Podcast, Science and Physiology, Training

Pacing science and training talk with Andy Renfree, PhD | EP#349

 August 1, 2022

By  Bernardo Gonçalves

LISTEN TO THE EPISODE HERE:

Andy Renfree - That Triathlon Show

Andy Renfree, PhD, is a sports and exercise scientist with a background in and passion for running. In this interview, we discuss one of Andy's main areas of expertise, the science of pacing. We also have a more general discussion about training, sports science, and the integration of the two.   

In this episode you'll learn about:

  • The science of pacing
  • Practical recommendations for improved pacing
  • The messy reality of training and the implications for athletes and coaches
  • The role of sports science in training and coaching

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Shownotes

Andy's background

03:12 -

  • I am a sports and exercise lecturer at the University of Worcester in the UK.
  • I teach undergraduates modules on exercise physiology and sports science. I do many different things in this world of sports.
  • I am a teacher and researcher.
  • I am a "frustrated" middle distance athlete but have never done a triathlon. My best results were 4th in the Indoor National Championships and 7th outdoors.
  • When I worked in Scotland, I also got a couple of national championships, but I probably could have done better.
  • My PhD looked at decision-making concerning pacing and exercise performance.
  • I got interested in sports science first because I wanted to know how to run faster. Therefore, I always focused on applying the different concepts in practice.

The complexity of athlete's physiology

05:05 -

  • I got a conference where I was going to talk about pacing, and on the plane, I tried to write down everything I knew about that topic and what caused pacing behaviours to be as it was.
  • Everything affects pacing, so I came across complex terms that I have not explained before in much detail.
  • So I went to look at complex systems. Everything in Biology is a complex system, but the complex is not the same as complicated.
  • In a complex system, the sum of its components is higher than the whole of its parts.
  • In an athlete, there are physiology, anthropology, and psychological characteristics; despite understanding them, I could not explain why an athlete would perform in a specific way in a race.

Implications of athletes being complex systems

06:46 -

  • Unpredictability is a crucial issue. It is complicated to understand what will happen with any intervention.
  • Moreover, some tiny aspects have a substantial impact on performance. For example, butterfly wings could have caused a tornado, but we could not understand the causes of a tornado only by looking at it.
  • It isn't easy to explain what causes some behaviours we see in athletes.
  • In my readings about complex systems, there is a concept of Anti-fragility. If I take a cup and drop it, it breaks, making it fragile.
  • While if I drop a metal cup, it bends and gets stiffer, so they are anti-fragile.
  • Biological systems are anti-fragile, meaning they do not break under stress but grow more robust and can support more inflammation.
  • When working with athletes, we have to stress the body to have some inflammation so they can get better.
  • Any biological system will respond to volatility and randomness.
  • Therefore, you need to add this to your training.

The futile search for the optimal training program

09:36 -

  • I wanted to learn sports science to run faster, and I thought the secret was in training.
  • I was self-coached, and I was reading many books on the subject.
  • I would learn that different athletes would have different training methodologies and achieve the same results.
  • Therefore, was the difference in the athletes, genetics, or what made the athletes improve with this training methodologies?
  • Some people might be performing well despite their training program.
  • Moreover, there are experimental, case studies and descriptive studies in the scientific literature.
  • I believe there are some limitations in every training study published. If you look at short-term training studies, high-intensity wins almost every time. However, elite athletes are not doing that all the time.
  • Furthermore, the participants in the study are usually active sports students because researchers have access to this type of people.
  • We also need to consider that the studies are 8, 10 or 12 weeks long, which does not reflect what athletes do in their preparation for races.
  • So, these studies do not show which training methods are the best, but the ones that show the quickest results.
  • However, you do not know what happens after that study.
  • Some athletes respond to the stimuli, and others do not. 
  • There is a study that had elite athletes and compared short interval training and long interval training. The headline was that the short intervals were superior to long interval training. However, if you look at the participants, some people respond negatively to short interval training.
  • So, if I am coaching someone, I want to know what is best for that athlete. Applying general concepts and ideas to every athlete is problematic.
  • Moreover, once you have done those sessions, you are not the same person anymore because your physiology has changed. Therefore, the same sessions might not have the same impact.
  • There are some limitations to the studies of that type.
  • Alongside you have case study papers about what athletes do.
  • However, if I read about how Lance Armstrong trained, I am not Lance Armstrong, so what he does might not work for me.
  • There are some studies on Kenyan athletes. However, if you do a Saturday morning session, you find 350-400 runners doing the fartlek session. So, it might be the case that those who survived the session will respond better to the session.
  • Maybe, training does not matter. For example, a study on Eritreans and European runners showed the only difference between them was in running economy.
  • If you look at anthropometric data, Eritreans have much lower limb volume. There are some limitations from the case studies on what we should apply to individual athletes.
  • You can retrospectively quantify the training intensity distribution of these athletes, but it is like looking at the cake and finding what the recipe was.
  • So, you need to know how they came with that intensity distribution.
  • I suspect that everyday decisions influenced that training distribution.

How an individual coach can find what works with each athlete

15:44 -

  • I do not know if you could have the optimal training for each athlete.
  • You have to evaluate if one thing works and then try something else. 
  • All you can do is focus on performance, health and well-being.
  • Health and well-being have to be the priority. If you are not healthy, you will not perform in the first place.
  • You can evaluate how different training strategies impact your performance as long as you are healthy.
  • For example, I would have done much slower interval sessions if I could go back in time. I used to go to the track and target specific times regardless of the weather, and I would do too intense intervals.

Collecting and analysing data

17:30 -

  • I do not have the most prevalent thoughts on this topic.
  • I am sceptical about collecting vast amounts of data to monitor training progress.
  • I am not sure what people are trying to capture (training data, load or physiology)
  • I became suspicious a few years ago when I was trying to quantify my training load. 
  • I would go to the track and do a challenging session (4x400m at 800m race pace), which is a session that would destroy me.
  • The load value of those sessions was lower than the recovery run I did the following day.
  • I understand that the load calculations undervalue the high-intensity efforts and emphasise volume in the training sessions.
  • We identified this problem, but we did not find a solution yet.
  • I understand people want to measure training load and physiological responses, but if you get too much data, it is challenging to distinguish noise from the signal.
  • For example, it is like addressing global warming by looking at daily local weather. It might snow some days, but that is only noise in the signalling.
  • Therefore, you must choose small changes instead of tiny differences in some parameters.
  • You must choose data to collect, but you must be aware of the best data.
  • Subjective measurements are some that I recommend using because changes in psychological state correlate with overtraining syndrome.
  • To measure psychological data, I use POMS, the feeling scale or only by talking with athletes.
  • One of the best physiological would use the "ultra short POMS": "How are you?"

Andy's perspective on exercise prescription

22:46 -

  • My background in middle-distance running meant we used pacing as a guide for training.
  • However, there are differences between goal paces and the pace you can hold on a given day, as it also depends on the weather.
  • Therefore, we use the perception of effort, so we use 3 km efforts, for example.
  • I have some concerns about stopwatches because they put numbers on people's minds, which do not link with physiology on a given day.
  • Most of the time, I would use the perception of effort or feeling rather than pacing targets.
  • Some coaches had much success by prescribing efforts with "fresh", "good", and "very good". Coaches would watch the athletes do the sets and prescribe them until running form crumbled.
  • I think the stopwatch is particularly dangerous. Athletes might crush themselves trying to hit targets, but it can also have obstacles.
  • For example, athletes might run faster because they feel good, but as they see, the pace slows down, becoming a barrier.

Role of sports science in the training and coaching

27:17 -

  • Sports science is fundamental for coaches that are evidence-based practitioners.
  • Some research shows that the best coaches refer to sports science as a guide to training.
  • There are some limitations in sports science. Most scientific papers are not "user friendly" because they are complex to read and extract information from reading them.
  • Therefore, sports scientists must share the implications of their work with the end users.
  • Rather than giving only tables and graphs, writing down the conclusions from that data is helpful for athletes and coaches.
  • I also think sports scientists should understand sports. Some scientists are asking the wrong questions about the sport.
  • Moreover, we must increase the dialogue between coaches and scientists to come up with research questions to answer those coaches' questions.
  • For example, last year, we had an event in the UK where the commentators were academics, and the audience was practitioners. That combination of people is valuable, and I hope we have more events like that in the future.

Pacing science

How a fast start might impact performance

31:03 -

  • A fast start is almost always bad if your fastest absolute time is the goal. However, for example, 800m performance correlates well with the speed of the first lap.
  • However, for every effort from 4 min upwards, an even pace or a negative split will benefit performance.
  • When Eluid Kipchoge went to break two hours in the marathon, they did an even pace. 
  • Even pace is optimal, but you rarely see it.
  • Some papers looked at pacing strategies and world records of 800-1500 and the mile.
  • In those efforts, you see athletes running consistently, but when I race, my first half is faster than the second.
  • In a paper I worked on, we looked at marathon times from the Berlin Marathon. We split the first 60 finishers, and we divided it into four groups: athletes finishing in 1-15º; 16-30º; 31-45º; 46-60º.
  • If we look at the PBs of the athletes, athletes with higher PBs finished in the first group.
  • However, when we looked at the differences between athletes, we saw that difference was higher than expected by their PBs.
  • The further the classification, the higher the difference between expectation and the results.
  • However, if you look at race splits, most riders initially run their marathons at similar paces. As the race progresses, athletes start to fall off the pace.
  • Some athletes running off the back had a 17 % running speed from the first to the second half of the event.
  • From a physiological view, athletes are depleted, dehydrated, overheated and losing motivation.
  • Most of these poor performances came from a wrong decision at the start of the race. And that was my Eureka moment, where decision-making would influence behaviours in events like this.
  • What happens is that, for example, athletes start relatively fast (e.g. cross country running) to be in the front group. And this is horrific as it is a test of survival.
  • In that situation, everyone is pacing themselves poorly, so the success in that event is to control intensity even if the temptation is high.
  • Things get interesting when you dive into cycling. If everyone goes fast, you must also go fast because of the draft.
  • A cycling peloton is a "superorganism" where we have 200 cyclists, and researchers try to model that peloton based on the abilities of the different riders.

Decision-making for better pacing strategies

37:28 -

  • At any point in a race or training session, you need to decide the intensity you will be riding at that moment.
  • The intensity range goes from stopping to going maximum. 
  • As the race progresses, your maximum ability declines. Therefore, you have many "paces" to choose from in racing.
  • There are two kinds of decision-making: rational and unrealistic.
  • Rationale means you are working as a statistician.
  • You are constantly evaluating the risks, rewards and probabilities of specific outcomes based on behaviour decisions.
  • However, as you need certainty in the probabilities you are measuring, it can only happen in a small world environment.
  • A small world environment is where you understand how the variables change.
  • Most life is not a small world because there are too many uncertainties.
  • If you focus on yourself, you probably understand what will happen if you increase your pace 1s/lap, but you do not know how your opponents will respond to that change.
  • As pacing and racing are not "small worlds", you cannot make rational decisions concerning pace regulation.
  • The other alternative is using heuristic decision-making. You ignore most of the data available to you and focus on some crucial features of the environment.
  • There is something called the "affect heuristic", which correlates with how positive and negative you feel. If you choose between two options, you will choose the only one that brings more positive responses.
  • For example, I always wanted to run a sub-four-minute mile. If I am heading into the last lap with 2min55s, in theory, I will beat that milestone, and it will bring a positive sensation where I will only try to maintain my speed at the end of the race.
  • On the other hand, if I see the clock at 3min10s, it will bring a negative effect. I will probably reduce my speed or pursue it even though I know it will not be possible.
  • There is no reward by picking up the same because I will not achieve my goal, but there is a reward risk of physiological damage.
  • Based on the information I got, I will change my perspective on things concerning my pacing behaviour.
  • RPE relates to the affect heuristic, but they are not the same. If I knew I could achieve my goal, my fatigue could be high, but because I was feeling optimistic, I would tolerate that fatigue better.
  • If I see a slow time on the track, my RPE would be equal, but I would probably drop out on the last lap because my affect response is low. (significant risk, small reward)
  • It seems the risk/reward seems essential.

Practical applications of data we should collect

42:59 -

  • There is some exciting stuff when looking at perception during time trials.
  • One of my undergraduate students did quite simple experiments, where he took participants four times to a lab and made them do a 10 km time trial on the ergometer.
  • He told them he was doing an investigation on familiarisation with the test, but there was much more into it.
  • The first trial was a familiarisation trial; the other three came to do a 10 km time trial, and there was a marker that would show them how far they were in the race and a clock to show them the time.
  • On one occasion, the clock was on time, the other was running too slow (3%), and on the last, it was running too fast.
  • Athletes thought they were going better than the clock was running slower and worse when it was running faster.
  • In all cases, RPE increased linearly until the maximum, but the results were better when they thought they were going faster.
  • They were probably comparing their performance against the baseline time trial, and the concept of "goal progression" seems essential.
  • Your performance will decline as soon as you see that you will not achieve your goal.
  • Therefore, you should get the goal set properly. If you set that goal wrong, you will get negative psychological responses as the event progresses.
  • The longer the event, the higher the risk of pacing errors.
  • Our goal setting is more straightforward when you have to target a time, but it is much more challenging when you want to win.
  • If my competitors are having a great day, I might also be performing well, but I underperform on the day because they perform better.
  • Some studies looked at this by evaluating runners in 10 km races. 
  • They did an individual 10 km time trial and a 10 km race.
  • Participants went faster when racing against each other, and in both cases, RPE increased linearly, but the affecting state was higher when they were running in a head-to-head situation.
  • The presence of competitors makes me feel less pessimistic about the race itself. (The RPE to speed ratio would increase)

Additional comments on racing scenarios

47:23 -

  • We did some studies where people competing against avatars would go faster or slower than the baseline trial.
  • When you compete against an avatar faster than you, your confidence in your abilities decreases.
  • However, many variables exist, and we are only starting to entangle these relationships.

Pacing ultra-endurance events

48:27 -

  • We were interested in the effect of ageing on pacing, and we grouped the masters' athletes in 100 km trail races.
  • We assumed that men and women would pace themselves differently because there were allegedly differences in competitiveness between genders.
  • The motivation pattern of athletes changes as they age. Younger athletes are more competition driven, while older athletes are more social and recreationally driven.
  • We found that women pace themselves far better than men do. (physiological and psychological reasons: I think women are more sensible, so they slow down during a race but to a slower extent)
  • We did not find any effect of age on pacing strategies.
  • Regardless of their age, status or race success, the event has a positive pacing strategy (the first half is faster than the second)
  • This paper had two studies on two different populations. One was a group of well-trained ultra-distance runners, and there was a risk-taking questionnaire. (quantifies the individual perception of risk)
  • There were 24 athletes, and we split them in half based on their risk profiles.
  • Athletes with lower perceived risk/perception rations started faster than the average participant and finished slower than the average participant.
  • The second study was on students that did a 5 km time trial in the lab, and the ones with a higher perceived risk started slower than the average pace.
  • However, there was no difference in actual performance based on different risk/perception ratios.

Practical applications for pacing events

52:49 -

  • In competition, we must consider race tactics, position and craft.
  • For example, Andy Jones analysed the 800m Olympic final in 2000, where the winner did 1min44s and the second was the record holder.
  • When we analysed the race, the record holder ran much faster than the winner, but he ran seven meters more. Therefore, tactical position on the bends is crucial.
  • Concerning race tactics, we need to work with probabilities. If athletes are in a specific position at different points of the race, what will be the odds of the athletes achieving good results?
  • We find in 800 m a strong correlation between mid-lap position and final race position, whereas, in the 1500 m, the correlation is not as strong.
  • Concerning practical application, focus on your race, but many distractions can pull you away from your ideal race strategy.
  • Connective behaviour is a feature that influences human behaviour. If you have to make a complex decision, the easier option is to do what everyone else is doing. For example, avatars will influence the pace of participants.
  • Therefore, you must overcome the trend to do what everyone else does.
  • The first thing you need to do is to perform some race efforts throughout your total race distance. It is easier for middle distance runners but more demanding for Ironman athletes.
  • For example, If I had a 1500m athlete, I would make him do 6x1500m splits at tempo speed, so athletes understand the feeling at different moments of the race.
  • Pacing is all about effort distribution. Therefore, learn how it feels to run those distances at reasonable efforts.
  • Even Kenyan marathon runners do long tempo runners as their most crucial workouts.
  • Moreover, try to make it difficult to control your pace with the influence of external athletes.
  • I also think visualisation is helpful. One of the most critical discussions I had was with a sports psychologist when I was not performing well.
  • He asked what I was thinking about when running, and I would say things like: "there is a long way to go, and I am hurting".
  • Therefore, he told me to think of the 1500m race as a set of 400m.
  • Then, he told me to visualise all the possible scenarios that could unfold in each race section.
  • And that felt a revelation to me, and I understood I was making decisions before the race, when I was feeling good, and could not change that mindset under physiological strain.
  • Therefore, I started implementing that in my workouts (I started splitting 1500m intervals into sets of 400,400,300,200,200m).
  • I trained that until it became automatic.

Three pieces of advice to improve performance

1:00:05 -

  • Visualisation and psychology have a remarkable impact on performance.
  • If in doubt, doubt - if you take small risks, you might get into the habit of doing it. And if you get that habit, those risks compile until it becomes an illness. Therefore, be cautious in a sporting career.
  • Finally, do not turn triathlon into a second job. The whole point of sport is recreation and enjoyment.
  • We do things for two reasons: the activity has intrinsic value or extrinsic value (you do it to get something else)
  • I go to work and teach to pay my mortgage (my job has extrinsic value)
  • If I am doing an interval session I do not enjoy doing because of the benefits it will bring me, it will be an extrinsic activity.

Rapid-fire questions

1:02:30 -

What is your favourite book, blog or resource?

Any book of Percy Cerutty, coach of Herb Elliot.

What is an important habit that benefited athletically, professionally or personally?

Reading outside of my focused disciplines (outside of endurance sports)

Who is someone you have looked up to or who has inspired you?

I will say Marco Tardelli, a footballer with the best celebration goal I have ever seen.

LINKS AND RESOURCES:


Bernardo Gonçalves

Bernardo is a Portuguese elite cyclist and PhD student in the field of aerodynamics at the University of Coimbra. He writes the shownotes for That Triathlon Show, and also produces social media content for each new episode.

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