Transcript

Sam: I would say don't take this as me wanting to beat up on that industry. I think a lot of it's well intended, but I also believe that we should hold them to account just like we would our athletes. It's okay to expect the best from what we deal with. That's what sport is. It's high performance. And if you can't deliver on that, whether you're an athlete or a coach or a tech company, then go away and improve on it.

John: Welcome to Human Science, a podcast exploring the human element behind the science that shapes our everyday lives. We're powered by LabFront, the go to tool trusted by researchers looking to automate their studies and transform real world data into health insights.

I'm your host, John Drummond, and today I'm talking with Professor Sam Robertson, known for his expertise in sports analytics and decision making, especially when it comes to integrations between humans and technology. We'll touch on the progression of sports science, the impact of Moneyball, his quality framework, and what's next for technology and data in sports.

So everyone, please welcome Sam.

Sam: Thank you, John. Pleasure to be here, and it's a very exuberant entrance from you. Thank you.

John: Really grateful you're making some time for us because I believe you just came back from Switzerland. Is that correct?

Sam: Yeah, I did and so I'm hoping I don't trip over my words as much as I normally don't today But yeah a little bit of jet lag kicking in but you do get used to it as you well know I think you're in a different location to normal as well today.

John: That is correct yeah out here on the west coast of North America and Yeah, the last time we spoke I think we were you were in Australia and I was in Taiwan. So It's going to be a fun one. The Jet Lag Podcast with Jon and Sam. Welcome.

Sam: Cool. Thank you. We'll do well.

John: I was hoping we could start with a little ease into the show in the sense of letting our audience here on Human Science get a feel for who you are and really what you stand for and kind of a little backstory.

Who is the man, the myth, the legend, Mr. Sam Robertson?

Introduction To Sam Robertson

Sam: Well, there's plenty of myth. And maybe a little legend, but, the man, that's tricky and I'm always quite poor at doing this, but I guess it probably makes the most sense to, work backwards. Maybe I'll try that as something different, but instead of, starting at the start.

Yeah. I mean, I'm interested in a lot of things and the area I'm most interested in and most working in these days is the intersection of technology, computers, AI with, with humans. It's, it's something that's kind of touches every part of our lives and sport is the area that I spend most of my time in.

And the reason I like that area is because it's, it's growing. It's something not a lot of people know a lot about. It's, it's very interdisciplinary. It pulls together people from all walks of life. It affects decision making and performance, which in, you know, my previous life in sport were two areas I was interested in a lot.

So that's really where I find myself spending a lot of my time now with, with various sporting teams, with my university research, with governing bodies. And I guess how I arrive at that, if we do go backwards in time, is it's a combination. Like a lot of things you do in life, it's a combination of what came before in terms of my studies, my experiences, my roles. So in that sense, I've worked across a lot of sports, as a practitioner initially, in lots of different elements of sports science from strength and conditioning in very early days through to skill acquisition.

My PhD was a representation of where my career's gone now, which had elements of, of, strategy, of skill learning, of early stages of computer analysis of, of sporting technique.

I probably got in the right place at the right time in Australia in terms of, getting the back end of the tale of Moneyball in the US, which was really quite new in Australia at the time. So, so even elements of the analytics revolution kind of worked their way into the back end of my PhD.

It's really been a journey from practitioner to academic to a mixed role to, university administrator to where I am now, which is the next stage of the journey, which is really recent, the last couple of months, which is, working for myself or starting to move towards working for myself and focusing on some really big projects that the sports industry has ahead.

John: You're such a humble guy, but it's really beautiful what you've done and touching all the different, facets of sports and technology.

Maybe we could dive in a little bit to the Moneyball reference because you were saying you came into that more in Australia as it was, as it was beginning in the U. S. as more of a understanding the data analytics of baseball in the U.S.

The 'Moneyball' Revolution

Sam: Yeah, it's all optics with this type of thing and sometimes, I mean I think the best analogy and it works in sport is the same thing happens in music, right? You have Seattle for that really short period of time in the nineties, putting all those great grunge bands out and then it kind of dies away and something else takes its place or emerges.

And it's something that's broader than that. And I think for Moneyball, it was exactly that.

John: The 90s grunge scene in Seattle, I love this reference here.

Sam: Well, where I'm going with this is, if you think about that origins of Moneyball, if people that know the story, it's a really niche area, right? And the people that have benefited from, who continue to benefit from that, uh, from a, particularly in the U. S., from a very specific background, training,and, well, the learnings from that have, have merged into other sports, not across the board, but other sports have picked them up, particularly again in, in the U.S. But again, that manifests itself differently in different sports and in different regions of the world. So in Australia, we, where, where I was based at the time, we had, a very different upbringing to what professional sport was and how it operated. And I would say in a, in a sense, I'm probably simplifying things here. We had a very strong sports science, sports performance background. We had very defined roles for people in physiotherapy or athletic training, as you'd call in the U. S. for, for biomechanics, for physiology, for skill acquisition.

Some of those roles that didn't exist in the professional codes in the U. S. And then as the data came along, which continues to be a revolution now, of course. We started to kind of find, well, how do we make sense of that, data for these people in these roles? And of course, then we started to emerge with these analytics roles that were so common already in the U.S.

On the flip side, what we're seeing now and we continue to see to this day in the U. S. is a lot of these roles, the ones that I just spoke about and now it's just starting to come in vogue over there after the data revolution after the money ball revolution. So it's interesting how we kind of like much closer to the same spot than we've ever been I think but we've got there in exactly opposite directions.

John: Yeah, it's fascinating. And would you say now the data optics of this is focused on skill acquisition, like you're saying, or, or is this really of, you know, recovery? Is this athletic performance? Is this, is this tackling all different aspects of the game now? Or is this really like, hey, how can a manager or a scouting report, you know, figure out an edge in a game? Is it now, is it for the whole team or is it really an individual?

The Data Dilemma in Sports

Sam: Yeah. I mean, it's opportunistic if, if I'm honest. So I think every sport or franchise or governing body sees their own opportunities. And frankly, there's opportunities everywhere in data and technology right now. It, there's almost nothing, I can't think of anything that's untouched by it. So, it really does depend on what the interest is and where the gaps are.

Sometimes it's a combination of things being in the right place at the right time. Sometimes it's, it's people driving it within the sport. I wish it was more fundamental than that and starting at first principles.

I don't know how often that is the case. So something along the lines of, we are a sport, this is our view of performance of this athlete or this team. Uh, here's our perfect world performance model about what good looks like. What's missing from that? So what new analyses or what new data do we need to collect on that to get a better picture of our athlete?

I think across the board that starting at first principles about what good looks like is, is somewhat missing and I think we often get into a situation of availability bias, right?

Like a tech company puts new data in front of us and we pick it up and maybe we don't do as much due diligence on whether we need that data or not. Or tech, and whether we know the quality of that data as well.

John: Yeah, it's interesting to think. And where do you envision this goes? I mean, you know, tech is here to stay. It's only going to be more advanced as AI, as you said, artificial intelligence, machine learning comes into more of the games we love, the sports we love, the athletic process we love. What's next in this journey? And how do you see yourself maybe helping, navigate teams and athletes and governing bodies through that?

Sam: Yeah, I'm glad you asked. I gave a presentation on this very topic last week, and so it's fairly fresh and front of mind, but it was to a closed audience, so I'm really kind of happy to share it.

But, hopefully I play a major role in it. I think it's something that I'm passionate about and I see a lot of need for. And there's plenty of people out there capable of having impact, but it's not front and center for them for various reasons. And to give you an example, people working day to day in professional sport are inundated like never before. Sometimes we're not kind of stopping to check about whether we need to do it all or whether it's adding value, but it's like our lives.

Yeah, we, we have things popping up and beeping us every 30 seconds. So it's the same in sport. So where do I see it going? I honestly don't know. I think it depends on whether you get me in an optimistic or a pessimistic mood, but I'm not always known for being the most optimistic, so I'm trying to work on that now, but I mean, a couple of things that come to mind in this space, we will hit a threshold or a tipping point, so to speak about what we can physically manage.

And realistically, we've actually gone past that in many respects in terms of what we can handle in sport. And in fact, our day to day lives. It will move past that further and further because we will, we will leverage AI and, and automation to help us take on more information. So, so we kind of have that up our sleeves.

And unsurprisingly, the areas that are most likely to benefit from tech and AI right now are the ones that will be will face the most resistance internally. And the one that eternally comes to mind for me is coaching. Coaches are often exempt from a lot of the scientific methods that we, we apply to things like how we develop speed in an athlete or how we develop skill.

We apply so much rigor and so much evidence and research to that. Coaching remains very much an art form. And I am generalizing this. There's scientific propensities behind a lot of that. In a sense, it often hides behind the fact that it's the touch point with athletes. It's relationship building.

Well, so is every other job in sport. And so I think it will be the remaining kind of bastion of, of exposure to scientific rigor, to tech, to AI, to automation, to dare I say it, replacing of parts of the coaching process with AI and tech, which again has to happen and will happen.

But which sport takes the leap of faith and does that? I'm not sure. So that's, that's one thing that will lie ahead. And the other thing that will lie ahead in terms of a, again, a tipping point or a hurdle. The consumer will have far more say in what they choose to accept if we do it right.

So for example, we don't need to accept poor technology. We don't need to accept things that drop out, tech that drops out and provides missing data. We don't need to accept data that doesn't consider our privacy, the ethical considerations around how we collect and store data and access data to people.

In our day to day lives, we won't have much saying that. I mean, governments are collecting all sorts of data that we would rather they weren't already. The athlete, hopefully, has more of a say in that in sport as they move forward.

John: I hope we can all hear the rest of that speech at one point if that becomes public, but I'm just thinking as, as a colleague and, you know, it doesn't feel like you have an overly, pessimistic view by any means. I feel, I felt that was, quite balanced. So thank you for that.

Do you mind though, if we explore that for the second, for a second, do you sense that there would be pessimism because it's a less of, you know, you've got an athlete with talent and they have a feel for a game. Now it's more of, hey, your numbers are not hitting this metric, therefore we need to change it.

Sports Tech Ethics and the 'Injury Prediction' Debate

Sam: Yeah, my pessimism stems from people that will take advantage of a lack of action or a lack of literacy. Or in some cases, just a lack of time and ability to drive this in the industry . And that happens, that's unavoidable, of course, that happens in every element of life.

And that's what I am most concerned of all, is there's no single entity out here, governing body, United Nations of sport, that's ensuring that governing bodies and leagues and, even governments have the best interests of the athletes at heart. And I think we all say that we would like that to happen, but it's moving really fast.

It's way, way, way faster than we can manage. There's no doubt about that. So who's in the driving seat? And the reality is nobody's in the driving seat. So that's what concerns me. And that's also where I am passionate because I think. I can't do a lot, but I can do my bit from the touch points that I have and the sports that I work with.

Coming back to the point around, I guess, a snake oil salesman, which is probably the best way to describe them. Although I think sometimes people don't intentionally mean to mislead. They just want to create a startup and they want to make money or they want to have an impact. They have the right reasons, but the quality may not be there.

And the example I've talked about very heavily, is what's going on right now in injury prediction. So there's thousands of companies all over the world selling injury prediction software. And I, again, I won't ad nauseum go into all of the issues I have with it scientifically, but it can't work.

The main reason why it can't work is if we're going to prove any of these algorithms, we have to let an athlete go out when we have a recommendation for, or a prediction that says an athlete will be injured. We have to ignore that and let them go out and see if they actually do become injured. Sports will never do that. And if they do do that, they're leaving themselves very, very liable or open to, to being sued, quite frankly, by the athlete.

Teams are not going to expose themselves, particularly in the U. S., to, to that type of risk. So we're, we're left with this situation where we could have the best prediction model in the world, but it's not going to work.

I'm just using that as an example because it's one that comes to mind. The first question is not whether it works or not. The first question is, is it the right question and should we be doing it?

John: I think about that, you know, kind of It's, you know, somebody is going to get injured, but you push them, or the athlete, you know, wants to push through anyway. So I thank you for diving a little deeper into that.

Quality Control in Sports Tech: Sports Quality Framework

John: Changing gears a little bit here, you and some colleagues wrote a wonderful paper, I believe, and also created a whole movement around a sports quality framework. Do you mind, Sam, diving into that a little bit, you know, it's inception and some of the intricacies that might be relevant to us.

Sam: Yeah. I mean, it's, it's certainly related to what I spoke about earlier around that notion of the industry, hopefully demanding more quality on more choice from the sports tech industry and,

I would say don't take this as me wanting to beat up on that industry. I think a lot of it's well intended, but I also believe that we should hold them to account just like we would our athletes. It's, it's okay to expect the best from what we deal with. That's what sport is. It's high performance. And if you can't deliver on that, whether you're an athlete or a coach or a tech company, then go away and improve on it.

So really the inception of that as an idea I can't take full credit for, but I remember very specifically on one of my many around the world trips visiting, you know, five or six countries. The single prevailing question a couple of years ago at this trip was all around the quality of technology, was all around things like, how can I find the time to go and evaluate all these potential offerings on the market and find the right one, purchase the right piece of equipment for my, my company or my organization?

And as we talked about up front, this is not going, not only not going away, this question, it's becoming larger by the day. And I think we've all probably experienced that. So really at that point, there was a couple of colleagues that talked about, well, could we all join forces and collect information and share it as a resource?

And certainly that's where the idea originated, but we kind of went one better. We went one step back to go one step forward in the sense of well, are we even talking about the same thing when we talk about quality? And so again, we went to the literature. We, we also did a survey through the industry.

And I'm really happy with where it landed. We came up with 23 items, which we called features of, of what a good piece of technology looked like. And of course the usual areas we're in there, validity, reliability, we all want to know that our tech's going to work when we need it.

It's going to be accurate. But, other things really came to the fore as well. Like a lot of people in the literature and in the sporting context said it has to be usable. It doesn't matter how good technology is, if our athletes don't want to wear it, it's not a good piece of tech. And new considerations also emerged, like is this tech, considering the environment, is it sustainable or am I going to have to throw out the sensor or throw out a device and replace it every 12 months? This is not a company I want to associate with.

These 23 features were fit, fit into a framework, which is available online now as a white paper. We were specifically with the end product, really quite open and not prescriptive about how an organization would pick that up and, and operationalize it to inform their decisions that they're making.

Because frankly, there's so many ways you could use it. use it. And six months out from when it's been released I've heard some really interesting ways companies and governing bodies, across the world are using it. And, there's a couple of ways we thought people would gravitate towards using it and others have caught us off guard and by surprise. And we're really interested to see what happens in the next six to 12 months with it.

John: I, I really appreciate that, Sam. and like you said, it, it hopefully allows sports, teams all around the world to, to be able to decipher that snake oil salesman from a legitimate piece of technology or, or a company that is, is, in it for the right reasons. You touched on a few of, of those frameworks, but is there anything, maybe now as, as that white paper is, evolving a little bit as, as the tech is evolving, is there any kind of big real world applications, of that framework that, that come to mind, that can be useful to our audience at the moment.

Established Benefit - Is the Evidence There?

Sam: I think the interesting thing I've seen with it already is that the intersection between some of the areas or the, again, as I call them, features of the framework and what organizations were already doing has been something that I didn't expect to see.

So, for example, there's a group of features in the framework that are all around this notion of established benefit. And what that means is exactly what it sounds like, which is does the tech or has the tech in question being shown to be valuable for whatever its purpose is. So its purpose might be to provide insights for fans in broadcast.

It might be to prevent injury. It might be to improve the performance of athletes. What's the body of work in the literature or in that organization that exists to, support that claim? Now, again, this is something that sounds really obvious and you would think that organizations are doing this, but by formulating into the framework, it's almost reminded some of them that, okay, we actually should check whether this thing works and if the evidence isn't there.

Who's responsibility is it to go and collect that? And that's, that's kind of been an interesting, probably the most interesting part about this, which, again, I don't want to seem like I'm critical too much of the tech industry, but it's certainly the guidance I provide. Any tech companies that I work with or mentor is go and create that body of evidence because I see so many startups pitch, as do other people, I think, with a new app, a new device, that's either very incrementally improving on what we already have now, and so may not be worth the trouble of developing. Or it's trying to answer some kind of problem that nobody was, nobody really had already. they didn't bother to ask industry.

John: Yeah, no doubt. No doubt. And it's, it just really speaks volumes to me to think about how vital you are now to a company You know, as you go in and mentor or, or advise and offer guidance of Hey, don't be swept away by the marketing and the gimmicks.

Sam reflecting a little bit here that on your own journey, the academic side and, and the data rigor and, you know, the scientific method is so important to you, but also just the, the love of sport is, is so near and dear to you. is there a sport where you're like, oh, here's where the, features of the framework , could go and really help evolve this sport for the fans, for the athletes and for the organizations.

Sam: It's interesting because I am a definitely a sport guy, I've spent most of my life in that, as you probably can tell from the last 20 minutes or so, I am more motivated now by these questions and problems than sport per se, because I think every question we've talked about today is relevant to outside of sport as well, but I'm just lucky enough that sport is a vehicle that I am working in and moving in and able to have an impact on, you know, who knows that might change in the future, but right now that's certainly the case.

And I think the other reason I find it a useful vehicle for some of the things we're talking about is, I've got no issue if someone's motivation is to start a tech company to make money. The point is, when you're working with athletes, which has been my kind of journey in the performance pathway, I'm, I'm sorry, but the, you need to show it works. I think making an impact or creating a company just for the sake of making money, that's not enough. And again, this is an interesting question though in sport because if you're in it just to make money for improving the fan experience, is that okay? So it's a different purpose. So this is the beauty of sport. There's all these diverse stakeholders and on diverse questions that you can have and they need different iterations of the framework, and some of them need it less than others, which is exactly to your point.

And even some of our work presents that. So I'll give you an example. If we were involved in assessing with FIFA, the world governing body for football, the accuracy of the semi automated offside line leading into the men's and women's World Cup recently. Now, in order for that system to work, it has to be extremely accurate, much more accurate than a human referee could adjudicate. This graphic is going straight to TV broadcast in front of billions of people all over the world. You have to have that stuff right. Otherwise your system doesn't work. Basically you're going to all over the world.

If you look at a alternative exam, if you need to provide some kind of visualization about how far Lionel Messi ran in the World Cup Final for fans or on the television broadcast or on the website. Of course, you want that to be accurate, but if it's out by a couple hundred meters, it doesn't really matter that much to the fan, as long as it's giving them some indication or some value add to what they had in the past.

And so if you go through the list of stakeholders there, you find that sure we'd like every single thing that we measure in a sport, and I'm using football as an example, we'd like it all to be right at the top of the framework. We'd like it to be super usable, super fun, really accurate, really reliable, good value for money, cares for the environment. Ticks all those boxes. And of course, I hope, like I think most of us, that that is, becomes the benchmark for companies as they move forward.

But, I think it's also important just to recognize that, at least for the moment, it's a journey for companies and some of those are going to take a long time to get there and if you're a company that specializes in fan engagement or insights for a marketing department, it's probably okay that your data is not as good as a company providing an offside solution. We need to be pragmatic about that.

John: Yeah. And I, I just think about my own experiences. I'm currently, watching baseball, you know, it's right before the World Series. And, they're giving so many little insights to what pitches being thrown and at what speed and, and that's such a value add to me. And at no point am I like, that is off one mile per hour or what, you know, two kilometers per hour, you know? But I bet if someone's calling balls and strikes in that, and they want to change the umpires out of that in the future, you, you better damn sure believe, believe that that's like right on the plate.

It better be exactly where it says it is.

Sam: Yeah, exactly the same.

Closing Thoughts

John: Well, Sam, it's really great to feel your passion and, and your honest approach to all of this and. I do feel you are quite optimistic about this and excited and and so I think as a good researcher and a good scientist you will have your hesitations and your reservations and I think that's absolutely necessary, so thank you for that. As we start to wrap up here , I was thinking, is there anything that you've seen now over your, your tenure of mentoring startups and being on boards that really strikes you, some wisdom you could share with our audience about that?

Sam: The main one that comes to mind is one that I have given in the past, and it's also been criticized by some of the people that have received it, and I understand why, and that is to do due diligence on the problem, and I think that's obvious based on the example I gave earlier.

I guess related to that is, is to collaborate on that. Now, the criticism I get on that piece of advice is, well, that's easier said than done. How do I go and get a audience with Real Madrid or New York Yankees when I have no experience, I have no contact in at that organization. And it's a good question. But the point is, they're not your only contact. Other people will have the ability to provide insights for you, so look far and wide with those people. Academics, of which I guess I have to consider myself one, are some of the worst at this. They reply to everything. And so, it's very easy to kind of contact a leading academic around the world and get an audience with them if you ask them a good question.

There's always going to be an insight or a way you can gain an incursion into any organization, even if it's not going to be the CEO or the general manager straight away. Yeah. Interrogate your question. That is much more important than having even a solution to the question. The question is the most important thing.

And then when it's an opportunity to fulfill it, you need to get a collaborative, multidisciplinary, interdisciplinary team. Some of the best startups I'm seeing right now, they have people working with them who have, not generalist skill sets, but they have more than one specialization. I think that's a really nice way, moving forward to ensure that you can get that with a smaller team as well. So again, it's probably an obvious thing, but it's often overlooked.

John: No, I think it's, it's, it can't be stated more. So, so thank you for that. And, you know, here on Human Science, we do love a little healthy controversy. So always, always happy to get those perspectives.

I would love maybe if we could just kind of end on a little bit about your show. I know it's, it's something you are, you're humble about, but you have an incredible show, I believe, really focusing on sports analytics. Is that the best way to summarize it?

Sam: Well, firstly, thank you for, for mentioning it. The show's called One Track Mind. It's, it's run for about two and a half years. It's certainly started on, on sports analytics, but really it relates to anything on the future of sports.

So we've covered topics from safeguarding children, athletes in, in sports through to what is the future of high performance sports. So it's a very broad topic. And if you're interested in anything around these, these high level concepts around sport, I'm sure you'll find it interesting.

John: Awesome, Sam. and where can people reach out to you? As you said, you dabble in the world of academics. Can people reach out to you if they have questions, concerns, and want to know more about your life?

Sam: Yeah, again, LinkedIn and Twitter, all the usual places. my website is, is samrobertson.com.au. So it's very easy to find me there.

John: You're a rock star. Thank you for your healthy level of skepticism, Sam. It is necessary and vital as we enter the world of sales and marketing tactics in AI. Correct. Awesome. Well, Sam, thank you so much for joining us and we'll talk to you next time.

Sam: Thanks, John. Been a pleasure. Thanks for the invite.

John: Thanks for listening to Human Science. If you enjoyed this episode and you'd like to help support the podcast, please share it with others or rate and review it. All the show notes and links can be found over at labfront. com slash human science.