Tag Archives: bicikli

Tour of Tenerife, Stage 1 (106 km, 2500 m+)

Today after I approved the buffet breakfast of the hotel, I left for the first longer ride. I did not leave too early, only after 10 AM, because I knew it would be more than enough. The weather was normal, cloud layer at around 800-1000 meters, so not sunny, but humid and warm. I had downloaded my plans for the day to my Garmin Edge 500 cycling GPS, so I did not need a map, I only had to follow the track displayed on the screen. This was extremely useful (and stressful too – but I will talk about that later), because La Orotava is full of one way streets, and finding the way out from here is not that easy. Also, cycling along the North coast of Tenerife to the East from La Orotava is not that easy when you have to leave out the TF-5 highway from your plans, because there are not that many roads besides that one. I took the TF-21 and TF-217 roads (with slight ups and downs, but nothing very serious), then after crossing above the highway, I rode to El Sauzal. Here after a short downhill, the first ascent started at the 16.79 km mark.

7.5 km @ 4.2% with a maximum over 100 meters of 13.7% (3rd category)

The average gradient does not tell the whole story, because there were long and painful sections above 10%, but the rest was not that difficult. Still, it did hurt. Meanwhile the Sun appeared from behind the clouds, so it started to became a bit too hot too. Then from the top, there was still a bit of rolling terrain, but soon the descent started to La Laguna on the TF-152 road. I did not spend any time in the city (I did that last November already), but I turned directly towards the Teide instead. After a quick bite (from my energy bars), I started the long ascent towards the Las Cañadas caldera at the 32.87 kilometer mark on TF-24.

38.71 km @ 4.6% with a maximum over 100 meters of ~10% (HC)

Again, don’t let the average gradient fool you, because there are even downhill sections included in this number! The typical gradient from the elevation plot is around 5-6%, when you only look at the uphill sections. But on top of the length, and the constantly changing steepness, the main difficulty was the temperature above 1900 meters, where the road leaves the beautiful pine forest behind, and enters the rocky volcanic desert of the Teide. Also, this is the point where the road quality basically stops to exist, because while the asphalt is extremely smooth during the first half (it is a pleasure to ride there), it becomes cracked and worn for the second. But the view above the cloud layer is truly amazing.

I could have taken hundreds of pictures, but I have already a lot from these islands, so now I only take a few for the blog, and try to concentrate on the biking itself instead. As I climbed higher the temperature also ‘climbed with me’ (my GPS showed 38°C, which was clearly not the real air temperature, but it felt like that), the Sun was shining extremely strong, so there were moments when I did not feel perfectly ok, even though I was riding with a zipped down jersey and I had enough liquids with me. It was very difficult during the last kilometers.

To make thing worse, around halfway into the ascent I realized that I forgot to pack my small toolkit and my small pump, so from that point I made sure I don’t ride over anything sharp… Luckily I had no flat tire during the day (and now I have already prepared these for tomorrow). The highest point for today was the entrance gate of the Observatory!

From here, it was basically a descent of more than 30 kilometers till home, which I enjoyed a lot of course, especially because the road quality was also quite good (TF-21). The temperature got much cooler when I entered the cloud layer, which felt really refreshing after the hours I spent riding in the scorching hot.

Thanks to the GPS (and my planning) the last road in La Oratava was a tiny, extremely steep (~30% or more) cobblestoned one way street, where I was really doubting if I would be able to stay on my bike, and stop at the end. Luckily, I did succeed in both, though getting off the bike was not easy on the slippery stones. Then the last stressful moment came when I wanted to save the ride in the GPS, but at first sight it seemed that it did not work out… At least it did not show up in the history, so I got a bit sad/angry, because loosing the data of such a ride would have ben really painful for a geek cyclist like me :) Luckily when I connected the GPS to my MacBook and tried to see if I can save something from the memory, the ride was there, and I managed to upload it to Garmin Connect! It was a huge relief. But this is not the first time that I have problems when I use the follow course option with the GPS, so it seems to me that when the GPS thinks that I left the course (because of, e.g., reception issues) then it has trouble recording data while it is trying to relocate itself along the course, and this leads to 1) minor data loss (I can see that in the file), and 2) problems with uncorrupted file-creation. So I will not use this ‘feature’ in the future, because I really don’t want to loose any data… I will have to follow road signs instead, like we did back in the previous century :D

Ps.: I have a feeling that being in Spain for three weeks and barely speaking to anyone in English has a terrible effect on my English, apologies for that…

Double observing run: done.

During the last three weeks I spent 17 nights working at the telescope. 11 nights at the 1.2 meter Mercator telescope on La Palma, and 6 nights at the 0.8 meter IAC-80 telescope on Tenerife (and I had one night in between, when I did not have to work). All these nights were clear, and though some were plagued by the Calima (dust from the Sahara blown above the Canaries), I managed to keep observing all the time, except for one hour, when the seeing got so bad, that I could not do anything anymore. It will be really hard to have a better observing run ;) It is not easy to admit that there are much better time-lapse movies than the one I made last year, but the video below really shows how beautiful my workplace is…

I still think that this is the best part of being an astronomer, but of course after so many days it is good to be back in the normal day-night rhythm, and on a lower altitude (almost sea level), where there is more oxygen ;) So after saying good bye to the view from the Observatory (see below – pretty, isn’t it?), I took a taxi down (to 2000 meters lower in elevation) to La Orotava.

I am staying at the Hotel Rural Victoria, and it is perfect. I have a quite big room, free wifi, an LCD TV (on a nice wooden work desk), a huge private terrace overlooking the city and the sea (with my own cacti :D), two big beds, separate bathroom and toilet, etc. And the dinner included in the half pension has three courses with some tidbits in between! Perfect for a tired cyclist, who does not want to look for food after a long day. I hope the breakfast will be as good too! (Update: it is damn good too! I have chosen the best hotel.)

Today I did not do anything special, just assembled my bike (for the 3rd time in three weeks…) while watching the Vuelta (I went on a ‘short’ 1.5-2 hour training on 5 out of 7 afternoons while I was working here on Tenerife, so today was a rest day), walked around the city for an hour (it is really nice, a bit like La Laguna), and got some cookies and chips for later. And a big bottle of water – the most important thing… Now I have 6 days to bike around the island (mostly up and down, not really around), and I made plans for 5. We will see how much I can handle. The rides are shorter (100-130 kilometers), but with a lot of climbing, so it is not at all easy… But the scenery is amazing, so it is definitely worth the struggling ;)

I will keep you updated on the rides! (If I have energy left to blog afterwards… :D)

The Performance Management Chart

Now that I can analyze and plot my workouts in detail, and I can estimate the Training Stress Score (TSS) of each ride, it is time to make use of all the statistics. It is nice to have numbers quantifying my trainings one-by-one, but how nice would it be to track my form and fitness during the season based on the volume and intensity of my rides. This is the concept behind creating the Performance Management Chart. I will write down (partly quote, or just rephrase) here the most basic things, but again, everything is based on the following two articles (take time to read them, they are very interesting):

What is the Performance Management Chart in TrainingPeaks WKO+?, by Hunter Allen
The scientific inspiration for the Performance Manager, by Andrew R. Coggan, Ph.D.

Andrew R. Coggan defines form as a combination of fitness and freshness (Form = Fitness + Freshness). You will have a really good day in the saddle when your fitness level is high, and when your fatigue level is low. Fitness is a response to training stress (or training load); the more you train the better you will be. Freshness is simply a result of rest. As TSS quantifies training load, knowing TSS values of workouts from the past enables us to determine the rider’s fitness in the present, and see how much training is needed in the future to raise this level higher.

The Performance Management Chart is based on Banister’s impulse-response model (see 2nd article for details), and we define the following components:

1) Chronic training load (CTL) provides a measure of how much an athlete has been training historically (chronically). It is calculated as an exponentially-weighted moving average of daily TSS values, with the default time constant set to 42 days. CTL can be viewed as analogous to the positive effect of training on performance in the impulse-response model. It is a relative indicator of changes in performance ability due to changes in fitness.

2) Acute training load (ATL) provides a measure of how much an athlete has been training recently (acutely). It is calculated as an exponentially-weighted moving average of daily TSS values, with the default time constant set to 7 days. ATL can be viewed as analogous to the negative effect of training on performance in the impulse-response model. It is a relative indicator of changes in performance ability due to fatigue.

3) Training stress balance (TSB) is the difference between CTL and ATL, i.e., TSB = CTL – ATL. TSB provides a measure of how much an athlete has been training recently, compared to how much they have been training historically. It can be viewed as an indicator of how fully-adapted an individual is to their recent training load, i.e., how “fresh” they are likely to be.

In the Performance Manager concept, an individual’s CTL determines their performance potential, but their TSB influences their ability to fully express that potential. Their actual performance at any point in time will therefore depend on both their CTL and their TSB, but determining how much emphasis to accord to each is now a matter of trial-and-error/experience, not science. (Really go and see more details in the original article.)

Some other interesting quotes before I tell you about my progress in the topic:

“The following approximate guidelines may prove useful when analyzing prior data: a TSB of less than ‑10 would usually not be accompanied by the feeling of very fresh legs, while a TSB of greater than +10 usually would be. A TSB of -10 to +10, then, might be considered neutral, i.e., the individual is unlikely to feel either particularly fatigued or particularly rested. The precise values, however, will depend not only on the individual but also the time constants used to calculate CTL and ATL, and therefore should not be applied too literally.”

“The optimal training load seems to lie at a CTL somewhere between 100 and 150 TSS/d. That is, individuals whose CTL is less than 100 TSS/d usually feel that they are undertraining, i.e., they recognize that they could tolerate a heavier training load, if only they had more time available to train and/or if other stresses in life (e.g., job, family) were minimized. (…) On the other hand, few, if any, athletes seem to be able to sustain a long-term average of >150 TSS/d.”

“In addition to the absolute magnitude of CTL, considerable insight into an individual’s training (and/or mistakes in training) can often be obtained by examining the pattern of change in CTL over time. Specifically, a long (e.g., 4-6 week) plateau in CTL during a time when a) the focus of training has not changed, and b) the athlete’s performance is constant is generally evidence of what might be termed training stagnation – that is, the individual may feel that they are training well, by being very consistent and repeatedly performing the same workouts, but in fact they are not training at all, but simply maintaining, because the overload principle is not being applied. On the other hand, attempting to increase CTL too rapidly, i.e., at a rate of >5-7 TSS/d/week for four or more weeks, is often a recipe for disaster, in that it appears to frequently lead to illness and/or other symptoms of overreaching/overtraining. Of course, since changes in CTL are driven by changes in ATL, this means that any sudden increase in the training load (e.g., training camp, stage race) must be followed by an appropriate period of reduced training/recovery, so as to avoid too great of an overload.”

So now that I have all my rides processed with my python script (even the indoor ones, so really all cycling workouts), I only had to write a script which grabs the TSS values from every statistics file (then calculates daily values if there were more rides on a given day), calculates the ATL, CTL and TSB values for every day in the given period, and plots them (plus saves the data as a table). This was not too hard ;) There is even an option to change the default time constants in the parameter file (the same parameter file which is used by the workout analyzer and plotter script), and to define a start date and end date for the plotting in the command line. Then after giving the

>python plotgarmintcx_makePMC.py 20101201 20110920

command (where the dates are optional and only affect the plotting limits, while the calculations are always done using all the data), you have your Performance Management Chart plotted and all the data saved in an ASCII file too. Let’s see how the result (with some additional comments photoshopped over in red) looks like (click and it will be bigger)!

You can see that I came into the season with some residual CTL (in blue) from last year, but one 1 hour trainer session per week was not enough to keep that on a steady level… Then as the spring was exceptionally warm and sunny I had some quite good trainings in March and April raising my CTL from 20 TSS/d to 55 TSS/d, which then dropped because of no training and very low training load during my observing run. Then the four hard days of cycling on La Palma gave a huge amount of training load (ATL in magenta), my CTL jumped from 45 TSS/d to 68 TSS/d, but my TSB (in yellow) fell to -87 TSS/d, which is extremely low, if you keep riding there then the chances are high that you will get an injury… It also resembles well that I was extremely tired after that week… Then, only after two days of rest, with a still pretty negative TSB I rode my personal best on the standard Leuven-Mechelen-Leuven route. Probably I should have waited a bit more, because another 4 days later I did a ride where I easily had a better average speed on 62 kilometers than my personal best on 48 kilometers from 2010. I was extremely surprised about my performance on that day. It is not such a surprise anymore when you look at this plot. My CTL was still very high, and as I was resting a lot in the days before, my TSB became almost zero. This is the perfect combination (high CTL, zero or slightly positive TSB – remember, actual form is a combination of fitness and freshness) for record breaking rides (or races), this is an example of a sweet spot on the Performance Management Chart. Unluckily after this really good period I could not ride for almost three weeks (work, work, and weather…), which had a huge impact on my CTL (dropping all the way down to 43 TSS/d). From this point, I started a quite systematic and serious training, as I wanted to finish July with at least 1000 kilometers. You can see that my ATL is almost always above my CTL, which leads to the increase of CTL. Then I crashed into a car just days before I was about to climb the Mont Ventoux, which gave me some time to rest (and a bit if pain in my right knee, which is missing from this chart…), so again I had a high CTL and an ~zero TSB when I went to France. And I was again very surprised how easy the first ride felt there, especially after an accident. But now from this chart, it is not so surprising anymore. My TSB was still only -13 TSS/d when I climbed the Mont Ventoux, so it was close to optimal, but I could have done better with a bit less riding on the first two days in France. It was also the day of my highest CTL with 71.3 TSS/d this year (till now). Then during my observing run, it dropped again (to 57 TSS/d), so now I am working on getting it back up to the region where is should be :) Next year I want to be at 100 TSS/d at this time. At least. Especially if I am serious about my plans for the summer…

As a conclusion, I think it is a very nice visualization and an extremely useful tool for a cyclist!

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Estimating cycling power and training load

Updated 1: I modified the script slightly to perfectly match the estimation method of Joe Friel (see below), and as this had a small but clearly positive effect on the results, I have also updated the last plot and the paragraphs describing it at the end.

Updated 2: I have also tested the power estimate on very low intensity efforts with known average wattages (check out the bottom of the post).

You might remember that I have written a python script earlier this year, to analyze my cycling workouts and besides the calculation of detailed statistics, also create all kinds of fancy (multidimensional) plots. If you do not remember, or you would like to refresh your memories, please click here before reading this post further.

The most important thing missing from my script was the ability to directly compare different workouts. Of course you can tell that a 150 km ride with 5000 meters of elevation gain is more difficult than an easy 50 km Sunday afternoon ride, but how much more difficult? And what about an easy 75 kilometer training and a short but hard interval session? How do these compare? How tired am I going to feel myself after these? I really wanted to create or find a metric which tells me how much I did on a training. Of course this is only a problem when you don’t have a power meter installed on the bike, because that would directly tell you the amount of work in SI units for every workout. But power meters are expensive, and most importantly I don’t have one. This situation might change in the future, but till then let’s see what can we do.

I kinda forgot about the problem (and I did not even start dealing with it earlier, I just simply made a note on my To Do list), but some days ago Strava (a similar site to Garmin Connect, but with competitive – social media based – extras, unluckily with basically no users in Europe, so it is not an alternative for me) introduced a so called suffer score on their website, basically giving a rating to every ride based on its intensity (estimated from heart rate – HR – data) and duration. I knew that the algorithm behind this will be quickly decoded on one of the blogs I frequently read, and indeed it did happen very quickly, go and have a look there. First I wanted to implement this into my script, but then I got some extra motivation from the post and the comments there, so I looked a bit into the literature, and thus I got to know about the Training Stress Score, or simply TSS (among others, but this is the most important thing for us right now). To save some space here, check out the following three sites, so I can skip retyping things which are already on the Internet.

Estimating Training Stress Score (TSS), by Joe Friel
Normalized Power (NP), Intensity Factor (IF), and Training Stress Score (TSS), by Andrew R. Coggan, Ph.D.
What does 100 TSS mean, and the connection to Functional Threshold Power (FTP)

Though TSS and all these metrics are used in and based on power measurements, you can see that it is possible to give a good estimate (as good as estimating the realtive power on climbs from the slope gradient and VAM, which my script already did before) of it from time spent in HR zones using the scaling values from Joe Friel. We have seen that by definition a TSS of 100 equals to a one hour ride at FTP (so as hard as you can go for one hour). This means that rides shorter than one hour can have a TSS/hour higher than 100, but longer rides will have a lover value. So the TSS value tells you how big the training load of a given ride was (and it is related to the amount of post-ride fatigue), and the TSS/hour value gives you the intensity of your workout. The way it’s calcualted, TSS varies by the square of intensity. That means if you’re only going at 90% for an hour then you’re only accumulating 81 TSS per hour (90% = 0.9, which squared is 0.81, then you multiply by 100 to get TSS). So from the TSS/hour you can calculate an intensity factor, which gives you your average power output in units of your FTP power for the whole workout. Given that you know your FTP power, you can have a good estimate of the average power of any of your training rides! Now that is pretty cool. So that is what I have built into my script, this way now the TSS, TSS/h, average power and total work estimate values are also given in the summary file. And I also performed some test calculations and comparisons – because you should never forget, these are estimates! You need to know your FTP, and your HR at FTP, and then you might even get a reasonable value…

First of all, I wanted to see what is the intensity factor of my personal best ride to Mechelen and back. It was an approximately 1 hour 20 min all out effort, and the script gave an intensity factor of 1.00, meaning that I was riding on FTP. Though the ride was a bit longer than one hour (when it should not be possible to ride on FTP anymore), but I had a small 10 min break between the two legs in Mechelen, so it might still be a valid approximation, but for sure it should be extremely close to reality. Also, though it was a very tough ride, because it was short, the TSS value is ‘only’ 135.0, meaning that in 24 hours I might have probably almost fully recovered. In comparison, a normal ride (in my terms, so an average of 32 km/h instead of the record 35.7 km/h) on the same route gives a TSS around 110, while a recovery ride is probably below 100, and my epic ride (147 km and a bit more than 5000 meters of elevation gain) climbing twice up from sea level to the highest point of La Palma had a TSS of 483.7, which – as you have already guessed it probably – is in the epic category. Just as comparison, the last short (46 km and 400 meters of ascent) and recovery paced ride from France had a TSS of 69.5 (which is approximately half the TSS of my personal best ride which was done on an almost equally long, but completely flat route, so this really shows how easy this French ride was). So as I got an IF (intensity factor) of 1.00 on a ride which is very close to the definition of FTP I was already quite happy with the result. (If for such a ride you get something like 1.05 or even higher as IF, then that is a sign that your threshold heart rate is now higher, so you should change it accordingly in the parameter file.)

As a second test I wanted to see how does this power estimate compare to the power estimate from VAM and average gradient, which is a widely accepted and used relation for climbing sections. If the power values from the two methods match, then it is OK to use the power estimate from the hourly TSS value on rides even with no climbing at all. So first I took my recent ride up to the Mont Ventoux, where I did 1 hour and 40 minutes of all out riding, so I expect to get an IF around 0.95 and of course I am very interested to see how the two different power estimates will differ from each other (if they will differ at all). So here is the statistics file (recent additions marked with a grey background):

So from the IF of 0.96 you can see that indeed I did all I could in this time frame. Now we can estimate the average power from my FTP, which is somewhere around 300 W (or maybe a bit more, but I have to admit I don’t have a recent measurement, so I can only guess this from my workouts on the trainer back in the first months of the year). The result from this is 288 W. What about the value from the other method? I was ~70.5 kg and my bike + drink and food + clothes is an extra ~11.5 kg, then with a total weight of 82 kg and the relative power estimate from the VAM and gradient you get an average power of 287 W. These values are surprisingly almost perfectly identical! This means that I can most probably trust the TSS based method, and use it for complete rides, while the VAM and gradient based method only works on climb sections. This is quite nice! Of course one measurement is not a measurement, so I wanted to check this on other climbs as well. Unluckily I don’t have to many climbs (as Flanders is pretty flat), but I still managed to put together a sample of 18 climbs from this year, mostly from my rides on La Palma in may, and some others from later (so there is a chance that my FTP was not the same at the time of the different rides, but I still calculated with the same value, while for my total weight – with equipment included – I could make small changes based on me having a backpack with 2 extra liters of water or not). The sample has climbs from short explosive hills to the long ascents of La Palma, steep climbs and not so steep climbs, climbs where I was really fit and well recovered, and also climbs where I was tired, to see what is the effect of these on the relation. So all these climbs are displayed on the plot below (the size of the circles is related to the length of the climb, while the colour resembles the steepness).

With a perfect 1:1 relation between the two different power estimates we expect the climbs to fall on the x=y linear (which is the grey dashed line here). The correlation is well visible, with of corse some noise, but the trend seems to be pretty clear. The biggest outlier is the Smeysberg on the top right, which is a very short (430 meter) but very steep (an average of 9.8%) climb, and the reason why it is an outlier is that it was a sprint effort well above threshold, but starting with a very low heart rate, so it took some time till my HR got up into the regime which really corresponds to the level of my power output, and this time was in the order of the full length of the effort, so of course the TSS based metric is lower. For such sprint efforts the heart rate based estimated TSS will be always lower than the power based, because 1) the already mentioned lag of HR behind the sudden raise of power output, 2) that much above FTP the HR will not get higher, it does not matter if you maintain 400 W or 600 W for 30 seconds, your HR will be probably stuck at you maximum HR value. But these problems only arise when you do very short above threshold sprints. This is also why one of the small blue dots is also a clear outlier. Still, in the region where most of my training rides are (~220 W to 300 W) the match is almost perfect. Fitting a linear [y = f(x) = mx + b] to all the climbs (solid grey line) or only climbs which were longer than 3 km (a.k.a. efforts where there was a significant heart rate lag were dropped, thick black line) gives the following equations:

a) m = 1.41±0.16, b = -99±41, but we don’t really care about this
b) m = 1.00±0.12, b = 0±31, which is a perfect 1:1 match (with some noise of course)!

The other problem with HR based power estimates, that your heart rate at, e.g., 250 W will not be the same for two workouts which were ridden in different conditions, as for example dehydration and the level of residual fatigue strongly affects the heart rate. So again, these can significantly change the resulting estimated power. Like in case of the medium sized light blue circle slightly above the 1:1 line in the bottom left – this was a very slow paced ride (I was not alone), but I was extremely fit (after more than 1000 km ridden already in the same month, but basically no hard workouts in the previous one week), so probably my heart rate zones were a bit shifted. On the other hand, the two other climbs where the difference between the two values is larger than 5% (the small blue circle and the larger dark blue circle slightly below the 1:1 line towards the bottom left) were the last climbs of my two hardest days on La Palma, so at those ascents I was already very tired, which led to the shift of my HR zones – but now to the other direction. (At least this is my explanation.) Furthermore, small ascending sections can effect the estimate from overall VAM and slope gradient, and to convert relative power to power you need to know your total weight with bike, clothes, and everything included. Taking all these factors into account it is easy to understand why there is a scatter around the 1:1 relation.

To see what happens when you go to lower power – so the region of true recovery workouts – I have modified the script to be able to handle TCX files which do not contain GPS position information (so files which contain indoor trainer workouts). I have only three rides where I maintained a constant power instead of doing intervals (where the heart rate based method is clearly off, but we know that already), but in case of these, I can compare the ‘real’ average power (from the Tacx Flow) to the estimate. So for these three rides, the real average power values were 156 W, 140 W, and 200 W, while the estimates for the same rides are 133 W, 136W, and 220 W (assuming an FTP of 290 W as these were done very early in the season). The difference between the measurements and the estimates is in the order of ~10% (of the trainer values), which is OK for an estimate.

Conclusions: we can say that indeed the TSS based method can be used to estimate the average power (and workload) of workouts (even from HR data) if they do not consist of (only) short sprint efforts (so sections where there is a significant lag in the change of heart rate compared to the power output), as it scales well with another widely used power estimation method, and as it is consistent with wattage data from indoor trainer workouts (though the latter is only tested for low intensities). As a second conclusion: I really need a power meter (to test these estimates, and to have real power data, damn it)…

And oh, this was my 500th post!

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Daily commute at the ORM

I am back again on La Palma at the Observatorio del Roque de los Muchachos (ORM) – for the 4th time already. I have 11 nights of work at the Mercator Telescope, then I will fly to Tenerife to continue there with another instrument. As always, I can only say that this island is amazing. Every time I take the taxi from the airport to the observatory, I can not stop staring out the window to look at the landscape. This is the first time I am here during summer, and I have to admit that the temperature is much nicer up here at 2300 meter, than it is in e.g. October. I don’t need my winter coat anymore, and I don’t need to put a kilogram of warm clothes (knee warmers, arm warmers, neck warmer, wind coat, rain jacket, etc.) on when I roll down with the bike on the morning, a simple jacket is enough. So during the day it is typically ~20°C (but it feels much more thanks to the Sun), while on the morning it is around ~15°C. Now again I took my racing bike with me (and I will have a week of cycling after my observing run on Tenerife), so already here I use it every day to commute between the Residencia and the Mercator Telescope.

I need to ride up once before dinner to start the calibrations (~5 PM), then I roll down to eat (~7 PM), and after a small nap I ride up to catch the sunset (which is around 9 PM) and start the observing night. Then I work till the Sun rises, and go down to sleep around 7:30 AM (and sleep from 8 AM to 4-4:30 PM). The ride itself is 2.65 km @ 6.9% with a maximum over 100 meters of 10.3% (a hard 3rd category climb in Tour de France terms), so it is not extremely difficult, but riding above 2000 m makes every climb a bit more challenging (click on the climb-plot and it will be bigger). But it is very good high-altitude training, and 3 times faster than walking! (I don’t even think about driving up with a car, that’s not my thing :D)

On the last afternoon I rode a new personal best up to the telescope, reaching the ‘summit’ in 11m 09s, which corresponds to a VAM of ~1000 Vm/h (yeah, I know it is not really a pro value, but who cares :D)! My previous bests from last October and this May were 12m 05s and 11m 36s, respectively, so the improvement is very clear. Oh, and these were ridden while carrying a backpack… After this record ride, I took the evening ride a bit easier and recorded the scenery (turn the volume down, if you don’t want to listen to my still quite hard breathing…). It is a nice place to work at ;)

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