## Chain reactions

At this year’s Royal Society Summer Exhibition, scientists and engineers from Bristol University presented some interesting work on improvements to the drive chains used by Team GB in the Rio Olympics. They reached clear conclusions about the design of the chain and sprockets, taken up by Renold. Current research is exploring the the problem of chain resonance.

Bicycle chains and sprockets and sprockets tend to receive less attention than aerodynamics, for several reasons. As noted in previous blogs, the power required to overcome aerodynamic drag scales with the cube of velocity, whereas frictional effects scale simply in proportion to velocity. Furthermore, a good well-lubricated drive chain typically has an efficiency of around 95% or more, so it is hard to make further improvements. Note that a dirty chain has significantly lower efficiency, so you should certainly keep your bike clean.

The loss of power comes from the friction between links as they bend around the chainring and the rear sprocket. Using a high precision rig, the researchers demonstrated that larger sprockets are more efficient than smaller ones. For example, with a gear ratio of 4:1, it is more efficient to use a 64/16 than a more conventional 52/13.

In fact, one of the experts told me that the efficiency of the drive chain falls off sharply as the sprocket size is reduced from 13 to 12 to 11. This is because the chain has to bend around a much sharper angle for a smaller sprocket. If you think about it, the straight chain has to bend to a certain angle that depends on the number of teeth on the sprocket. Recalling some school maths about the interior angles of polygons, for 16 teeth, the angle is 157.5º, whereas for 11 teeth, the angle is 147.3º. For the larger sprocket, each pair of links overcomes less friction bending through 22.5º and back, compared with a more dramatic 32.7º and back for the smaller one.

Note that this analysis of the rear sprocket applies to single speed track bikes. On a road bike the chain has to pass the two derailleur cogs, which typically have 13 teeth, whatever gear you choose. However, the argument still applies to the chainring  at the front, where the gains of going larger were shown to exceed the additional aerodynamic drag.

The Bristol team also explored the effect of a number of other factors on performance. Using different length links obviously requires customised sprockets and chainrings. This would be a major upheaval for the industry, but it is possible for purpose-built track bikes. Certain molybdenum-based lubricating powders used in the space industry may be better than traditional oils. Other materials could replace traditional steel.

A different kind of power loss can occur when the chain resonates vertically. A specially designed test rig showed that this can occur at frequencies, which could be triggered at certain pedalling cadences. Current research is investigating how the tension of the chain and its design can help mitigate this problem (which is also an issue for motor cycles).

In conclusion, when we see Tony Martin pushing a 58+ chainring, it may not be simply an act of machismo – he is actually be benefitting from efficiency gains.

## Surrey Cycle Racing League Courses

Cyclists in southeast England are indebted to Glyn Durrant, who with his team of supporters and local club members, runs the Surrey Cycle Racing League. These very popular races are held throughout the year on a number of courses around the region. This article takes a look at the characteristics of the courses raced in the Surrey League.

Thanks to Russell Short, Surrey League course are available as routes on Strava. I have created a map that displays all thirty-one circuits. You can view it, if you go to the trouble of opening to this page, then you have to press on the download icon and open the file in your browser. If you hover over the marker on each course, it indicates the usual finish line and the name of the course. The different colours help identify where loops pass over the same roads.

The courses have a range of characteristics, each one suiting some riders better than others. Although they are all road races, shorter circuits like the 4.6km lap around Kitsmead Lane are repeated many times, giving more of a criterion feel, in contrast with longer routes like the 24.1km Lingfield loop that offer less opportunity to memorise the finishing straight.

The amount of climbing is a key feature. For example, the Newchapel course is almost pan flat, whereas Beachy Head has an elevation gain of 228m over a length of 11.7km. Since you end up at the same point on each loop, we can assume that you are climbing for about half of the distance, giving an average climb (and descent) of 3.9%.

The organisers tend to position the chequered flag at the top of a rise, but the final effort required on Milland Hill is far greater than, say, Seale, which is slightly downhill for the indicated finish line. The rider leading the last Milland Hill climb will tend to be someone who can generate a high level of power at the end of the race. In order to identify steep finishes, the gradient of the final kilometre is calculated using the finish line as it appears in the Strava route. This is not 100% reliable, because the actual finish line can be different on the day.

Some courses are more technical than others, involving twists, turns and sharp corners. These favour riders with good bike-handling skills. One way to assess this is to calculate the “fractal dimension” of the route. This method is a bit experimental, so it would be interesting to receive comments from riders on whether they agree with the ranking. The metric gives a figure close to one for smooth, straight courses, but higher numbers for twisty circuits. None of the routes is very technical, but Sharpethorne and South Nutfield score more highly, due to some very sharp turns.

Putting all this together leads to some interesting results. Consider the amount of climbing versus the length of each circuit. Beachy Head is a medium length route that is well suited to climbers, though heavier riders have a chance to catch up on the fast descent. Sharpethorne, Staplefield and Lingfield are long and hilly, whereas you have less time to recover from the climbs at Ladies Mile, Staple Lane and Seale, because they are much shorter. Meanwhile Milland Hill and Bletchingly form part of a medium-length hilly group, contrasting with the Laughton, Coolham, Kirdford group of flatter courses. The very flat Newchapel course should suit the heavier diesel-powered TTers.

Taking account of the technical nature of the courses, Newchapel, Sharpethorne and Beachy Head form a triangle of extremes. The flat non-technical Newchapel course can be ridden at a very high average speed. Sharpethorne requires both climbing ability and good bike handling skills. Beachy Head is hilly but not too technical. Most of the other routes are intermediates.

The hilliest course does not necessarily have the steepest finish. That honour is reserved for the brutal climb that concludes Milland Hill, though the Bletchingly finish is also tough. Note once again that this depends on where the finish line is positioned on the day – I’m sure the finish of Beachy Head was at the summit, last time I did it!

The Surrey League offers a range of courses that provide opportunities for different types of riders. None of them is super-technical. Unfortunately we don’t have any mountains so strong puncheurs tend to do well. Remember that it is well worth reminding yourself what type of course you are riding, before you start the race. Please take a look at the spreadsheet summary of the data with useful links available here: SurreyLeagueRoutes

## Update on cycling aerodynamics

A recently published paper provides a useful review of competition cycling aerodynamics. It looks at the results of a wide range of academic studies, highlighting the significant advances made in the last 5 to 10 years.

The power required to overcome aerodynamic drag rises with the cube of velocity, so riding at 50km/h takes almost twice as much power as riding at 40km/h. At racing speed, around 80% of a cyclist’s power goes into overcoming aerodynamic drag. This is largely because a bike and rider are not very streamlined, resulting in a turbulent wake.

The authors quote drag coefficients, Cd, of 0.8 for upright and 0.6 for TT positions. These compare with 0.07 for a recumbent bike with fairing, indicating that there is huge room for improvement.

Wind tunnels, originally used in the aerospace and automotive industries, are now being designed specifically for cycling, though no specific standards have been adopted. These provide a simplification of environmental conditions, but they can be used to study air flow for different body positions and equipment. Mannequins are often used in research, as one of the difficulties for riders is the ability to repeat and maintain exactly the same position. Some tunnels employ cameras to track movements. Usually a drag area measurement, CdA, is reported, rather than Cd, thereby avoiding uncertainty due to measurement of frontal area, though this can be estimated by counting pixels in a image.

One thing that makes cycling particularly complex is the action of pedalling. This creates asymmetric high drag forces as one leg goes up and the other goes down, resulting in variations of up to 20% relative to a horizontal crank position.

Cycling has been studied using computational fluid dynamics, helping to save on wind tunnel costs. These use fine mesh models to calculate details of flow separation and pressure variations across the cyclist’s body. The better models are in good agreement with wind tunnel experiments.

Cycling speed is a maximum optimisation problem between aerodynamic and biomechanical efficiency

Ultimately, scientists need to do field tests. The extensive use of power meters allows cyclists to experiment for themselves. The authors provide two practical ways to separate the coefficient of rolling resistance, Crr,  from CdA. One based on rolling to a halt and the other using a series of short rides at constant speed.

Minimising aerodynamic resistance through rider position is one of the most effective ways to improve performance among well-trained athletes

Compared with riding upright on the hoods, moving to the drops saves 15% to 20% while adopting a TT position saves 30% to 35%. Studies show quite a lot of variance in these figures, as the results depend on whether the rider is pedalling, as well as body size. The following quote suggests that when freewheeling downhill in an aero tuck, your crank should be horizontal (unless you are cornering).

Current research suggests that the drag coefficient of a pedalling cyclist is ≈6% higher than that of a static cyclist holding a horizontal crank position

The authors quote the figures for CdA of 0.30-0.50 for an upright position, 0.25 to 0.30 on the drops and 0.20-0.25 for a TT position. Variation is largely, but not only, due to changes in frontal area, A. Unfortunately, relatively minor changes in position can have large effects on drag, but the following effects were noted.

Broker and Kyle note that rider positions that result in a flat back, a low tucked head and forearms positioned parallel to the bicycle frame generally have low aerodynamic drag. Wind tunnel investigations into a wide range of modifications to standard road cycling positions by Barry et al. showed that that lowering the head and torso and bringing the arms inside the silhouette of the hips reduced the aerodynamic drag.

Bike frames, wheels, helmets and skin suits are all designed with aerodynamics in mind, while remaining compliant with UCI rules. Skin suits are important, due to their large surface areas. By delaying airflow separation, textured fabrics reduce wake turbulence, resulting in as much as a 4% reduction in drag.

In race situations, drafting skills are beneficial, particularly behind a larger rider. While following riders gain a significant benefit, it has been shown that the lead rider also accrues a small advantage of around 3%. It is best to overtake very closely in order to take maximal advantage of lateral drafting effects.

For a trailing cyclist positioned immediately behind the leader, drag reduction has been reported in the range of 15–50 % and reduces to 10–30 % as the gap extends to approximately a bike length… The drafting effect is greater for the third rider than the second rider in a pace-line, but often remains nearly constant for subsequent riders

For those interested in greater detail, it is well worth looking at the full text of the paper, which is freely available.

### Reference

Riding against the wind: a review of competition cycling aerodynamics, Timothy N. CrouchEmail authorDavid BurtonZach A. LaBryKim B. Blair, Sports Engineering, June 2017, Volume 20, Issue 2, pp 81–110

## The fractal nature of GPS routes

The mathematician, Benoît Mandelbrot, once asked “How long is the coast of Britain?“. Paradoxically, the answer depends on the length of your measuring stick. Using a shorter ruler results in a longer total distance, because you take account of more minor details of the shape of the coastline. Extrapolating this idea, reducing the measurement scale down to take account of every grain of sand, the total length of the coast increases without limit.

This has an unexpected connection with the data recorded on a GPS unit. Cycle computers typically record position every second. When riding at 36km/h, a record is stored every 10 metres, but at a speed of 18k/h, a recording is made every 5 metres. So riding as a lower speed equates to measuring distances with a shorter ruler. When distance is calculated by triangulating between GPS locations, your riding speed affects the result, particularly when you are going around a sharp corner.

Consider two cyclists riding round a sharp 90-degree bend with a radius of 13m. The arc has a length of 20m, so the GPS has time to make four recordings for the a rider doing 18km/h, but only two recordings for the rider doing 36km/h. The diagram below shows that the faster rider will have a record of position at each red dot, while the slower rider also has a reading for each green dot.  Although the red and green distances match on the straight section, when it comes to the corner the total length of the red line segments is less than the total of the green segments. You can see this jagged effect if you zoom into a corner on the Strava map of your course. Both triangulated distances are shorter than the actual arc ridden.

It is relatively straightforward to show that the triangulation method will underestimate both distance and speed by a factor of 2r/s*sin(s/2r), where r is the radius of the corner in metres and s is speed in m/s. So the estimated length of the 20m arc for the fast rider is 19.4m ridden at a speed of 35.1km/h (2.5% underestimate), while the corresponding figures for the slower rider would be 19.8m at 17.9km/h (0.6% underestimate).

We might ask whether these underestimates are significant, given the error in locating real-time positions using GPS. Over the length of a ride, we should expect GPS errors to average out to approximately zero in all directions. However, triangulation underestimates distance on every corner, so these negative errors accumulate over the ride. Note that when the bike is stationary, any noise in the GPS position adds to the total distance calculated by triangulation. But guess what? This can only happen when you are not moving fast. The case remains that slower riders will show a longer total distance than faster riders.

The simple triangulation method described above does not take account of changes of elevation. This has a relatively small effect, except on the steepest gradients, thus a 10% climb increases in distance by only 0.5%.  In fact, the only reliable way to measure distance that accounts for corners and changes in altitude is to use a correctly-calibrated wheel-based device. Garmin’s GSC-10 speed and cadence monitor tracks the passage of magnets on the wheel and cranks, transmitting to the head unit via ANT+. This gives an accurate measure of ground speed, as long as the correct wheel size is used (and, of course, that changes with the type of tyre, air pressure, rider weight etc.).

According to Strava Support, Garmin uses a hierarchy for determining distance. If you have a PowerTap hub, its distance calculation takes precedence. Next, if you have a GSC-10, its figure is used. Otherwise the GPS positions are used for triangulation. This means that, if you don’t have a PowerTap or a GSC-10 speed/cadence meter, your distance (and speed) measurements will be subject to the distortions described above.

But does this really matter? Well it depends on how “wiggly” a route you are riding. This can be estimated using Richardson’s method. The idea is that you measure the route using different sized rulers and see how much the total distance changes. The rate of change determines the fractal dimension, which we can take as the “wiggliness” of the route.

One way of approximating this method from your GPS data is, firstly, to add up all the distances between consecutive GPS positions,  triangulating latitude and longitude. Then do the same using every other position. Then every fourth position, doubling the gap each time. If you happened to be riding at a constant 36km/h, this equates to measuring distance using a 10m ruler, then a 20m ruler, then a 40m ruler etc..

Using this approach, the fractal dimension of a simple loop around the Surrey countryside is about 1.01, which is not much higher than a straight line of dimension 1. So, with just a few corners, the GPS triangulation error will be low. The Sella Ronda has a fractal dimension of 1.11, reflecting the fact that alpine roads have to follow the naturally fractal-like mountain landscape. Totally contrived routes can be higher, such as this one, with a fractal dimension of 1.34, making GPS triangulation likely to be pretty inaccurate – if you zoom in, lots of corners are cut.

In conclusion, if you ride fast around a wiggly course, your Garmin will experience non-relativistic length contraction. Having GPS does not make your wheel-based speed/cadence monitor redundant.

If you are interested in the code used for this blog, you can find it here.

## Strava Fitness and Freshness

The last blog explored the statistics that Strava calculates for each ride. These feed through into the Fitness & Freshness chart provided for premium users. The aim is to show the accumulated effect of training through time, based on the Training-Impulse model originally proposed by Eric Banister and others in a rather technical paper published in 1976.

Strava gives a pretty good explanation of Fitness and Freshness. A similar approach is used on Training Peaks in its Performance Management Chart. On Strava, each ride is evaluated in terms of its Training Load, if you have a power meter, or a figure derived from your Suffer Score, if you just used a heart rate monitor. A training session has a positive impact on your long-term fitness, but it also has a more immediate negative effect in terms of fatigue. The positive impact decays slowly over time, so if you don’t keep up your training, you lose fitness. But your body is able to recover from fatigue more quickly.

The best time to race is when your fitness is high, but you are also sufficiently recovered from fatigue. Fitness minus fatigue provides an estimate of your form. The 1976 paper demonstrated a correlation between form and the performance of an elite swimmers’ times over 100m.

The Fitness and Freshness chart is particularly useful if you are following a periodised training schedule. This approach is recommended by many coaches, such as Joe Friel. Training follows a series of cycles, building up fitness towards the season’s goals. A typical block of training includes a three week build-up, followed by a recovery week. This is reflected in a wave-like pattern in your Fitness and Freshness chart. Fitness rises over the three weeks of training impulses, but fatigue accumulates faster, resulting in a deterioration of form. However, fatigue drops quickly, while fitness is largely maintained during the recovery week, allowing form to peak.

In order to make the most of the Fitness and Freshness charts, it is important that you use an accurate current figure for your Functional Threshold Power. The best way to do this is to go and do a power test. It is preferable to follow a formal protocol that you can repeat, such as that suggested by British Cycling. Alternatively, Strava premium users can refer to the Strava Power Curve. You can either take your best effort over 1 hour or 95% of your best effort over 20 minutes. Or you can click on the “Show estimated FTP” button  and take the lower figure. In order for this to flow through into your Fitness and Freshness chart, you need to enter your 1 hour FTP into your personal settings, under “My Performance”.

The example chart at the top of this blog shows how my season has panned out so far. After taking a two week break before Christmas, I started a solid block of training in January. My recovery week was actually spent skiing (pretty hard), though this did not register on Strava because I did not use a heart rate monitor. So the sharp drop in fatigue at the end of January is exaggerated. Nevertheless, my form was positive for my first race on 4 February. Unfortunately, I was knocked off and smashed a few ribs, forcing me to take an unplanned two week break. By the time I was able to start riding tentatively, rather than starting from an elevated level, my fitness had deteriorated to December’s trough.

After a solid, but still painful, block of low intensity training in March, I took another “recovery week” on the slopes of St Anton. I subsequently picked up a cold that delayed the start of the next block of training, but I have incorporated some crit races into my plan, for higher intensity sessions. If you edit the activity and make the “ride type” a “race”, it shows up as a red dot on the chart. Barring accident and illness, the hope is to stick more closely to a planned four-week cycle going forward.

This demonstrates how Strava’s tools reveal the real-life difficulties of putting the theoretical benefits of periodisation into practice.

### Related posts

Modelling Strava Fitness and Freshness

Supercompensating with Strava

See other blogs on Strava Power Curve, Strava Ride Statistics or going for a Strava KOM.

## Strava Ride Statistics

If you ride with a power meter and a heart rate monitor, Strava’s premium subscription will display a number of summary statistics about your ride. These differ from the numbers provided by other software, such as Training Peaks. How do all these numbers relate to each other?

### A tale of two scales

Over the years, coaches and academics have developed statistics to summarise the amount of physiological stress induced by different types of endurance exercise. Two similar approaches have gained prominence. Dr Andrew Coggan has registered the names of several measures used by Training Peaks. Dr Phil Skiba has developed as set of metrics used in the literature and by PhysFarm Training Systems. These and other calculations are available on Golden Cheetah‘s excellent free software.

Although it is possible to line up metrics that roughly correspond to each other, the calculations are different and the proponents of each scale emphasise particular nuances that distinguish them. This makes it hard to match up the figures.

Here is an example for a recent hill session. The power trace is highly variable, because the ride involved 12 short sharp climbs.

 Metric Coggan TrainingPeaks Skiba Literature Strava Power equivalent physiological cost of ride Normalised Power 282 xPower 252 Weighted Avg Power 252 Power variability of ride Variability Index 1.57 Variability Index 1.41 Rider’s sustainable power Functional Threshold Power 312 Critical Power 300 FTP 300 Power cost / sustainable power Intensity Factor 0.9 Relative Intensity 0.84 Intensity 0.84 Assessment of intensity and duration of ride Training Stress Score 117 BikeScore 101 Training Load 100 Training Impulse based on heart rate Suffer Score 56

### Weighted Average Power

According to Strava, Weighted Average Power takes account of the variability of your power reading during a ride. “It is our best guess at your average power if you rode at the exact same wattage the entire ride.” That sounds an awful lot like Normalized Power, which is described on Training Peaks as “an estimate of the power that you could have maintained for the same physiological “cost” if your power output had been perfectly constant (e.g., as on a stationary cycle ergometer), rather than variable”. But it is apparent from the table above that Strava is calculating Skiba’s xPower.

The calculations of Normalized Power and xPower both smooth the raw power data, raise these observations to the fourth power, take the average over the whole ride and obtain the fourth root to give the answer.

Normalized Power or xPower = (Average(Psmoothed4))1/4

The only difference between the calculations is the way that smoothing accounts for the body’s physiological delay in reacting to rapid changes in pedalling power. Normalized Power uses a 30 second moving average, whereas xPower uses a “25 second exponential average”. According to Skiba, exponential decay is better than Coggan’s linear decay in representing the way the body reacts to changes in effort.

The following chart zooms into part of the hill reps session, showing the raw power output (in blue), moving average smoothing for Normalised Power (in green), exponential smoothing for xPower (in red), with heart rate shown in the background (in grey). Two important observations can be made. Firstly, xPower’s exponential smoothing is more highly correlated with heart rate, so it could be argued that it does indeed correspond more closely with the underlying physiological processes. Secondly, the smoothing used for xPower is less volatile, therefore xPower will always be lower than Normalized Power (because the fourth-power scaling is dominated by the highest observations).

Why do both metrics take the watts and raise them to the fourth power? Coggan states that many of the body’s responses are “curvilinear”. The following chart is a good example, showing the rapid accumulation of blood lactate concentration at high levels of effort.

Plotting the actual data from a recent test on a log-log scale, I obtained a coefficient of between 3.5 and 4.7, for the relation between lactate level and watts. This suggests that taking the average of smoothed watts raised to the power 4 gives an indication of the average level of lactate in circulation during the ride.

The hill reps ride included multiple bouts of high power, causing repeated accumulation of lactate and other stress related factors. Both the Normalised Power of 282W and xPower of 252W were significantly higher than the straight average power of 179W. The variability index compares each adjusted power against average power, resulting in variability indices of 1.57 and 1.41 respectively. These are very high figures, due to the hilly nature of the session. For a well-paced time trial, the variability index should be close to 1.00.

### Sustainable Power

It is important for a serious cyclist to have a good idea of the power that he or she can sustain for a prolonged period. Functional Threshold Power and Critical Power measure slightly different things. The emphasis of FTP is on the maximum power sustainable for one hour, whereas CP is the power theoretically sustainable indefinitely. So CP should be lower than FTP.

Strava allows you to set your Functional Threshold Power under your personal performance settings. The problem is that if Strava’s Weighted Average Power is based on Skiba’s xPower, it would be more consistent to use Critical Power, as I did in the table above. This is important because this figure is used to calculate Intensity and Training Load. If you follow Strava’s suggestion of using FTP, subsequent calculations will underestimate your Training Load,  which, in turn, impacts your Fitness & Freshness curves.

### Intensity

The idea of intensity is to measure severity of a ride, taking account of the rider’s individual capabilities.  Intensity is defined as the ratio of the power equivalent physiological cost of the ride relative to your sustainable power. For Coggan, the Intensity Factor is NP/FTP; for Skiba the Relative Intensity is xPower/CP; and for Strava the Intensity is Weighted Average Power/FTP.

An overall assessment of a ride needs to take account of the intensity and the duration of a ride. It is helpful to standardise this for an individual rider, by comparing it against a benchmark, such as an all-out one hour effort.

Coggan proposes the Training Stress Score that takes the ratio the work done at Normalised Power, scaled by the Intensity Factor squared, relative to one hour’s work at FTP. Skiba defines the BikeScore as the ratio the work done at xPower, scaled by the Relative Intensity squared, relative to one hour’s work at CP. And finally, Strava’s Training Load takes the ratio the work done at Weighted Average Power, scaled by Intensity squared, relative to one hour’s work at FTP.

Note that for my hill reps ride, the BikeScore of 101, was considerably lower than the TSS of 117. Although my estimated CP is 12W lower than my FTP, xPower was 30W lower than NP. Using my CP as my Strava FTP, Strava’s Training Load is the same as Skiba’s Bike Score (otherwise I’d get 93).

### Suffer Score

Strava’s Suffer Score was inspired by Eric Banister’s training-impulse (TRIMP) concept. It is derived from the amount of time spent in each heart rate zone, so it can be calculated for multiple sports. You can set your Strava heart rate zones in your personal settings, or just leave then on default, based on your maximum heart rate.

A non-linear relationship is assumed between effort and heart rate zone. Each minute in Zone 1, Endurance, is worth 12 seconds; Moderate Zone 2 minutes are worth 24 seconds; Zone 3 Tempo minutes are worth 45 seconds; Zone 4 Threshold minutes are worth 100 seconds; and Anaerobic Zone 5 minutes are worth 120 seconds. The Suffer Score is the weighted sum of minutes in each zone.

The next blog will comment on the Fitness & Freshness charts available on Strava Premium.

## Which Strava KOMs will fall in the Tour of Flanders?

This series looking at Strava leaderboards now turns to the action in Belgium, where the spring classics season is under way. Greg Van Avermaet, Philipe Gilbert, Michal Kwiatkowski and Peter Sagan are among the riders in top form ahead of the Tour of Flanders, not forgetting former winners Tom Boonen, Alexander Kristoff and Stijn Devolder. This year’s race includes 18 climbs, finishing with a loop that takes in six famous ascents in the last 50km. Will the pros to be setting KOMs on these Hellingens?
Making the top 10 on any leaderboard, towards the end of a 260km race, sounds like a tall order. KOMs are more likely to fall on longer faster climbs where riders can benefit from drafting in a group. In fact the riders will be climbing the Oude-Kwaremont three times and the Paterberg twice, so the top times are more at risk on those two, if someone decides to make a strong attack. The weather forecast is good: sunny, about 16°C, with a light breeze from the WNW. The wind will be against the riders on the Taaienberg, but it will provide a small benefit on the other final hills, which happen to be ridden in directions between East and South.

### Koppenberg

Surface: Cobbles, Distance: 444m, Avg Grade: 14.3%, Elevation Gain: 64m, Bearing: 104°

 Rank Name Time Date Race 1 Reinardt Janse van Rensburg 00:01:27 19-Feb-14 – 2 Joris Van Der Auwera 00:01:28 01-Jan-10 – 2 gijsade holstege 00:01:28 01-Jan-10 – 2 Dries Devenyns 00:01:28 27-Nov-15 – 5 Cameron Bayly 00:01:30 06-Sep-15 – 6 Dylan Kennett 00:01:32 26-Jun-15 – 7 FOCUS Rides 00:01:34 01-Jan-10 – 7 Korneel De Viaene 00:01:34 06-Aug-15 – 7 Arjen Palstra 00:01:34 02-Apr-16 – 7 Korneel De Viaene 00:01:34 06-Aug-15 –

Pro rider van Rensburg holds the KOM up the Koppenberg, set on a pre-race recce. Dries Devenyns is not far behind, but none of the top ten times appear to have been set in races. Although there will be a weak tailwind, it seems unlikely that a new record will be set in this year’s Tour of Flanders.

### Steenbeekdries

Surface: Cobbles, Distance: 724m, Avg Grade: 2.8%, Elevation Gain: 24m, Bearing: 121°

 Rank Name Time Date Race 1 Niki Terpstra Racing 00:01:22 01-Apr-15 – 2 Jasper Stuyven 00:01:25 06-Apr-13 Flanders U23 2 Lawson Craddock 00:01:25 06-Apr-13 Flanders U23 4 Michal Kwiatkowski 00:01:26 01-Apr-16 – 5 Jered Gruber 00:01:27 06-Apr-12 – 5 Walter Eikelenboom 00:01:27 04-Aug-15 – 7 Pierre-Henri LECUISINIER 00:01:28 06-Apr-13 Flanders U23 7 Marcus Burghardt 00:01:28 02-Apr-15 Three Days of De Panne 9 Michael Schär 00:01:29 02-Apr-15 Three Days of De Panne 10 Stijn Steels 00:01:30 29-Mar-17 –

Two riders made the top ten in the 2015 edition of the Three Days of De Panne and three others in the Flanders Under 23 race in 2013. We can also see quick times in recce rides by Terpstra in 2015, Kwiatkowski, last year, and Steels this week. There’s a chance this KOM could go on Sunday.

### Taaienberg

Surface: Cobbles, Distance: 639m, Avg Grade: 7.8%, Elevation Gain: 46m, Bearing: 262°

 Rank Name Time Date Race 1 Daniel Oss 00:01:12 25-Mar-16 E3 Harelbeke 2 Jasper Stuyven 00:01:13 25-Feb-17 Omloop Het Nieuwsblad 3 AlliGator Junior 00:01:14 06-Mar-13 – 3 Edward Theuns 00:01:14 25-Feb-17 Omloop Het Nieuwsblad 3 Greg Van Avermaet 00:01:14 24-Mar-17 E3 Harelbeke 6 Wesley Van Dyck 2090874575184 00:01:15 28-Mar-17 Three Days of De Panne 7 Arnaud Demare 00:01:16 22-Mar-17 Dwars door Vlaanderen 7 Johnny Cecotto 00:01:16 22-Mar-17 Dwars door Vlaanderen 9 Jarl . 00:01:17 18-Mar-14 – 9 Bryan Coquard 00:01:17 24-Mar-17 E3 Harelbeke

This is another segment that is dominated by the pros and tends to be smashed in races.  The problem is that there will be a slight headwind, so this KOM will probably hold on Sunday.

### Kruisberg (Oudestraat)

Surface: Asphalt, Distance: 1813m, Avg Grade: 4.9%, Elevation Gain: 89m, Bearing: 142°

 Rank Name Time Date Race 1 Daniel Lloyd 00:04:03 03-Apr-11 Tour of Flanders 2 Cor ~~ 00:04:05 03-Apr-11 Tour of Flanders 3 davide COM 00:04:06 03-Apr-11 Tour of Flanders 4 Jeremy Cameron Ⓥ 00:04:24 29-Jun-16 – 5 Pascal Eenkhoorn 00:05:00 13-Mar-16 – 6 Pieterjan Spyns 00:05:02 06-Jul-15 – 7 dylan de kok 00:05:03 21-Feb-14 – 8 Eloy Raas 00:05:04 21-Feb-14 – 9 Lenard Maes 00:05:06 21-Jul-15 – 10 kobe vdv 00:05:09 30-Mar-17 –

The KOM is held by GCN‘s Daniel Lloyd, set in the Tour of Flanders in 2011, in very similar weather conditions. The three leading times stand a long way ahead of the rest. It will be very interesting to see how the current pros perform on this climb. Daniel’s time could be at risk.

### Oude-Kwaremont

Surface: Cobbles, Distance: 2509m, Avg Grade: 3.6%, Elevation Gain: 91m, Bearing: 163°

 Rank Name Time Date Race 1 Niki Terpstra Racing 00:04:55 25-Mar-16 E3 Harelbeke 2 Daniel Oss 00:04:58 25-Mar-16 E3 Harelbeke 3 Tiesj Benoot 00:04:59 25-Mar-16 E3 Harelbeke 4 Nikolas Maes 00:05:01 23-Mar-16 Dwars door Vlaanderen 5 Michal Kwiatkowski 00:05:02 25-Mar-16 E3 Harelbeke 6 Scott Thwaites 00:05:04 23-Mar-16 Dwars door Vlaanderen 7 Greg Van Avermaet 00:05:06 28-Feb-16 Kuurne–Brussels–Kuurne 8 Oliver Naesen 00:05:07 24-Mar-17 E3 Harelbeke 9 Stijn Vandenbergh Racing 00:05:08 28-Mar-14 E3 Harelbeke 10 Antoine Duchesne 00:05:10 28-Feb-16 Kuurne–Brussels–Kuurne

The leaderboard of segment is packed with pro racing performances, led once again by Niki Terpstra. With the last ascent coming at a crucial time in this year’s Tour of Flanders, the KOM could fall again.

### Paterberg

Surface: Cobbles, Distance: 358m, Avg Grade: 11.7%, Elevation Gain: 42m, Bearing: 96°

 Rank Name Time Date Race 1 Eli Iserbyt 00:00:53 27-Jul-16 – 2 Dries Devenyns 00:00:54 19-Feb-14 – 2 mathias Declerck 00:00:54 01-Sep-16 – 4 Jarl . 00:00:55 23-Mar-14 – 5 Pascal Eenkhoorn 00:00:56 04-May-16 – 6 Joeri Calleeuw 00:00:57 24-Feb-14 – 6 Frederik Vandewiele 00:00:57 19-Mar-14 – 6 Antoine Loy 00:00:57 18-Jun-14 – 6 Aaron Midgley 00:00:57 20-Aug-14 – 6 Merten De Wever 00:00:57 06-May-15 –

This short, steep climb seems to be the target of specific KOM hunters. None of the top ten times were set in the big races. Although this will be the final place to attack in this year’s race, the riders will be fatigued by 247km of tough roads. The top time is likely to hold firm, especially if barriers are used to block smoother edges of the road.

### Conclusion

KOMs to hold: Paterberg, Koppenberg, Taaienberg

KOMs at risk: Kruisberg, Oude-Kwaremont, Steenbeekdries

Watch out Dan!

## Pro Cyclist KOMs on Strava

This series has explored what it takes to get a KOM on Strava, but what about the pros? Don’t they come home with a sackful of KOMs after every training ride? Which pro rider tops the most Strava leaderboards?

You can follow over a thousand pro athletes on Strava. These include runners, triathletes, mountain bikers and professional cyclists. Although you will not find Peter Sagan, Chris Froome, Nairo Quintana or Alberto Contador  (you can ignore all the fake Strava ids with these names), there is a good selection of UCI team riders. There are also riders who do not claim Strava pro status, like some guy called Phil who recently went out for an afternoon ride.

Some pros upload just a limited number of rides, for example, Marianne Vos only has 243 rides on Strava, with nothing new since December. Other riders, such as Ian Stannard, upload their rides, but withhold their (monstrously high) power data. Nevertheless, many pro riders are more open about making their data available on Strava, including power. Take a look at the Col d’Eze segment on the final stage of Paris Nice. The little lightning bolt symbol indicates that the rider was using a power meter, but rather confusingly, some pro riders (Team Sky) are able to hide their average power for the ride, in which case the figure is a Strava estimate. But you can find the real number by highlighting the segment in the analysis view of the ride.

This review considers over 200 active professional road cyclists who are on Strava. The riders with the highest number of KOMs need to have uploaded a lot fast rides, in regions where many segments have been recorded. Here are the current top 10 pro riders from the sample.

 Rank KOMs Name Team 1 1907 Laurens ten Dam Team Sunweb 2 1381 Elisa Longo Borghini Wiggle Honda 3 1296 Annemiek van Vleuten Orica AIS 4 1230 Niki Terpstra Racing Quick-Step Floors 5 1162 James Gullen JLT Condor 6 1070 Thibaut Pinot FDJ 7 1035 Dan Evans Cannodale-Drapac Pro Cycling Team 8 978 Romain Bardet AG2R 9 864 Joe Dombrowski Cannodale-Drapac Pro Cycling Team 10 852 Dani King Cylance

A bit further down the list, Michal Kwiatkowski has 559 KOMs, including eight that he picked up in his Milan San Remo victory. After riding the first 140km at a relatively easy 35kph and an average power of just 124W, he upped the effort to traverse the Passo del Turchino. His power and heart rate rose progressively all the way to the Cipressa, from which point he earned a KOM for the segment to the finish. He claimed four KOMs as he followed Peter Sagan’s dramatic attack on the Poggio, though these would have undoubtably been Sagan’s, if he’d put his data on Strava. Viewing the ride analysis, we see that after over seven hours of riding, Michal ascended the 3.6km 4% climb at 37kph, generating 443W (about 6.5 W/kg) for 5 minutes and 47 seconds, rather than the 536W estimated on the leaderboard. He peaked at over 900W near the summit as he an Alaphilippe desperately fought to get onto Sagan’s wheel.Lauren ten Dam has the most KOMs by a long way, though he does match Maryka Sennema’s haul of QOMs. Interestingly there are three women in the top ten, in spite of the fact that most of the riders in the sample were men. It is no surprise to see Elisa Longo Borghini and Annemiek van Vleuten at the head of the women’s rankings. Niki Terpstra follows his Dutch compatriot, while James Gullen is the leading Brit, followed by Dan Evans representing Wales alongside Dani King. Thibaut Pinot and Romain Bardet are the kings of the French mountains. Nice-based American rider Joe Dombrowski also makes the top ten.

The next blog will explore some more feats of the professionals.

## Going for a QOM on Strava

In exploring how to chase a KOM on Strava, this series of articles has fallen into the trap of under-representing the achievements of the Queens of the Mountains (QOMs). Although this is partly because Strava tends to attract male data geeks, there are plenty of women who use the platform to monitor their fitness and performance in a social way. This blog looks at the performance of women cyclists, once again featuring the popular Tour de Richmond Park segment.

More women are riding their bikes as the interest in women’s cycling continues to grow. Top riders like Lizzie Deignan, Marianne Vos and the Drops Cycling Team are receiving broader recognition for their amazing performances. This year’s Women’s Tour will benefit from broad media coverage, as it finishes in the heart of London. The Cycling Podcast Féminin is now into its ninth episode.

Analysis of the top 1000 (mostly male) riders on the Tour de Richmond Park leaderboard established that the majority of personal bests (PBs) were set during the summer months, either early in the morning or in the evening, with Saturday and Wednesday being popular days or the week, especially when the wind was blowing from the East. The charts below compare these statistics from the male and female leaderboards.

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Female PBs are a little more evenly spread over the year, peaking in July. Women have tended to achieve their best times later in the morning, perhaps reflecting a stronger preference for cycling around the park on the weekend, particularly on Sunday, when men seem to be off chasing KOMs elsewhere.

An Easterly wind has also been helpful, though the effect has been less marked than for the men. In fact only three out of the top 25 women benefited from a favourable wind direction. This suggests that, as the weather warms up, there’s an opportunity to post a very good time when there is a strong tailwind up Sawyers Hill, perhaps seeing the first woman under sixteen minutes for the segment. So watch the forecast and get out there girls!

The last post noted that riders can be classified according to their strengths as sprinters, climbers or time trialers. Whatever kind of rider you are, it is important to balance dietary energy intake with exertion. Given the non weight-bearing nature of the sport, this is particularly important for very lean female cyclists, who may experience disruption of hormonal function, resulting in reduced bone mineral density. See Nicky Keay’s blog for more information on Relative Energy Deficiency in Sport, which is also relevant to men and young athletes.

No discussion of Strava QOMs could fail to mention the incredible performance of Maryka Sennema. Her dedication to training and cycling at the highest level has earned her over 2,200 QOMs, making her the undisputed Goddess of the Mountains.

The next blog will continue to apply the scientific microscope to cycling data, in search of helpful insights on pro cyclists.

## The best rider for a Strava KOM

So far this series of article has explored to the time of year, wind and weather conditions when riders have set their best times on the Strava leaderboard, using the popular Tour of Richmond Park segment as a case study. This blog considers how the attributes of the cyclist affect the time to complete a segment. The most important components are power, bodyweight and aerodynamic drag area or CdA. Your best chance of picking up a KOM is to target a segment that matches your strengths as a cyclist.

A power curve plots the maximal power a cyclist can sustain over a range of time periods. Ideally, the curve is plotted from the results of a series of maximal effort tests performed over times ranging from 5 seconds to an hour. Alternatively, Strava Premium or software such as Training Peaks or Golden Cheetah can generate power curves from a history of power data files. Power can be expressed in Watts or in Watts per kilogram, as in the example below.

The shape of the power curve reveals a lot about the characteristics of the cyclist. Dr Andrew Coggan explains how this information can be used to define a cyclist’s individual power profile. In the chart above, the 5 minute and functional threshold (1 hour) Watts/kg rank more highly than 5 second and 1 minute figures, indicating that this cyclist can generate fairly high power for long periods, but has a relatively weaker sprint. For a heavier rider this profile would be consistent with a time trialer, who can generate a high absolute number of Watts, whereas a light rider with this profile may be a better climber, due to a good sustainable power to weight ratio.

If you have a power meter or access to a Wattbike, it is well worth gathering this data for yourself. It can help with training, racing or selecting Strava segments where you have the best chance of moving up the leaderboard.

The power required to maintain a constant speed, V,  needs to balance the forces acting on a rider. Aerodynamic drag is due to the resistance of pushing the rider and bike frame through the air, with some additional drag coming from the rotating wheels. Drag can be decreased by reducing frontal area and by adopting a streamlined shape, while wearing a skinsuit. Additional mechanical factors are due to gravity, the rolling resistance of the tyres on the road surface and drive chain loss.

Power = Drag Factors * V3 + Mechanical Factors * V

Since the power needed to overcome aerodynamic drag scales with the cube of velocity, it is the dominant factor when riding fast on flat or downhill segments. However, on a climb, where speed is lower, the power required to do work against gravity quickly becomes important, especially for heavier riders.

Consider a rider weighing 60kg, call him Nairo, and another weighing 80kg, say Fabian. Suppose they are cruising along side by side at 40kph. Under reasonable assumptions, Fabian rides at 276 Watts or 3.4 Watts/kg, while Nairo benefits from a smaller frontal area and lower rolling resistance, requiring 230 Watts, though this equates to 3.8 Watts/kg. Reaching a 5% hill, they both increase power by 50%, but now Nairo is riding at 27kph, dropping Fabian, whose extra weight slows him to 26kph. You can experiment with this interactive chart.

Climbers are able to sustain high force on the pedals, taking advantage of their ability to accelerate quickly on the steepest slopes. Time trialers generate high absolute power for long periods, on smoother terrain, while maintaining an aerodynamic tuck. Sprinters have more fast-twitch muscle fibres, producing extremely high power for short periods, while pedalling at a rapid cadence.

The following chart shows the gradient and length of 1364 popular Strava segments from around Britain. Distances range from 93m to 93km, with an average of 2.3km. Gradients are from 21% downhill to 32% uphill (Stanwix Bank Climb).

You should be able to click on the chart (no need to sign up) for an interactive version that allows you to zoom in and display the names of the segments that suit your ability: short segments for sprinters, steep ones for climbers and longer flat ones for TTers. The Tour de Richmond Park segment is 10.8km with an average gradient of zero, so it is no surprise that the KOM is held by an accomplished time trialer.
The next blog takes a look at QOMs. Are women different?