at the end of the day it's possible to merge two types of an analyses like that, but it takes a fair bit of work to be able to get in and see something of value.
And so the R-O-R-O-I and finding that is, is a challenge. In the future though again when you have AI to look at stuff as opposed to people. Then that's probably better, right? If you don't have a, have to have a person kind of staring at video and looking at metrics, then you've got an AI looking at stuff, then it can actually find things you might not be able to find otherwise.
So it's not to say that won't happen in [00:59:00] the future but it's probably gonna be something like big data and AI doing it rather than people using the tools that are synced, if you will.
Jason Jacobs: Well that, I mean, that's one of the things I've been thinking about is just if if you sit with these player development coaches through. Enough reps and you document like when they think something and then what it is in the video that they saw that made them think that. And you almost just start like annotating all the, these different moments, right?
Then if you get enough scale to it, right, and you structure it properly then presumably you can start to teach the machines to, to take on more of the load over time and also just account for more edge cases as you get more reps. So, and they, I mean the benefit there would be that, the, that these highly manual processes, human time is expensive and if you want to make these types of services more accessible, you need to make that human time go further.
It's the only way to lower the cost because otherwise your rate is your rate because, 'cause you gotta feed your family and you need X amount of money [01:00:00] to make a living.
Stephen McGregor: Well, that, that's just it. Like, so I think at this point I think people are. Being less surprised at about what AI can do? I think ironically, I think a couple years ago people were overconfident what AI could do in certain things, right? So people thought that AI could be applied to video and could be used to do stats, right?
And that hasn't worked out so well, right? But at the, on the other hand the things that AI is doing now are is improving exponentially and so fast, and people are becoming less and less surprised about the rate that AI is improving and being able to do things. So I do think that in the near future.
We're gonna able to be able to use AI to, for what people thought AI could be used for five and 10 years ago. And then once you can do that, then yeah. To, to your point, you can start to to find things you're looking for because AI is finding them. And then identify what are the things that are coincidental if you will.
Like why, what is the situation here that plays out and whether it's [01:01:00] a load metric or a skate sensor, power output, that type of thing like that, that, that's a value. I think right now where we are with with things like the skate sensor is we can we can train the way people do off ice, on ice in game scenarios.
So puck retrievals in the corner you can go into a puck retrieval in the corner. And how did you. Perform that puck retrieval to maximize power output on your exit, that's gonna give you greater acceleration to beat the demand that's coming in to cover you. Right? So there's tactical hockey element of that, that, you do your shoulder check, then you do a fake to get the to get the pursuer to go the other way.
And then you've gotta figure out a way to translate that fake into separation, right? And if you don't do that, you don't get outta there, right? So, so we can get at the physiological, just like you would with the force plate, that type of thing. And then merge that with the tactical and strategic [01:02:00] to optimize performance and train both physiology and hockey IQ and tactics and strategy in the same setting and get more bang for the buck from a training standpoint.
So that's where I see merging. Things in, in, in in, in, in development cases, right? So doing hockey specific tasks and then using power output analysis to to see how you're optimizing those tasks from a physiological standpoint to merge and coincide with the tactical elements that are gonna be successful.
Because if you do a successful fake, but you don't optimize it the corresponding acceleration, then it is, it's of no use, right? So you actually have to have a couple different pieces of that.
Jason Jacobs: Yeah, I mean, I'm no computer vision or machine learning expert, but I would imagine it would probably be a lot more straightforward to use the ai, to train the machines, to analyze your stride, for example, than it would be to like, train the machines to analyze how well you're moving without [01:03:00] the puck and finding space.
It just, I mean, maybe I'm wrong, but it just seems like it's more straightforward, like a golf swing seems more straightforward than than hockey iq. Hockey IQ just seems like a beast.
Stephen McGregor: Well, honestly as somebody has done a little bit of both I ironically following a person and how they're finding space, if you define those parameters, is actually relatively straightforward, right? Because if you can. Whether it's AI or wherever you're tracking the player as long as you can find the position, those types of things are relatively easy as long as you define the parameters of open space and those types of things, actually relatively straightforward.
The stride, especially in the, all contexts, it's challenging because game there's almost never a scenario whereby you're taking more than three or four strides, right? There's almost never, right? Like if you think about it a sprint from the goal line to the blue line is about two seconds.
For a high level hockey player, two and a half seconds for [01:04:00] high level hockey player, it's four or five strides, right? Like very seldom do you take that, blue line go line to blue line, in a straight line without any deviation in a game. It is not, it never happens, right? So there's no scenario whereby you can analyze stride in the game or practice setting.
And even so if we back up even more and we say, okay, let's do it in a standardized testing scenario. Okay, well, in that scenario where you do that test, you get four or five strides to work with, right? And so you have to do 10 or 15 of them to get a number that's sufficient to really do analysis.
So stride analysis, skate analysis in skating and hockey is challenging. It is challenging, right? So, we've we've got some of the pieces figured out, but it's not easy. And following players around in the ice is actually at this point is relatively easy as I can define your parameters.
So anyway they're both, they both have their challenges, I guess is what I'll say.
Jason Jacobs: Well cool. Gosh, we've covered so much [01:05:00] ground. Is there anything I didn't ask that you wish I did or any parting words for listeners?
Stephen McGregor: No, I honestly I don't. I I don't know what would it be of interest to anybody And I actually I'm, I'd be surprised if anybody's interested in this conversation. That's just the way but we talked about stuff I'm interested in, so that's great. And,
Jason Jacobs: Me too. That's all that matters to me. If anyone else is interested, it's gravy.
Stephen McGregor: So anyway but no, like, so I think that we pretty much covered it and I think we really just scratched the surface on the load management piece, but it's hard to get deep in that without actually showing stuff and talking about stuff in more detail.
So it's, that's fine. So,
Jason Jacobs: Got it. Well, Steve, thanks again for coming on the show. Best of luck and looking forward to seeing that skate sensor when it comes out as well.
Stephen McGregor: okay. Thanks very much Jason.
Jason Jacobs: Thank you for tuning in to the next, next. I hope you enjoyed it. If you did and you haven't already, you can subscribe from your favorite podcast [01:06:00] player, whether it's Apple, Spotify, or any of the others. We also send a newsletter every week on the journey itself. The new content that we publish, the questions that we're wrestling with, how the platform itself is coming along, that we're planning to build for player development, and where we could use some help.
And you can find that at the next next.substack.com. Thanks a lot and see you soon.