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Coach Jason Koop covers training, nutrition and recent happenings in the ultramarathon world.
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Insights from Ultramarathon Pacing Strategies with Baptiste Morale | KoopCast #243
Baptiste Morel-Prieur is a author on a recent paper titled Submaximal force-velocity relationships during mountain ultramarathon: Data from the field
Research from the 2023 UTMB offers ground-breaking insights into trail running performance by analyzing how runners handle different gradients throughout a race. Batiste Moral shares his data-driven approach that identifies specific areas where athletes can improve from year to year.
• Analyzing Strava data from 600 UTMB participants reveals distinct "force" (uphill ability) and "velocity" (flat ground speed) components of performance
• These components have no correlation with each other, meaning they represent different physiological abilities requiring targeted training
• Case study of Jim Walmsley shows 76% of his time difference behind Kilian Jornet in 2022 occurred on gradients steeper than 20%
• After focused training in the Alps, Walmsley specifically improved his steep climbing ability before winning UTMB 2023
• Durability analysis suggests flat terrain speed deteriorates more than uphill capability during ultras, indicating flat-ground training after fatigue might be more valuable
• Western States vs. UTMB comparison shows dramatically different terrain profiles despite both being 100-mile races<br>• Simple heart rate-based test can help identify your personal gradient-specific weaknesses
Papers discussed-Decoding Ultramarathon: Muscle Damage as the Main Impediment to Performance
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Trail and ultra runners. What is going on? Welcome to another episode of the coop cast. As always, I am your host, coach Jason coop, and on this podcast we oftentimes glean insight from the lab and then we use it in the field of competition. Other times, we look at what is actually going on in the field of competition and we glean insights from that on how to train and race smarter. And today's guest was an author on a paper that did just that.
Speaker 1:Welcome to the podcast today for the first time, batiste Moral, who took publicly available Strava data from the 2023 UTMB to determine what pacing strategies worked better, and he also used those same techniques to determine how people in, or how the elite field specifically, evolve over time. And the results of all of this analysis is something that I feel we can take to training and racing, and it's something that I'm taking into practice with many of the athletes that I work with currently. I'm going to let Batiste explain exactly what he did and what the results of his research actually are and how we can apply it. So with that out of the way, I'm getting right out of the way. Here's my conversation with Batiste Moral.
Speaker 1:Batiste, welcome to the podcast today. I appreciate you coming on. I'm really excited to talk about some specific pieces of research that you've kind of recently published. But before we get into it too much, can you give the listeners a little bit more of a background of who you are, how you got involved in trail running and looking at this from a scientific perspective in the first place?
Speaker 2:and looking at this from a scientific perspective in the first place. Yeah, thank you for the invitation. First, I'm a researcher right now, so it's been 10 years. I defended my PhD and I started working on performance sports performance, but more on rugby players, but we will see later. There is some comparison that we can do with trail runners. So I work on physiology and biomechanics fields and I do research on sport performance, but also on health and even on animals in ecology. That's another question, but you will see there is some crossroads between all of these topics. So that's my background.
Speaker 1:Perfect.
Speaker 1:So, the part of your research that really piqued my interest really gets to the core and the soul of what athletes go through every year. But most ultramarathon athletes, they're experienced athletes. They're not novel or new to the scene. They have marathon PRs and they've done ultramarathon races before and things like that. And the coaches who are listening to the podcast can resonate with this run of show.
Speaker 1:At the end of every year and at the beginning of the next season they're going through their Rolodex of athletes, they're doing their consults and the question that comes up how am I going to get better? Where am I going to get better? What am I going to do to take my performance from X to Y based on the previous year? It's something that's kind of like top of mind for everybody and really, when you think about it, it's kind of the root of all interventions whether it's interval intervention or a nutrition intervention, like a supplement or anything that you can kind of like throw at an athlete is all kind of aimed to improve them specifically from one year to the next. And a lot of your and a lot of your research has looked at that specifically, like where have athletes actually improved? And so I want you to kind of like take it from there with this. Maybe we can look at how certain athletes have improved from year to year and how we can actually take this really broad brushstroke of looking at a race and trying to dissect it in terms of where can those athletes actually improve? That's a big question.
Speaker 2:that's a big question because it's not easy with so much performance factors, especially in trail running and ultra trail running even more probably, and there is multiple way to to approach and to address this question, and I tried to do some new way to analyze the athlete performance based on the data that we. You know, there is a lot of big data coming from the sports performance and this is something that we can go deeper on it to see how were the performance on one year and how it can evolve, and so you can have like objective metrics like that help you to choose, to look at if the intervention and the training, the thing that you change, maybe the nutrition and all we mentioned how it effectively and change my profile and so making me more performant.
Speaker 1:And the way that you kind of like looked at this was looking at race profiles, right, and specifically UTMB race profiles, and we can take a couple of high level athletes, but let's take it at like kind of like a population level first. Right, you wanted to look at UTMB from an entire race perspective. What is the entire you know, the entirety of the race actually doing? What did you glean from that? First off, what are you specifically looking at? We're going to have to get into force velocity relationships and some of the things specifically that you're looking at and define those for the users, but take it from the highest level possible. How are you trying to dissect this problem of where can athletes actually get better?
Speaker 2:yeah, actually the first thing I did was in 2023, at the end of August, just before the UTMB this year. And that was for fun, because some of my colleagues just asked me who will won the UTMB this year and I have no idea. But I say, okay, we'll take a look at the data and see if there is some piece of information in there. So I look at the race, I analyze the race, coming from Kilian John that won the 2022 edition and I look at one of the guys who will probably win this year that was Jim Wemsley in 2023. So I look at the race in 2022 and say, okay, let's dissect the race and we will cut it into segments, into portion, every 100 meters of this race. And I wanted to compare their profile on each 100 meters of the race to see who is the best on which segment.
Speaker 2:And there was a result really interesting because you know, there is flat portion, there is uphill with more or less steep gradient, really steep gradient. And the thing that was really interesting, that was that if you look at the race and all the segments when it was flat or low gradient, or downwind or uphill, there is not that much differences between the race of kian and jim. But when you compare them, only looking at the steep gradient let's say above 20% gradient, there was a big difference between Kylian and Jim this year. Actually, jim lost. Almost all of the difference on the finish line was because of the steep gradient. So that was the first approach to say, okay, he seems to have kind of a weakness on this specific part of the race, so that's only one way to approach it because the determinants of the performance are complex. But there was like a first step. Okay, there is something different between these two top elite athletes on this specific portion and to get into that specifically 76%.
Speaker 1:So three quarters of the time difference was lost on sections above a 20% grade. That's what your analysis found correct, yeah yeah, that's correct.
Speaker 2:So more than one hour was only on those portions, but they only represent less than 10% of the whole race, so most of the difference was done on very specific portion of the race.
Speaker 1:Okay. So that's kind of like we're going to take this like Jim versus Killian and now we're going to move it into like Jim versus Jim, because I do think that this sets this up for the population level thing. A lot of people listening will say, okay, yeah, big deal, killian's like pushing it harder on the steepest climbs, because that story is very good. But there's another analysis that you looked at which is gym versus gym. So gym pre-winning, utmb, gym training in the Alps for a couple of years very famous story here in the US. Some you know an American moves over here specifically to train for here. I'm now located in Chimney, france. So for the listeners out there who are wondering why I'm using that vocabulary, it's because of that Comes over to the Alps specifically to train and win UTMB and ends up doing it. Do you analyze Jim's performances before that transition and then after that transition? What did that actually reveal?
Speaker 2:Yeah, actually that was the purpose of this analysis, because everybody knew that he came to the ops probably trying to win the utmb on the next year and actually he did a 100 race in april this year. It was in croatia, if I remember well. So so I we found the data on. We found the data of the race and analyze it to see if there is a changing in the profile of gym, especially in this specific steep slopes. So we did exactly the same analysis, comparing the profile of gym in the utmb 2022 and the profile of the istria, the 100 istria race in croatia in 2023.
Speaker 2:And actually we saw a big improvement on the ability to maintain a good speed in the steep slopes, in the steepest slopes. That's why we thought that probably the training he did this year in the Alps helped him to improve in this specific part, the part that was a weakness for the last, the year previous, the previous year. So he was better on this specific slopes and he was as good as before on all the other segment of the race. So that's why we, at this time, I thought that he was probably able to be one of the top athletes and probably winning the race because of this analysis. So it's always a very challenging race and with a lot of random aspects that we can't control. But they're training hello him to be better on this specific part of his profile, so probably better at the utmb next year.
Speaker 1:Well, okay, so now we're going to get it into the general population right With that as a little bit of a back, with that as a little bit of a backdrop. That happens specifically, most likely because of an athlete's environment changes. They change their environment. They train more in steeper types of train. It makes all the sense of the world with that. That's where they are going to improve in the most. But let's back this, let's broaden this out a little bit, because we're talking about two very good athletes that are training for a very specific purpose and, as you mentioned, they have narrow margins to improve upon. And usually those narrow margins get even narrower because it's a very specific element that they're improving with.
Speaker 1:I know I go through this with my elite athletes every single year when are you going to improve? And it's usually not a general fitness proposition. We try to maintain that as much as possible. Usually we're trying to improve a specific component of their fitness, maybe a specific component of the entire of the preparation arc where we screwed up one phase or something like that. Maybe it's something with strength training. Maybe it's something with strength training, maybe it's something with nutrition, but it usually is because they are so good it's sliced out and it's focused on in a very narrow perspective.
Speaker 1:You contrast that with your general pop athlete, somebody who's only been training for three or four years. They kind of improve across the board with just generalize training. So let's take that bigger picture approach. You were part of a study that looked at 600 athletes. Use the Strava data and it can generalize athletes into a few different like profiles. So first off go over like what the profile is that you're actually looking at and then what the actual research found, and then we'll get into some practical takeaways from that.
Speaker 2:Yeah, actually, at the beginning it was an analysis of just two top elite athletes for fun, with my team and we did a meeting at the end of August and say, oh, that's very interesting, we probably need to go to see all of the other participants to see what we can identify in terms of profile.
Speaker 2:So, to improve, so I bring my team at home and we open all of our computers so to bring back all the data and say, okay, now we have the database that we can analyze to see how we can describe the profile of all the athletes during this specific race of the under edition of 2023 of the UTMB. So the idea was to do the exact same thing that for the NMGM look at every 100 meters segment of the race and to see at which speed they were able to run for depending on the gradients. That was the idea. We all know that there is a grade adjusted pace. You can find that on your watch, on your Strava or Garmin account. But most of the time this grade-adjusted pace is just computed on the mean of a population. So it gives you an idea of how you can translate a speed when you are going up compared to the flat.
Speaker 1:But actually what we observed was that there were really big differences on how an athlete will run, depending on his own profile okay, before we go any further, I want to take some time and explain what ngp and gap actually are, because both of these terms are kind of an alphabet soup, so to speak, and they try to do the same thing although they get about it at different ways. So NGP or normalized graded paste, is a is something proprietary to training peaks and all it tries to do is it tries to make the pace that you are actually seeing on a run during a post hoc analysis when you're actually looking at the file and make an equivalent for that pace of what it would be on flat level ground. Now the two things that training peaks is adjusting for, our first off, the normalization piece of it. That's the first part of the term normalized graded pace and the second piece of it is the graded part of it. Now, most people will be familiar with the grading part of it. It just means running uphill is harder than running on the flats and running downhill is easier than running on the flats and running uphill.
Speaker 1:The second part of that, the normalization part it is a little bit trickier and honestly doesn't have that big of an influence on the number, but it normalizes the pace to what you could run if you ran the pace completely evenly. So for example, if you go out and run one mile in six minutes and you do every single lap of that mile at 90 seconds, so the first quarter is 90 seconds, at the halfway mark or at 800 meters you hit 130. At the 1200 meter mark you hit 430. And then at the end of the mile you hit six minutes, you've run that mile completely evenly. Your normalized graded pace would be six minutes per mile. Now if you decided to mismatch that pace, or you decided to run the first quarter in 60 seconds and the second quarter slower and the third quarter faster, the normalization component of that would be faster than six minute miles, because the physiological toll is harder than six minutes per mile.
Speaker 1:So all it's trying to do is it's trying to make a physiological equivalent of both the variation in pacing and the variation in grades and normalize it to flat level terrain.
Speaker 1:Strava's equivalent of this, which is gap or grade adjusted pace, only takes the grading component of it. So it looks at how fast you're running and what the grade you're running on and it tries to make a physiological equivalent for that for flat level terrain. Now, as Batiste mentioned, both of these are based off of research that is done on tons and tons of people and, as we know, certain people are more efficient or effective at running uphill and downhill and so their particular physiological cost might not be quite accurately reflected in these grade adjusted paces and normalized graded paces. And if we can look at that across a whole host of athletes we might be able to glean some insight. But, needless to say, with both of these, all of all that they are trying to do is it's trying to look at the variables around the actual pace that you ran uphill, downhill, the variation in those paces and training peaks case and saying what is the physiological equivalent, or, more accurately, the oxygen cost equivalent of if this athlete actually ran on flat level terrain, what would the pace actually be?
Speaker 2:I would say you all know people that are really good on flatland and on level running but when it's going up it's hard and you have guys that not that much fast on flatland but when you are going up they are really good. And that was this idea how we can model the profile of an athlete to determine his own grade, adjusted pace so we know he is he able to run with a lot of speed. This is the velocity part of the profile, and when you are going up you have to push more, produce more force so you can elevate your center of mass in a biomechanical perspective. So how force and velocity interact within a profile, can we characterize an athlete's speed on this profile? And that will probably open some new information on how we can train for the next year or for the next race.
Speaker 1:And so what were the different profiles that you found throughout all of the UTMB participants, and how did those profiles correlate to their end performance, so to speak? Was there a profile that was more advantageous for the race, least advantageous for the race, more advantageous for the second half of the race versus the first half of the race? What did the data mining actually glean out if we were to build this perfect athlete that we wanted to put on the start line for UTMB?
Speaker 2:For the comparison between the first half and last half of the race, we will talk about that later because it will imply durability aspects, so let's talk to that after. But for the force, velocity, how you're able to run fast when it's steep, with steep gradient, or how you're able to run fast when it's flat, if you look at all the participants, actually we had the race data coming from the winner of this edition and the race data coming from the last guy who crossed the finish line, so that's evident. But, yes, the best athletes are better on each portion of the race, right?
Speaker 2:they run faster on flat and faster when it, when you have, when they are going uphill. But but we ran a second analysis looking at people and runners with quite the same performance. So we cut all the population in 10 percentiles. So let's take the 10 best athletes of this race, or the athlete between the 40 and 50 percentile, so we can analyze a comparable performance. And in this specific analysis, the very interesting thing with that was that there was not optimal profile. You can do the exact same race time but with a very different profile, and probably the guy you would cross the finish line with him you never saw him because when you are behind and when it's going down you are below.
Speaker 1:Everybody's had that experience. I mean, that's like a really realistic thing. You can cross the finish line. You look to the person who finished in front of you, the person who finished behind you and two places behind you, and maybe you saw one of those people for a part of the race. Aside from that, they're like you said they're faster in different sections and slower on different sections, particularly races that have a lot of different features steep uphills gradual uphills, steep uphills, gradual uphills, steep downhills, gradual downhills, flats and things like that.
Speaker 2:Yeah, and I feel like this is something that everyone who did a trail, who ran a trail race, know that, because you know that you will be better on some portion, and then the guy that let you down you will come back, et cetera. But the thing interesting is that now we have a model so we can precisely evaluate this. And since we can evaluate this, we have indices indexed that can use for testing and for optimizing training or to open new idea of how I can train now so to be better. For example, if you compare your profile with the guy who finished the race at the same time of you, but if you see that for every flat portion he was better, you probably need to train more on these flat sections and it's probably this training that can help you to be better on the next race.
Speaker 1:Let's check in with Coach Adam really quickly on how we as coaches determine the strengths and weaknesses with athletes.
Speaker 3:So short answer. It's not always easy, but the first place I start is actually just asking the athlete, and you can kind of get this when you have a call after a race and they have some perspectives on what went well and what didn't. And I also want to combine that with the real, objective data that we're seeing in the race files. So a lot of times people might finish a race and say, man, I suck at climbing, and I always want to make sure that is that the sensation of sucking at climbing, or is it a potentially a reality? Because there's a lot of things that are simply hard running in the heat, things like that.
Speaker 3:I mean, it's easy to perceive a weakness when it's really just everyone kind of performs worse in those conditions. So we see if, potentially, there's a mismatch with your normalized graded pace in the climbs versus the descents in something that would be significant or material. Usually we probably would have seen that in training already, but hopefully we can see that is some sort of weakness. But it might be something that shows up only late in a race too. So that's something we want to look at.
Speaker 3:Another thing that we have that's a tool at some races is the kind of live tracking where it shows your position throughout the race and that can actually give you some good ideas of your strengths and weaknesses, based on where are you passing people and where are you being passed. I like to use that quite a bit, actually. So there's no perfect answer, and I think one of the weaknesses that we have right now is that only the more strong edge cases stand out, maybe the people on the edges of the bell curve. But with some more tools what was done in this study we might be able to use a bit more of a fine tooth comb.
Speaker 1:So that's what I wanted to kind of get into next before we get into the first half back half piece, and just as a little bit of a depth backdrop. The listeners who have been kind of constantly tuned into this podcast will realize that this concept of durability that is, a relatively newer phenomenon or concept that we've started to talk about in sports science and in performance has continued to come up in this podcast via a lot of different angles and sometimes ones that where I'm not really like seeking it out, it just happens to be part of the fabric of the dialogue. And so this podcast runs true to form, where we're going to talk a little bit about durability as well. But we're going to put it, we're going to put a pin in that.
Speaker 1:For just one second, the synopsis of how to look at this data can really be boiled down to how can one person get better knowing their profile from year to year? Not necessarily and I was kind of baiting you with the way that I phrased the question or like how to build the perfect athlete for the race, right? I don't think that's the value in knowing this information. It's taking Jason Koops' data from UTMB or from other races or whatever looking at it and then extracting something from that to train for the next year and the year after that. How might one actually do that? I mean, this is something that you and your team put together, but can we put this in more practical aspects for the coaches and the athletes that are out there that do look at their race files and do look at other people's race files a lot? How can we actually go about doing that?
Speaker 2:yeah, actually there is something interesting that we are doing also in my lab.
Speaker 2:This is an experiment and research that we're doing right now, but that had really practical application.
Speaker 2:The idea is that I told you that when you're going up, you are producing more force, and something that we did was to compare the force that you produce when you are going up by running or using sled training, like like in track and track and training resisted sprinting or resisted pushing right.
Speaker 2:Yeah, exactly, and what we saw was that actually this is almost the same biomechanical organization of your body when you are pulling a sled or when you are going up. So that's opened new opportunities to train, for example, if you have a profile that is not that good in the force portion. So when you're going up and let's say, maybe you are living in a city with not that much gradients, so you are not able to train in this kind of gradients you probably can find some alternative with this kind of training. So that's just an example. But to say, you can be better and be more endurance in the fourth part of your profile, not only by running uphill, but you can find other alternatives with strength training and, in this specific example, with sled training that can mimic a slope.
Speaker 1:And so what you're trying to do is, like you said, improve the force part of the equation.
Speaker 2:Ah, because the thing interesting was that there is no correlation between the velocity part and the force part. So, just to be clear, the velocity part is your when we are talking about train, is your ability to run fast on flat land, and the force part is are you able to run fast or to walk fast when you are going up? So this is how we can differentiate these two parts, and the interesting thing is that there is no correlation between these two parts of the profile. That's what we were talking about at the beginning of the talk. So if it's not the same ability, you can probably train that differently. And when I said you can improve the force part of your profile, you probably need to find a way to train your ability of your muscle to produce higher level of force for a longer time and it probably will improve better your profile on this part, but not necessarily changing the other part being the velocity part the.
Speaker 1:So the analogy that I've always used to to that where you're saying the force part is not related to the velocity part, or let me kind of colloquialize that a little bit the uphill performance is not related to the flat part, or let me kind of colloquialize that a little bit the uphill performance is not related to the flat level performance is that a fair colloquialization of what you're trying to describe from a, from a biomechanics standpoint?
Speaker 2:yeah, totally. Actually, we all. What we are doing is definitely not a revolution. You don't know that, that kind of aspect, because we saw that in training and we saw that when we're racing. But the thing interesting is that we can know model it, we can know evaluate it. So our objective informations so we can stand on to build the training of your athletes yeah.
Speaker 1:so the analogy that I've always used for that the uphill component and the flat component and I actually add two more components to that that we go completely off the rails, we can talk about those for a little bit is the downhill component and the walking component, and I always liken those to different sports. They're sure linked by the cardiopulmonary system and the musculoskeletal system to some certain degree and have a little maybe a little bit of Venn diagram overlap. Although you're describing the uphill component and the flat component as not being related, but I still think that from a training standpoint we do need to think about training all four of these. Just like a triathlete trains all three swim, bike run I think that analogy kind of plays through and the more statistically we look at race performance, the more that analogy starts to actually come. It actually comes to that realization that it's not just an analogy, it's actually what's going on.
Speaker 2:Yeah, but you agree that if you have a limited time to train, a limited volume, you cannot do all the training you want, because you can enrich, over reaching or over training or that especially for elite athletes. So, to be more precise, and if you more tailored the intervention and the training that you do because you know that you cannot do infinite training probably you should find the the best part of your profile to train. And just something that I wanted to say before. But it also depends on the race, because 100 miles race training, race can be really different and the profile can be really different, and not only on the total elevation, because it's something that is often looked at okay, there is, let's say, 10,000 meters of elevation gain in this race, but you can do 10 000 meter elevation gain in very different way correct and if you're going really with really steep slopes at more than 40 or 50 percent, or only a long portion with 15 percent slopes, that's not at all the same in terms of the physical ability that your trainer needs to have.
Speaker 1:Yeah. So I get this question actually a lot because there's this section of my book where we talk about how to tailor the uphill, downhill components of training to the race. And the way that I illustrated in the book was with the broadest brushstroke, and I'm going to do it in. I'm going to do it in us units as opposed to metric units, so we can create the. We can create the translation later. But if your race has 200 feet of elevation gain per mile, which would be approximately what the Western States 100 has, try to average that over the course of a week of training. Now to your point. You can come up with that a hundred different ways. You can do all of that elevation gain in one run up a 40% slope, or you can do it up 5% slope, 7% slope, 5%. There's a lot of different ways to do it. I guess is what I'm saying, and so I get a lot of questions on my book related to just that. How do I come up with that? So the first step is just hit the average. Second step is try to hit the grades that you're actually going to experience during the race.
Speaker 1:Interestingly enough, I watched a presentation that you gave. It was in French, but the English subtitles are apparently pretty good, according to my French speaking counterparts. And one of the really neat slides that you had during the course of that presentation compared UTMB and Western States in terms of the percentage of the distance that the runners would spend at each grade. And the point that you're trying to make is between zero and 15%. They're about equal, meaning runners are going to spend about the same amount of time running across those grades 15 to 30%.
Speaker 1:Utmb has over twice the amount of distance as compared to UTMB at that grade. And then over 30%, utmb has seven times the amount of distance at that grade compared to Western States, and there's hardly any part of the Western States and there's hardly any part of the Western States course. Having been on both of those courses, that is at that percent grade. But what that really illustrated to me is how two marquee 100-mile races, the marquee 100-mile races in the world, can be so dramatically different. And I look at it through the lens of they're dramatically different from a demands perspective, because the gradient that they're actually running on not just the total elevation gain, elevation loss, but the actual gradient that they're running on can be thought of as a specific demand of the race yeah, yeah, definitely.
Speaker 2:That's really important to understand that, because we often look at the race.
Speaker 2:Metrics are the mean big thing, but we we definitely need to go more into detail on how the race occurred really and what are the gradients that you will find in this race, because it's not the same to do one kilometer at 30 percent or two kilometers at 15. It's just not the same in terms of how your body will react. And maybe another thing interesting is that we're a lot talking about the steep gradient and the flat and the level running, but actually there is also a component for intermediate gradients. I mean that we found some profiles of runners that were able to run at the same speed in strip gradients, the same speed in flats land, but they were not able to run at the same speed for the intermediate gradients, especially in the area of the walk-run transition and when you have to walk at high speed or to run at low speed, some of the assets are not good and other ones are good, and that's another component of the profile that we analyze with the model we proposed.
Speaker 1:I have a colleague of mine, jackson Brill. Shout out to Jackson, who was actually an intern with us a long time ago. He was actually trying to work on this in Roger Cromwell's lab at the University of Colorado and find out where the right walk to run transition actually exists. And does it actually exist? And the answer is no, you've got to use your own intuition for it. It might be a conversation for another day, but I'll link that conversation up that I had with Jackson in the show notes for anybody that's interested in maybe trying to find it or trying to find a better way to determine when that walk to run transition for you should actually be.
Speaker 1:I want to spend a little bit more time on this Western state versus UTMB component of it.
Speaker 1:One of the things that that that illustration that I just mentioned the percentage of distance that the runners are spending at each kind of like gradient bin or gradient chunk being so different between those two races, also highlights to me how difficult it is to get both of those races right, even though they're both a hundred mile races, even though they're both long and you know duration, and it's very far out to the right on the power duration curve which we haven't really gotten into yet, and they seem like low intensity activities and things like that.
Speaker 1:You're running at a lower percentage of your VO2 max way lower than a marathon or anything like that. But because the courses are so different, it's hard to get both of those right in a short, in a really short timeframe, and we've seen that play out especially really short timeframe, and we've seen that play out especially in the elite field over the years, that it's hard to have an athlete do both of those. And I happen to have an athlete, Katie Scheid, who did both of those successfully one year. But it is exceedingly rare. And one of the reasons it's exceedingly rare is because we see this kind of market difference in the course profiles, which is kind of a no-duh moment. But your research is really bringing to light how different they can actually or how different the demands actually are. I guess is what I'm trying to say.
Speaker 2:Yeah, yeah, actually we can think that it's the same race, because just it's 100 miles right 160K. But if I say, if you take a 100-meter dash sprinter, you will never perform on marathon and the opposite, that's evident for every everybody. But actually that's the same for various different race of training race that do not require the same abilities and physical capabilities yet exactly okay, before we go on to this durability component, I kind of want to like rehash some of this from a practical standpoint.
Speaker 1:So the athletes out there and they look at they can look at this conceptually and say, ok, you know, I understand that if I looked at my performance on this race for a particular year and how I rack and stack compared to the people around me, I'm going to notice different strengths and weaknesses. And I want to focus on the weaknesses part generally in order to kind of extract more time out of it. But how, aside from the observation piece, people are going away from me on the climbs and I'm bringing them back on the flats, right? So the climbs are my weaknesses and the flats are my and the flats are my strengths.
Speaker 1:And then, taking that to an intervention standpoint, can an athlete at home statistically go through this? Are there tools or methods out there that are similar to what you actually went through to, where an athlete can like break down their own stuff, or a coach can actually break this down for an athlete? Because I've done the same thing with my athletes and it's, trust me, it's a painstaking process. We've like we generalize it into like a five minute summary, but maybe you can kind of take the athletes through what are the listeners, through what you would actually have to do in order to extract this from a statistical perspective, as you've done.
Speaker 2:I'm not sure I understood you. You mean more how everybody can do that kind of analysis at home.
Speaker 1:Yeah, exactly Like if I were to just take, you know, my race file from the. The Cocodona two fifties is just happening and I happened to do that race a couple of years ago, so it's at the top of my Cocodona two, 50 or whatever race. How can I take that information and say, okay, statistically speaking, this is where my strengths and weaknesses lie.
Speaker 2:Okay, actually, that's something that we try to develop. We have a patent on all the process that we developed and now we are currently discussing with some companies to try to implement this kind of analysis.
Speaker 1:Somebody's going to charge us for it. That's what you're telling me. There's going to be an app that I'm going to have to download in order to tell it. Okay, I get it.
Speaker 2:Yeah, and actually because we talked a lot about the race analysis. But actually we can do the same kind of thing than the record profile instead of the data of all your training and not only your race and to look how your profile evolved by taking all your records for all the gradients that you encountered in your training. But we also try to develop something that I think that you can try at home. It's a test that we try to design. So let's say you go to run outside and you will try to keep your heart rate at a given intensity that interests you for your training, let's say 100 and 60 RPM, because it's the intensity that interests you.
Speaker 2:And then you can go running in various gradients and look at the speed that you can maintain with this specific heart rate. So you have an idea of what is the velocity that you're able to run on this specific gradient. And you can do that for another gradient, etc. And with only a few segments of various gradients you only need a few minutes to stabilize the heart rate. So you can have the full relationship of what is the velocity that I can use for this given heart rate and in this given gradient and then you can compare to others, but you can also compare to yourself.
Speaker 2:Let's say, I need to train in the steep gradient. So you try to train, you start to train for a few weeks and you want to see okay, I am improving in this specific part. You can do again the same segment, going on this steep slope, and look at the same heart rate Are you going faster? So that's a simple way, a quite simple way that you can use, with a simple testing to, to see if you improve in this specific ability that you targeted in your training yeah, what everybody's searching for in trial running right now is analogous to the power duration curve in cycling.
Speaker 1:so you can take any cyclist who has a reasonable amount of power data and you can produce a power duration curve for cycling. So you can take any cyclist who has a reasonable amount of power data and you can produce a power duration curve for them, and then you could fit that power duration curve against a Tour de France rider, a world class time trial athlete, somebody who runs or somebody who rides the kilometer on the track any of those riders. You can kind of pit them against each other, so to speak, and see where rider A is going to be better than writer B and how they can like rack and stack and they even do a lot of like still some old school like team selections, kind of based off of these, based off of these profiles. People are searching for that in trail running, which you're you know what you're kind of proposing, and I think that this is a good tool to have. This is essentially a velocity grade curve curve as opposed to a power duration curve, to complete some part, some component of that analysis.
Speaker 2:Yeah, actually it's a force velocity duration Right, because in cycling you can change the gears so you can just stick to the best condition, but when you are going up you do not have gears when you're running, so you have to go up with your foot and no way other. So you have to distinguish the force and the velocity part in the power. That's why you need to distinguish force and velocity. And then how force and velocity evolve with duration is another component, and force and velocity not necessarily evolve the same way in the duration and for different individuals they can have a better durability in velocity or better durability in force. And that's another thing.
Speaker 1:And another question and another that we can analyze the statistics nerds out there will certainly get a lot of a big kick out of some of the material that will be in the show notes.
Speaker 2:Let's kind of move the conversation forward, because we've kind of gone through this from a practical standpoint in terms of how athletes can actually analyze this no-transcript component and how athletes kind of exhibit some of the or how some of the things that we were looking at earlier change over the course of a big, long race like utmb so if we stick to the analogy of the force and velocity part or the flat and happy component that that's what I was just saying that there is not correlation between the ability to continue being good and running fast on flat and all uphill we can start with the first picture being the mean picture of all of the UTMB finishers that we analyzed. There is something interesting is that on average the runners are better to. They conserved more. They were more close to their initial capacity in the fourth component. So when it's going up you do not lose that much speed.
Speaker 2:When the race is ongoing and because of the durability components, this is an average analysis. But on flats running there is a big difference between the beginning of the race and the end of the race. So if we just stop here for a few seconds, I would say on average you probably need to improve your ability to run on flatland for the durability and very long effort. That means that probably when you prepare this kind of race you can imagine having a pre-fatigue boot or you will run for, let's say, five, six, eight, ten hours, as you want for your trail running training, and then go to a flat portion and try to maintain your high speed. For the second part in this static state, that could be a recommendation based on this kind of analysis.
Speaker 1:Yeah. So this intervention, a finish fast run or a progression run or doing intervals at the end of the run as opposed to the beginning of the run. Our coaching group has this debate kind of like ad nauseum, and fundamentally you know some people in our coaching group, that's probably why you're laughing. Fundamentally there's a lot of nuance to this, but fundamentally you're kind of trading two things right, you do the hard work at the beginning, you have the best capacity. Right, you do the hard work at the beginning, you have the best capacity right. So if you have the best capacity, theoretically you're going to do the most kilojoules of work. You're operating at the highest, higher percentage of your VO2 max. You're introducing kind of more stress which theoretically would lead to more adaptation. Right, more stress, more adaptation. You do the intervals at the end. You're probably at a reduced capacity. Right, you can operate at a smaller percentage of your VO2 max or maybe for not as long of a period of time, and so the interval quality is certainly reduced. I don't think anybody would argue that, right, we might argue the impact of that reduced quality session, but it certainly is more specific to an ultra marathon where durability becomes exceedingly important.
Speaker 1:Now you're adding in this analysis, which, which is once again, there's a ton of practical takeaways to this, and I think that this is actually a big one. What your analysis would suggest is that if you are doing intervals at the end of the run, particularly to accentuate, or hard work at the end of the run, a progression run, something like that you're probably better suited doing that in a flat level condition, because that is the gradient, or lack thereof, that gets impacted the most when an athlete is fatigued. It's not the uphill component. So a better way to structure a durability type of workout would be run for two hours and then do an interval on flat level terrain, versus run two hours and do uphill intervals, which you could still have that component of it based on the race that you're actually seeing. But if you have, if the race has uphill and flats, the better piece of it would be to do it on, to do it on the flats. Is that what the research is suggesting? Am I reading between the lines well enough?
Speaker 2:there. Yeah, actually it's really hard to. We do not have an easy, right answer on the durability question, because it's a hot topic, it's new even in the scientific field, so I can't say you absolutely must do that. But based on the analysis that we did and since we saw with evidence that you lose more speed and you lose more speed at the end of the run up because of the flat running capacity, if you have to choose what to do, I would probably suggest yes, to train your durability component on flats.
Speaker 3:Okay, let's check back in with coach adam on this really neat concept of when we would actually want to target flat ground durability to improve performance and how we would actually do it yeah, well, first of all, I do think that this, when I heard this, it kind of made sense to me, because I pace people quite often at races and this is something that I do see quite a bit where you're hiking uphill with someone late in an ultra and they're actually moving pretty well, and then you hit flat ground where you have to run, and it's a different story.
Speaker 3:So it probably varies person to person, race to race, but I actually do think it's a theme that you can just see out there on the ground as well. As far as actually training it, I know in the podcast you guys brought up potentially doing some sort of faster work or intervals on flat terrain at the end of a long run. And another intermediate step that I might bring in an athlete is just, you have your hilly long run and in the last 60, 90 minutes do it on flat terrain. Maybe not even with the speed with it, but just placing that terrain at the end could be one step along the way.
Speaker 1:Yeah, you know, western States is the classic course that accentuates this because it gets faster as the race generally goes on, particularly as you leave Robinson flat and then Michigan bluff and then forest hill. It just kind of gets easier and easier from a surface standpoint and the athletes that do really well, the ones that can just kind of run past run specifically, not in quotes actually run past forest hill and not do a lot of the prototypical hiking and things like that. But when Batiste actually mentioned this, I thought of a paper that a mutual colleague of mine was a co-author on, and I'll link that paper up in the show notes, and it posited the co-workers, nick Tiller along with Guy Mier, who are the two authors of this paper. Their primary thesis is the limiting factor in ultra running is at the level of the muscle, and particularly muscle damage, which would be accentuated during running more than it would hiking, because running is a more, let's just say, a muscularly intensive mode of locomotion as opposed to hiking.
Speaker 1:Uphill, downhill, running would probably be the most muscularly demanding component of all the three disciplines, or all the disciplines that we can like that we experience in ultra running. And so maybe if we took this research step further and further the speculation that we would see this. We would see this component diverge even further on the downhills, where people get disproportionately people who are doing or who are slower get disproportionately worse, specifically on the downhills the most, then on the flats the second most, and then don't really deviate that much on the uphills, but who knows? But some interesting concepts, so to speak. And the final caveat that I'll add before we go back to the main conversation is remember this analysis was done on one specific race and we have to put that into context with all the races that athletes are doing and that may or may not look like the same course profile.
Speaker 2:But this was an average relationship because we evidenced, even if they are not the case, that we saw the most, but it could be the opposite for some of the athletes that were able to maintain quite a good speed and flat but was really slower on the steepest gradients. So you do, we cannot have just one answer for everybody, but on average I tell you that small flatland that you can play, but specifically individually it can change well, and if an athlete wants to apply it to themselves, they can look at.
Speaker 1:This is why post-race notes and post-workout notes become so incredibly important During the end of the race. I could do this and I couldn't do that. That's the lens that you can look through to determine okay, we can improve this or improve that. You can also corroborate that with the actual file itself and this is something that I do with my athletes is I look at how each segment deteriorates over time the uphill segments, the flat segments and the downhill segments and the gauge. I'm kind of like revealing the coaching process. There's not like it's like proprietary to me or anything.
Speaker 1:I didn't come up with it.
Speaker 1:Somebody taught me how to do this and then I'm just doing it, you know 20 years later, but we're simply using intensity factor, as the intensity factor is just the ratio of the speed that they're running to their threshold speed.
Speaker 1:So if the if they're running faster than the threshold speed, the intensity factor is over one. If they're running slower than their threshold speed, intensity factors under one. It's almost always under one in any ultra marathon event, but we look at how that deteriorates over the course of a race and do the uphill segments deteriorate more than the flat segments or do they deteriorate less than the flat segments as compared to the downhill segments? And those are the three buckets that I essentially put it in and that becomes a lens to look through to say, okay, we're going to improve here from year to year or based on this race, and that improvement can come through training, it can come through pacing, it can come through nutrition interventions. There's a whole host of things. It's not just do progression runs or anything like that. That's not the answer to everything. But my point with that is we have tools that we can look at it from a statistical perspective as well as a subjective perspective, how the athletes actually experience things.
Speaker 2:Yeah, and how the runner and the athletes perceive something is really important, but I really think that we should always mix the perceived thing with the objective information, because sometimes you can have a difference between how the athlete perceives a thing and what actually the watch and the time say.
Speaker 3:Exactly.
Speaker 2:Especially at the end of an ultramarathon, where your mind is, in a way, not in the fresh state that you have at the beginning, so you can have some discrepancies between the perception and what actually occurred.
Speaker 1:Okay, this has been amazing because I like getting the.
Speaker 1:I like getting the curtain peeled back a little bit with the people who do these types of analyses, and I do think that this is one where we can come up with a lot of functional ways or functional takeaways in terms of how we can start to analyze, you know, race performance and things like that. I know you've got a lot of things on your wishlist right now. In fact, the people who are viewing the video version of this will see a whiteboard behind the Baptiste's shoulders that has a lot of equations and quadratic formulas and things like that, written meaning you're at work doing something. But I kind of want to know, like, what's on your wishlist to develop. Do you now have like, this framework to say, okay, we can analyze race performance like this, how do we like what's on? What would you like to do with that? If you could just like instantly wave a magic wand and put it in the hands of your average or elite trail runner in order to improve their performance with, like, your knowledge base, what would you want that to look like?
Speaker 2:We have several things that we could do all seven.
Speaker 2:We can go all day with this.
Speaker 2:Well, the thing that is, I think, really important is to develop some tool that we can provide to the community.
Speaker 2:So so we can use it and we can not just look at two elite runners or not just at the finisher of the 2023 edition of the utmb, but but, as you say, it's a descriptive way to see at the performance, but actually we don't know if the durability of the force or the velocity is related to pacing, is related to the nutrition or related to the heat of the race.
Speaker 2:There's a lot of factors that we want and we want to use this framework to analyze all of that, all of the nutrition, the heat, the pacing, etc. And, for example, I've been solicited by a team in the UK that will run an experiment in the Western States this year in a few weeks. They are really interested in the heat acclimatization aspects for this race and we will run this analysis that we did for the UTMB and the Western State and a few participants to see how the duration, the durability profile on force, on velocity, on intermediate slope, on downhill, etc. How all of that is potentially related to the heat, to the core temperature of the athletes. So that's an example of what we want to do with this framework that we developed and that can be useful tomorrow for analyzing and reanalyzing again some of aspects that are related to performance in trail running, but with a new, potentially more sensible tool that we developed.
Speaker 1:Amazing. I hope all of those come to light. I'm going to leave links in the show notes to some of the research that we developed Amazing, I hope all of those come to light. I'm going to leave links in the show notes to some of the research that we've either directly referenced or kind of tangentially referenced. I'll get some of those links from you after the fact as well, because there's probably some things that I was missing in the research that our fellow colleague Fred and I did. But if people just want to like look you up and look up the research and the work that you do, how are they going to go and find you? How they can find me?
Speaker 2:Yeah, Research gate Twitter, Instagram they're going to direct email address.
Speaker 1:Some people give their cell phone numbers out every once in a while on this podcast, which I'm kind of like a little leery and actually put on the air, but like, how do you want people to find out more about you?
Speaker 2:Yeah, they can find me on all the social media and twitter, instagram, etc. I will send you the link so they can reach me. And we have also a project named predict trail where we want to develop this tool that we want to spread. You can find on wwwpredictrailcom. You can share your link that that's another project that we have and where you can find me.
Speaker 1:Brilliant. Well, thanks for your time, thanks for your research. I wish you all the best. We're going to bring you back on this podcast whenever any of these probably gated or paywalled or whatever versions of these performance analysis tools actually come out and you can explain them. More power to you. It's absolutely going to happen. I'm here for it.
Speaker 2:Any way that we can slice and dice performance is something that I'm going to be a part of, and we'll nerd out on how to analyze performance probably at another future point. Thank you very much.
Speaker 1:All right, folks, there you have it. There you go. Much thanks to Batiste and Coach Adam for coming on the podcast today and, as I mentioned during the intro, I do think that this is something that we can deploy in our day-to-day training with athletes and also think about how we can race smarter. As always, this podcast is brought to you sponsorship and advertisement free, so if you would like to support this podcast, the one thing that you can do is subscribe to Research Essentials for Ultra Running. We are actually going to take some of Batiste's research and break it down even further in that newsletter, in an upcoming version of that newsletter, and so if you really want to geek out on all the stats, statistics and find out what we think about it in a more detailed version, more detailed manner, research Essentials for Ultra Running is your ticket. All right, folks, that is it for today and, as always, we will see you out on the trails.
