Chances are, many of you reading this are already using some kind of data in order to at least log your rides. The focus of this post is the use of data for training (no, you don’t need a power meter for this to be relevant). This should provide a good overview, explaining which figures to pay attention to and how to use them to measure improvement, find out your strengths and weaknesses and prevent overtraining.

Lets begin with what not to do, that is to say what I did;

I first started recording rides back in January 2015, having been given a Garmin for Christmas. It came with a cadence sensor and heart rate monitor, at the time I knew what these were but hadn’t ever considered what they could be used for. I fell into a very common trap – the only figures I paid any attention to were average HR and average speed, believing that higher always equated to better.

Every ride became a time trial – regardless of the conditions, if my average speed on a given route was lower than last time, I would assume this meant I’d lost fitness and needed to train harder. I noticed over time that my average heart rate began to decrease – again I interpreted this as being a loss of fitness.

In actual fact average speed is a very poor figure on which to judge improvement, there are simply too many variables for it to be a reliable measure. Wind speed and direction, air temperature, how much weight you are carrying, which bike you are riding and your riding position – just to name a few. A gradual increase over the course of a few months is probably a sign of improvement, anything short of that isn’t worth looking at.

A decrease in average heart rate over time is generally a positive sign. It is almost certain that your fitness has increased if your average HR for a given power is lower. Anyone who read my recent post on training (click here for it) – might remember a brief discussion of training zones. Heart rate zones remain relatively constant, what you should notice with increasing fitness is power zones shifting relative to HR zones.

The message here is simple, though it is one that I took a long time to learn. Don’t just look at a number and assume it is good or bad, think smart. Again, to most readers the above will be very well know – I’m just using the example to prove a point.

So, how can you use data to measure improvement in fitness? To start off with, there is no need to get overly technical. Simply comparing times on Strava segments can be a good way of testing whether or not your fitness is improving. Once again, conditions do have a part to play. In general, uphill segments provide more reliable measures – as the effects of wind resistance are much smaller at lower speeds. I’ve found the best method is to ride a segment as fast as you can and take note of the conditions. Ride it again, in as similar conditions as possible (e.g. same time of day & on the same bike) to draw a reliable comparison.

If you are following a training plan, you can Set weekly distance or time goals using Strava. Speaking from personal experience, this can be very motivating if you are feeling lazy on a given day and are tempted to skip a ride for no good reason. It’s satisfying to look back at the miles you’ve covered and hours spent in the saddle before a big race. For some useful Strava tips, click here.

A more accurate, and still cost effective way of measuring improvement is to use a turbo trainer fitted with a speed sensor. Indoors, the usual factors affecting average speed are removed – if you can sustain a given speed for a longer period of time, it’s a positive sign. That said,  be sure to make a note of the resistance setting used on the trainer during your first test – if you can, also record the room temperature and make sure it is similar for future attempts.

Be sure to test yourself regularly – if not it can be difficult to know if your training is working (sounds obvious but it is a mistake that many make, myself included). There are various tests relevant to racing, for example a 20 minute threshold test. Stick to the same protocol each time you perform a test in order to make it as reliable as possible.

In my experience, important factors in racing are CP6 and CP1. That is to say the maximum power output that can be sustained for a duration of 6 minutes and 1 minute, look here for more. Race outcomes are often determined by these short intense efforts. My greatest improvements came after working hard on improving both values. You don’t need a power meter to test for improvements, just use average speed on the turbo trainer for  6 minutes and 1 minute or look at your times on segments taking approximately those times to ride.

I’ve left out information on training with power. I’m assuming that anyone with a power meter has good knowledge in this area anyway, just in case – for a guide on getting started with power, click here.

Ride data is at its most useful if things go very well or very badly. If you do happen to get dropped in a race, note down the time at which it happened and take a look at the data afterwards.I’ll illustrate how this can be useful with an example from my own season;

In the first two races I managed to finish, I was dropped virtually on the startline. My first thought was that this was due to a lack of fitness. I looked at the Strava data of some of my fellow riders, those who finished in the points, noticing that their power outputs were broadly similar to my own.

One thing I did notice, albeit after much head scratching was that all of them rode at an average cadence of 80 or above. Mine on the other hand was 68, much lower. For the next month I worked hard in training, setting my Garmin to tell me if my cadence dipped below 80. After a while, I became used to riding at a higher cadence and began to do it instinctively.

Afterward, I never got dropped early on again. Riding at a higher cadence made it much easier to accelerate quickly and as such respond to the frequent surges in pace that occur in all races. My power output did not significantly improve in this time, (FTP increase of 10 Watts only) technique alone made all the difference.

Your weaknesses can very quickly become apparent if you take some time to analyse what was going on during a poor performance. Do you, for example struggle to close gaps that open after technical corners during closed circuit races? In which case, your CP1 is probably a weak point. This was another problem I ran into, if I had to close a gap I’d be-able to stay with the bunch but not have much left for the finish.

It can also be very useful to take a look at the numbers, if you should happen to do very well in a race. The more you information you have recorded the more useful this will be, for example; What did you eat the day before? What training had you done in the 8 weeks before the race? How long did you taper for? Did you sleep well in the days leading up to the event? All of these questions are of course also relevant in the aftermath of a bad performance.

In short – data can really help to determine your strengths and limiters. The more you know, the easier it will be to correct any problems – just make your training as specific as possible to your limiters.

This also applies to other factors relating to race performance. For example it is worth noting down any changes in diet, sleeping pattern and stress levels. For this reason, I’d suggest keeping a training diary rather than just recording rides on Strava or similar.

Data can be useful in preventing overtraining. Keep track of your weekly volume and the intensity of workouts – this is worth doing even if you don’t follow a specific training plan. If you’ve been putting in more hours or just more hard sessions than usual lately and notice that you are feeling tired, depressed and demotivated then it’s probably time to take a break.

That’s all for today – and probably for the next few days. Goodnight.

 

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