Testing Schwalbe's Super Ground versus Super Race tyre versions

Last month I posted the results of the tyre rolling resistance testing I did, in which I compared the Schwalbe's Thunder Burt Super Race against the Continental Race King Protection.

Both are fast tyres, but the Super Race version of the Thunder Burt has never been tested by BRR (BicyleRollingResistance.com). Only the more robust Super Ground version of the Thunder Burt has been tested (see here).  Nevertheless, that Super Ground version of the Schwalbe Thunder Burt is so far the fasted MTB tyre tested by BRR, marginally faster than the Continental Race King Protection. 

In principle, the thinner carcass of the Super Race version of the Thunder Burt should be faster than the Super Ground version.  This is at least what Schwalbe claims.  However, some tests on BRR in the past gave some unexpected results, with the Super Ground version of the Racing Ralph testing a few Watts faster than the Super Race version.  This showed that it's not guaranteed that the Super Race version should be faster tyre, which is what motivated me to do this test.

Test details

For this test I was fortunate to have two new 2.35" Schwalbe tyres, that were brand new and unused until this test.  I had bought both the Super Ground and Super Race tyres in late 2023, so there was minimal uncertainty coming from the effect of any tyre ageing.

I tested the tyres using the same protocol used previously, which I explained in a previous post here.  As for my most recent test though, I used my MTB with its Power2Max spider-mounted power meter which measures total power.  As a consequence, this was one of the best executed and best controlled tests I've done.

I tested the two tyres in an A-B-A-B manner, so both tyres had a repeated test.  In addition, I also test my older 2022 Schwalbe Thunder Burt Super Race tyre first, to check that it gave similar results to last week.

Results

The results are shown again below.  The results are fairly conclusive, showing the Super Race version of the tyre (plotted with purple symbols/lines) being faster than the Super Ground version (plotted in red).

Validating my power delivery optimiser: Step 4

Step 4: Calculating the optimum power delivery profile using my optimiser.  

In the previous blog post (Step 3), I showed the BestBikeSplit recommended power profile and the results of my Zwift ride when I followed BestBikeSplit's recommended power profile.  It saved me 9 seconds compared to the constant power effort, for a similar 150W normalised power.

I will now see if my Excel-based power optimiser gives similar results when I model the Whole Lotta Lava segment using the same set of 29 intervals that BestBikeSplit used to model the segment.

Results of my power optimiser

The plot above, at the top of this blog post, shows the BestBikeSplit recommended power profile (dashed red line) and then the power profile recommended by my Excel-based optimiser (solid red line), when targeting a 150W normalised power. The agreement between the two plots is remarkably good.  In the past I've done a crude comparison between my optimiser and BestBikeSplit (see here), but I've not plotted the powers on the same graph before.  This really shows the two methods give the same results, and so it shows my Excel-based optimiser is a free alternative to BestBikeSplit.  It could actually be even better than BestBikeSplit if I'm able to model more intervals than BestBikeSplit.  I just need to write the script that can automate the creation of the intervals.

Predicted ride times from my power optimiser

For the constant power situation, with the power fixed at a constant 150 Watts, my optimiser predicts a time of 27 minutes and 17 seconds. Note that this is only 2 seconds different (0.1% different) to my actual Zwift ride time of 27 minutes and 15 seconds, which I've obtained on two occasions riding at a constant 150 Watts.

To achieve this constant power of 150 Watt simulation in my optimiser, I set-up a multiple goal-seek macro in Excel.  That was an interesting learning exercise in itself, because I have never programmed in Visual Basic before.

Running the optimiser to achieve an optimal power profile, my optimiser predicts a time of 26 minutes and 37 seconds, which is a fairly significant 40 seconds (2.4% saving).  That's very similar to the 2-3% savings that I was getting when I first created my power optimisation spreadsheet back in 2016 (see post here).  The speed and power differences between the constant power case and the optimised power case are shown in the plots above.

Summary

Riding at a constant 150 Watts, my optimiser predicts a time of 27:17.

(average power = 150W, normalised power = 150W)

That 27:17 time agrees very well with the actual ride time in Zwift of 27:15.

Riding at the optimum power, my optimiser predicts a time of 26:37 (40s faster)

(average power = 143W, normalised power = 150W)

I haven't yet tried following this power profile in Zwift yet, and although the BestBikeSplit power profile is similar, the speed modelling deficiencies in BestBikeSplit (described here) was insufficient to test this.

Next Steps

The next step is to create a script that can process the Whole Lotta Lava segment profile, and any other ride profile, in a more efficient way with less manual effort by me.

Validating my Power Delivery Optimiser: Step 3

Step 3: Using BestBikeSplit to create an optimum power delivery profile.  

In my previous blog post, I explained that the following values are needed to simulate the Zwift ride:

• Total weight (rider + bike) = 80 kg (176.4 lb)
• CdA (drag coefficient x Frontal area) = 0.2415
• Crr (coefficient of rolling resistance) = 0.004
• Air pressure = 101,250 Pa
• Air temperature = 15 degrees C
• Air density = 1.225 kg/m^3
• Drivetrain losses = 2.5%

BestBikeSplit modelling via ZwiftInsider

I first tried to use this BestBikeSplit link that is available via the ZwiftInsider Whole Lotta Lava route page to access Best Bike Split.  The page assumes a fairly light rider weighing 145lbs, and makes some other assumptions. However, the Time Analysis tab allows the user to change the value of various parameters using sliders to adjust the parameters in 1% increments to the necessary value.  Unfortunately, this method didn't allow me to enter the exact values above, so I used the closest values possible instead:

• CdA = 0.24 (i.e. 0.0015 too low)
• Average power = 144.5 Watts
• Normalised power = 149.9 Watts (0.1 Watts too low)
• Weight = 177 lb (i.e. 0.6 lbs too high)
• Crr (coefficient of rolling resistance) = 0.004 (exactly as required)

Note that I tried to get the normalised power as close as possible to 150 Watts.  It's unclear what assumptions BestBikeSplit has made for air density and drivetrain losses.  Nevertheless, with these values, BestBikeSplit predicts a route time of 27 minutes 42 seconds, which strangely is 27 seconds slower than my constant power time.  I didn't understand this, so I needed to investigate why.

BestBikeSplit dedicated race plan

Since the results above seemed to make no sense, I decided to create a dedicated BestBikeSplit race plan, which is possible even with BestBikeSplit's free account.

I was able to change the settings so that the parameters were exactly matching what I wanted:

• Rider weight = 160 lbs, Bike Weight = 16.4 lb -> Total weight = 80 kg (176.4 lb)
• CdA = 0.2415, for all yaw angles
• Crr = 0.004
• Drivetrain losses = 2.5%
• Air pressure = 101,250 Pa
• Air temperature = 15 degrees C
• Zero wind speed
• Drivetrain losses = 2.5%

These values give a route time of 27:47 for a normalised power of 149.94W and an average power of 141.93W, which is bizarrely still 32 seconds slower than my constant power Zwift time.

The final section of the BestBikeSplit Race plan above provides 'race intervals' which show the segment distances and gradients, then the power recommended by BestBikeSplit, together with the resulting speeds.  I copy and pasted those 29 race intervals into Excel and calculated segment distances in metres, instead of miles: 

I then copied the distance, gradient and speed values into my power optimiser spreadsheet to allow me to compare the power values calculated by BestBikeSplit and values calculated by my spreadsheet.  Surprisingly, the power values agreed for some of the route, but were significantly different for the downhill section, as shown in plot below for the solid red and dashed red lines:

As soon as the gradient becomes negative, BestBikeSplit (the red dashed curve) is showing that a higher power is needed to achieve the downhill speeds that BestBikeSplit has calculated.  I really don't understand this because, for example, it is calculating speeds of 23.08 mph on a -4.36% (downhill) gradient while applying 84 Watts of power.  That is clearly wrong and I didn't know why BestBikeSplit is doing that, unless it is somehow assuming a large headwind

To investigate this, I plotted the BestBikeSplit powers and speeds versus the gradient values, shown withe red symbols on the plot to the left.  The plot looks reasonably sensible, although the scatter seen in the corresponding speeds is slightly strange. Regardless of these doubts, I decided to try to ride the route using the BestBikeSplit power profile to see what time it would give.

Riding the BestBikeSplit power profile

To do this, I decided to use the BestBikeSpilt's feature where they allow the power profile to be exported as a TrainerRoad workout.  This is a nice feature, because it allowed me to ride exactly the prescribed powers by doing the workout in ERG mode, with my phone (for TrainerRoad) and also Zwift (via my iPad) connected to my Wahoo Kickr trainer.  The workout included the 29 intervals contained in BestBikeSplit's race intervals.  As shown above, I also added a long 150W interval afterwards to allow me to do a second lap at a constant power, if I wanted to.

I started the TrainerRoad workout as I got to the archway that marks the start of the Whole Lotta Lava segment.  As a reminder, the segment time I did previously with a constant 150W power output was 27 minutes and 15 seconds.  Using the BestBikeSplit power profile, I did a time of 27 minutes and 6 seconds, so 9 seconds faster than the constant 150W effort.  It's worth noting that although the normalised power for the BestBikeSplit run was about 150W (actually slightly lower, at 148W), the average power was substantially lower at 140W.  This is interesting in itself, and a positive results, that the average power was 10W lower than the constant 150W effort, but the time was 9 seconds faster.

However, something I noted when riding the segment with the BestBikeSplit power profile was that at several times the target power did not correlate well with the gradient.  At most times it was good.  For most of the uphill sections, the target power was higher than 150W, as  expected.  At some points, though, the target power didn't correlate with the gradient.  For example, the high power interval stopped about 10 or 20 seconds too soon, before I reached the top of the climb, on the steepest part, which seemed odd.  Therefore, I think that due to the BestBikeSplit speed issue mentioned previously, I think there was a synchronisation problem between the BestBikeSplit simulation and what actually happened in Zwift when those powers were ridden.  I have the feeling that the most likely explanation is that BestBikeSplit is assuming some wind that is not actually there in Zwift.

It's worth also be noting that the 27:06 time is significantly faster that the 27:47 time predicted by BestBikeSplit, indicating that their calculation of Zwift speeds are wrong, despite me entering the details such as Crr and CdA correctly. 

Finally, it's interesting and encouraging that my second lap of the Whole Lotta Lava segment was done again at 150W constant power, and again this gave exactly the same 27 minute 15 second segment time as I achieved before (described in my previous blog post).

Summary

This has been quite a long post, so let's recap:

Riding at a constant 150 Watts gives a time of 27:15.

(average power = 150W, normalised power = 150W)

Riding at the BestBikeSplit optimum power profile gives a time of 27:06 (9 second saving)

(average power = 140W, normalised power = 148W)

Next Steps

The next step is to see whether my own Excel-based power optimiser gives similar results, and to do that I will be to create a scripts that can process the segment profile in a more efficient way, with less manual effort by me.

Validating my Power Delivery Optimiser: Steps 1 & 2

Steps 1 & 2: Gathering data and rider parameters for the constant power case.  

The introduction post written previously (here) explains the objective and context of this Step 1 & 2 of the study.

Route Selection

Firstly, I needed to decide a suitable route.  Strange as it may seem, I didn't want to use an outdoor route.  The difficulty of doing a test like this outdoors is that the conditions are so variable. The wind changes, traffic interferes with aerodynamic drag and gets in the way, road junctions have to be avoided, etc.  Then I would also have to ensure my bike, my power meter and my body position stayed consistent between runs.  

I really wanted to avoid those constraints and difficulties associated with doing the test outdoors, so I instead decided to use a virtual route on Zwift.  This is also not ideal, because the bike speed in Zwift is calculated with a computational model, so I'll effectively be using a model (Zwift) to validate another model.  However, I feel that the benefits of having a fully controlled environment to test the power delivery optimiser outweighed the doubts and downsides that may come from using Zwift.

For the route, I decided to use the Whole Lotta Lava route.  I picked this route because it was reasonably short at 12.3km (7.6 miles), so it would take less than half an hour to ride.  Also, it includes a good mix of terrain, with a flat section, a climb and a descent in approximately equal amounts, as shown in the route profile to the left.  The amount of climbing, with 160m (525ft) climbed in 12.3km is the kind of elevation change that I typically see in my local rides and the terrain in the South West of England where I live.

Finally, the route is almost entirely pavement, which makes the treatment of rolling resistance somewhat easier.  There is a short section of wooden boardwalk, perhaps 200m in length that has to be crossed on climb and the descent, but I'm hoping that is not significant enough to complicate the modelling.

Bike Choice

For this type of test, a TT bike is the best bike to use, because Zwift does not allow a TT bike to gain a benefit from drafting other riders, unlike for their road bikes.  This means that any ride I would do on a TT bike, and the associated segment time for the Whole Lotta Lava route, would not be affected by the presence of other riders.

In Zwift I have the Canyon Speedmax TT bike with Zipp 808/Super 9 wheelset.

Riding the route at constant power

I decided to perform the constant power reference case at a fixed power of 150 Watts.  This is approximately 60% of my FTP, so it makes it a comfortable long-slow-ride kind of pace.

I ensured that that my ride was done at exactly 150W by using TrainerRoad in ERG mode to control my Wahoo Kickr trainer, ensuring that I held a power of exactly 150W, then linking the Kickr trainer to Zwift via it's second bluetooth connection so that the Zwift ride was done at 150W.  It can be seen in my Strava activity file (screenshot shown above, link to Strava activity here) that the power trace is constantly at 150W.

The entire Whole Lotta Lava route was done first, then I also performed most of a second lap too.  The second lap was useful because the translucent pace partner (which follows the pace of my best time) was a demonstration that Zwift was reproducing the pace almost exactly, as shown in the screenshot to the left.  This was the top of the climb, approximately half way through the route.  Note that the pace partner (riding at the pace of my previous lap) is very close to my avatar on the second lap.

This is to be expected, of course, but it was reassuring to see.  This is the kind of repeatability that would be impossible if riding a route outside!

Reference parameters

For the subsequent power delivery optimisation, I will also have to decide values to use for the following parameters:

• Total weight (rider + bike)
• CdA (drag coefficient x frontal area)
• Crr (coefficient of rolling resistance)
• Air pressure
• Air temperature (which together with air pressure, gives the air density)
• Drivetrain losses, if indeed Zwift even account for these.

The choice of these values should match as closely as possible as what Zwift models, some of which it's possible to determine, but some of which are unknown.

Weight

I entered my weight of 73kg and my height of 5'10" into Zwift.  I'm not sure what weight Zwift assumes for the bike, so this is something I had to figure out as part of the virtual elevation (VE) analysis.

Coefficient of rolling resistance (Crr)

According to Zwift Insider, Zwift assumes a Crr value of 0.004 for road and TT bikes on pavement.  I will use this 0.004 value for the VE analysis.  The small section of wooden boardwalk apparently pushes the Crr up to 0.0065 for that short period of time.

Drag coefficient (CdA)

Previously, when I unlocked the Zipp Super 9 disc wheel, I calculated (here) that the CdA was 0.2415 m^2.  I will use that value for VE analysis.

Air Pressure and Temperature

As I did previously, I assumed International Standard Atmospheric Sea Level conditions (101250 Pa, 15 degrees Celsius), which results in an air density of 1.225 kg/m3.  It's worth noting that pressure, temperature and the resulting air density only affects aerodynamic drag and nothing else.  Therefore, if the assumption is wrong, it will simply bias the CdA value which is also chosen, because it's the product of CdA and air density that affects aerodynamic drag.  As the CdA is obtained from the VE analysis, the choice of pressure, temperature and air density is rather arbitrary and intrinsically linked to the CdA value selection.

Drivetrain losses

I used a value of 2.5% as starting point, to be checked with the VE analysis.

Virtual Elevation Analysis

I used the .fit file from my Zwift ride to perform a virtual elevation analysis using Golden Cheetah's Aerolab Chung Analysis feature. The plot to the left shows the final fit between the real elevation curve (from Zwift, in green) and the Chung Method virtual elevation curve (in blue).  As can be seen in the plot, keeping the values above and changing only the total weight to 80kg gives an excellent fit between the blue and green curves, to the point where it's almost impossible to see the two different elevation profiles.  I also tried using other combinations of parameter values, but using this combination of parameter values gave the best match.

Summary and Results

My constant 150W ride took 27 minutes and 15 seconds to complete the Whole Lotta Lava route, which equates to an average speed of 16.8mph (27.0 kph).

My virtual analysis showed that the values used by Zwift which I need to use later for the power delivery optimisation are:

• Total weight (rider + bike) = 80kg
• CdA (drag coefficient x Frontal area) = 0.2415
• Crr (coefficient of rolling resistance) = 0.004
• Air pressure = 101,250 Pa
• Air temperature = 15 degrees C
• Air density = 1.225 kg/m^3
• Drivetrain losses = 2.5%

I'll publish the next blog post to describe the improvements to my power delivery optimiser, once I've done that.

Validating My Power Delivery Optimiser: Introduction

In one of my earliest blog posts (here) from 2015, I described the power delivery optimiser that I created using Microsoft Excel.

I also used it to help a friend try to improve his personal best time for his favourite bike route (here). In doing so, I showed that my power delivery optimiser gave a similar optimal power profile to the profile that BestBikeSplit gave.

However, I have never properly validated my optimiser, or BestBikeSplit either, to prove that following those power targets does indeed give an improvement in performance.  It should do of course, because there is no reason why the modelling should have deficiencies.  Nevertheless, it would be satisfying to validate it properly, and this is what I intend to do in the coming weeks.

I have several steps in mind, because I also intend to improve my Excel-based power delivery optimiser so that it's generally easier to use.  This blog post is the first step, where I'll outline what I intend to do.  I'll then write additional blog posts as I make progress, and will update this post to include the relevant links.

The summary below describes briefly what I'll do and includes links to the more recent blog posts that will describe in more details the studies and the results.

Summary and links

• Step 1:  Gathering data for the reference case (constant power).
• Step 2:  Determine rider/bike parameters (CdA, Crr, weight).
• Step 3:  Use BestBikeSplit to create an optimum power delivery profile.
• [To be done] Step 4: Calculating the optimum power delivery profile using my optimiser.
• [To be done] Step 5: Improve my power delivery optimiser.
• [To be done] Step 6:  Calculate the optimum power profile for the route cycled in Step 1.  Do the same for BestBikeSpilt and compare.
• [To be done] Step 7:  Re-ride the route following the optimum power profile, to see whether it improve the average speed versus the constant power approach done in Step 1.

Repeating the Thunder Burt vs Race King CRR test

Yesterday (27th Dec '23) I repeated the rolling resistance test that I did last year, in which I compared two of the fastest mountain bike tyres that are available: The Schwalbe Thunder Burt Super Race tyre and the Continental Race King Protection.

Why repeat?

A couple of things have changed since I did that previous test in February 2022, which I think will improve the quality of my testing.

 

Firstly, last year I bought a Power2max spider-based power meter for my MTB.  A spider-based power meter has the benefit over my single-sided Stages power meter that I used last year because it measures total power.  Any single-sided power meter assumes that the total power is double the power that is measured on the left hand side, so has some inherent uncertainties coming from that assumption.

Secondly, I now have a second pair of wheels for my MTB.  This enables me to mount a 2nd tyre on the other rear wheel and perform a back-to-back test with less effort to swap the tyres over, and less delay between tests.

Testing protocol

I performed the testing in exactly the same manner as I did previously.  The process is briefly summarised below:

1)  Mount tyre/wheel on bike.  Measure how much weight is on the back wheel when I'm sitting on the bike (which in this case, was 54.9 kg).

2)  Warm up the tyre for five minutes at 80-85 rpm and ~150W / 24mph.

3)  Adjust tyre pressure (takes ~1-2 mins) to the desired value.  During this time, the Power2Max power meter will automatically calibrate its zero offset value.

4)  Pedal for 4 minutes in same gear, at a similar 80-85 rpm.  For the final 2 minutes, measure the average power and speed.

5) Results were recorded in the spreadsheet and the CRR was calculated using the standard Tom Anhalt method. 

For this test, I performed an ABABA style test (A=Thunder Burt, B=Race King), so with two repeats for the Thunder Burts and 1 repeat for the Race Kings.  The reason for repeating the Thunder Burt a second time was because I got a strange results for the first test at 16 psi.

Results and observations

As shown in the CRR plot below, the Thunder Burt is clearly the faster tyre of the two. During test, the power needed to keep the rear wheel spinning at ~23mph was noticeably higher by 15-20 Watts for the Race King.  The results from my 2022 test are shown in grey in the plot, for reference.  Note that those 2022 tests were performed with a butyl inner tube, instead of tubeless.  This difference, together with the single-sided power meter, might account for the difference in results.

Note also that the Bicycle Rolling Resistance test results in the plot below have been updated to show the tubeless BRR results instead.  In previous years, all of the BRR testing was performed with a butyl inner tube. It's only recently in 2023 that BRR changed it's protocol and updated their results to show data for a tubeless setup whenever appropriate.

What's also noticeable in the plot above is that the results are incredibly consistent and repeatable, which I am really pleased with.  This is probably due to my new power meter which avoids errors coming from inconsistent left/right leg balance.  

There is one outlier though, one of the low pressure results for the Thunder Burt (denoted by the red circle in the plot above).  As this is a clear anomaly in the otherwise consistent data, I ignored it when of creating the green Thunder Burt trendline.

What I'm not so pleased about though (and this is a massive "DOH!"), is that I have just realised that I mounted the Race King in the wrong direction.  This is really annoying and it might be the reason why the Race King has higher CRR values than the Thunder Burt and why the differences seem to be larger than I measured previously.

I need to think what to do next.  At the moment, I am not inclined to repeat the whole test!

Addendum 28/12/23 - Correcting my mistakes

I really couldn't leave this test as it was, with Race King results for the tyre mounted in the wrong orientation.  On the following day (28th Dec '23), I decided to repeat the Race King test with the tyre mounted the correct way round.

Before re-mounting the tyre, I first performed a repeat test with the tyre exactly as it was the previous day (i.e. the wrong way round), to ensure I could get get consistent results with the previous day.  The results from this repeat test are shown with the light blue triangles in the plot below.  It's pleasing to see that the repeatability of the test results with the previous day's testing is very good, so on this basis, I didn't feel that it was necessary to repeat any of the Thunder Burt tests too.

I then removed the tyre, flipped it around and remounted it so that it was now rotating in the correct direction.  The new results for the Race King tyre mounted in the correct direction are shown with dark blue circular symbols in the plot below:

It's interesting to see that when the Race King was mounted in the correct direction, the rolling resistance seems to be similar.  In fact, if anything the CRR values are slightly higher than when it was mounted backwards, which was a little surprising. The differences are very small though, and are the same order of magnitude as the repeatability.

Conclusion

After this additional testing, I think it's safe the conclude that the 2.35" Schwalbe Thunder Burt Super Race is a faster tyre than the 2.2" Continental Race King Protection.  The differences seem to be ~4-6 Watts of rolling resistance for a 85kg rider+bike cycling at 25kph (~15.5mph), depending on the tyre pressure.

It's worth noting that the 2.2" Race King measured slightly narrower by 0.11 inches (=2.8mm or 4.7%), which would penalise the rolling resistance for Race King relative to the wider 2.35" Thunder Burt at a given pressure.  The effect of the width difference should be quite small though I think, based of tyre width effects I've seen on BRR.com, like this test for example.

As with all roller or drum testing, it is worth remembering that testing of this nature only detects the tyre hysteresis effects of the tyres.  These tests cannot capture the other losses associated with riding off-road, such as the so-called suspension losses that that are created from the rider 'jiggling around', or the hysteresis losses that occur in the ground itself.  So for example, the CRR relationship versus tyre pressure seen in roller testing or drum testing goes in the opposite direction to what the tyre pressure effects I have seen when doing proper off-road tyre testing (see here, for example).

Nevertheless, I believe that the relative tyre hysteresis losses that are captured by roller testing of this nature are still relevant to off-road riding, and will be one element of the total rolling resistance that remains present when riding off-road.  Therefore I think this result and results from tests like it (on BRR.com for example) will still show which tyre is relatively faster.

The Drop Bar MTB - Is it faster for Cyclocross?

In my previous blog post, I described the drop bar conversion that I did for my hardtail MTB.  I did that conversion in an attempt to create a bike that's as fast as possible for 'light' off-road duties - faster than a hardtail MTB and faster than a traditional gravel or cyclocross bike.

Based on the testing that I've done in recent years, this drop bar MTB bike should be faster on a grass surface like a cyclocross (CX) course than my CX bike, somewhat counter-intuitively.

During the latest 2023 CX season I therefore decided to use my drop bar MTB for any CX races that were dry enough to be suitable for the semi-slick Schwalbe Thunder Burt tyres.  For the muddy races, I used my CX bike with mud tyres, knowing that the grip from the Thunder Burt tyres would be terrible in mud.  It's worth mentioning that my local Cyclocross League doesn't apply the UCI rule of 33mm maximum tyre width, and doesn't have other bike restrictions, so anything is allowed.  MTBs are often used by people that don't have CX bikes.  Given the lack of restrictions around bikes, my general approach is to use the fastest bike possible, rather than sticking with tradition.

The question is, was the drop bar MTB actually faster or not?

How I judged the bike performance

I did seven CX races this season, from September through to early December.  The last one I did was a regional race, with riders entering from two Leagues (Western League & Wessex League), so that 7th race was much more competitive than the other six races, so my result was worse than the others.

For the first six Western League races, I felt that my fitness was broadly similar, and therefore my results in those six races, and my speed relative to my competitors, would provide a good indication whether or not the drop bar MTB was faster than the CX bike.

It has been a very wet autumn here in the South West of England, so unfortunately there weren't many dry races this year.  Of the six races, only two races were dry enough to use the drop bar MTB.  Even for those two, the courses were still muddy in places.  So for two of the races, I used the drop bar MTB, and for the other four races I used my CX bike with 33mm Challenge Handmade Tubeless Ready Clincher mud tyres, with the tyre model choice based on the level of muddiness (either Baby Limus or Limus).

I analysed my performance relative to my competitors in two ways:

1) What my finishing position was relative to the size of the field, calculated as a percentile.  For example, if I finished 20th out of a field of 50 people starting the race, that's a 40th percentile finishing position.

2) Secondly, what my finishing time was relative to the winner.  For example, if the winner finished in 1 hour and I finished in 1 hr 6 minutes, my 6 additional minutes make me 10% slower than the winner's time of speed.

Of these two methods, I think the first one is a slightly more reliable method, because the second method is influenced by a single person; the winner's performance. It therefore depends on whether the fastest rider in the region raced in a particular weekend and how he performed in that race.

It's worth noting that although I changed bike and tyre width for each race depending on the conditions, almost everybody else in the field, especially the top riders, used the same cyclocross bike with 33mm tyres for all their races. 

Results

The plot below shows the results using these two methods.  Regardless of the method, it's clear that I achieved better race results using the drop bar MTB than my CX bike.  Like many things, this is not 100% conclusive, but I still think it's a strong indication that the drop bar bike is faster.

Mountain bike drop bar conversion

This is the drop bar conversion that I've done for my Scott Scale hardtail mountain bike.

This post describes why I did this conversion, what I components I used, and how much it cost me.

Why?

I admit it's a strange-looking bike, and unusual build; a MTB with a drop bar.  It may not be obvious why anybody would want to swap out the flat MTB handlebar for a drop bar.  There are three mains reasons why I did this conversion, which I've explained below:

1)  Wider tyres are faster off-road.

Firstly, over the last few years I've down a lot of testing that has consistently shown that wider MTB tyres are faster than narrower gravel/cyclocross tyres.  Those tests are summarised briefly below, with links to my previous blog posts that describe the tests in more detail:

• In 2021, I did a back-to-back test around the same road loop, riding my road bike, then my gravel/cyclocross bike, then my hardtail MTB.  The really surprising result from that test was that MTB was actually no slower than my gravel/cyclocross bike.  For both of those bikes, the tyres were reasonably fast tyres in their own category, both scoring quite high on the individual tables of Bicycle Rolling Resistance's gravel and MTB tables.  Because the test was done on a road circuit, you'd think that would favour the gravel bike versus the MTB.  However, the results indicate that the improved rolling resistance of the MTB tyres versus the gravel tyres was cancelling out the less favourable aerodynamics of the MTB (due to wider tyres, the flat bar, the suspension fork etc).

• Then, in 2022, I performed a virtual elevation test (a 'Chung method' test) for my MTB around a grass field, to determine the optimum off-road tyre pressure.  An interesting observation from that test was how much faster the 2.35" Schwalbe Thunder Burt tyres were than the 35mm Schwalbe X-one cyclocross tyres that I tested back in 2020.  The rolling resistance coefficient difference between them was enormous, equivalent to 30-35W at 15mph. 

• Then, later in 2022, I performed a similar virtual elevation grass test using my cyclocross/gravel bike which this time was fitted with 40mm Continental Terra Speed gravel tyres.  The aim was to determine whether the CX/gravel bike was quicker overall than my MTB with it's Thunder Burt tyres, to help me decide which bike to use for an upcoming cyclocross race.  Surprisingly the MTB was faster, despite the worse aerodynamics.  This again indicated that the lower rolling resistance of the MTB tyres was outweighing it's aerodynamic disadvantages, to give a net speed benefit.

All of this showed that wider tyres, especially the Schwalbe Thunder Burts that I had tested for two of those tests, were much faster than narrower gravel and cyclocross tyres, even faster than very good tyres like Conti Terra Speeds. 

That got me thinking, whether the best combination for a fast and light off-road bike would be the wide Schwalbe Thunder Burts coupled with a more aerodynamic bike and riding position.

2)  MTB tyres don't fit into the frame of my cyclocross/gravel bike

Sadly the frame clearances of my Planet X Pickenflick cyclocross/gravel bike does not allow me to fit my Schwalbe Thunder Burts.  Even if I bought the 2.1" version instead of the 2.35" size, they wouldn't fit.  The maximum tyre size for the frame is about 50mm, which is fairly large by cyclocross bike standards, but is not big enough for MTB tyres.

Hence, getting fast MTB tyres onto my cyclocross/gravel bike was unfortunately not an option for me.

Interestingly, around this time, I saw a Twitter post from Tom Anhalt who also said that his favourite gravel tyre is actually a MTB tyre.  He commented that he was looking to convert his gravel bike to take 650B wheels.  I didn't fancy going in that direction though, because it would require new 650B wheels etc.

3) My new full-suspension MTB

The third and final reason that persuaded me to convert my hardtail MTB to a drop bar bike was getting a new full-suspension XC MTB in early 2023.  Until then, I had ridden my Scott Scale hardtail MTB as my 'best' MTB for any fast MTB rides.  However, having bought a nice new full suspension MTB in the spring of 2023, a Specialized Epic Evo, I found that I was then rarely using my Scott Scale.

My Scott Scale hardtail seemed somewhat redundant in my garage, in the presence of the full-suspension XC bike and my gravel/cyclocross bike.  There were few types of rides and terrain where it was a better option than those other two bikes.

It was these three things that persuaded me to convert my Scott Scale MTB to a drop bar bike.  I also had a spare pair of SRAM Rival 1x hydraulic shifters, which I could re-use for my my bike build, helping to reduce the cost of the conversion. 

Components and cost

I had to buy the following items for this build:

• Ribble Level 3 Carbon Aero Road Bar:    £39.00   [RRP £149.99, no longer available]
• SRAM Rival 1 Long Cage Rear Mech:    £34.00   [RRP £124.00]
• 2 x SRAM road hydraulic disc replacement brake hoses:  £75.19   [RRP £92.00]
• Schwalbe Thunder Burt Super Race 2.25" front tyre:   £52.79   [RRP £68.99]
• Planet X Selcof Carbon Rigid Fork:   £99.99   [RRP £199.99]
• Syncros semi-integrated tapered headset:   £15.99   [RRP £45.99]
• 100mm -> 110mm boost axle adapter spacers:   £7.85   [RRP £8.33] 
• Lifeline professional bar tape:   £11.43   [RRP £12.99, no longer available]

I already had the following items spare, so these didn't cost me anything at the time of this build:

• SRAM Rival 22 11- Speed Shift Brake Lever Set    Already owned   [RRP £532.00]
• Ritchey Comp 4-Axis 30 degree 60mm Stem:    Already owned   [RRP £42.99]

Note that I only had to buy the front tyre because I wanted to move my old 2.35" Thunder Burt front tyre onto my new full-suss MTB as a rear summer tyre, and 2.35" Thunder Burts were out of stock everywhere.  Hence I had to get a 2.25" version instead.

The total cost to me for the items above was £336.  As shown above, some of the items I managed to get were real bargains, bought at a significant reduction compared to their recommended retail price (RRP).  The total RRP of all the items I had to buy is £702.   If I had to also include the cost of the shifters and stem, which I had already as spare items, the total RRP cost would be £1277.

In summary, at just over £300, I think this was a fairly cheap conversion.    However, if I had to buy all of the items at full price, the £1200+ cost would have been significantly higher.

The Outcome

I'm really pleased with the way the bike has turned out.  Not only was it a fairly cheap conversion to do, but the bike feels great when riding off-road trails.  It feels faster, more forgiving and more comfortable than my cyclocross bike with 40mm gravel tyres, but also faster than my old hardtail on road sections and whenever the off-road speeds are higher.  I feel that for the majority of off-roading riding I do, it's the ideal of bike to be on.

Looks wise, I have to admit that it's a bit ugly and unusual looking, but I'm okay with that.  The weight of the bike is 8.67 kg (19.10 lb), including pedals, bottle cage and out front Garmin mount.

I have used the bike for a few cyclocross races in recent months, and it has performed well, helping me to get better race results than normal.  In a future blog post I'll show more details about these Cyclocross race results.

It's interesting that in 2023, the racer Dylan Johnson has also started to use a drop bar mountain bike for his gravel/MTB Life Time Grand prix Races in the US.  In a recent YouTube video he explains his reasons for using a drop bar MTB this bike and tyre choice, which are similar to mine (although Dylan favours Conti Race Kings instead of Schwalbe Thunder Burts).