Ironman NZ 2017 Analysis

Those who completed IMNZ 2017 will (un)happily tell you the conditions were tough. Now we can quantify exactly how tough.

It was, in fact, the slowest day ever at Taupo (of the 17 full distance events). If you’re not fond of numbers, that is the executive summary and you should read no further (but you’ll be missing out).

Overall Averages

On average, 2017 was most of 40mins slower than 2016. Comprised of 15mins in the swim and 22mins on the bike.

Compared to the fastest year of the last 10 – 2011 – there is just over an hour difference in average time (though this is not only related to conditions – more on this further down).

The corollary to slow times is the high drop out rate. A full fifth of the field did not complete the event. This is extremely unusual – you can see the typical completion rate is much higher over the years.

Not a great day for your first IM!

Repeat Competitor Analysis

For athletes who’ve competed more than once we can track performance over the years. This same-athlete analysis offers a more precise comparison across the years.



Note that the 2017 result is not included in the individual averages. So these numbers are a sum of individual performances in 2017 compared to 2016 and earlier.

We can (of course) break this down further to look at the impact on athletes of different levels in each discipline.



It’s always been obvious (and logical) that the better swimmers are affected less by rough conditions – this analysis makes it clear how rapidly the impact scales.

This carries on to the Bike

The low sample numbers distort the fast riders values a bit and there are a few riders in the 4.40 to 5.00 category who have progressed significantly over the years, thus even a slow day is faster than their long term average.

The impact is not as marked as it is in the swim but it’s still a clear trend that tough conditions impact mid and back of pack athletes a lot more.

Finally the Run.

The average difference showed it to be a slow year, stratifying the data provides more detail.


In this case the front half of the field had good or expected run times. But the back half of the field showed the impact that arriving at the start of the run 40mins (or more) later has on your performance. The extra fatigue accumulated by being out there longer makes it impossible to run well.

Environmental Analysis

I posted data on facebook leading into the event detailing the impact that conditions were likely to have.  Those predictions worked very well for riders under 6.30. Above that the observed differences were much higher than the theoretical. Rather than a breakdown in Newtonian physics (which doesn’t happen until things are much faster or much smaller) it is likely that this difference stems from pacing and psychological factors. As well as the aforementioned fatigue – the longer you’re out there the more tired you get so the longer you’re out there…


Pacing Analysis

Here is the breakdown of splits for different bike times


I’ve only listed the model output as a generalisation. There is fine graduation in the predicted splits if you’re out there longer (as the wind picks up), but it wasn’t worth making the chart bigger for.

It’s clear that pacing is a problem for all but the fastest riders.

For comparison, 2016 shows that there was less variation in pacing between front and back of the field. Which suggests that a lot of people got over-excited in the tailwind at the beginning of the 2017 race and paid for it later.




Overall averages vs average repeaters vs Cameron Brown.


I had thought that Mr Brown might be a reasonable indicator of the conditions each year. But no, he doesn’t like to vary much from his average time of 8.24.54, so he is no use as a gauge of how tough the day was.

Repeat athlete average time is generally ~10mins faster than the overall average. Additionally they are less affected by poor conditions.


Time Distribution

You might wonder at the overall upwards trend of average times – from other races I’ve analysed there was a significant dropoff in the depth at the front of age group racing (from 2013 onwards), which meant that average times rose.

The same holds true here






Finisher numbers have been higher since 2012 but there has been a significant reduction in the percentage of sub 12hr finishers.

I hope to dig further into this in future.



None of these were troubled this year, but I’ll post for completeness.




Every way you slice the data – 2017 was a slow year. Tough swim and bike conditions had a disproportionate impact on the back half of the field, yielding a higher than normal number of DNFs and unpleasant toil for those who survived.

To those who managed to record personal bests – well done – the odds were stacked against you so even a small improvement in time reflects a large performance improvement.

And for the first timers – next year will likely be much easier, so you should be able to go substantially faster, even without significant personal improvement (though you should aim for that too).

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