*** many thanks for Finn "Dataman" Zwager for this blog post ***

As many TriDubai members raced the recent Abu Dhabi International Triathlon (ADIT) and have done so more than once, Dataman thought it would be interesting to look more in depth at comparative pre-race training data as well as the data from the race itself.

Training data - volume

So, what could my training data tell me about my readiness for this race?

Last year’s ADIT was during the build/peak period for my ‘A-race’ a few weeks later, Iron Man South Africa. This year it was a few weeks after the Dubai Marathon, which had been an ‘A-race’ in an attempt to get under 3 hours (it worked: 2:59:06!). Clearly these different A-race goals meant a very different preparation for the ADIT training race.

Time

The charts below show training hours for each discipline in the 3 months prior to ADIT:

In 2012 I trained 32 more hours (or almost a quarter more) compared to the same period in 2013. I also spent a lot more time, 41 hours or 70 percent, on the bike, which is typical for IM preparation. The emphasis on running in 2013 was clear.

Training data – intensity

Training intensity also varies according to your training goal. Iron Man distance training generally consists largely of longer, lower intensity work, while (arguably!) preparation for a fast marathon should include quite a lot of high intensity speed work as well. The heart rate distribution chart below comparing percentage of time in each heart rate bin (a bin is 10 beats wide in this graph) reflects this approach.

Note: The 2012 distribution is skewed to the right in contradiction to the ‘long, steady’ statement on IM training above. This is partly due to brick sessions; running after bike causes higher heart rates, and participating in sprint races.

Based on this pretty basic way of looking at my data, I expected that:

  1. My swim time would be similar or a bit slower compared to last year.
  2. The bike would be slower. Or, at least my average power output would be lower while the speed could still be similar depending on the circumstances on race day.
  3. My run would perhaps be faster.

There are certainly other more sophisticated ways to determine race readiness for instance using Trainingpeaks or WKO+ software and (preferably) a bike power meter. We will discuss these in a future blog.

Race day!

Weather

You have got the deal with the weather on the day, which in some races can vary wildly from one year to the next. The circumstances at ADIT are pretty predictable: Hot and windy!

As it turned out, last year it was a bit windier and cooler, this year a bit calmer but hotter as can be seen on the graphs below.

Course

The run and swim course were the same, but measured by GPS the bike course of the short distance was 5 km longer than last year and the routing was different as well. The main change was taking out crossing the big bridge twice and not going all the way into town before heading back out, which made for a flatter (440 meters less climbing this year using uncorrected GPS elevation data), more wind exposed course.

Results

Swim

My swim time didn’t really change 24min13sec for 1522 meters this year, versus 24min44sec for 1566 meters last year, or 1min30sec per 100 meters. While some members of TriDubai swim that time using one arm while towing the Titanic, I was quite pleased with it because I had kept the pace the same despite my lower swim training volume (and intensity). It is likely that improved technique and more (open water) swimming experience gained over the year paid some dividends.

Bike

The bike results are in the table and charts below (some of the terms will be discussed in a next blog):

 

 

The 2013 graphs show a consistently higher heart rate and from around 2 hour 20 minutes you can see the blue power bars drop and the red heart rate line rise. The decoupling taking place here may have had to do with some stress and dehydration at the end of my race: When I reached for my drink bottle behind my back on the bike, I discovered it was gone. I did not have a drink for the last part of the race, while I desperately needed and expected one. Bummer!

The last interesting bike graph is a power/heart rate scatter plot where each 1 second power measured is plotted against the heart rate in that same second.

This graph again shows that overall in 2013 I had higher heart rates producing less power, indicting decreased bike fitness compared to 2012.

Run

Unlike my prediction, the 2013 9.5KM run turned out to be slower at 41min versus 39min16sec in 2012. The average heart rate was the same at 154 bpm.

The difference is well explained by the lack of hydration in the last part of the bike (I had to slow down to drink lots on the run) as well as the higher (perceived) effort I had to put in the bike because of my lower bike fitness

Conclusion

Your training data can predict your race results quite well, especially if you have previous race data as well.

To end this episode and to get you back out there training, I found a good quote in Alan Hunter’s book ‘Cutting-Edge Cycling’:

…nobody wins because they’re the best data logger! On the other hand, careful ongoing analysis of the data, along with honest assessment of goals and communication…may help.”

Yours in data, DataMan.