*** many thanks to Finn “Dataman” Zwager for this blog post ***

Dataman?  CP came up with this nickname and it’s stuck with some in the Dubai tri community.  I much prefer the recent miss-print on my new team tri-suit: ‘Dateman’.  A name is a bit far fetched for a married-with-kids-and-a-mortage-45-year-old-who-has-difficulty-remembering-names, I am not too sure I am worthy of ‘Dataman’ either.  So, reading these blogs, please keep in mind that I am not a tri coach, nor a sport scientist.  I have competed in several sprints, some halves and one full Iron Man distance race where I usually ended up near the front in my age group, but I have not exactly won yet either.  Still, since I have been asked to write a blog about my tri data collection and analysis efforts, I’ll give it a go.  I hope that your feedback and comments will make it a learning exercise for all.

Why collect data, can’t I just train?

My interest in collecting data, the easy part, and trying to base my training on it, the hard part, is based on bitter experience.  While at university I rowed for my student club.  After a few years of rowing eights, twos and single sculls I was good enough to be selected to stroke the coxed four in the U23 World Championships.  Switching from my rowing coach at my club to the national coach, there were no questions about, or measurement of, my recent training load, just a one off test on a rowing ergonometer.  Over the next few months I became totally over trained as I had done too much before and continued to put my everything in every training session as I thought that was my responsibility as stroke (‘to lead by example’ and all that).  The end result was that I suffered physically (mentally I was fine, this was not a psychological issue), sleeping badly and waking up most nights in a pool of sweat with a through-the-roof heart rate.  Needless to say, as a boat we did poorly at the Championships.  So, I decided never to repeat this failure and have mostly kept a close track of my training ever since.

Over training

Clearly, one reason you should collect training and racing data is to prevent possible over training.  I believe amateur tri-athletes who lack a good sports knowledge background or are without a coach (which is different from a trainer) or plan (or trying to stick to the wrong plan no matter what) can quite easily get over trained.  The fact that you are a triathlete already proves you have a driven personality.  If you combine the feeling that you can always swim another kilometer, bike one more hill and go around Safa Park one more time, with the lack of proper rest and sleep due to the demands of jobs and family/friends, there is a good chance you could over train.

Under training

By the same token, if you are a less-driven personality and for instance joined the tri-scene to meet some people in a setting other than work or a bar, you may miss out an opportunity to actually get quite good at the sport.  Maybe you haven’t done much sport in the past.  Perhaps during the swim you always feel like you are drowning.  Or you are one of those athletes who says ‘I just can’t run’.  Keeping track of your data may show that you are actually making improvements, or that you can push yourself a little harder although you are sure your heart is about to give up.

Group training

If you train a lot in groups without keeping track of your data you may well be either over or under training most of the time.  While training in a group can be very motivating and in some cases, such as sea swimming, be highly advisable as going out on your own could be dangerous, you may not always be doing yourself a favour by following the group.  If the cycling group sets out to do 32 km per hour, what does that mean to your personal training load?  If there is a head wind should we still cycle at that speed?  Does speed actually have anything to do with the amount of work I am putting into my training session?  Measuring will help you answer these questions.  Once you know your data, joining small specific groups of athletes of a similar output (watts/speed) level and, importantly, in a similar phase of their training plan, could work very well for you.

Measuring loop

How can you make your data, once analysed, work for you? It will involve four steps in a continuous loop.

  1. Bench marking – finding your training zones.
  2. Making a training plan.
  3. Follow the training plan while collecting data.
  4. Going back to step 1 to find out if your plan is working, setting your new training zones and adjusting or taking the next step in your training plan.

Before starting the measuring loop, you must set yourself some Tri goals first.  This can range from ‘getting fit/lose some weight’ to trying to win your age group at the Iron Man World Championships.  If the latter is the goal you have been working on, I am pretty sure your own knowledge will go far beyond what I have to offer in this blog.  Either way, data collection may help you figure out sooner rather than later if your goal is too ambitious or too modest.

What data?

Over the next year we will be collecting training data such as a heart rate, speed, distance, power (wattage), time, lifts, reps and cadence. We will look at how we collect this data (but I won’t go into detailed ‘gadget reviews’).  I’ll go over a few data storage and analysis solutions (programs, websites) and ways of bench marking will be discussed.

We’ll also go over the non-training metrics such as resting heart rate, body weight/fat percentage and food in-take (and calorie expenditure).  Like over-training, measuring food intake and weight can be a dangerous area for triathlete types.  The sport does arguably attract a disproportionate share of obsessive/compulsive personality types; you need some of those traits to be able to commit and be any good at such an intense sport.  If your motivation to do tri is an (unrecognized) distorted body imagine or you’re trying to run away from having to deal with a past experiences or psychological issues, then there is nothing like a weight obsession to push you over the edge. Then again, by measuring things you may actually be able to stay sane and healthy!

The aims

The ultimate aims of collecting and analyzing training data are to reach your goals faster and to make the training journey more enjoyable and injury/illness free. This blog may help you achieve those aims.

Have a great 2013!

Dataman

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