As we move into our 40s, 50s and beyond, we may become aware of changes in our bodies. Performance peaks level off or start to decline. Even if you don’t feel old, it becomes harder to keep up with younger sprinters. It takes longer to recover from a hard ride, injury or illness.
Muscle, Fat and Bone
The cause of these age-related changes is a decline in the production of specific hormones. Growth hormone falls insidiously from the time we reach adult height. From the age of 50, testosterone levels drop slightly in men, while oestradiol levels fall dramatically as women reach menopause. The key thing to note about growth hormone and testosterone is that they are anabolic agents, i.e. they build muscle. As they decline, there is a tendency to lose muscle and to increase fat deposition. Sex steroids also play a pivotal role in bone formation.
Protein, Carbohydrates and Vitamin D
Fortunately there are measures we can take to counter the effects of declining hormones. Nutrition plays an important role. Understanding the physiological effects of hormonal changes makes it easier to recognise beneficial adaptations in your diet.
Protein provides the building blocks required for muscle. Taking an adequate level of protein, spread out through the day, is beneficial.
Carbohydrates are the key fuel for moderate to high intensity. Fasted training is not advisable. The body’s shock reaction to underfuelled training is to deposit fat.
The UK government advises everyone to take vitamin D supplements, especially over the winter. In addition to supporting bone health, studies have shown improved immunity and muscle recovery.
Nutrition as you get older
Nutrition, Exercise and Recovery
When combined with adequate nutrition, exercise, particularly strength training, stimulates the production of growth hormone and testosterone. It is important to ensure adequate recovery and to follow a regular routine of going to be early, because these hormones are produced while you are asleep.
Everybody is unique, so you need to work out what works best for you. For further insights on this topic, Dr Nicky Keay has written a book full of top tips, called Hormones Health and Human Potential.
One of the big differences between amateur and professional cycle racing is the role of the breakaway. Amateur racing usually consists of a succession of attacks until a group of strong riders breaks away and disappears into the distance to contest the win. There is rarely enough firepower left in the peloton to close down the gap.
This contrasts with professional racing, where a group of weaker riders typically contests for the breakaway, hoping that the pursuing teams will miscalculate their efforts to bring their leaders to the head of the race in the final kilometres. Occasionally a solo rider launches a last minute attack, forcing other riders to chase.
One minute for every ten kilometres
Much of the excitement for cycling fans is generated by the tension between the breakaway and the peloton, especially when the result of the race hangs in the balance until the final metres. Commentators often say that the break needs a lead of at least one minute for every ten kilometres before the finish line. Where does this rule of thumb come from?
It’s time for some back of the envelope calculations. On flat terrain, the breakaway may ride the final 10km of a professional race at about 50kph. A lead of one minute equates to a gap of 833m, which the peloton must close within the 12 minutes that it will take the breakaway riders to reach the finish line. This means the peloton must ride at 54.2kph, which is just over 8.3% faster than the riders ahead.
On a flat road power would be almost exclusively devoted to overcoming aerodynamic drag. The effort rises with the cube of velocity, so the power output of the chasing riders needs to be 27% high. If the breakaway riders are pushing out 400W, the riders leading the chasing group need to be doing over 500W.
The peloton has several advantages. Riding in the bunch saves a lot of energy, especially relative to the efforts of a small number of riders who have been in a breakaway all day. This means that many riders have energy reserves available to lift the pace at the end of the race. Teams are drilled to deploy these reserves efficiently by drafting behind the riders who are emptying themselves at the front of the chasing pack. Having the breakaway in sight provides a psychological boost as the gap narrows. The one minute rule suggests these benefits equate to a power advantage of around 25%.
Not for an uphill finish
If the race finishes on a long climb, a one minute lead is very unlikely to be enough for the break to stay away. Ascending at 25kph equates to a gap of only 417m and now the peloton has 25 minutes to make up the difference. This can be achieved by riding at 26kph. This is just 4% faster, requiring 13% higher power to overcome the additional aerodynamic drag. This would be about 450W, if the break is holding 400W.
The chasing peloton still has fresher riders, who may be able to see the break up the road, they do not have the same drafting advantages when climbing at 26kph. The other big factor is gravity. The specialist climbers are able to put in strong accelerations on steep sections, quickly gaining on those ahead. They can climb faster than heavier riders at equivalent power.
If we take the same figure for the power advantage over the break as before, of around 25%, the break would need to have a lead of 1 minute 55 seconds as it passes the 10km banner. However, experience suggests that unless there is a very strong climber in the break, a much bigger time gap would be required for the break to stay away.
Chasing downhill
This analysis also explains why it is very difficult to narrow a gap on a fast descent. Consider a 10km sweeping road coming down from an alpine pass to the valley. Riding at 60kph, a one minute gap equates to 1km. The peloton would have to average 66kph over the whole 10km in order to make the catch in then ten minute descent. In spite of the assistance of gravity, the 10% higher speed converts into a 33% increase in the effect of drag, where riders begin to approach terminal velocity.
Amateur breaks
Amateurs do not have the luxury of a directeur sportif running a spreadsheet in a following team car. In fact you are lucky if you anyone gives you an idea of a time gap. The best strategy is firstly to follow the attacks of the strongest riders in order to get into a successful break and then encourage your fellow breakaway riders, verbally and by example, to ride through and off, in order to establish a gap. As you get closer to the finish, you should assess the other riders in order to work out how you are going to beat them over the line.
The critical power model is one of the most useful tools for optimising race performance, but why does it work? The answer lies in the connection between the depletion of energy reserves and the accumulation of waste products.
Variation of W’ Balance over a race
A useful overview of the critical power model can be found in a paper by Clarke and Skiba. It applies well to cycling, where power can be measured directly, and to other sports were velocity can play the role of power. Critical power (CP) is the maximum power that an athlete can sustain for a long time without suffering fatigue. This measure of performance is closely related to other threshold values, including lactate threshold, gas exchange threshold, V̇O2max and functional threshold power (FTP). An advantage of CP is that it is a directly related to performance and can be measured outside a laboratory.
The model is based on empirical observations of how long athletes can sustain levels of power, P, in excess of their personal CP. The time to exhaustion tends to be inversely proportional to the extent that P exceeds CP. This can be described by a simple formula, where excess power multiplied by time to exhaustion, t, is a constant, known as W’ (read as “W-prime”) or anaerobic power capacity.
(P-CP)t=W’
Physics tells us that power multiplied by time is work (or energy). So the model suggests that there is a fixed reserve of energy that is available for use when we exceed our CP. For a typical athlete, this reserve is in the order of 20 to 30 kilojoules.
Knowing your personal CP and W’ is incredibly useful
Suppose you have a CP of 250W and a W’ of 21.6kJ. You are hoping to complete a 10 mile TT in 24 minutes. This means you can afford to deplete your W’ by 0.9kJ per minute, which equates to 900J in 60 seconds or a rate of 15W. Therefore your target power should be 15W above CP, i.e. 265W. By holding that power your W’ balance would slowly fall to zero over 24 minutes. Theoretically, you could burn through your entire W’ by sprinting at 1250W for 21.6 seconds.
Replenishing W’
While it may be possible to maintain constant power on a flat TT or on a steady climb, most race situations involve continual changes of speed. A second aspect of the critical power model is that W’ is slowly replenished as soon as your power drops below CP. The rate of replenishment varies between individuals, but it has a half-time of the order of 3.5 minutes, on gentle recovery.
This means that in a race situation, W’ can recover after an initial drop. By hiding in the peloton and drafting behind other riders, your W’ can accumulate sufficiently to mount a blistering attack, of precisely known power and duration. The chart above, generated in Golden Cheetah, shows the variation of my W’ balance during a criterium race, where I aimed to hit zero in the final sprint. You can even download an app onto your Garmin headset that measures W’ in real time. It is great for criterium racing, but becomes less accurate in longer races if you fail to take on fuel at the recommended rate.
Physiology
Although I am completely convinced that the critical power model works very well in race situations, I have always had a problem with the idea that W’ is some kind of magical energy reserve that only becomes available when my power exceeds CP. Is there a special biological label that says this glycogen is reserved only for use of when power exceeds CP?
One possible answer is that energy is produced largely by the aerobic system up to CP, but above that level, the anaerobic system has to kick in to produce additional power, hence the name anaerobic work capacity. That sounds reasonable, but the aerobic system burns a mix of two fuels, fat and glucose, while the anaerobic system burns only glucose. The glucose is derived from carbohydrates, stored in the liver and muscles in the form of glycogen. But it is all the same glucose, whether it is used aerobically or anaerobically. The critical power model seems to imply that there is a special reserve of glucose that is held back for anaerobic use. How can this be?
The really significant difference between the two energy systems is that the byproducts of aerobic metabolism are water and exhaled CO2, whereas anaerobic glycolysis produces lactic acid, which dissociates into H+ ions and lactate. Note that two H+ ions are produced from every glucose molecule. The lactate can be used as a fuel, but the accumulation of H+ ions presents a problem, by reducing the pH in the cells and making the blood more acidic. It is the H+ ions rather than the lactate that causes the burning sensation in the muscles.
The body is well equipped to deal with a drop in pH in the blood, in order to prevent the acidity from causing essential proteins to denature. Homeostasis is maintained by buffering agents, such as zwitterions, that mop up the H+ ions. However, if you keep producing more H+ ions by furiously burning glucose anaerobically, the cell environment become increasing hostile, with decreasing levels of intramuscular phosphocreatine and rising inorganic phosphate. The muscles eventually shut down because they simply can’t absorb the oxygen required to maintain the flux of ATP. There is also a theory that a “central governor” in the brain forces you to stop before too much damage ensues.
You don’t “run out of energy”; your muscles drown in their own waste products
It is acknowledged that the magnitude of the W′ might also be attributed to the accumulation of fatigue-related metabolites, such as H+ and Pi and extracellular K+.
If you reach the point of exhaustion due to an accumulation of deleterious waste products in the muscles, why do we talk about running out of energy? And what does this have to do with W’?
Firstly note that CP represents the maximum rate of aerobic exertion, at which the body is able to maintain steady state. Oxygen, inhaled by the lungs, is transported to the muscles and the CO2 byproduct is exhaled. Note that the CO2 causes some acidity in the blood, but this is comfortably managed by the buffering agents.
The connection between H+ ions and energy is evident in the following simple chemical formula for anaerobic glycolysis. Each glucose molecule produces two lactate ions and two H+ ions, plus energy.
C6H12O6 → 2 CH3COCO−2 + 2 H+ + Energy
This means that the number of H+ ions is directly proportional to energy. A W’ of 21.6kJ equates to a precise number of excess H+ ions being produced aerobically. If you maintain power above CP, the H+ ions accumulate, until the muscles stop working.
If you reduce power below CP, you do not accumulate a magic store of additional energy stores. What really happens is that your buffering systems slowly reduce the accumulated H+ ions and other waste products. This means you are able to accommodate addition H+ ions next time you exceed CP and the number of H+ ions equates to the generation a specific amount of energy that can be conveniently labeled W’.
Conclusion
W’ or anaerobic work capacity acts as a convenient, physically meaningful and measurable proxy for the total accumulated H+ ions and other waste products that your muscles can accommodate before exhaustion is reached. When racing, as in life, is always a good idea to save energy and reduce waste.
Supercompensation sounds like a reference to an investment banker’s salary, but in fact it describes the body’s ability to adapt positively to a training stimulus. The idea is to attain a higher level of fitness, following a training session, than you had before. In fact, that is generally the point of training. This concept is closely linked to Strava’s Fitness and Freshness charts.
The development of athletic performance requires a delicate balance between an adequate stimulus that drives adaptation and the provision of sufficient recovery time to allow these adaptations to take place
Endocrinology of Physical Activity in Sport, Third Edition
Much has been written about supercompensation, but, as the quotation above highlights, improving your own personal performance depends on – applying the optimal amount of training stimulus and – allowing the correct amount of recovery time.
How does supercompensation work?
A hard training session puts your body under stress. An athlete who is perspiring profusely and complaining of aching limbs experiences similar symptoms to a patient with a severe fever. The stress induced by both of these situations is picked up in the brain by the hypothalamus, which triggers a range of hormonal responses, putting the body into recovery mode.
Physical exercise challenges the muscular-skeletal, cardiovascular and neurological systems. The hormonal response elicits a range of actions around the body, including muscle repair, replenishment of glycogen stores, increase in mitochondria and reinforcement of neural pathways. These processes do not begin until activity has ceased, so, in fact, you become fitter during the rest and recovering phase, rather than while you are actually exercising.
The recovery processes take time and energy. In addition to fuelling before and during exercise, it is important to refuel after a hard training session, particularly during the first 20 minutes.
Optimal training stimulus
Training stimulus is a function of duration and intensity. Strava measures this as Training Load, which shows up as Training Impulse on your Fitness & Freshness chart. This is similar to other commonly used measures. You should also have in mind what aspect of fitness you need to develop for your target events (endurance, power, sprint etc.).
I recently rode over 200km from London to Brighton and back, which Strava calculated as a Training Load of 400. Unfortunately this probably did not make me much fitter, because it left me greatly fatigued. During the next two days that I spent recovering, my body probably just about reattained its previous base line level of fitness and failed to achieve supercompensation. It was a great ride, but it was also an example of excessive training stimulus .
On the other hand, going for a gentle ride without any strong effort is unlikely to put the body under enough stress to give rise to the desired hormonal response. Any supercompensation is likely to be minimal. Some people might call this “junk training”, because higher duration or intensity is needed, in order to become fitter.
So what is the optimal training stimulus you should aim for? A simple answer is to check your Strava Fitness & Freshness page and set a target Training Load equal to about 1.3 to 1.5 times your current Fitness (quite a hard session). This all links back to how to ramp up your fitness.
The right recovery time
As mentioned above, you get fitter while you are recovering. Ideally your next training session should be timed to match the peak of supercompensation. The colour coding of the chart provides a traffic light system. If you train again too early, your body will not have time to recover. But if you leave it too long, you miss the opportunity. As a general rule, it is sensible to follow a hard training day with an easier day. It is also very important to take one full rest day per week, where activity is limited to nothing more than a short walk or some stretching. When is comes to recovery, remember that sleep is “Chief nourisher in life’s feast”.
Functional overreaching (FOR)
Good periodisation of training stimulus and recovery results in beneficial performance adaptation, known as functional overreaching. This stimulates anabolic (muscle building) hormones, such as IGF1 and testosterone, while stress hormones, like cortisol remain low. The athlete sees a steady improvement in performance.
Nonfunctional overreaching (NFOR)
Nonfunctional overreaching occurs when an athlete is too eager to train again. Without sufficient recovery, the body is only just back to base line when it is hit with another bout of exercise. No time is allowed for the anabolic response. This is throwing away the potential benefits of supercompensation and leads to a stagnation of performance.
Overtraining syndrome (OTS)
Overtraining syndrome occurs when the next training session begins before the body has fully recovered from the last one. This can be a problem for athletes juggling a high number of training hours with a full-time job. When the endocrine system is put under this level of stress, cortisol, prolactin and creatine kinase tend to rise, while sex steroids become depressed. This results in an accumulation of fatigue and a progressive deterioration of performance.
When were you last in a fully recovered state?
You can tell which of these situations applies to you, by asking how long has it been since you were in a fully recovered state? If it is days, you should be able to get fitter. If it is weeks, you may be in a state of nonfunctional overreaching. If you have not been in a fully recovered state for months, you have overtraining syndrome. The period taken to recover to a healthy state often has the same timescale.
How do I know if I am in a fully recovered state?
Various apps use heart rate variability (HRV) as an indicator of recovery. Alternatively, you can activate the sliders for Fatigue and Form on your Strava Fitness & Freshness page and look for positive Form. This is when Fitness is greater than Fatigue. My chart below shows a sustained period of high Fatigue and negative Form in April, suggesting that some of the training in that heavy block may have been somewhat counterproductive, but at least I took a rest week in early May.
Super compensation
Supercompensation is the underlying mechanism of periodised training. It works on a number of timescales from the days in a weekly plan, to the weeks in a monthly plan and up to the months in the season’s plan. I hope that this read has provided you with super compensation.
Suppose you are in a Zwift race that comes down to a sprint finish. How long does it take for your avatar to respond to your heroic effort in the final dash for the line? Could a time lag cost you the race?
Consider the steps involved. First the ANT+ signal travels from your power meter to your device (i.e. computer or phone) then it goes to your router and on to Zwift’s server somewhere on the cloud. At some point your watts per kilo are converted into a velocity, taking account of your previous speed, the gradient, rolling resistance, drafting and any PowerUps in play. This calculation can be performed pretty much instantaneously compared with signal transmission time.
The ANT+ signal travels at the speed of light to your device, which is likely to be very close by, so there is little to be gained as long as there is a clear line of sight. The next step, to the router, can be slower, especially if you are relying on a wireless signal from your garage, while running a raft of other applications on your device (best to shut these down). Serious e-gamers often use a direct wired link to the router. It also helps if you have a super-fast high bandwidth internet connection. However, the time taken for the signal to travel from your router to Zwift’s gaming server, called latency, typically introduces the longest delay, especially if it has to go halfway around the world.
We don’t know the precise location of Zwift’s server, but let’s suppose it is in San Francisco. You can check the latency from your location to other parts of the world on web sites like this one. When I looked, the latency from London to San Francisco was 136ms (milliseconds) and from Cape Town it was 281ms.
In the past, banks have moved their trading desks as close as possible to exchanges, in order to obtain prices nanoseconds earlier than their rivals. As a general rule for interactive online gaming, you need a latency of less than 100ms for acceptable gameplay and over 150ms can become frustrating. But we are not talking about playing DOTA, so how do these figures apply to Zwift?
Zwift not DOTA
Let’s go back to our sprint finish, where the bunch is riding at 60kph. This equates to 16.7 metres per second, which is just a bit less than one bike length every 100ms. However, your ability to overtake your rival depends on your relative speed, not the absolute figure. Imagine a situation where you make a Herculean effort to increase your speed to 18 metres per second (64.8kph), drawing level with the leader’s rear wheel with 30 metres to go. To win the race, you have to make up a bike length, say 1.8m, travelling at a measly 1.3m/s faster than the leader. Who will cross the line first?
If you have 30m to go and the leader is a bike length ahead, he only has 28.2m left, taking 1.69 seconds. But at your higher speed you will cover 30m in 1.67 seconds, so you win by about half a wheel. However, if your avatar had responded to your acceleration with a 100ms lag, you would certainly have lost the race. If you experience this level of latency, a slower rider could beat you, just because he is located closer to the gaming server. The speed of your avatar really is limited by the speed of light.
However, sometimes it can feel like a zPower rider is overtaking you at an appreciable proportion of the speed of light. If this really were the case and Zwift wanted to represent the avatar correctly, what would it look like?
The physicist George Gamov posed this question back in 1938. He highlighted the effect of relativistic length contraction, predicted by Einstein’s theory of special relativity. In fact, the avatar would change colour, due to the Doppler shift, and light intensity would fluctuate. These effects would be further be complicated by our binocular vision, causing an unnerving blurring effect. This is helpfully explained in detail by physicists in a recent scientific paper. Surprisingly, there are practical applications for this work that may help interpret data gathered by spacecraft passing objects at very high speeds.
The Covid-19 epidemic provided a huge boost to the Zwift streaming service. Confined by a global lockdown, cyclists freed themselves from the boredom of pedalling on a static turbo trainer by logging into one of a broadening range of online virtual worlds. Zwift racing has become particularly popular. While it is relatively straightforward to simulate variations in gradient and even the effects of drafting, it is not possible for riders to demonstrate superior bike handling skills. Nor can racers benefit from adopting a superior aerodynamic position on the bike, in fact this may prove to be a disadvantage.
Setting aside e-doping suspicions, such as riders understating their weights, in the artificial world of a Zwift race, the outcome largely comes down the the ability to sustain a high level of power (watts per kilo). The engagingly competitive nature of simulated races encourages everyone to push their limits. However, since Zwift offers no penalty against maintaining a non-aerodynamic body position on your trainer, it is quite possible that regular Zwifters might become habituated to riding in position that is far from optimal for the road.
Fresh aerodynamics
Once out in the fresh air again, many riders may have noticed improvements in the levels of power they are able to sustain, thanks to the high levels of exertion required to compete on Zwift. But in the real world, when it comes to beating other riders in a race or a time trial, the principle force a rider has to overcome is aerodynamic drag, not electromagnetic resistance.
Maximum speed is attained by adopting a riding position that provides the optimal tradeoff between the ability to generate power and a low level of aerodynamic drag. Drag depends on a rider’s CdA, which represents the drag coefficient multiplied by frontal area. Since power rises with the cube of velocity, there comes a point where it is better to compromise on power in order to reduce frontal area. This is the key to time trialing and successful breakaways.
When the race season begins, skilful and more aerodynamic racers will be able to benefit from drafting in the huge wind shadow created by Zwift diesels, while offering back much less assistance when they pull through. So after prolonged training on Zwift, racers and time trialists really need to focus on improving their aerodynamics
There are various ways to reduce drag, starting withs some basics as described in an earlier blog. Post ride analysis can be performed using Golden Cheetah, BestBikeSplit or MyWindSock. There is also a range of devices that claim to offer real time measurement of CdA. These have been primarily targeted at the TT/triathlon market, but there’s no doubt that these could be incredibly useful for both training or even, perhaps, a race breakaway. Cycling Weekly recently reviewed the Notio device, but, while useful, these tools remain expensive and a bit clunky.
Whatever you choose to do, stay safe and stay aero.
Since my blog about Strava Fitness and Freshness has been very popular, I thought it would be interesting to demonstrate a simple model that can help you use these metrics to improve your cycling performance.
As a quick reminder, Strava’s Fitness measure is an exponentially weighted average of your daily Training Load, over the last six weeks or so. Assuming you are using a power meter, it is important to use a correctly calibrated estimate of your Functional Threshold Power (FTP) to obtain an accurate value for the Training Load of each ride. This ensures that a maximal-effort one hour ride gives a value of 100. The exponential weighting means that the benefit of a training ride decays over time, so a hard ride last week has less impact on today’s Fitness than a hard ride yesterday. In fact, if you do nothing, Fitness decays at a rate of about 2.5% per day.
Although Fitness is a time-weighted average, a simple rule of thumb is that your Fitness Score equates to your average daily Training Load over the last month or so. For example, a Fitness level of 50 is consistent with an average daily Training Load (including rest days) of 50. It may be easier to think of this in terms of a total Training Load of 350 per week, which might include a longer ride of 150, a medium ride of 100 and a couple of shorter rides with a Training Load of 50.
How to get fitter
The way to get fitter is to increase your Training Load. This can be achieved by riding at a higher intensity, increasing the duration of rides or including extra rides. But this needs to be done in a structured way in order be effective. Periodisation is an approach that has been tried and tested over the years. A four-week cycle would typically include three weekly blocks of higher training load, followed by an easier week of recovery. Strava’s Fitness score provides a measure of your progress.
Modelling Fitness and Fatigue
An exponentially weighted moving average is very easy to model, because it evolves like a Markov Process, having the following property, relating to yesterday’s value and today’s Training Load. where is Fitness or Fatigue on day t and for Fitness or for Fatigue
This is why your Fitness falls by about 2.4% and your Fatigue eases by about 13.3% after a rest day. The formula makes it straightforward to predict the impact of a training plan stretching out into the future. It is also possible to determine what Training Load is required to achieve a target level of Fitness improvement of a specific time period.
Ramping up your Fitness
The change in Fitness over the next seven days is called a weekly “ramp”. Aiming for a weekly ramp of 5 would be very ambitious. It turns out that you would need to increase your daily Training Load by 33. That is a substantial extra Training Load of 231 over the next week, particularly because Training Load automatically takes account of a rider’s FTP.
Interestingly, this increase in Training Load is the same, regardless of your starting Fitness. However, stepping up an average Training Load from 30 to 63 per day would require a doubling of work done over the next week, whereas for someone starting at 60, moving up to 93 per day would require a 54% increase in effort for the week.
In both cases, a cyclist would typically require two additional hard training rides, resulting in an accumulation of fatigue, which is picked up by Strava’s Fatigue score. This is a much shorter term moving average of your recent Training Load, over the last week or so. If we assume that you start with a Fatigue score equal to your Fitness score, an increase of 33 in daily Training Load would cause your Fatigue to rise by 21 over the week. If you managed to sustain this over the week, your Form (Fitness minus Fatigue) would fall from zero to -16. Here’s a summary of all the numbers mentioned so far.
Impact of a weekly ramp of 5 on two riders with initial Fitness of 30 and 60
Whilst it might be possible to do this for a week, the regime would be very hard to sustain over a three-week block, particularly because you would be going into the second week with significant accumulated fatigue. Training sessions and race performance tend to be compromised when Form drops below -20. Furthermore, if you have increased your Fitness by 5 over a week, you will need to increase Training Load by another 231 for the following week to continue the same upward trajectory, then increase again for the third week. So we conclude that a weekly ramp of 5 is not sustainable over three weeks. Something of the order of 2 or 3 may be more reasonable.
A steady increase in Fitness
Consider a rider with a Fitness level of 30, who would have a weekly Training Load of around 210 (7 times 30). This might be five weekly commutes and a longer ride on the weekend. A periodised monthly plan could include a ramp of 2, steadily increasing Training Load for three weeks followed by a recovery week of -1, as follows.
Plan of a moderate rider
This gives a net increase in Fitness of 5 over the month. Fatigue has also risen by 5, but since the rider is fitter, Form ends the month at zero, ready to start the next block of training.
To simplify the calculations, I assumed the same Training Load every day in each week. This is unrealistic in practice, because all athletes need a rest day and training needs to mix up the duration and intensity of individual rides. The fine tuning of weekly rides is a subject for another blog.
A tougher training block
A rider engaging in a higher level of training, with a Fitness score of 60, may be able to manage weekly ramps of 3, before the recovery week. The following Training Plan would raise Fitness to 67, with sufficient recovery to bring Form back to positive at the end of the month.
A more ambitious training plan
A general plan
The interesting thing about this analysis is that the outcomes of the plans are independent of a rider’s starting Fitness. This is a consequence of the Markov property. So if we describe the ambitious plan as [3,3,3,-2], a rider will see a Fitness improvement of 7, from whatever initial value prevailed: starting at 30, Fitness would go to 37, while the rider starting at 60 would rise to 67.
Similarly, if Form begins at zero, i.e. the starting values of Fitness and Fatigue are equal, then the [3,3,3,-2] plan will always result in a in a net change of 6 in Fatigue over the four weeks.
In the same way, (assuming initial Form of zero) the moderate plan of [2,2,2,-1] would give any rider a net increase of Fitness and Fatigue of 5.
Following my recent update on the Tour de Richmond Park leaderboard, a friend asked about the ideal weather conditions for a reverse lap, clockwise around the park. This is a less popular direction, because it involves turning right at each mini-roundabout, including Cancellara corner, where the great Swiss rouleur crashed in the 2012 London Olympics, costing him a chance of a medal.
An earlier analysis suggested that apart from choosing a warm day and avoiding traffic, the optimal wind direction for a conventional anticlockwise lap was a moderate easterly, offering a tailwind up Sawyers Hill. It does not immediately follow that a westerly wind would be best for a clockwise lap, because trees, buildings and the profile of the course affect the extent to which the wind helps or hinders a rider.
Currently there are over 280,000 clockwise laps recorded by nearly 35,000 riders, compared with more than a million anticlockwise laps by almost 55,000 riders. As before, I downloaded the top 1,000 entries from the leaderboard and then looked up the wind conditions when each time was set on a clockwise lap.
In the previous analysis, I took account of the prevailing wind direction in London. If wind had no impact, we would expect the distribution of wind directions for leaderboard entries to match the average distribution of winds over the year. I defined the wind direction advantage to be the difference between these two distributions and checked if it was statistically significant. These are the results for the clockwise lap.
The wind direction advantage was significant (at p=1.3%). Two directions stand out. A westerly provides a tailwind on the more exposed section of the park between Richmond Gate and Roehampton, which seems to be a help, even though it is largely downhill. A wind blowing from the NNW would be beneficial between Roehampton and Robin Hood Gate, but apparently does not provide much hindrance on the drag from Kingston Gate up to Richmond, perhaps because this section of the park is more sheltered. The prevailing southwesterly wind was generally unfavourable to riders setting PBs on a clockwise lap.
The excellent mywindsock web site provides very good analysis for avid wind dopers. This confirms that the wind was blowing predominantly from the west for the top ten riders on the leaderboard, including the KOM, though the wind strength was generally light.
The interesting thing about this exercise is that it demonstrates a convergence between our online and our offline lives, as increasing volumes of data are uploaded from mobile sensors. A detailed analysis of each section of the million laps riders have recorded for Richmond Park could reveal many subtleties about how the wind flows across the terrain, depending on strength and direction. This could be extended across the country or globally, potentially identifying local areas where funnelling effects might make a wind turbine economically viable.
If you have ever had the feeling that it is becoming harder to rise up the Strava leaderboards and that KOMs are ever more elusive, you are right. I took a snapshot of the top 1000 entries for the Tour de Richmond Park segment in April 2019 and compared it with the leaderboard from February 2017 that I used for an earlier series of blogs.
The current rankings are led by a team of Onyx RT riders, who rode as a group at 6:02am on 25 July 2018, beating Rob Sharland’s solo effort by 6 seconds, with a time of 13:51. Some consider that targeting a KOM by riding as a team time trial is a kind of cheating. Having said that, many riders have achieved their best laps around Richmond Park while riding in the popular Saturday morning and Wednesday evening chain gang rides. In fact, if the Onyx guys had checked my blogs on the optimal wind direction and weather conditions, and chosen a warm evening with a moderate Easterly wind, they would have probably gone faster.
Survival of the fittest
The Darwinian nature of Strava leaderboards ensures that the slowest times are continually culled. Over the two year gap, the average time of the top 1000 riders improved by 35 seconds, which equates to an increase in speed of about 1.6% per annum. In 2017, a time of 17:40 was good enough to reach the top 1000. You now need to complete the rolling 10.8km course in less than 17:07, averaging over 37.8kph, to achieve the same ranking. The rider currently ranked 1000th would have been 503rd on the 2017 leaderboard, making the turnover about 50%.
Strava inflation produces a right shift in the speeds at which riders complete the segment. Rider speeds exhibit “long tailed” distributions, with just a few riders producing phenomenal performances: although many people can hold an average of 38kph, it remains very hard to complete this segment at over 42kph.
More faster riders
A total of 409 names dropped off the bottom of the 2017 leaderboard, to be replaced by new faster riders. Some of these quicker times were set by cyclists who had improved enough to rise up the leaderboard into the top 1000, while others were new riders who had joined Strava or not previously done a lap of Richmond Park.
Riders riding faster
Of the 591 riders who appeared on both leaderboards, 229 improved their times by an average of 53 seconds. These included about 90 riders who would have dropped out of the top 1000, had they not registered faster times.
Getting faster without doing anything
One curious anomaly arose from the analysis: 32 efforts appearing on the 2019 leaderboard were recorded on dates that should have shown up on the 2017 leaderboard. Nine of these appeared to be old rides uploaded to Strava at a later date, but that left 23 efforts showing faster times in 2019 than 2017 for exactly the same segments completed by the same cyclists on the same rides.
For example, Gavin Ryan’s ride on 25 August 2016 appeared 8th on the 2017 leaderboard with a time of 14:23, but now he appears as 16th on the 2019 leaderboard with a time of 14:20! It seems that Strava has performed some kind of recalculation of historic times, resulting a new “effort_id” being assigned to the same completed segment. If you want to see a list of other riders whose times were recalculated, click here and scroll down to the section entitled “Curious anomaly”.
Summer is the time to go faster
Strava leaderboards were never designed to rank pure solo TT efforts. Although it is possible to filter by sex, age, weight and date, it remains hard to distinguish between team versus solo efforts, road versus TT bikes and weather conditions. The nature of records is that they are there to be broken, so the top times will always get faster. The evidence from this analysis suggests that there are more faster cyclists around today than two years ago.
As the weather warms up, perhaps you can pick a quiet time to move up the leaderboard on your favourite segment, while showing courtesy to other road users and respecting the legal speed limit.
Unfortunately an increasing proportion of the population of western society has fallen into the habit consuming far more calories than required, resulting an a huge increase in obesity, with all the associated negative health consequences. At the opposite end of the spectrum, a smaller but important group experiences problems stemming from insufficient energy intake. This group includes certain competitive athletes, especially those involved in sports or dance, where a low body weight confers a performance advantage. A new infographic draws attention to this problem and highlights the fact that the individuals have control over the factors that can put them on the path to optimal health and performance.
RED-S
The human body requires a certain amount of energy to perform normal metabolic functions, including, maintaining homeostasis, cardiac and brain activity. The daily requirement is around 2,000 kcal for women and 2,500 kcal for men. Additional energy intake is required to balance the energy requirements any physical activities performed.
Athletes and dancers need to eat more than sedentary people, but they can fall into an energy deficit in two ways.
Reducing energy intake, while maintaining the same training load. This is typically an intentional decision, in order to lose weight, in the belief that this might improve performance. It can also arise unintentionally, perhaps due to failing to calculate energy demands of the training programme.
Increasing training load, while maintaining the same energy intake. This can often occur unintentionally, as a result of a more intensive training session or a shift into a higher training phase. Some athletes or dancers perform extra training sessions while deliberately failing to eat more, in the hope, once again, that this might improve performance.
While most of the population would benefit from a period of moderate energy deficit. High level athletes and dancers tend to be very lean, to the extent that losing further weight compromises health and performance. The reason is that the endocrine system is forced to react to an energy deficit by scaling back or shutting down key metabolic systems. For example, levels of the sex hormones testosterone and oestrogen can fall, leading to, among other things, reductions in bone density. Unlike men, women have a warning sign, in the form of an interruption or cessation of menstruation. Both men and women with RED-S are likely to suffer from a failure to achieve their peak athletic performance.
Achieving peak performance
Fortunately athletes have control over the levers that lead to peak performance. These are nutrition, training load and, of course, recovery. Consistently fuelling for the energy required, whilst ensuring that the body has adequate time to recover, allows the endocrine system to trigger the genes that lead to the beneficial outcomes of exercise, such as improved cardiovascular efficiency, effective muscular development, optimal body composition, healthy bones and a fully functional immune system. These are the changes required to reach the highest levels of performance.
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