But now I'm abandoning endurance training in favour of sprint cycling.

Here's why.

First, I'd taken it just about as far as I could. My best aerobic power and VO₂max numbers (see below) are either awesome, quite good or mediocre, depending on whether you compare me to the average man, the average weekend club rider, or the average competitive amateur racer.

3 hours: 244 Watts 1 hour: 272 Watts 20 minutes: 307 Watts 5 minutes: 392 Watts VO₂max: 61 mL/kg/minute

At this point, I doubt I could add more than a handful of extra Watts to my 5+ minute power numbers, even with a hard focus on longer duration work. Apart from my 5 minute power (more on which below), these numbers haven't really increased much over the last 18 months; it's pretty likely that I'm close to my physiological ceiling.

Actually, it was clear to me quite early on that I'm naturally far stronger anaerobically than aerobically, and this idea has subsequently been borne out by all the data I've collected.

Comparing my data with those from other riders makes this crystal clear. For example, intervals.icu currently has over 5,000 male cyclists in my age bracket. Presumably, a large majority of these cyclists are quite serious and well-trained (since they've each taken the trouble to invest in a power meter and sign up for a service to analyze their data). My power percentiles in this group are:

5 sec: 96th 15 sec: 97th 30 sec: 98th 60 sec: 99th 3 min: 96th 5 min: 93rd 20 min: 82nd 60 min: 59th

Quite a drop-off! And this is after almost four years of strongly endurance-focused training.

Look again at my best 5-minute power, and compare it with my best over 20 minutes. Most riders who could do only 307 W for 20 minutes would be somewhere around 350 W for 5 minutes, not 392. This relatively high 5-minute power is due to my huge anaerobic capacity (estimated by three different training tools at close to 40 kJ — itself in the top fraction of the 99th percentile), and so a five-minute effort tends to greatly overestimate my aerobic capacity (VO₂max), which is actually around 350 W. But clearly this 5-minute:20 minute ratio is another big clue that I'm an anaerobic kind of guy.¹

Unfortunately, anaerobic capacity on its own doesn't really do much; if you also have a large aerobic capacity you could be a good criterium racer, while a large anaerobic capacity + high thresholds would make you a good road racer. But I don't have those. However, a huge anaerobic capacity plus large anaerobic power (i.e., peak power) would make me a good sprinter, especially over longer efforts of 30-60 seconds. And that's what I'm now working towards: keep topping up my capacity while aiming to increase my peak power via weight training in the gym and sprint work on the bike, and then see what kind of performances I can do over 1 kilometre.

Interestingly, it is commonly reported in the scientific literature that the anaerobic:aerobic crossover point (the duration at which there is a 50% contribution from both (shorter-term) anaerobic and (longer-term) aerobic energy sources) is around 60 seconds. But it has recently been argued that, due to a decline in metabolic efficiency over durations of up to several minutes, anaerobic energy contribution to total energy expenditure for maximal efforts is underestimated by about 30% in studies that do not account for this (i.e., just about all of them to date; see Haugen et al., 2021).

This means that the crossover point could in fact be around 3-4 minutes, making events like the 400 metre run and 1 kilometre cycling time trial overwhelmingly anaerobic, rather than somewhere close to a 50/50 split. This makes a lot more sense to me intuitively, as well as in explaining my own numbers and when looking at the training (and physiques) of athletes like Michael Johnson and Chris Hoy.

Furthermore, for sprint exercise, it appears that the musculoskeletal intensity level that can be achieved determines energy release and sprint performance. This is in contrast to endurance exercise, where sustainable aerobic energy availability determines performance (Bundle & Weyand, 2012). In other words, your sprint power is not limited by metabolic factors; it's determined by the force you can generate through the pedals.

The relevance of this is that if I want to maximize my 1-minute power (and I do), then I need to focus far more on anaerobic training than aerobic training. This is very important, due to the other reason I switched to a sprint plan: arrhythmia.

My first bout of tachycardia happened when I'd been cycling for just under a year. I was making my first attempt at the hardest ride around (the fast ride in Savannah). I managed to hold onto the back of the group for about 10 minutes, but just as I was getting dropped and easing off I got several consecutive heart rate spikes (my HR instantly increased by about 40-50 bpm, well above maximum) of up to 20 seconds each. The only symptom was a fluttering feeling in my chest. A similar thing happened a few weeks later when I sprinted up the first part of a climb on the Asheville Gran Fondo.

This 30-second ride excerpt shows tachycardia in action.

At first these events were few and far between (sometimes months apart), and only ever occurred when I went very deep for several minutes and during an extended period of increased training load. So I didn't worry too much about it. But this spring (again, several weeks into a training load build) they started happening regularly. As in earlier cases, hard riding was still needed to trigger them, but the level of exertion at which this happened was considerably lower than before: they'd started showing up on many of my regular intervals sessions.

I did a lot of research on this phenomenon, and it's actually quite common amongst older endurance athletes. It's apparently most strongly correlated with total lifetime endurance hours (I'd already racked up 2,500 hours in the first 3 ½ years!), and to a lesser extent with time accrued in the heavy-severe intensity domains (i.e., doing long aerobic efforts between threshold and VO₂max), due to damage caused by increased pressure and volume of blood flow.

Arrhythmias in athletes are related to the cardiovascular changes that universally occur in response to endurance training (so-called Athlete's Heart): heart and blood vessel remodelling, low resting heart rate, heart murmors, etc. These changes are usually benign, but scarring of the heart chambers can also occur, which in some individuals can interfere with the electrical signalling that controls the heart rate (see O'Keefe et al., 2012).

So it usually shows up in people after a couple of decades of endurance activity, but in my case I imagine various 'lifestyle factors' may have begun the scarring process earlier, long before I started serious cycling!

In any case, I obviously don't want this to progress; at this point none of my individual tachycardia episodes have lasted longer than a few seconds, and have always resolved themselves on their own. However, if ignored, the disorder can progress to a point where the arrhythmia lasts for hours or days, necessitating a visit to the emergency room to have your heart restarted. No thanks.

Given all the above, it wasn't a difficult decision to switch from endurance training to sprint training: to resolve (or at least avoid any progression of) my symptoms, I have to let myself detrain enough so that my heart can repair the damage. The most important thing for me to do is reduce training volume, and also lay off regular high-intensity aerobic riding.

This whole saga has the benefit of giving me an excuse to spend many enjoyable hours researching the best way to go about this (I'd got bored of revisiting my old endurance training plan)², and I now also get to train how I really like to train. No more tempo or threshold or VO₂max intervals; no more interminable endurance sessions. Just short base and recovery rides (often commutes), various sprint workouts, and a few anaerobic sessions (typically widely-spaced 30-second efforts), combined with plenty of time lifting weights in my new garage gym.

In other words, just the fun stuff: flat out, or dead slow. It's great.

The one downside is that group rides aren't part of the equation, as they fall squarely into the 'endurance' category, and don't really help me towards my new goals (which revolve around things like 1-rep maxes in the squat and deadlift, and increasing my 5-, 30- and 60-second maximum power on the bike). But I'll try to find a way to sneak in the occasional social ride every now and then.

In the meantime, hopefully my new approach to training will result in a resolution of my symptoms, so I can keep on riding.

UPDATE: 12 June

Soon after writing this article, it came to my attention that there have been increased reports of myocarditis/pericarditis (inflammation of the heart, one of the symptoms of which is arrhythmia) in individuals taking two does of mRNA COVID-19 vaccinations.

I had my second dose on 30th March, and the increased incidences of tachycardia began a couple of weeks later. It's quite likely only a coincidence, but there is still a possibility that some slight inflammation occurred which had an additive effect with whatever (presumably fibrosis) has caused my tachycardia previously.

Nevertheless, this doesn't really change anything: I'm happy that sprint training is the right way for me to go and I'm really enjoying developing and implementing my new plan.

1 Even the 20 minute:60 minute ratio is much bigger than normal (13% difference). This demonstrates why you can't just use predefined formulas (like 20-minute power – 5%) to set training zones: if you want to know your real 60-minute power you have to ride as hard as you can for 60 minutes! Sorry.

2 My new sprint training plan has gone live, here.

#cycling #training #endurance #arrhythmia #tachycardia

It's well worthwhile to read through the linked article, but I'm going to summarize the main points here.

Most cyclists with a power meter are familiar with the concept of Training Stress Score (TSS). This is an attempt to correlate training load with changes in fitness, and was itself developed based on the earlier heart rate-based TRaining IMPulse model.

Every ride is assigned a TSS, based on its intensity (fraction of FTP) and duration. Quite simply, the longer and harder you ride, the higher the training load (TSS), and the higher the training load the higher your fitness should become. High training loads also lead to high fatigue levels and the potential for overreaching and injury if you increase loads too quickly or beyond levels you can tolerate; the key to effective training is to balance optimal load with recovery.

The problem with the TSS concept is that it tries to track fitness via a single one-size-fits-all number. The way it's calculated means you can get a TSS score of 100 in many different ways. Three examples: riding for 60 minutes at FTP; riding for 60 minutes doing 30 second sprints at 250% FTP every few minutes with easy recovery in between; or riding for 4 hours at 50% FTP.

Anyone who's done these workouts knows that each has very different physiological effects, so clearly they're not interchangeable.

Each of us has a different natural ability and response to training. Some of this is down to genetics, which has a large effect on body type, cardiovascular capacity, muscle typology etc. So it stands to reason that we'll all respond differently to different methods of training.

In the linked article, Couzens discusses two of his athletes, both of whom are fast — winners of major endurance events — but each responds very differently to training. One of them thrives on 25-hour weeks of long slow distance, with low average intensity. He's a 'volume responder': the more he rides, the fitter he gets, almost regardless of how hard his riding is. The other is an 'intensity responder', who does much better on lower volumes at a much higher average intensity: he does a lot of high-intensity interval training and fewer endurance miles.

If we want to optimize our training, it would clearly be beneficial to us to be able to find out if we fall into one of these categories, or if a more balanced approach would suit us better.

The first step in doing this is to move beyond TSS, by separating out the two main terms in the training load equation: volume and intensity.

Couzens shows how to do this using your power and heart rate data. You look at each of your training blocks and get the totals for volume (hours ridden) and intensity (TSS per hour). You also have to calculate your relative VO₂ capacity, which requires an extended maximal effort at the end of every block.

Once you've got this information, the first thing to do is plot VO₂max score versus training load (TSS per day). This gives an idea of how fitness responds to just the raw TSS numbers, with volume and intensity not separated. Mine are shown below.

I took the liberty of adding in r² values for each of these graphs, to judge the strength of the relationships plotted. As you can see, for me there is only a very weak correlation between my training load and fitness; it appears that a higher TSS is probably beneficial, but the effect is rather underwhelming. You can see that there were a couple of blocks in which I had a much higher training load than in the others, but considering the extra effort involved, this didn't make a great deal of difference.

Hopefully we can get more insight by separating out volume and intensity. First, fitness versus training volume:

Ouch! Not only does increasing volume not help, if anything there's an inverse relationship: the more I ride, the worse I get! I'm not taking that too seriously, though, since the r² value is so tiny (you can see the data points are all over the place, and don't cluster tightly around the line of best fit, especially given a big outlier on the left that's skewing things).

What it does mean is that, at least in the range of the data (about 13-18 hours riding per week), extra volume doesn't do much for me: I may as well ride at the bottom end of the range so I can save some energy and recover faster.

At this point all my cycling buddies who've been trying to get me to ride less over the last couple of years are laughing their asses off at me. Still, there is one way that higher volume helps: calorie burning. I'm currently on a weight loss program, and due to the way VO₂max is calculated (the body's oxygen consumption, in millilitres per kilogram body weight per minute), any loss in body weight does actually improve relative fitness. And clearly, the more miles I rack up the easier it is to lose weight.

Furthermore, one important long-term factor in fitness improvement is cycling economy. This increases very slowly over many years, and the biggest factor in its development is total hours ridden. So maybe that's another reason to ride more, but not if it's at the expense of other aspects of my training.

Other than that, there's no reason for me to ride more than (at most) the low end of the volume range I've been riding at. Okay, great. Let's move on and look at fitness versus training intensity.

Now we get to the good stuff: a tight cluster of data points with a good strong correlation (r² = 77%), and, even better, it's pointing in the right direction!

This is a nice steep line, and needless to say it's given me all kinds of ideas about how to experiment with my training over the next few months. Lately I haven't really been enjoying my very long rides, and now I don't have to think twice about reducing their frequency, while I instead shift my focus onto intensifying my training.

The first thing I'd like to see is a wider range of values on the X-axis: I need to do some blocks with an average intensity score in the 60s to see what happens. I don't want to extrapolate too far, but I'm optimistic that if I do this (and get the training/recovery balance right) I'll see some good improvements in the near future. But more data points are certainly required.

What about improvement over time? When I started riding three years ago my fitness level was very low, so there was a lot of scope for improvement, and I certainly saw huge gains over the first twelve months or so. Considering the low starting point, this would probably have happened almost no matter what kind of riding I did. But since I wasn't training with power during that time, I can't include this in my analysis.

But over the last year that I've had my power meter there's been no obvious relationship between my fitness and time, so it seems that I'm at a point where if I want to see significant further improvement, I've got to pay attention to the details of my training. Just guessing what's best won't work anymore: I need to put together a series of specific, focused and consistent blocks with a test at the end of each one, modifying my training based on the results.

Here's a figure from the article, showing how athletes group by type:

The guys who win everything are in the green group, most people are in the blue group, and we all know a few purples!

It appears that I'm in the yellow group (actually, off the top of the scale). Unfortunately, until recently, I spent most of my time training like a volume responder; I nearly killed myself for much of 2019 repeatedly stringing together 20-hour weeks, during which time I seemed to get slower and slower, although this was probably down to exhaustion.

In fact, the likely explanation for the total lack of correlation between volume and fitness for me is that any gains from extra volume were offset by fatigue. If I'd taken time out for recovery when I was in the high volume period last year things might have looked a little better. Indeed, this year I've done just that, recording my best performances across the board in the last few weeks.

This analysis has reinforced what I've increasingly been suspecting: that I need to focus on riding harder, not longer. But intensity isn't a monolithic entity, so I'll need to go into even finer detail. I'm going to be keeping the volume relatively low for a while (averaging around 12 hours per week for the next couple of blocks), while I focus on intensifying my training. But there are several ways I could do this.

If I want to bring my average intensity for a block up to 65 TSS/hour, I could just do a lot of tempo rides (riding at 80% FTP), or I could do a combination of endurance rides and threshold and/or VO₂ sessions, or just do a mixture of fairly short base training rides and very hard anaerobic sessions. And these will all have different physiological effects.

The first option is out, as I want a combination of easy and hard rides, not a lot of moderate ones. So it's mostly a question of getting the right balance between work above and below VO₂ capacity, within the right proportion of hard and easy days. It's time to experiment, and, as ever, I'll be posting updates on how things are going over on my Training Notes page.

If you've got your own power and heart rate data, and you perform regular maximal long efforts of at least 20 minutes, then you've also got the information you need to do this analysis¹. I think it would be well worth your while; maybe it'll lead you to a new way of training that works better for you.

1 You can calculate TSS scores and training hours using the Strava plugin intervals.icu (as I discuss here). Get the numbers and plug them into a spreadsheet.

#cycling #training #volume #intensity

The social aspect enables what I find to be the most valuable part of the Strava experience: the motivation that comes from seeing improvement in my own rides, moving up the leaderboard on segments, and getting inspiration from other athletes' performances. A bit of (mostly) friendly competition I find to be a good thing.

But when it comes to serious training, many people turn up their noses at Strava. And it's true that the analytics that Strava offers, especially on the free tier, are quite limited compared to the competition.

There's a couple of things to say about this.

First of all, when it comes to training, I think there's a lot of over-analysis and over-complication out there: all it takes to achieve success is, to quote Morgan Freeman, pressure and time. Regular, consistent riding with the right balance between easy and hard sessions, a progressive increase in training load, and a recovery period whenever you need one should get you where you want to be. The finer details might make some difference, especially at the highest level, but for amateurs I think if you do this for a few years you're all set.

Certainly that's what has (eventually) worked for me. My current training plan is pretty simple, and all the changes I've made to it recently have moved it further in that direction, which has coincided with me getting fitter than I've ever been. All you really need to stay on track is to follow the old truism to 'listen to your body'. However, that can prove more difficult than it should be for a lot of us.

If that sounds like you, then maybe you'd benefit from a little computerized assistance? If so, as I demonstrate below, you can get all the training detail you could want from Strava (optionally supplemented by a freely-available stand-alone software package for the real data monsters).

While basic, free Strava isn't going to be enough by itself for athletes who want more in-depth analysis of their training, there are ways to enhance the experience. The first option is to pay a few dollars per year for their Premium plan, which actually adds a decent list of features.

The best of these include Relative Effort and the associated Fitness & Freshness trends (these are based on heart rate, and I actually find them to be far more accurate than any power-based training stress balance measures such as found in TrainingPeaks), basic power analysis (zone accumulation per ride and a power curve) and Live Segments (real-time data on segment efforts). There are also custom power and segment goals (the former providing motivation when chasing a season's best, and the latter being most useful in conjunction with Live Segments, as you can then see in real time whether you're on target to beat your goal time).

Strava Relative Effort

Strava Fitness & Freshness

Strava Live Segments

However, aside from this, there's another way to make Strava better: using add-on apps. There are many out there, but the best two that I've found are intervals.icu and Elevate (both of which are currently web browser extensions and thus only work on the strava.com website, not on the phone app).

Elevate gives you a ton of ways to customize your activities, adding many enhanced statistics and graphical breakdowns. It also replicates the TrainingPeaks fitness trend model if that's your bag, and even allows detailed customization of the Strava homepage (my favourite is hiding indoor activities like Zwift from my feed).

Elevate Activity View

Elevate Extended Stats

Elevate Fitness Trend

If you train with a power meter, intervals.icu lets you go even deeper. It again gives you the standard power-based fitness breakdown, plus a calendar detailing all your activities with their information, your weekly training load and training distribution, etc. It lets you know when you get a power PR for a given time period, gives you power profile analysis such as athlete type and where you stand relative to other intervals.icu users, gives FTP estimates from extended maximal efforts (if you want this to be accurate, make sure to set the minimum duration to at least 20 minutes), and provides a very detailed breakdown of individual activities, plus lots more. At the time of writing it is undergoing rapid development, and is already a must-have for any Strava user with a power meter.

intervals.icu Training Diary

intervals.icu Activity Details

intervals.icu Fitness Chart

I personally use all the above, and find it easily covers everything I need. But in case that's still not enough, the standalone (not Strava-related) WKO clone Golden Cheetah takes you well and truly down the rabbit hole. I use this as well, but generally just for a couple of esoteric features that I can't get elsewhere.

Golden Cheetah

Between all these, it's safe to say you can have all the data analysis you want, and probably a lot more than you actually need.

#cycling #strava #training

The ideas behind it are interesting, and promising:

Firstly, a software-based adviser that recommends workouts based on your current training status. Tired or fresh? Building or peaking? The recommendations will vary based on your current situation. And if you're targeting a road race you'll get different advice than if you were peaking for a criterium. If you miss a couple of days training, it will seamlessly adapt its recommendations, as there is no 'plan' as such. If you're feeling fresh and energetic, you can increase your ramp rate to 'aggressive' and Xert will adjust. If you're tired, select 'taper' or 'maintenance' and it will slow down.

Secondly, the automatic generation of your power profile, without any need for specific testing. This uses the power data from your rides to estimate your abilities. The only requirement is that you regularly perform maximal efforts (intervals, group rides, races). From this Xert will gather information about you, summarized with the key parameters Peak Power, High-Intensity Energy and Threshold Power. And from that they give you the familiar power curve 'fitness signature', which tells you your current abilities from 1 second out to 5 hours (even if you haven't done a maximal effort at most durations). Your progress is tracked over time, so you can see how things are going, and what works and what doesn't work for you.

The key to the fitness signature is the 'breakthrough': when you perform in excess of what Xert's model regards as your maximum. For instance, if I have a predicted maximum of 800 Watts for 20 seconds, and I go out and hit 814 W, this will generate a breakthrough and Xert will update one or more of the parameters (in this case this would definitely include High-Intensity Energy and possibly also Peak Power, but not Threshold Power).

They also have a fairly extensive selection of workouts, which can be loaded onto your smart trainer (should you have one), and for Garmin users they have an app which provides useful information live during a ride, such as carbohydrate and fat consumed and current maximal energy available.

It all sounds great, and for $10 per month seems like decent value. I used it for about six months, but recently cancelled. Here's why.

I wasn't interested in the training adviser, other than as a curiosity, but here is an overview.

In practice, the adviser will default to either: a) recommending an intervals ride with progressively decreasing interval durations depending how far out from your next scheduled event you are, and what you set your 'athlete type' to. So for instance, if you're targeting a road race two months from now and you are a punchy type cyclist, you might set your athlete type to 'breakaway specialist'. This would mean that the recommended training would start out as mostly endurance, then progress to intervals of gradually decreasing duration (20 minute, 10 minute, 8 minute, 5 minute) until they matched the athlete type as you reach the peaking phase; b) if you're judged by Xert to be too tired for a high-intensity ride, it will recommend an endurance or recovery ride. There are multiple rides to choose from in each category, but that's basically it. Very straightforward.

The other side is the fitness signature. This obviously has great appeal: doing specific threshold tests every few weeks is arduous, and even if you do them they don't track changes in between and only cover the specific durations that you test; the rest of the curve is extrapolation. Xert promises a continually-updated fitness signature without the need for specific testing. The problem was that my fitness signature provided by Xert became wildly inaccurate.

I'd been growing increasingly suspicious of the numbers for some time, but as detailed elsewhere, I'd been greatly overdoing my training for just about the whole period in question, so I just put the discrepancy down to fatigue. Eventually though, something had to give, as attempting to use the fitness signature to set training zones led to failed workouts.

At the end of the season when I'd finally accepted I'd been chronically overreaching, I backed off for a couple of weeks, freshened up, and went out for a fun ride for a couple of hours, just doing random efforts. I threw in a set of my favourite intervals: a warm-up culminating in a 20-second neuromuscular effort with 1 minute recovery, followed by 6 reps of 15 seconds full gas, 30 seconds recovery (I actually got interrupted on the third and fourth reps, so ended up doing 8). These are the intervals that always gave me breakthroughs on Xert, and sure enough I got another on that day too.

Above is a screenshot of the ride, which gives you an idea of how Xert works. It has values for my Peak Power, Maximal Power Availability (MPA; a dynamic value that changes during a ride based on fatigue) and Threshold Power (FTP). At first, your MPA is equal to your Peak Power, but efforts greater than your Threshold Power cause your MPA to decrease at a rate that varies between individuals due to differences in high-intensity energy (also known as anaerobic work capacity, or W').

You can see I did a couple of these efforts earlier in the ride (at around 14 and 26 minutes), but because I backed off afterwards my MPA returned to its initial value. Now we'll zoom in on the set of sprint repeats:

I started with a 20-second effort, then about 1 minute later I started the repeats, during which my MPA gradually declined, until during the last repeat it dropped lower than the power I actually generated. This gave me a breakthrough. Down at the bottom you can see my numbers following this ride: Peak Power 991 Watts, High Intensity Energy 22.2 kJ and Threshold Power 322 Watts. Those are the numbers of a 'slow-twitcher', which itself was a red flag for me, but those are what the model produced, so let's just go with them for now.

As I've said, I was suspicious. So four days later, once I was nice and fresh again, I went back out and did a 20-minute maximal effort to see what I really could do on a longer, continual interval. According to Xert my FTP was in the 320s so I should have been able to get around 340 W for 20 minutes. I buried myself and got 301 W (obviously nowhere near a breakthrough), which at best would put my FTP in the 280s. I cancelled later that day.

Afterwards, Xert sent me a cancellation note asking if I had any feedback, so I wrote back and explained my concerns. The response seemed to imply that I just wasn't trying hard enough on the 20-minute effort, but after I insisted that I really was giving it everything (my average HR was 5 bpm above my threshold HR and perceived exertion was through the roof), they adjusted the numbers: dropping my Threshold Power down from 321 W to 285 W and increasing my High Intensity Energy from 22.3 kJ to 25 kJ. This is shown in the next figure:

According to Xert, those numbers match my performance on the 20-minute threshold ride (where it now shows me getting a breakthrough during a surge I did near the end of the effort, as you can see above). But the problem is, if you go back and plug these new numbers into my intervals ride from four days earlier, you get this:

My efforts on the sprint repeats are far greater than I should have been able to do given these new parameters. In other words, I broke their model.

It seems that Xert isn't properly accounting for recovery between reps on short repeat intervals. There's a lot of cool biochemistry going on here involving depletion and repletion of the phosphagen energy system, which I suspect Xert isn't properly modelling. The only other plausible explanation is that I just wasn't trying on the 20-minute interval. That's what Xert hinted at (and I've noticed them doing this with other clients on their help forum), but I can assure you that it isn't the case.

For what it's worth, I think the revised numbers are far closer to reality than the original ones; as much as I'd love my FTP to climb upwards of 320 W, that's just not happened (at least, not yet!). The revised power curve derived from these numbers does match quite closely my actual best efforts. But even so, Xert isn't any use to me because as soon as I do a repeat-type intervals ride, the numbers will be overridden due to Xert's inability to handle this type of workout.

So that was my experience with Xert. Maybe they'll improve things later on, or maybe it really is my own fault. Either way, it's not for me at the moment.

They offer a 30-day free premium trial, so if you have a power meter it's worth giving it a go. Let me know if you have better results than I did.

#cycling #training #xert

Aerodynamic drag in cycling team time trials Blocken et al., 2018

Technology has always been an integral part of cycling. Obviously, bicycles are themselves technology, and also rely on technology (i.e. good quality road surfaces) to be a practical means of transportation. Cycling has also taken its fair share of the spoils obtained from the increase in the rate of technological development of recent years; innovations such as carbon fibre, power meters, and GPS computers (amongst many others) have transformed cycling at every level.

Technological innovations are also helping push forward our knowledge of the physics of cycling, and a great deal of this progress has come from the study of aerodynamics. Field tests and wind tunnels were a starting point, subsequently complemented by computational fluid dynamics (CFD), which utilizes modern information-processing capabilities to simulate highly complex physical situations in a much more comprehensive way than the relatively two-dimensional analysis possible in a wind tunnel.

The importance of aerodynamics is readily apparent to all road cyclists, especially those who have ridden at speed in a group. It's been known for decades that the energy saving obtained from riding immediately behind another rider (or other vehicle) is very large, and it has recently been shown that in a very large peloton the aerodynamic drag of a rider near the back centre of the group can be reduced to below 10% of that experienced by an individual cyclist.

The work outlined here utilized wind tunnel measurements and 3-dimensional CFD simulations to study drafting of up to 9 cyclists riding in single-file formation at various distances. Much of the work consisted of designing and validating CFD simulations. We won't get into the theoretical concerns (although aerodynamics is a fascinating subject, it gets extremely complicated very quickly); suffice it to say that the CFD simulations aligned very closely with wind tunnel measurements, and that reasonable assumptions regarding rider and bicycle geometry were made. All results used a velocity of 15 m/s (54 km/h, 33.5 mph), at which speed aerodynamic drag accounts for 90% of the total drag forces on a rider; for lower speeds this figure would be less, and the magnitude of the energy savings indicated would be smaller.

The main results are shown in the following figures (identical pacelines except for the inter-rider distances, which are 15 cm, 50 cm and 1 m respectively).

Distances of 5 cm and 5 m were also modelled, but aren't shown here (5 cm is unrealistically close, while 5 m is far larger than found in all but the most disorganized groups¹).

Interesting points to note include:

1) the lead rider in a group receives a small aerodynamic benefit compared to riding solo (due to upstream disturbance, i.e. following riders 'pushing' air forward);

2) upstream disturbance also means that the rider experiencing the greatest drag reduction is not the last rider for groups with six or more members;

3) riding second in a paceline reduces drag to 60-70%, and riding third to 50-60%, compared with an isolated rider. Beyond fourth place in a long paceline, however, additional benefits are much smaller. But remember that this analysis is for a single line of riders; as noted above a large, multi-column peloton reduces drag even more, since it produces a much larger wake from multiple lead riders.

The most illuminating point to me, though, was how little difference there is between the three distances shown in the charts: the middle rider of a 5-man group, for example, experiences 50%, 52% and 54% of the drag of an isolated rider at 15 cm, 50 cm or 1 m, respectively.

Although this is certainly significant for a team time trial or pursuit race, for amateur riders, even when racing, I'd suggest that the 2% reduction of drag when going from 50 cm to 15 cm isn't worth the additional risk of a crash: if the lead rider is doing 400 Watts this difference amounts to just 8 Watts², but 15 cm (6 inches) gives you almost zero reaction time, whilst 50 cm (over two-thirds of a wheel length) allows much more room to manoeuvre. In practice you might be somewhere in between these two distances, but the main message is not to worry about squeezing out every last centimetre, especially in a situation with unknown riders on a technical course.

To conclude with a personal anecdote: on my most recent group ride there was a 15-minute period where I rode in a 3-man line, during which I averaged 240 Watts and the group as a whole maintained a steady pace with evenly-spaced changes every 90 seconds or so. We know and trust each other quite well, so kept the gaps small. My average power during this period was as follows: on the front 320 W, on the back 180 W (56%) and in second 210 W (66%). These numbers are remarkably close to those shown in the charts. Each of us could probably keep up a 240 Watt average for two or three hours of riding, but wouldn't last more than a few minutes at 320 Watts.

That's the power of drafting.

1 although even gaps this large still produced substantial beneficial effects.

2 actually even less, because to keep things simple I'm crudely (and incorrectly) assuming that aerodynamic drag is the only force acting against cyclists.

#aerodynamics #drafting

#cycling #research #science

The 4000-m team pursuit cycling world record: theoretical and practical aspects Schumacher and Mueller, 2002

This is an extremely interesting research article, not least because it gives a detailed description of the training practices of world-class athletes. Such information is generally hard to come by, so when I find some I give it my close attention!

The paper actually begins in quite unremarkable fashion, detailing what were at the time the necessary power requirements (an average 480 Watts for the four team members over 4 minutes¹) to secure a world record in the track cycling Team Pursuit event, modeling the workload and physiological demands of the event, and giving an equation to estimate winning times in future competitions based on regression analysis of previous winning times.

However, for our purposes the most interesting material is the included discussion of the team's training program. Given that the event in question takes place over a distance of just 4 kilometres and thus has a high anaerobic component (with a need to maintain 100% VO₂max power – well above lactate threshold – for the full duration of the race), you might imagine that the riders would focus heavily on anaerobic training, such as high-intensity intervals. In fact, there was surprisingly little of this until the final days immediately prior to the event, at the 2000 Olympic Games.

Overall, the training in the months leading up to the event was dominated by high mileage, low intensity rides (3-8 hours/day; 29,000-35,000 km/year), referred to in the article as 'basic training'. This is exactly the same as the old-fashioned base training rides employed by road racers in the off-season, done at 30-50 bpm below lactate threshold heart rate (Z2 in a 5-zone system).

This training led into periodic workload peaks in the form of one or more road stage races, which increased in frequency and difficulty as the Olympics date grew nearer, and culminated in a short period of track training to finish each of 3 macrocycles (see Figure 1; note that the darkest boxes are the track training, and that blank sections involved unstructured light training at home).

The final track training period covered the 8 days immediately prior to the Olympic Games, and the two earlier ones were just 4 days each. That's 16 days of event-specific training in 6 months! This track training was divided into 'evolution' training (6 minute blocks within 5bpm either side of lactate threshold heart rate), and event-intensity 'peak' training (1- or 2-minute blocks at maximal 4-minute power). Both evolution and peak training were performed at around 130 rpm with 20 minute breaks between repeats. As shown in Table 2, a maximum of only 7 evolution and 2 peak blocks were carried out in a given day.

The training distribution outside of the stage races and track training (i.e. 70% of the training days) was 94% below anaerobic threshold, 4% around threshold, and 2% above threshold. These numbers would change with the inclusion of the stage races, although even the tougher races would also consist predominantly of low-intensity riding, punctuated with periods where increased effort was demanded.

The purpose of the track training was to boost anaerobic capacity, as well as allowing focus on discipline-specific technical and motor skills. As we have seen, however, this didn't require a great deal of time to accomplish (about 8% of total training days in the six months leading up to the Olympics).

The remainder of the paper discusses in further detail specifics related to the determination and track-order of team members.

However, for us the lesson is clear (and there's a reason that base training is so named): aerobic adaptations are the major physiological determinants of performance, even in an event consisting of just 4 minutes of maximal effort. Anaerobic capacity must be added on top of this.

Because of the much more rapid recovery following low-intensity riding compared with training at threshold or higher intensities, this type of training can be repeated consecutively for many days, allowing a training volume (and associated adaptations) to be amassed that is much larger than would be possible when training at higher intensities. This is an ideal way to build a potentially huge fitness base.

Furthermore, in addition to being built upon and benefiting from aerobic adaptations, many anaerobic adaptations can develop over a much shorter time frame than aerobic ones (partly because they actually have less development potential). Understanding why this is true necessarily involves examining the specific physiological changes that we're talking about in some detail, which is a large topic that we'll look at in future, across multiple articles.

The big caveat for amateur riders when applying this to ourselves, of course, is that most of us don't have the time and resources necessary to emulate this kind of training. So we must make compromises. This also is a huge topic that I'll repeatedly address in future articles. A clue for now: if we can't match the volume, we must modify the other term of the training equation i.e., intensity. But exactly how we do this is of great importance; some ways are much better than others.

1 Note also that, prior to the Team Pursuit, two of the four riders on the team finished first and second in the Individual Pursuit event.

#training #intensity #olympics #worldrecord

#cycling #research #science

Effects of saddle height on economy and anaerobic power in well-trained cyclists Peveler and Green, 2011

Bike fit is a crucial aspect of cycling, both for maximizing performance and minimizing the risks of injury. And probably the most important single component of bike fit is saddle height. There are various methods used for determining optimum height, including the heel, LeMond, and 109% inseam methods. However, the most direct method is to measure a precise knee flexion angle at the maximum extent of the pedal stroke. The current experiment complemented previous ones in determining the optimal knee angle for performance.

This study took it as well-established that the safe range of knee angles is 25°-35°, but investigated whether there were any differences in performance between the lower and upper end of this range. It had been shown earlier that the 109% inseam method leads to wildly variable knee angles (from 19° to 44°, and outside the recommended 25°-35° range more than half the time), presumably due to inter-individual variability in femur, tibia and foot lengths.

There were indeed differences, albeit fairly small. Two tests were performed for each knee angle, measuring economy and power. The economy test consisted of 15 minutes of pedaling at fixed resistance and cadence, measuring oxygen consumption as an indication of economy (lower oxygen consumption indicating greater economy). The power test consisted of a 30 second maximal effort. The subjects were well-trained males.

There were small but significant differences favouring 25° over 35° in oxygen consumption (1.0%), perceived exertion (3.5%) and mean power (2.7%). Like I said, these differences are quite small — likely unnoticeable for the recreational cyclist — but potentially significant during an actual race. Certainly, people do all kinds of crazy (and expensive) things in an effort to gain a couple of watts, so potentially getting some for free seems like a good deal.

The study excluded a high number of subjects, and so ended up being quite small, so I'd definitely like to see a larger one looking at multiple knee angles around 25° in future, and another thing that would have been very interesting would be a follow-up of athletes a few weeks later. There was no indication in the article about what the prior knee angle of each athlete was and therefore no way of knowing how different a particular angle was from their usual angle. If 25° was a significant departure from normal, having the athletes continue to train and race using this 'optimum' knee angle might actually increase the observed gains as they got used to the new setup.

#bikefit #saddleheight

#cycling #research #science

Anthropometric comparison of cyclists from different events Foley, Bird and White, 1989

Whether you're a serious cyclist, a novice or simply prefer watching others do the suffering, you're probably aware that, although clearly an endurance sport, different types of cycling suit different types of rider. Even the casual fan knows that there are climbers, sprinters and time trialists.

Obviously, there are certain essential characteristics that are shared by all strong riders and there is a large overlap between the categories, but nevertheless there are specific physical traits that predispose a rider to excel in one discipline relative to the others. Some of these traits are based on unchangeable skeletal features.

This study looked at this, based on rider specialization. Riders were placed into one of four categories based on their strengths: sprint (track and road), pursuit, time trial (including ultra distance) and all-rounders. The most significant differences between the groups were found in height, femur, lower leg and total leg lengths, foot length and somatotype (the ratio of endomorphy, mesomorphy and ectomorphy).

In all cases, the sprinters had the lowest values, i.e. they were the shortest in stature, leg and foot measurements and were the most mesomorphic (muscular). The time trial specialists were the tallest with the greatest leg and foot measurements and highest ectomorphy (slenderness). The pursuit and all-round riders were intermediate.

These results were consistent with earlier studies. They were explained in light of the crucial importance of strength and cadence in sprinting, respectively facilitated by a relatively muscular physique and short legs. In contrast, for time trial riding a more effective approach is to push a large gear at a relatively low cadence, so therefore there could be mechanical advantage to having longer legs and a leaner body frame.

This is quite an old study, and I failed to find any more recent follow-ups, which is a shame because I'd love to see a larger, more comprehensive analysis.

For what it's worth, I followed the authors' methods on myself which emphatically placed me in the sprinter category. This didn't come as a surprise, as (although I've never done a proper power profile test) I'm clearly stronger at short efforts relative to longer ones. Does this mean I should use this as an excuse to cut out the long rides, and just focus on intervals and pumping iron? Of course not; cycling is an endurance sport, and even short criterium races demand a lot of endurance; there's no point having a devastating sprint if you're too tired to produce it at the end of an actual race.

What it might do is give me more confidence that the specialization phases of my training should indeed be focused on power and top end speed and that my best chance for good results will be in criteriums as opposed to long road races or hill climbs. But my overall training plan should be quite similar to any other amateur cyclist, just with a different emphasis.

#anthropometrics #bodytype

#cycling #research #science