## Kings and Queens of the Mountains

I guess that most male cyclists don’t pay much attention to the women’s leaderboards on Strava. And if they do it might just be to make some puerile remark about boys being better than girls. From a scientific perspective the comparison of male and female times leads to some interesting analysis.

Assuming both men and women have read my previous blogs on choosing the best time, weather conditions and wind directions for the segment that suits their particular strengths, we come back to basic physics.

KOM or QOM time = Work done / Power = (Work against gravity + Drag x Distance + Rolling resistance x Distance) / (Mass x Watt/kg)

Of the three components of work done, rolling resistance tends to be relatively insignificant. On a very steep hill, most of the work is done against gravity, whereas on a flat course, aerodynamic drag dominates.

The two key factors that vary between men and women are mass and power to weight ratio (watts per kilo).  A survey published by the ONS in 2010, rather shockingly reported that the average British man weighed 83.6kg, with women coming in at 70.2kg. This gives a male/female ratio of 1.19. KOM/QOM cyclists would tend to be lighter than this, but if we take 72kg and 60kg, the ratio is still 1.20.

Males generate more watts per kilogram due to having a higher proportion of lean muscle mass. Although power depends on many factors, including lungs, heart and efficiency of circulation, we can estimate the relative power to weight ratio by comparing the typical body composition of males and females. Feeding the ONS statistics into the Boer formula gives a lean body mass of 74% for men and 65% for women, resulting in a ratio of 1.13. This can be compared against the the useful table on Training Peaks showing maximal power output in Watts/kg, for men and women, over different time periods and a range of athletic abilities. The table is based on the rows showing world record performances and average untrained efforts.  For world champion five minute efforts and functional threshold powers, the ratios are consistent with the lean mass ratio. It makes sense that the ratio should be higher for shorter efforts, where the male champions are likely to be highly muscular. Apparently the relative performance is precisely 1.21 for all durations in untrained people.

On a steep climb, where the work done against gravity dominates, the benefit of additional male muscle mass is cancelled by the fact that this mass must be lifted, so the difference in time between the KOM and the QOM is primarily due to relative power to weight ratio. However, being smaller, women suffer from the disadvantage that the inert mass of bike represents a larger proportion of the total mass that must be raised against gravity. This effect increases with gradient. Accounting for a time difference of up to 16% on the steepest of hills.

In contrast, on a flat segment, it comes down to raw power output, so men benefit from advantages in both mass and power to weight ratio. But power relates to the cube of the velocity, so the elapsed time scales inversely with the cube root of power. Furthermore, with smaller frames, women present a lower frontal area, providing a small additional advantage. So men can be expected to have a smaller time advantage of around 9%. In theory the advantage should continue to narrow as the gradient shifts downhill.

## Theory versus practice

Strava publishes the KOM and QOM leaderboards for all segments, so it was relatively straightforward to check the basic model against a random selection of 1,000 segments across the UK. All  leaderboards included at least 1,666 riders, with an overall average of 637 women and 5,030 men. One of the problems with the leaderboards is that they can be contaminated by spurious data, including unrealistic speeds or times set by groups riding together. To combat this, the average was taken of the top five times set on different dates, rather than simply to top KOM or QOM time.

The average segment length was just under 2km, up a gradient of 3%. The following chart plots the ratio of the QOM time to the KOM time versus gradient compared with the model described above. The red line is based on the lean body mass/world record holders estimate of 1.13, whereas the average QOM/KOM ratio was 1.32. Although there is a perceivable upward slope in the data for positive gradients, clearly this does not fit the data.

Firstly, the points on the left hand side indicate that men go downhill much more fearlessly than women, suggesting a psychological explanation for the observations deviating from the model. To make the model fit better for positive gradients, there is no obvious reason to expect the weight ratio of male to female Strava riders to deviate from the general population, so this leaves only the relative power to weight ratio. According to the model the QOM/KOM ratio should level off to the power to weight ratio for steep gradients. This seems to occur for a value of around 1.40, which is much higher than the previous estimates of 1.13 or the 1.21 for untrained people. How can we explain this?

A notable feature of the data set was that sample of 1,000 Strava segments was completed by nearly eight times as many men as women. This, in turn reflects the facts that there are more male than female cyclists in the UK and that men are more likely to upload, analyse, publicise and gloat over their performances than women.

Having more men than women, inevitably means that the sample includes more high level male cyclists than equivalent female cyclists. So we are not comparing like with like. Referring back to the Training Peaks table of expected power to weight ratios, a figure of 1.40 suggests we are comparing women of a certain level against men of a higher category, for example, “very good” women against “excellent” men.

A further consequence of having far more men than women is that is much more likely that the fastest times were recorded in the ideal conditions described in my previous blogs listed earlier.

## Conclusions

There is room for more women to enjoy cycling and this will push up the standard of performance of the average amateur rider. This would enhance the sport in the same way that the industry has benefited as more women have joined the workforce.

## 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!

## 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?

## The best weather conditions for a KOM on Strava

This is the third in a series of articles investigating factors that determine the best times on Strava leaderboards, using the popular Tour de Richmond Park segment as a case study. So far we have established that the fastest times have tended to be in the summer, with a decent wind blowing from the East. This blog investigates how atmospheric conditions affect the density of air, which, in turn, determines the aerodynamic drag that a cyclist needs to overcome.

The power required to offset the mechanical forces, of gravity and rolling resistance, increases in proportion to speed, but the power needed to overcome aerodynamic drag rises with the cube of velocity. When riding fast, your effort goes principally into overcoming drag: maintaining a speed of 50kpm requires almost double the power of riding at 40kpm (503/40= 125/64 = 1.95). The aerodynamic drag force is proportional to the density of the air though which a cyclist is pushing both body and bike. So you have a better chance of winning a KOM (or QOM) when the air density is low.

previous blog noted that most personal bests (PBs) on the Richmond Park leaderboard were set in the summer. The following chart superimposes, in red, the average air density in London on a histogram showing the number of PBs set in each month. The trough in the air density implies that aerodynamic drag is about 5% lower during the warmer months.

So how much difference would a 5% reduction in air density make to your time round Richmond Park? For the same power, the cube of your speed can go up by 5%, resulting in a reduction of your PB time of 1.6%. For example, a cyclist completing a lap of Richmond Park in 16 minutes and 16 seconds (averaging 40kph) in December, would finish in 16 minutes dead, at exactly the same average power, in the less dense air of July. The difference is a second per minute, which equates to a saving of a minute for a one hour TT.

The air density depends on temperature, pressure and humidity. The reason that air density is lower in the summer is that temperatures are higher: warm air expands. Monthly mean atmospheric pressure is pretty much the same all year round. Humidity tends to be higher in the winter. Contrary to what most people think, higher humidity reduces air density (because water vapour, H2O, with a molecular mass of 18, is lighter than the main constituent of air, nitrogen, N2, which has a molecular mass of 28). However, as the following chart shows, changes in humidity have a tiny effect on air density relative to changes in temperature.

Although temperature is the primary determinant of seasonal variations in air density, both atmospheric pressure and humidity can vary significantly from day to day, so it is important to consider these factors when aiming for a KOM. The next chart shows the variability of air density, measured on a particular day, for an extreme range of temperatures, pressures and humidities.

When Bradley Wiggins was going for the hour record, he became obsessed with the weather forecast, because even though it was possible to raise the temperature and humidity in the velodrome, he ideally needed a low pressure weather system to pass over the UK at the same time, as this would have further reduced the density of the air that he was riding through. On 2 May 2015, the air pressure in Manchester, which is close to sea level, was 1009hPa. If it had been about 3% lower, at say 980hPa (historically very low), he should have been able to go about 1% further, to exceed 55km.

Since Strava segments tend to be outdoors, your priority should be to choose a very warm day, ideally with low atmospheric pressure and not worry too much about humidity, though higher is better. Returning to the leaderboard for the Tour de Richmond Park segment, the final chart shows the temperature and pressure on the days that the top 1000 PBs were set, split into quartiles of 250 riders (fastest riders in Q1).

Observe that most records were set when the temperature was well above the annual mean of 12 °C, shown by the vertical red line. Slightly more PBs were set when the atmospheric pressure was disadvantageously higher than the average horizontal red line. There was no significant difference in air density for the top 250 riders versus the other groups of 250. Clearly the best place to be is the lower right quadrant. Finally, we have found something that would have allowed Rob Sharland to improve upon his KOM, as the prevailing conditions were 21 °C and 1022hPa – a warmer day with a lower atmospheric pressure would have helped him go faster – but then he might not have had the ideal wind conditions noted in the previous blog. The relative importance of wind versus air density is something I hope to come back to.

The next blog explores the factors relating to the rider and bike that influence the time to complete a Strava segment.

## The best wind for a KOM on Strava

Two key aspects of the weather influence the time to complete a Strava segment: the wind and the air density. This blog considers the direction and speed of the wind. The following blog will examine how aerodynamic drag is affected by changes in air density.

Clearly, on an exposed, arrow-straight segment, the most favourable weather would be a hurricane tailwind. Like other KOM hunters, I have searched for segments that align with the predicted wind direction when a gale is forecast, though I’ve usually ended up going kitesurfing instead.

When the segment is a loop, such as the Tour de Richmond Park, discussed in the previous blog, the question becomes more interesting. Consider a light aircraft flying above the Richmond Park segment at an altitude of 300m. Any constant wind, regardless of direction, will result in a slower time than completing the circuit in still air. Why? Since any headwind slows down the plane, it hinders the pilot for more time than the tailwind provides assistance, resulting in a net increase in the total time.

However, cyclists do not ride in constant winds. Trees, buildings and the terrain all affect the wind’s speed and direction. Variability is so strong that it is recommended that multiple anemometers should be positioned at intervals alongside the 100m track at important athletics meetings.

All this means that it is quite likely that there are optimal wind conditions for all Strava segments. Most people suggest that a tailwind up Sawyers Hill is best for Richmond Park, as this part of the segment is an uphill drag that is exposed to the wind, whereas other sections of the route are much more sheltered. The bearing of a tailwind would be from just North of Easterly.  Historically, this is not a very common wind direction for London. The following charts shows the prevailing wind direction over the year is Southwesterly.

Easterly winds are even rarer in July and August, when many PBs have been set, though in September they have been a little more frequent. (An interactive version of the chart can be found on this site.)

Now, if the wind had no effect on the Strava segment, we would expect the distribution of wind directions on which riders set their PBs to be similar to the historic distribution. So we are interested in the difference between the distribution of wind directions on the dates derived from the leaderboard relative the background average. The following chart compares the segment against the historic average annual average. The compass rose clearly shows a much higher frequency (13%) of the PBs of the top 1000 riders were set when the wind was blowing from the East and a relatively lower incidence in the opposite direction.

The next hand chart “unwraps” the two curves to show the relative difference, which is statistically highly significant (p<0.01). A forensic analysis of the data confirms that the best wind direction for a PB around Richmond Park is indeed an Easterly tailwind up Sawyers Hill.

So far we have not considered the strength of the wind. The next chart shows the average windspeed on the days that PBs were set, according to the direction of the wind. This shows a bias towards stronger winds from the East, consistent with the frequency of PBs.

Combining this with the results of the previous blog, the following conclusions may be drawn. However good a cyclist you are, your best chance of achieving a high ranking on the Tour de Richmond Park leaderboard is to choose the evening or morning of one of the rare summer days when the wind is blowing strongly from the East. And, you guessed it, on the evening of August 2015 when Rob Sharland achieved his KOM, the wind was blowing at 11mph on a bearing of 80° .

The next blog will examine how temperature, pressure and humidity, as well as altitude, change the air’s density. This is the principal environmental factor affecting your aerodynamic drag, when you are going for a KOM.

## When to go for a KOM on Strava

The dates and times that people achieved their PBs on the Tour de Richmond Park

As the number of cyclists using Strava continues to grow, it is becoming increasingly difficult to achieve a high ranking on the leaderboard of any popular segment. Whilst it is possible to hunt for a top performance on some obscure route, attaining a KOM (or QOM) on a segment attempted by tens of thousands of other athletes is a real challenge.

Consider the Tour de Richmond Park, in southwest London. On 17 February 2017, the leaderboard had 35,833 entries. Note that the leaderboard does not show the 35,833 fastest times, rather it displays the personal best (PB) times of 35,833 individuals – it doesn’t matter how many times you do the segment, you only have one entry on the leaderboard. The current KOM is held by Rob Sharland, who completed the 10.8km segment in 13 minutes 57 seconds.

The top 1,000 entries on the leaderboard reveal some interesting patterns. This initial blog explores the dates and times that people achieved their PBs. The first striking observation is that hardly anyone sets a PB during the winter. The following chart shows that most records were set between June and September.

This suggests that riders tend to be in better form in the summer and that conditions are more favourable. In fact, it turns out that hours of daylight play an important role, as demonstrated by the following chart showing that most PBs are set either in the evening, around 7pm or in the early morning, between 6am and 9am. These represent times before or after work, when car traffic is lighter. Very few records are set in during the middle of the day and none at night.

A look at the days of the week, when record are set, reveals that Wednesday and Saturday are particularly popular. It turns out the most Wednesday records were achieved in the evening with some in morning, whereas almost all Saturday records were completed by 10am.

So the best time to achieve a PB around Richmond Park is on a Wednesday evening in August. And it turns out that Rob Sharland set the KOM at 8:31pm on Wednesday 12 August 2015.

But Rob deserves additional kudos, because quite a few riders have set their personal bests riding in groups, whereas it looks like Rob was riding solo. Nine other riders set their PBs on the same day, but these were all earlier than Rob’s. There were three other dates on which 10 or more riders achieved their fastest times. It is easy to spot those riding as a group, because they all start together and finish with similar times (show in red here). So chapeau to Rob for beating them all.

The next blog explores the prevailing weather conditions.