All podcasts

Data And Analytics In Olympic Sports, With Elliot Schwartz

When you’re competing among the best, you are expected to provide your best performance. In the sports space, competing in the Olympics is a test of not only talent and ability but also preparation. That means having the right data analytics in place to drive athlete performance and maximize the rules of the game. In this episode, J.R. Lowry is with Elliot Schwartz to talk about data and analytics in Olympic sports and the changes they are driving in the field. Elliot is a data analytics consultant for the US Olympic and Paralympic community. He has played a vital role in supporting athlete performance, health, and wellness. With his expertise and experience, he takes us deep into the behind the scenes of working with athletes and teams to set them up for success. Elliot then shares with us the changes in the judging system, his outlook for our US Olympic team, and the lessons he found in data analytics that we could apply to our career paths. Don’t miss out on insights about the world of Olympic sports analytics from this conversation!


Check out the full series of “Career Sessions, Career Lessons” podcasts here or visit A full written transcript of this episode is also available at

Watch the episode here


Listen to the podcast here


Data And Analytics In Olympic Sports, With Elliot Schwartz

Data Analytics Consultant For The US Olympic And Paralympic Committee

My guest is Elliot Schwartz. Elliot is a Data Analytics Consultant for the US Olympic and Paralympic communities. In this capacity, he helps apply data and analytics to support athlete performance, health, and wellness. Elliot has done a data analytics consultant for clients, including US figure skating and a leading orthopedics research center.

He has been an official in US figure skating for more than twenty years. He was with Procter & Gamble for seventeen years in a variety of data research and engineering-focused roles. Earlier career stops included Speedline Technologies, Alcoa, and Los Alamos National Research Laboratory. Elliot earned both his Bachelor’s degree in Applied Mathematics and Material Science and Engineering and his PhD in Materials Engineering, both from MIT. He lives in the state of Maine. Elliot, welcome. Thanks for joining me.

Thanks for inviting me to join you.

I appreciate it. I’m glad that Beth Benatti Kennedy connected us. Let’s start with discussing your work with the US Olympic and Paralympic committee. I talked a little bit about it in the introduction, but can you provide some color on the work you do with them and with the national governing bodies, and with the athletes themselves?

I’m part of a team at the USOPC that’s called Performance Innovation. Our team is all about delivering data analytics and technology solutions to answer key questions related to athlete performance, health, and wellness. My role in particular is to serve as almost a program manager for Performance Innovation.

I spend a lot of time talking to the national governing bodies. Those are what we call our national sports federations, USA swimming and US figure skating. Pick your favorite Olympic and Paralympic sport. I work with them to understand the key questions they are trying to answer, understand the tools they are using, and whether they are serving their needs or whether there are other solutions I can help connect them with. It may be within our organization or maybe college or universities with whom we are partnering or have a relationship, or also could be other solutions as well, or sometimes I do the work myself.

There’s been a massive jump in the extent to which data and analytics, maybe even big data, are used to drive athlete performance. What sports do you tend to focus on when you talk about working with the national governing bodies? Are there particular sports on which you focused more than others?

To an extent, yes. With the national governing bodies, a wide range of staff sizes and funding is available. Just like the way that data analytics has evolved in professional sports, it’s a story of following the money. You can imagine that organizations that have more money and more resources are doing more in the data and analytics spaces. Some of them have one or more full-time analysts of their own.

With those nationally, some of those might be like USA track and field or USA swimming. My role with those NGBs is to be in touch with them and provide a sounding board if needed, but they are largely self-sufficient. I’m working more with the NGBs that are a little bit smaller. Also, unsurprisingly, I work more with NGBs that are maybe a little bit closer to my experience.

I have been a figure skating official for over twenty years, and that’s how my sports analytics journey started. I started answering questions using data that I was being asked by coaches and athletes. Eventually, I got connected with the high-performance or senior high-performance director for US figure skating and started working with him.

Then from there, I started working with the USOPC. In addition to figure skating now, I’m working with a lot of other acrobatic sports. Artistic swimming, men’s gymnastics, and diving. I have done some work also for some of the winter sports like free ski aerials and halfpipe. Anything that is judged I have naturally gravitated toward anything that’s judged because I have a lot of experience with doing analytics in that space.

What are some of the ways that you particularly help apply data and analytics to these governing bodies or athletes?

On the performance side, it’s about understanding how our athletes are comparing with other athletes across the world in terms of their general performance, but also finding ways to break down performance into its component parts and looking critically at how our athletes are faring versus other athletes. Also, there’s a strong component of understanding the judging systems, what’s valued, and what’s going to get our athletes points because the sport is paramount. You also have to understand the system by which you are being assessed and game it a little bit.

That’s part of the analytics side. You might be something from a gap analysis to something called a Monte Carlo Simulation, where I look at all the possible results based on a set of variable inputs. I do that both to understand our medal chances. It’s probably most useful when we are looking at a team event, like gymnastics has a team event, and so does figure skating.

In gymnastics, you accumulate points based on the athlete’s performance. In figure skating, they are in the team event. In the Olympics, there are eight event segments, and it operates like a swim meet or track meet where within each of those event segments, the athletes collect points for their teams based on their placements.

Something we have done in figure skating is looking at what are our medal chances based on different choices of which athletes we use. In figure skating, the team event comes first. We certainly want to do as well as we can in the team event, but we also want to give our athletes a chance to do their best in the events that follow. We are also looking critically at how much competition time we want an athlete to have before their event and look at what impact that might have on the team event result. That’s something I have done as well.

You are doing a bunch of simulations, Monte Carlo or otherwise, and looking at how to optimize the configuration of the team, how to optimize the potential team results versus potential individual results for the athletes when they are in individual competitions. Is there an element of it? That’s my words. You’re hacking the judging algorithm where you are looking at how past events have been judged and what drove the decisions and trying to unpack how to maximize your points.

My most experience is in figure skating, and I have done a lot of analysis in the past to understand the relationship between scores on the technical elements like the jumps, the spins, the lifts, and the artistic marks. I’m now expanding that to other sports and trying to amass data that’s going to give us a better understanding of exactly that, like how the events are judged. In figure skating, it’s the one acrobatics where you get a very detailed protocol on element-by-element scores and also the artistic marks.

Gymnastics, diving, and artistic swimming. Artistic swimming is about to change, but in the other sports, none of that exists that’s published. One of the projects we are working on is how we amass that data to be able to give more insight to our high-performance teams, coaches, and athletes.

Are you also doing things to help the athletes in individual activities, like looking at how somebody may stick a landing in gymnastics to help them figure out whether there’s something they are doing in the tumbling part of it that drives whether or not they are able to come down and hold that landing? I’m envisioning the athletes all wired up with the sensors all over their body and that type of thing where you are measuring all that stuff. Is that part of what you do as well?

I do not do that. Some of the things around, like jump mechanics and the coaches and the athletes are the best experts in that space, and then some of the biomechanists we have in our team. We will work with athletes on that as well. In some cases, we are using sensors or devices to measure elements of supports and the proper biomechanics. In some projects, I’m involved with basically being a person that contributes to how to use quantitative information to guide action, but I’m not the expert in the spaces around biomechanics, so I leave that to the expert.

Interesting that it divides up in that way. In terms of some of the work you are doing with these individual sports, how much of a difference does it make in the analyses you do between letting the coach determine the best configuration for the team and what the data and analytics are showing? Is it a hard sell in a sport like gymnastics to convince the team coach to go in a different direction than what he or she might think in their gut?

It’s one that I have been addressing my entire career, including my engineer engineering career at Procter & Gamble. I view that experience as valuable because when I was working with product designers, especially when computer-rated engineering or engineering modeling and simulation was newer, the people were less familiar with it.

Some people were a little bit threatened by someone who, outside of their design process, provided input on their design. Some people reacted in a way almost akin to me telling them their baby was ugly. One key thing is building trust, and some people will grant that trust at the beginning. Some people have to work for it to earn it, and I would say in the sports space, it’s the same thing. What I try to do to make as good a process as possible and build that trust is make sure I listen before I recommend. If an athlete, coach, or team has questions that can be answered with analytics, I focus on their questions before I introduce my questions and my analytical approaches.

We have undoubtedly come a long way since the days of Sabermetrics and Billy Bean in the baseball world. I have to imagine it’s still winning parts and minds. One Heart and mind at a time.

I’m another provider and I’m another provider of a tool just like your strength and conditioning coach, sports scientist, nutritionist, or sports psychologist. We provide tools meant to support the athlete and the coach, but the most important skill you can bring to that work is the ability to listen and build trust.

CSCL 52 | Olympic Sports Data

Elliot Schwartz: We are providing tools that are meant to support the athlete and the coach, but the most important skill you can bring to that work is the ability to listen and build trust.


These are elite athletes at the top of their sport. What’s the difference between an athlete at this level who’s taking full advantage of these capabilities and one who isn’t? Have you modeled that difference?

I haven’t looked specifically at that difference. I talk about analytics and using data as an example of one tool like a lot of the other tools that are available to coaches and athletes. I think in every case it’s different and which one has the biggest impact, or even the relative impacts are hard to discern. The idea is to give the athlete the best chance for success possible. In some cases, you have athletes who are just prodigious talents and are going to win almost no matter what.

At the Olympics and the Paralympics, I think we are seeing now that the competitive field is becoming more and more competitive. Sometimes very incremental or seemingly small advantages can decide whether someone wins gold or whether someone wins a medal or whether they don’t. We are just trying to figure out what tools we can bring to bear and use in the right way with each athlete and each coach.

How amenable overall do you feel like our US Olympic movement is applying these techniques across the various sports?

The USOPC where I work is very open to it and amenable to it and the fact that my position was created and that other positions in the data and analytics space, both on the performance and the health and wellness side, have been an area where we have staffed up. That tells me that the USOPC believes in data and analytics as a learning tool and setting our athletes up for success. Across the movement, I would say part of my role is also to build awareness and build comfort with data analytics and technology. I think across our Olympic and Paralympic movement in the US, but like anything, there’s variability. Probably more people are open to it than not.

I know this is probably just a guess. There are roughly 200 countries in the world. How many countries that are participating in our Olympics when they come together do you think are using these techniques to a meaningful degree?

From an intelligence standpoint, we should probably understand better than we do. If I had to throw out a number, I would say out of those 250, maybe even 100. If I remember correctly, I think over 100 different countries won medals at the Summer Olympics. If you have an athlete with immense talent, then that can trump everything. I would guess that a vast majority of them are using analytics, at least to some extent, data and analytics doesn’t have to be big data. It can be relatively small and still be useful.

Data and analytics doesn't have to be big data. It can be relatively small and still be useful. Click To Tweet

I think the best coaches are using data to some extent in how they coach their athletes, design their training, monitor their health and wellness, and help keep them injury-free. Almost every coach does this naturally and internally and now I think we are just getting a little bit more explicit and maybe a little bit more rigorous about how we do that and also the fact that we have specialists in that space now who are providing that service.

In some sports and I like baseball and basketball, there’s been a little bit of criticism of these techniques changing the game. Baseball with the players swinging for the fences ends up meaning more strikeouts and fewer balls in play. In basketball, the death of the mid-range jumper. Have you seen in the sports that you are working on similar controversies develop about how data and analytics are changing the nature of the sport?

I’m thinking about figure skating again because that’s always my reference point. This is what I know best. I would say there’s a lot of discussion in figure skating about the impact on the sport of changing the judging system, which predated the use of data and analytics. Moving to a judging system that’s less holistic and more granular has led to a proliferation of data that we didn’t previously have and has opened us to analytics.

I would say, in that case, the change in the judging system, but that’s what’s changed the sport more than using data and analytics. Having a more quantitative system has led to maybe a change in the balance between the focus on individual elements and the program as a whole. Some people feel that’s cost the sports and fans. I wouldn’t argue against that fact. I don’t know if people have data to prove it, but we would love to increase our fan engagement and our viewership in figure skating. I would say probably an effort to be more transparent and objective in judging has led to changes in the sport, which are both positive and negative.

In the case of figure skating, there’s been a belief that new approaches to judging have emphasized athleticism, maybe more than grace. How far along do you think we are on this data journey for the leading athletes? Are we hitting a point where we are tapping out the potential of data and analytics or are we still closer to the beginning?

With the Olympic and Paralympic sports, I think in a lot of cases, we are closer to the beginning because there’s still more we can do in the performance space. One of the things about Olympic and Paralympic sports is we don’t have a third-party data provider giving us a lot of data on a silver platter like the NBA. All the teams get incredibly detailed data from Second Spectrum. Baseball and tennis using Hawk-Eye. In the Olympic and Paralympic sports, we don’t have that.

A lot of our work right now is more in the data collection space than in the analytics space. It’s like how do we even get the data that would enable us to answer the questions that would have an impact on athlete performance? That’s something that I’m very passionate about. A lot of that comes down to, in some fashion, analyzing video.

Right now, it’s a very manual process and very painstaking. A lot of countries in a lot of sports have a lot of effort going in. How do you automate that work? It’s surprisingly difficult to automate that and so that’s something that we are working on now because I think that’s essential to progress. I would say in the health and wellness space, a lot of athletes are now wearing devices such as Apple Watch, garments, rings, or WHOOP devices.

There are a lot of players in this space now. They are all competing with each other for the athletes’ dollar or even the recreational athletes’ dollar. I think they all have some algorithm that they are using to provide some output metrics, but it’s a black box that we can’t see into for the most part. There’s still a lot of work going on for individual athletes, coaches, and sports as far as figuring out, “Can I trust this metric? What is it telling me about my health and wellness that’s relevant to my sport?” There’s still a lot of understanding that’s going on now of that.

There are still a lot of people figuring out how I collect that data and how I use it. We are finding that any data collection with elite athletes needs to be easy, convenient, and essentially seamless and invisible. Finding ways to do that with these wearables that offer that. It means that we need to understand the output metrics and what they can tell us that will benefit the athletes. That’s something that we are still working through.

Any data collection with elite athletes needs to be easy, convenient, and essentially seamless and invisible. Click To Tweet

Are data analytics being used to look at diet, recovery time, workout frequency, duration, and all those kinds of things? The kinds of things an athlete in and of themselves probably wouldn’t be able to get off their wearable.

Yes, yes, and yes. The ones that you named are important ones and those are also ones that are harder to capture with a wearable. As I mentioned before, it’s important that we take as little of the athlete’s time and effort as possible. We are still working through how to collect data on some of the factors you mention without burdening the athlete.

Like them writing down everything they eat and it gets plugged into a computer is a pretty arduous thing to ask an elite athlete to do.

If some want to do that, if that’s part of their process, then great. I have seen some athletes do that because they feel it helps them. It helps them stay on track and accountable. Yes, it’s not something that we can ask of every athlete.

In your work with the individual governing bodies, how deeply into the sports or these techniques now going?

We are still at the point where we are doing at least as much effort into data collection as we are into the analysis. I think there’s a lot of variability in the sports and where they are. Just like with baseball and the other pro sports baseball sprinted out to the lead in terms of analytics because there was interest and also it was a much more simple matchup between pitcher and catcher. It was a simpler problem than the problem of trying to look at the impact of 22 football players on a result of a single play. Out of the work that’s going on is figuring out what defines performance.

We have a lot of data on what I call “metadata” on a result. What was the date? What was the competition? Who won? Who lost? What was the placement? Some expression of the result, whether it’s a time, distance, or score. The hard work is going into how we collect data about performance that’s more meaningful and can be utilized to impact training and strategy.

Different sports are in different places. I will take swimming for example. Swimming, it’s easy to quantify the results. You have time. Understanding things like stroke count and how well an athlete gets off the blocks. How well does the athlete execute their flip turns and how do they pace themselves through the race? There are a lot of things to understand. How do the stroke mechanics change through a race? There’s a lot of detail around the full race that requires a lot of effort to break down. There’s no easy way to measure any of that. It’s a lot of work to figure out and collect that data that can then be analyzed and then be used to create insights that would affect strategy and training.

You can send them, in that case, into the water with a body suit that has all the sensors built into it in a practice setting so you can do all of the biomechanical analysis you mentioned. You are not going to get that from a race that forces you back into what you described as a pretty manual process to code video into something that can then be used for analysis.

I know figure skating is probably your reference point. Do you see parents going over the top to get their kids access to these kinds of analytic techniques before they have proven themselves to be on a trajectory? At what point does it make sense to start using these without being over the top?

I probably won’t provide a great answer to this. I would say I haven’t had many or maybe even any conversations with parents about analytics and wearables. I don’t think the parents, in most cases, are not thinking that far into the development of their child/athlete. What I see is sports have become a year-round endeavor. Youth sports have become a huge economic activity in the United States. Almost every sport started off as seasonal. At some point, a child and a family are forced to decide, “Am I going to remain a recreational athlete and do this seasonally or am I going to commit to this year-round?”

Almost every sport has evolved to the point where you can play it or practice it year-round, and if you want to be among the best, you have to do that. That’s where I see the big change right now, making it a year-round endeavor and trying to get to the best coaching. If it’s a team sport, how do you get onto the best team? I see a lot more focus on that level than on the utilization of data and analytics. I wouldn’t be surprised if 1) I’m naive about that, and 2) That word not be increasing in the future as parents try harder to make their children into elite athletes.

CSCL 52 | Olympic Sports Data

Elliot Schwartz: Almost every sport has evolved to the point where you can play it or practice it year-round, and if you want to be among the best, you have to do that.


You get parents who have used those things in their own athletic endeavors and given their kids access to the same thing.

I think we see that in a lot of sports now where you have elite athletes who are the children of elite athletes.

Switching gears, can you talk a little bit about the work that you have done with US figure skating? You referred to it during the discussion. Can you describe a range of things that you are doing as an official consulting on data and analytics and other things?

First, I will explain the different roles that I play in US figure skating. Some of them are related to my now-paid job. Some of them are things that I do as a volunteer. I started with US figure skating as a pure volunteer. All of the officials in US figure skating, the people that officiate at the competition, the judges, referees, accountants, music people, and announcers. All of those people are volunteers. They are not paid for their time.

We are reimbursed for travel, hotel, food at competitions, or those things that are provided to us. Unlike some of the typical sports, baseball, basketball, and football, you are not paid for judging an event or a competition. That’s how I started in figure skating and that’s something I continue to do as an official.

The other thing is that in US figure skating, a lot of the work that happens in US figure skating is done by committees. It might be an official’s training committee or it might be an official’s performance committee. There are committees in the organization for parents and coaches and for the development of different disciplines like singles, pairs, ice dance, and synchronized skating.

Those committees are staffed by officials, former athletes, coaches, and current athletes and that’s all done on a volunteer basis. No one’s paid for that work. That’s something I have done a lot of committee work on in the past. Now I’m on the board of my skating club, and that, again, is a volunteer endeavor.

As far as my analytics work goes, first, I started doing it for free for people when our judging system changed in the mid-2000s and people had questions that could be answered with data, such as a lot of it was around risk and reward choices. All of these judging systems in these acrobatic sports are what I call risk and reward systems where you get more points for incurring more risk or higher difficulty of the tricks you are doing, but then you are also assessed on the quality of your execution, and it’s harder to execute something well if it’s more difficult. It’s risk and reward.

Coaches and athletes back in the 2000s were asking me questions about the selection of elements that would optimize their scores and also impact their training time. Is it worth it to spend the time in training to acquire a difficult element, or is that time better spent on increasing the quality of a less difficult element? Things like that. I was doing that.

At one point, with the new judging system and the more overt assessment of difficulty. Some of the judges felt like the judges used to take care of everything. Assessing difficulty, quality, artistry, and expressing it in a holistic mark. Now, that work is divided up among a technical panel and the judges.

The judges at the beginning of the implementation of this new judging system, we are believing that there are marks no longer mattered and that the technical panel was just determining the competition. Some of the work that I did was assessing how true that hypothesis was. After doing some work with coaches, one of the coaches whom I assisted, his name is Bobby Martin.

He coached a pair in 2014 and connected me with the senior high-performance director of US figure skating, and I started working for him. Initially, that work was paid on a contract basis. When I joined the USOPC in my full-time role, we rolled that work into my work at the USOPC because the USOPC was funding that work anyway, and USOPC employees are not allowed to also be paid by an NGB. In order to continue that work, we rolled it into my role, which spans more sports now.

Back to the hypothesis. Was it true that the technical panel influenced the outcome more than intended?

I’m not going to give a definitive answer here, but I will say a few things. In singles, especially on the men’s side, but even to an extent on the women’s side, the increase in the number of quadruple jumps has caused an imbalance between the points that can be earned from technical elements versus what can be earned from program components or artistry.

CSCL 52 | Olympic Sports Data

Elliot Schwartz: The increase in the number of quadruple jumps has caused an imbalance between the points that can be earned from technical elements versus what can be earned from program components or artistry.


That’s something that continues to be debated as is that something that needs to be rebalanced as far as what decides the results. It depends on the event and how close the athletes are in terms of performance, but there are some competitions where the difficulty carries the day. There are other cases, especially true in ice dance, pairs, and synchronized skating, where a good portion of the top skaters or teams are able to achieve the same difficulty. In those cases, the result is entirely determined by ultimately how the skaters execute and perform.

In terms of the officials, it comes down to the judge’s marks. The technical panel’s impact on differentiating the athletes or the teams in some cases becomes almost negligible because the teams are achieving the same difficulty. It all comes down to the quality of execution of the elements and the artistry.

I appreciate that it’s got to be a constant struggle for the governing bodies in these sports as the athletes get stronger and more capable, in this case, of landing those quadruple jumps that a generation ago wouldn’t have been possible. It creates an imbalance, to your point, in terms of the people who are just accumulating points for their athleticism to others who can’t do that and have to rely more on their artistry. I know your job as a judge is to be impartial, but how often does it happen where you see an athlete and you just go, “Wow.”

For me, it happens a lot because I was never an elite athlete or anything close, so I marveled at all the athletes. I would say in terms of like a specific wow trained to take that something makes us say, “Wow.” We very quickly ask ourselves, “Why are we saying, ‘Wow?’” What is it about what the athlete is doing that is a wow and how do we translate that into our judging criteria to reward it appropriately? We are trained to assess quickly if we find ourselves saying, “Wow,” or even enjoying a performance. We have to break it down into what it is we are enjoying and make sure that we are rewarding it in the right way in the right place.

CSCL 52 | Olympic Sports Data

Elliot Schwartz: We have to break it down into what it is we are enjoying and make sure that we are rewarding it in the right way in the right place.


I would imagine that if those things start happening enough that it ends up driving further changes in the scoring systems.

There was a scoring system that changed in figure skating after 2018. Ever since this judging system was created in the mid-2000s, the range of marks that could be given for the execution of elements was -3, -2, -1, 0, 1, 2, and 3. As for what the athletes were achieving, there was a feeling that a seven-point scale was not enough to differentiate what the athletes were capable of. We now have a system that’s -5 to +5 and an 11-point scale that we can use to evaluate how well each element was executed. A lot of the athletes do turn it up to eleven. They are amazing.

Let’s switch gears again. Talk a little bit about the work you were doing at Procter & Gamble. You were there for a decent amount of time. Give us a sense of the mix of roles you had over the years.

I started working at the Gillette company in 1998. At that time, Gillette had a pen business. They owned Paper Mate, Waterman, and Parker pens. I joined a research team that was responsible for developing new writing implements. That was fascinating for me because I have always been a big fan of pens and writing instruments. Being able to apply technology to what the experience of the hand feels in writing. What was the right ink viscosity? All these things about writing that most people would never worry about.

Also, even just consumer understanding of how pens are used because you have some pens that we used to refer to as jewelry, like the pen that an executive would put in her or his pocket. I would essentially have the same writing implement inside of it like carrying the ink as something that you can get for a couple of bucks off the shelf. It was all about the packaging of that and so that was something that we looked at as well. It was just to the packaging of the writing implement and also which consumer segments cared about that stuff.

I worked on pens for a while. Just about a year and a half after I joined the company, Gillette sold those businesses. I then started working in an oral care group and the Gillette owned the Oral-B business. I started working in a group similar to what I did with the pens, was all about understanding the cleaning of teeth and the health of teeth.

We were looking at toothbrushes, which types of bristle designs would penetrate between the teeth effectively and create the best cleaning experience. Also, were there new types of oral care that we could bring, that we could develop that there’d be a market for that we could bring to consumers at home?

I did that for a while, for a few years. All the while, I was utilizing modeling and simulation where I could based on my experience. At that point, I was looking for an opportunity to use modeling and simulation full-time. The blades and razors engineering department decided to create a full-time position. I applied for that and had the good fortune to be able to move into that position and launch that effort in that organization.

That led to what was the bulk of my career, which was similar to what I’m doing now. Talking to the different design groups about a problem they wanted to solve, and then how could I bring a modeling and simulation solution to that? What ended up happening was I would identify a tool and I would work with the designers. We’d come up with a work process that was effective and did help us move faster and innovate.

There’d be another question that I was asked to answer that required a different modeling and simulation technology. In most cases, I would hire somebody to come in and work with the technology that I had developed and proven out and then move on to exploring the next one, and I did that over a number of years.

I did that working with blades and razors. I got to the point where we were doing a variety of modeling and simulation techniques to look at product, process and equipment design. Also, just manufacturing flows like how many of each type of component we need to have between each type of machine to keep things going, even when a machine went down for a repair or down in an unplanned way.

During that time, we were acquired by Procter & Gamble in 2005. That was wonderful for me in the modeling and simulation space because Procter & Gamble had a well-developed effort in that space. I am immediately able to have a lot more colleagues who are experts in various modeling and simulation technologies. It was a lot of access to people and tools that I hadn’t enjoyed previously. That was great.

Eventually, I started as a solo or an individual contributor and then I grew my team. I became more of a people manager and did less of the technical work myself. Toward the end of my career at Procter & Gamble, I was asked to lead an effort in bringing modeling to consumer research. I led a group that did modeling and also operated the tests of having people try our razors before they hit the market or even while they were in the market to see what experience they were having with the product and what our opportunities were to innovate or improve the experience.

Your degree work at MIT and your early career work focused on fluid mechanics. Your PhD thesis I read included research that has been performed in the space shuttle Columbia, which is pretty cool. How did that develop as your initial professional interest?

It was just a convergence of things that I liked and things that were interesting to me.

When I started college, my intent was to major in Math and that’s what I did. I took a Computer Science course my freshman year because MIT had just created a Math with Computer Science major. That first Computer Science class caused me to march myself into the office and change my major back to Math because computer science was not my path.

I also was enjoying my Physics and Chemistry classes at MIT. I saw other students in my Math classes who were far more talented than I was, and I thought, “I need to do something else here too.” Since I liked Chemistry and Physics, I thought about the different engineering majors. Thankfully at MIT, it’s very easy to move among different departments in different schools. I was fortunate that I was able to take classes in Material Science and Engineering. By my process of elimination, that’s the one I converged on. It uses chemistry and physics. It was still a bit of a newer discipline, and I had heard the professors were good, so I decided to take that as my path.

In my junior year, I took a class on what they call transport phenomena which are fluid mechanics, heat transfer, and mass transfer and that’s a very mathematical discipline. That was the right combination of math and science, so I became interested in that particular aspect of Material Science and Engineering.

I approached the professor, and again, this is thanks to MIT. He has a very active undergraduate research program. I approached the professor for that class and asked him if there was an opportunity to work on a project with him. Fortunately, at that time, he had been thinking that there was this opportunity with this multi-user facility on an upcoming space shuttle mission.

It was an electromagnetic levitation facility, and my professor had an idea, experiments that could be performed on it to measure the properties of what are called metastable materials in a non-contact way. They were all based on very old hydrodynamic principles. He asked me to research those and assess the feasibility of the project and the approach. I did that as an undergraduate. More fortune. During my senior year, my professor received a grant or proposal was granted to work on that NASA program, and he offered that to me as a project. That became my project for graduate school and my doctoral thesis.

Data and analytics have been a pretty consistent theme for you professionally. Do you feel you have been data-driven or more opportunistic in your career choices?

I would say probably more opportunistic and experiential. Data can be qualitative as well as quantitative. There was an aspect of the qualitative data in assessing my career path, my experiences, and my choices. At times, I would employ some data techniques, like when I’m making decisions.

I like to use an approach I learned at Procter & Gamble called Best Value Options Analysis. This is a fancy term for just figuring out the factors in a decision that is important to you. How would you weight for each one, and then how would you rate each one? You can do a little math to figure out how close is this to the ideal and how these different options compare to each other.

Putting your data analytics consultant hat on. Are there ways that you think someone could apply those techniques in navigating their career journey?

Back to my career journey and with a lot of help from Beth Kennedy. Beth was very big on understanding yourself. There are various tools out there or maybe more rooted in science than others, and some are more hotly debated than others. I’m thinking about things like Myers-Briggs, DiSC, or StrengthsFinder. I think they are all helpful in helping you to understand yourself and what your natural skills and preferences are. I think taking some of those assessments are ways to use data because they have algorithms behind them. You are responding to questions.

You may not be doing the analysis yourself, but someone’s come up with an algorithm to help identify you and your tendencies or preferences. That’s something that I have done with Beth that’s always been helpful. I would say it’s something that I think can be valuable to everyone but as they go forward in their careers. Was it Socrates or Plato who said, “Know thyself?” I think that’s important to a successful and enjoyable life and career.

I don’t think we are at the point where you could use a Monte Carlo simulation to figure out whether you are going to be successful at the next job you are considering.

Maybe not Monte Carlo and then I would also say far as maybe more quantitative data and analytics. You could analyze things about a company, such as its financial performance and turnover and other factors that may be important to you in a decision you are making, where they fit in their industry, and what their prospects are. I think the most important thing is to know yourself.

Apart from knowing yourself, the last question. What are the 2 or 3 things you feel you have learned over your career that would want to pass on to our audience?

The knowing yourself part and specifically about the knowing yourself part, knowing your strengths and appreciating them. I would say in my career, progression has been somewhat opportunistic. It’s also been driven by trying to move into roles where I would have an opportunity to leverage my strengths to a greater extent than I was in the moment and also to work to do work in areas that were of interest to me.

I would say knowing and appreciating and leveraging your strengths is number one. When I discovered Marcus Buckingham, I think that was probably with Beth’s help as well. That changed how I thought about myself and my career. He’s the one that did a lot of the research to create the initial versions of the Strengths Finder, which I think is an amazingly useful tool.

Know, appreciate, and leverage your strengths. Click To Tweet

I would say know your strengths. I would also say to know your values and what’s important to you, which can change over your life and career. I can guarantee you that it will. Knowing what’s important to you at a given time is important. I will give you an example. When I left Procter & Gamble and decided to try to work in sports analytics, I was fortunate to make it to the final round after a number of rounds of projects, interviews, and applications. I made it to the final round of interviews for an analyst role at NFL, a pro football team.

I was invited to the teams’ facility and it was fascinating. I’m so glad I got to see that world. I was 48 or maybe even closer to 50 at the time. I looked at the role. I looked at what life was for the people working for that team, and I said, “If this job existed when I was 22, 23, or 25, this is the job I would have wanted.”

You are basically on call 24 hours a day for the entire year, except for the three weeks between optional training camp and mandatory training camp in the summer. At 48 or 50, that was not the right work for me. That’s just a simple example for myself. Know what’s important to you in a given role. It is how much you earn. The most important is what you learn the most important.

How much time do you need for yourself, your family, friends, or loved ones? How much autonomy do you want? These are all things to be understood. Are you looking to learn something new? Those are all important. I would also say being in touch with your manager is important.

Understanding what your manager is because your manager will have the biggest say in your progression in your career. You have got to meet that manager’s needs and so understand what’s important to them. The best manager is someone that will serve as a partner to you, will guide you, and challenge you but will also advocate for you.

The best manager is someone that will serve as a partner to you, will guide and challenge you, and will also advocate for you. Click To Tweet

It’s important to develop that relationship. I would say as part of the relationship with the manager, if I gave some advice to my former self, I think I would say if you find yourself spending a significant period of time with a manager that’s not providing those things to you, move on. Some people will say that an experience with a bad manager, or maybe more kindly, a bad manager for you, is a good learning experience and helps you build skills.

I would argue that it’s not worth your time. Move on to someone that’s going to value you. Life is too short to deal with someone that’s going to be frustrating and that’s who’s not going to support you. Sometimes you can have success by waiting for that manager to leave. If you feel like you are in the right role and the right company can choose to wait it out and hope that the manager leaves. I have had that happen a couple of times. If you find yourself in a situation that’s not good and it’s largely because of the manager, move on either within the company or out of it.

Thank you. This has been interesting. I am deeper into the world of Olympic sports analytics than I probably ever have, but I learned a lot. I appreciate you taking the time.

Thank you so much for the opportunity. I love your show. I have been listening to and learning from many of them.

I appreciate that. Thanks again, Elliot, and have a great day.

Thank you. You, too.

I’d like to thank Elliot for joining me now to discuss his work with the US Olympic and Paralympic committee, his broader data analytics-focused career, and what is learned a long away. If you are ready to take control of your career, visit If you’d like more regular career insights, you can become a PathWise member. It’s free. You can also sign up on the website for the PathWise newsletter and follow us on LinkedIn, Twitter, and Facebook. Thanks.


Important Links


About Elliot Schwartz

CSCL 52 | Olympic Sports DataElliot Schwartz is a data analytics consultant for the US Olympic and Paralympic Committee. In this capacity, he helps apply data and analytics to support athlete performance, health, and wellness.

Elliot has done data analytics consulting for clients including US Figure Skating and a leading orthopedics research center. He has been an official for US Figure Skating for more than 20 years. He was with Procter & Gamble for 17 years in a variety of data, research, and engineering-focused roles. Earlier career stops included Speedline Technologies, Alcoa, and Los Alamos National Research Laboratory.

Elliot earned both his Bachelor’s Degree in Applied Mathematics and Materials Science and Engineering and his PhD in Materials Engineering, both from MIT. He lives in the state of Maine.

Share with friends

©2023 PathWise. All Rights Reserved