Dan Webb is the Director of Performance Analytics at the United States Olympic and Paralympic Committee (USOPC) in Colorado Springs and an accomplished leader in performance analytics and sports science, using innovative data science and statistical modeling methods to solve challenging performance problems. Dan has led efforts to develop and implement cutting-edge solutions to model and predict Olympic-level performance, enabling data-informed decision-making for both the USOPC and NGBs. Under Dan's leadership, the Performance Innovation department continues to provide integrated and sustained competitive advantages for Team USA by deriving insights from data to improve athlete performance and optimize training, competition, and resource allocation strategies.
Episode Description
Athletes around the world are preparing to live out their Olympic dreams in Paris this summer. Many of those athletes have been competing in national and world championships before participating in Olympic trials in order to join their national teams. But how can an athlete be sure they’re peaking at the right time? How can they know whether adding an additional element to a routine or changing the angle of a throw will give them an advantage over the competition? Well, there’s data for that and that’s the focus of this episode of Stats and Stories, with guest Daniel Webb
+Full Transcript
Rosemary Pennington
Athletes around the world are preparing to live out their Olympic dreams in Paris this summer. Many of those athletes have been competing in national and world championships before participating in Olympic trials in order to join their national teams. But how can an athlete be sure they're peaking at the right time? How can they know whether adding an additional element to a routine or changing the angle of a throw will give them an advantage over the competition? Well, there's data for that. And that's the focus of this episode of Stats and Stories, where we explore the statistics behind the stories and the stories behind the statistics. I'm Rosemary Pennington. Stats and Stories is a production of Miami University's Department of Statistics and media, journalism and film, as well as the American Statistical Association. Joining me as always is regular panelist John Bailer, emeritus professor of statistics at Miami University. Our guest today is Team USA’s Dan Webb. He's the director of performance analytics at the United States Olympic and Paralympic Committee in Colorado Springs. Webb has led efforts to develop and implement cutting edge solutions to model and predict a Olympic level performance, enabling data informed decision making the performance innovation department works to provide integrated and sustained competitive advantages for team USA by deriving insights from data to improve athlete performance, and optimize training, competition and resource allocations and resource allocation strategies. Dan, thank you so much for joining us today during what must be a very busy time for you.
Dan Webb
Yeah, busy, busy, but exciting, and really glad to be here.
John Bailer
It's, you know, it sounds like he gets to do some really cool stuff. And so, I'm curious, how long has there been an analytics group associated with the US Olympics and Paralympics efforts?
Dan Webb
Yeah, there's been a couple different iterations of it. I got here in 2014. So that's basically as far back as I can speak to, I know, the competitive analysis program, which was really what kind of seated what we are today, started out of the London Games in 2012. And from there, it's grown a lot to what we have today as our performance innovation department that Rosemary just described, that kind of came together out of the Beijing Winter Games in 2022. So yeah, as our current iteration is performance, innovation is pretty new. And we're excited for this to be our first games with the team that we have today.
Rosemary Pennington
What's been driving the growth of this particular I guess, area? For team USA?
Dan Webb
Yeah, I mean, one thing that's important to understand is the USOPC is one of the only Olympic and Paralympic committees in the world that's not government funded. So our whole team is actually funded by donors. And our development team has done a great job raising funds to drive and expand our work. So it's, it's a huge area of growth for us going into LA and an area that, thankfully, really resonates with donors. As you guys said, it's cool, interesting stuff. So we have a good story there to tell, to raise funds, but so that's one side of it. And the other side is that the demand for data analytics and technology projects has really exploded in the last 10-15 years. Yeah, starting with Moneyball, and how that has expanded into the rest of sports and really the rest of business, you know, Olympic sport and Paralympic sport is no different and has seen the competitive advantages that this type of approach and thinking can provide and has been right there alongside of the growth of data and analytics.
John Bailer
You know, I was reading in one of the some of the material you sent that there are 329 metal events in the Summer Olympics, in 45, sports in 549, medal events in Paralympics in 23 sports, and I'm just thinking, you know, you have to be kind of busy with all of this. So, you know, how do you, within this collection of huge numbers of sports and events within sports? Where do you kind of put the analytics?
Dan Webb
Yeah, it's a great question and yet no shortage of areas where we can focus for sure. That's why I've been here for 10 years and still not bored. Still plenty to do. So yeah. I would say there's like a baseline set of analytics that we have across every sport in every event. That's pretty automated, that doesn't take a ton of hands-on time and effort at this point. And then from there, you know, our team is still relatively small, at least at the scale that you described, when there's that many events in sports, we're only about 10 people. So from there, we kind of choose places to do deep dives, where we feel like we can have the greatest impact, or where there's the greatest opportunity. And I will say we have a big opportunity to still improve what we do on the Paralympic side. Most of our work to this point has been focused on the Olympics because of the availability of data, and just some of the maturity of the competition and international competitive field. But Paralympics is definitely a huge, huge area of opportunity for growth moving forward.
Rosemary Pennington
I was watching your presentation of one of your colleagues, I think, at a sport analytic conference, and he was talking about how sort of what Team USA is doing is different from what some other countries are doing, because there are countries I mean, they made the May the example of the Netherlands, where like, you know, for Winter Olympics, they're gonna double down on speed skating, because that is sort of a space they have owned, and how that sort of frames the day data analytics work in a different way than Team USA, we're in both the Olympics and the Paralympics, we are competitive across multiple sports. And I wonder how it is that the fact that we are competitive across like multiple sports impact the approach of your team, to the gathering and the analysis of data?
Dan Webb
Yeah, it's definitely a good problem to have, that there's this abundance of sports and events in the US where we're competitive, and where we have athletes that are capable of making Olympic and Paralympic teams and winning Olympic and Paralympic medals. But I think it goes back to kind of the last question where, you know, with a team our size, we can't do it all. And I mean, I should give credit to the sports themselves, the way that the Olympic and Paralympic movement is set up in the US might not be familiar to all the listeners. So where I sit is at the USOPC level, then there's the NGBs, the National Governing Bodies of each of the sports, so that would be like a USA basketball or a USA Track and Field, for example. Some of those NGBs have their own analytics staff as well. So when we're looking for areas across the whole landscape, where we can have the biggest impact or where we need to prioritize NGBs with their own analytic steps, we might partner with them, or they might be beyond what we can even do. So you guys are good, we'll go focus somewhere else.
John Bailer
You know, there are many different areas of focus that I read about in some of your work ranging from Performance Analytics, to match analysis, to athlete 360. Could you give an example of each of the types of data and some of the decisions that are supported in those areas?
Dan Webb
Yeah, so we've really structured our team around three core pillar programs. One would be the competitive analysis program. And that was kind of the earliest one that I mentioned, starting out of the London Games, and has been around for about 10 years now. And that would be when you think of more traditional sports statistics, sports analytics, that would be through that program. That's, you know, rating and ranking athletes against their international competition, doing the match analysis to try to find edges in certain competitions. Yeah, pretty traditional, like sports analytics work. The Tekken Innovation Grant Program is the second one and that is a pool of money that the donors I mentioned earlier, have committed. Our team then accepts grants from the NGBs, so then our team is really structured around three core programs. And the first is a competitive analysis program, which I mentioned earlier was really the first thing that got our work kicked off about 10 years ago. And that would be most of the things that you think about when you picture traditional sports analytics. So, rating and ranking of athletes, how competitive are we against our international competitors, diving into match video to see where there might be opportunities or strategies or creating scouting reports. And then, the second program is the tech and innovation grant program. So the donors that fund our work have pooled a good amount of money to support NGBs in tech innovation, data analytics projects, and so NGBs can apply to that program for funds. And when we approve those, it comes with funding, but also support from our team in making sure those projects are implemented or connected to the right vendors. So that's, yeah, a really huge area of impact for us. And then the last one would be the athlete 360 program that you mentioned. And that is our comprehensive performance management system, where there's so much data around training and performance, and athletes nowadays, and it's just the central hub where we can store all of that data, integrate all of that data and start to look at trends. So that could be anything from if an athlete's using a wearable. If they're tracking their heart rate in training or competition, if they're using a sleep monitor, things like that. Or just subjective data about how am I feeling today? How did I sleep? It centralizes all of that and allows us to start looking at trends and performance.
Rosemary Pennington
I could say that the athlete 360 program seemed really interesting to me when I was looking through this information. Could you explain how this might actually be used in a sort of real world kind of scenario?
Dan Webb
Sure, yeah. So our goal with the athlete 360 program is to truly identify and understand the underlying variables that correlate to performance on the field of play. And so one example of that could be something like, as simple as travel, how far are teams traveling? When are they traveling? And how close to competition are they traveling? And then we've seen after travel, there tends to be an increase in illness or sometimes injury. And so make sure that you travel with enough time to recover. That is something you know, just a simple example of the type of data that we're collecting there and the impact that can have on the field of play.
Rosemary Pennington
You're listening to Stats and Stories. And our guest today is Dan Webb, Director of performance analytics at the United States Olympic and Paralympic Committee.
John Bailer
You know, I was really intrigued at some of the examples that you had described in a slide deck. You had shared one, it was the throws analysis, you know, looking at kind of the track and field events, where you're measuring kind of angles of throws and velocity and sort of optimizing kind of where the athlete feels most comfortable and where performance is best. Can you talk just a little bit about what you do with that performance, the throws initiative, and how that helped the athletes competing in those events?
Dan Webb
Yeah, so that's kind of a good example of the lifecycle of an analytics project that hits on all of the different programs that I mentioned. So initially, through our kind of competitive analysis of the international field, we realized that there was a huge opportunity in the throws. I think, in the London Games, our throwers won one medal. And even more importantly than that, only 13% of the throwers on the 2012 Olympic team hit their personal best at the Olympic Games. So a lot of them had the capability to perform at a medal winning level, but they just weren't hitting their best at the Olympics. And that's always the goal, right at the peak at the Olympic Games. So through the International Pettibon analysis, we identified this as an area of opportunity. There were some things that I think we're done around the scheduling of trials or like I mentioned before the travel that could be optimized a little better. But another piece of that was through a tech and innovation and grant where if you've ever watched golf, then on a drive, or a tee shot when it traces the ball through the air, using flight scope and trackman technology worked with those vendors to adapt those same same tools to track a shot put or a discus or a javelin. So now throwers have that instant feedback and training every day, if they want to be able to see how far their throws go. And then to your point on their farthest throws, what's their release angle? What's the velocity? And start to say, Okay, this is where I hit my best throws and really train and dial in on that. So yeah.
Rosemary Pennington
I was actually gonna ask you about another example that you included in the information you'd share with us, which was the biomechanical analysis. So I was a sprinter in my very young age in high school. And I'm sort of looking at this slide in sort of seeing, you know, the two different moments where this runner is getting off the block and sort of how you're analyzing it. And so could you just talk us through this sort of biomechanical analysis that has been done for some of these athletes? And how the impact that might have had on their performance, because I just, it's sort of, it's really interesting to me, obviously, as a former runner to sort of see that.
Dan Webb
Yeah, so the biomechanics analysis has always been a huge part of sprinting. The key is that previously at either you have to be done in a lab, which isn't always a great representation of the competition or training environment. Or you had to fly biomechanics, to observe the training and give the feedback there. So the project that you're referring to, allows the track team now to take video and training, just super, super simple video clips. And then send them through a program that processes things like their joint angles, their ground, ground, contact time, stride length, stride rate, you know, all the most important, all the things that contribute to sprint speed. And give really quick turnaround on insights and pointers on when you are at your fastest, or were there opportunities to get faster. So again, the biomechanics has always been a part of it, it's more about the volume and turnaround time that we've been able to achieve through that program and the USA Track and field staff has told us that, in the last couple of years, when this program has really been up and running, that they've been able to analyze and make interventions on about 15 times the amount of, of training data. Whereas before, you know, it was all manual.
Rosemary Pennington
It's interesting, because in both of these cases, both with the sprinters and with the throwers, like it seems like it's their adjustments that could be pretty small, that could really have a major impact on their performance that you're able to capture because you're able to do this data analytic work.
Dan Webb
Yeah, the details in the technique are just really mind blowing, when it really gets down to it. Sprinting and throwing, look, to my untrained eye, and probably a lot of the people watching, like a pretty, pretty simple task. But when you really get into it, the medals are one in the details for sure.
John Bailer
You know, we've been talking here about about individual performance and sort of improving these the, you know, the actual execution of particular tasks to, you know, whether it's the sprinting or throwing, you know, I also really was fascinated by the example that was shared it part of the materials you sent related to deciding whether or not a skater might skate in the team event. Were before skating and an individual event, that's that taking the presence of this skater going to really change that the team medal chances, or was it sort of just putting them at risk of maybe some other adverse outcome for the individual event? So that kind of, you know, analysis in advance of the events to help you determine strategies for team events and individual events, I thought was really interesting. Can you talk a little bit about those types of applications?
Dan Webb
Yeah, so that type of work is really the expertise of one of my colleagues, Elliot Schwartz. And yeah, the work that you were referring to was speed skating originally in a team event, where there's all sorts of rules about how many times individual athletes can skate and how, you know, only a certain number of teams progressed to the medal round. So you have to strategize appropriately, to make sure you're using your skaters at the right time. And then to your point, not fatiguing them for their individual event, which comes later. And so yeah, he did a great job with that work and has adapted the framework to other kinds of judged and scored sports in the same. So, like gymnastics. We'll be seeing each other here in Paris shortly. And he's been working really closely with that team on a similar type of approach.
Rosemary Pennington
What has been the response of athletes and coaches to having this kind of data?
Dan Webb
So with where my team sits at the USOPC, it's not every day that we would provide these insights directly to an athlete, a lot of the time it's channeled through a coach or another high performance staff member, since they know the right ways to message these things to the athletes. But I'd say overall, the reception has been really positive, you know, Olympic sport and Paralympic sport, just doesn't have the same level of resources as a lot of pro sport or even college sport in the US. And so really, anything that we can provide any support? Yeah, there's just a huge appetite for it. And, yeah, I'll leave it there.
John Bailer
So in some of the analyses you do, there's some prediction of probability of medaling, or probability of having a higher score than a particular competitor? Do you look back then after you've done after the games have been completed? And say, you know, hey, we nailed it on this one? Or we missed that one? And say, what are some of the reasons why we made it or missed it?
Dan Webb
Yes, absolutely. And that's a challenge in this line of work is, you know, the Olympics and Paralympics only come around once every four years. And so, definitely dealing with a small sample size problem, there were, to your point, after a game, if we look back, and there was something that we were projecting, or the analytics were pointing towards some action that a coach or a team should take, and it didn't play out, takes a lot of really good thought and discussion and bringing in the subjectivity from from the experts on the ground, to say, you know, were we off? Or was this just a once in every four years thing that, you know, the, the analytics was right, and it's, it's a well calibrated projection, it just didn't play out in this specific scenario.
Rosemary Pennington
As we're recording this, we're just coming out of the national championships for gymnastics, and that their trials will be coming up, not too far from now from where we're recording. And one of the things that kept coming up a lot during the conversations around the national championship, as Simone Biles was, you know, kind of just dominating that competition was, again the the jockeying of like, who is going to be number two, who's going to be number three, you know, who are they going to bring in a bar specialist or a beam specialist? And obviously, we're recording this before the Olympic trials, but I wonder if you have any advice for those of us who are watching these sports, and we're seeing those guys who are, you know, sort of throwing around this like armchair data analysis. What should we be keeping in mind? As we're sort of hearing people make these kinds of prognostications?
Dan Webb
Yeah, well, gymnastics is a really interesting one, because most of their team selection is discretionary. So they do get to factor in all those different strategic pieces that you mentioned, of how do we structure the best team and then also, how do we factor in the individual medal opportunities? I would say in gymnastics, definitely keep an eye on the difficulty score. That's where a lot of the strategy comes into place. But I would also say for sports, like track and field and swimming, which will have trials going on at the same time, a lot more objective in track, it's usually the first three across the line, or on the Olympic team. And same thing with swimming. It's the top two in that event there. And so yeah, the discretion doesn't exist. And, and the team is who wins on that day, which is another, just completely different, but equally interesting environment.
John Bailer
So what do you think's next for the analysis that your team might be doing? You know, what's, what's the future of work in the Olympics and Paralympics analytics?
Dan Webb
Yeah, so I mentioned earlier, the huge opportunity that we have in the Paralympic space. And I don't think it's as simple as just applying the same approach and models and data that we have in the Olympics to Paralympics, because it is just such a different and different competitive environment internationally. And so I think, you can expect to see a lot of work and development put in that space between now and when we host in LA in 2028. Another big area of opportunity that we see is, you know, with the the match analysis that we alluded to earlier, where you're kind of having to break down the video of a competition, the super time intensive process right now, it's usually a human sitting and just tagging everything that happens in a game or a competition to then produce the stats that you then analyze on the back end. And that I would say is probably where we see the biggest opportunity for AI. In the computer vision and video tagging space. It's not quite there yet, in the Olympic and Paralympic spaces, it is in some professional sports, you know, with NFL or NBA, where you have these fixed arrays of cameras in every stadium, and they're all exactly calibrated to get these stats coming out on the back end. That's not what we see in Olympic sports. These competitions are happening all around the world. And the best video feed we might have from it is from the coach's iPad, it's just propped up in the stands. So we don't have it. It's rare that we have the same quality of video. So that's why it's been a challenge. But it seems like the tools are getting there. And it's an area where we're super interested in.
Rosemary Pennington
Well, that's all the time we have for this episode of Stats and Stories. Dan, thank you so much for joining us today.
John Bailer
Yeah, thanks. Thanks, Dan. And good luck to the teams that are competing, but also your team that's supporting them.
Dan Webb
Yes, thank you, and we look forward to your support as we get to Paris. Should be good games.
Rosemary Pennington
Well, that's all the time we have for this episode of Stats and Stories. Dan, thank you so much for being here today. Stats and Stories is a partnership between Miami University’s Departments of Statistics, and Media, Journalism and Film, and the American Statistical Association. You can follow us on Twitter, Apple podcasts, or other places you can find podcasts. If you’d like to share your thoughts on the program send your email to statsandstories@miamioh.edu or check us out at statsandstories.net, and be sure to listen for future editions of Stats and Stories, where we discuss the statistics behind the stories and the stories behind the statistics.