Are Ironman Races Close to Kona Easier to Qualify At?

What do the statistics tell us about qualifying at races that fall close to Kona.

I’ve been asked this question a lot. I don’t really like the assumptions behind it: that all the fastest athletes are in Kona and not racing at other events. I get the logic, but what’s the evidence? My position has always been that it doesn’t have much impact. Or, at least that I’ve not seen anything to convince me it was the case.

The argument cropped up again during the debate over the new qualification methodology. Ironman itself has used it as a reason why the initial system wasn’t delivering as many slots for women as expected. Honestly, I felt this missed the point. If a third of your races have fewer women qualifying (because the fastest are in Kona) the remainder will need significantly more to bring the numbers up. (I did the maths: at the point of change women had around 20% of slots, to get to 30% they would need to be taking 35-40% of slots at every other race. Quite a change.)

There isn’t a monolithic, unchanging group of top age groupers – athletes come and go. Many – but not all – of the fastest age group athletes race in Kona.

And then it occurred to me that looking at how athletes perform in Kona and when they raced during the qualifying season might give an insight into the strength of qualifiers. I’m not sure why I’ve never thought of that before. Over the last 3 days I’ve been plotting graphs and examining data. I’ve taken tangents I felt necessary to support my case. I went very deep. I was going to put it all in this article, but there is so much it’s only reasonable to break it up into digestible chunks. At least I’ll start with my conclusions to spare those of you who don’t like wading through endless charts.

Conclusion

No, if you’re a man. Also, no^*, if you’re a woman.

^* There may be a small difference for women, but I’m not convinced it’s enough to affect their race selection

If you’re happy to take my word on this, you can stop here. Otherwise, buckle-up we’ve got a lot of data to review.

Finishing Times and Places

The basis for believing early season qualifiers are easier is the idea that the fastest athletes aren’t there. If the fastest athletes aren’t there, does it follow that the Kona finishing times from qualifiers at these races aren’t as fast as those from later races (further from Kona in the qualifying season)? Seeing a difference in the pattern of finishing times might be an indication in a difference in strength of qualifiers.

The chart above plots the distribution of age group male finishing times in Kona for 3 different groups. Those who raced early-season (late August to December), mid-season (March to May) and late-season (June to early August). Along with the distribution there are two sets of lines indicating the top 5% and the median for each group. In this instance I’ve considered Kona between 2015 and 2019 for consistency. Recent years with two championship locations and the impact of Covid make the data less clear.

All 3 groups show remarkably similar distributions with their top 5% and median lines tightly bunched together. There’s no significant difference in performance for these qualifying groups. Although, we might observe that the red March to May group have a slight edge. This certainly wouldn’t lead me to conclude the men who qualified early season were weaker than those that came later.

Notes on the data: the Ironman World Champs results are not included for any athletes in the September-December data even if they raced there. Only Ironman races are considered in this dataset. Athletes who raced multiple Ironmans in the season will be included in multiple groups.

The picture is different for women. The separation between groups is clearer, although no more than 15 minutes at the median and 10 minutes at the top 5% points. It’s enough to suggest that the qualifiers from the early-season races aren’t performing as well at Kona, although it doesn’t necessarily indicate why. That uncertainty, plus my own bias in believing there’s no real difference lead me to dig further.

Rather than looking at the distribution of results, scattering the results gives a visual representation of the very front of these races. Each point represents a female finishing time at Kona between 2015-2019 plotted against the date of a race they competed in during the qualifying season. Segregating this by Age Group would probably make some sense, but there are limits to how long I can make this article.

This graph includes results from any athletes who completed the World Champs during the qualifying season (in red). When we look at the lower half of the graph we can reasonably assume that most of those red diamonds are not blue crosses in that region. With women making up a smaller proportion of total Ironman numbers it’s not unreasonable that some of the fastest women aren’t racing elsewhere in that period, but there’s not a huge difference in the scatter of those early races from those higher up in the chart.

I’ll leave myself a reminder here that I need to make an interactive version of this particular graph to see how it works at an individual age group level

I was so dissatisfied with that last chart that mid-article I had to make the above graph to clarify the point. Rather than considering finishing times, looking at finishing places gives a clearer picture of competitive potential and eliminates differences between age groups. March through May remains the time period with the highest ranking female age groupers in Kona. A 7 place difference at the median, but only a difference of 1 place for the top 5%. June to August is comparable with the early-season qualifiers.

None of this suggests a huge difference in performance and that variance in age groups is a factor in the time distribution charts. Racing in October won’t avoid you facing someone with the potential to podium in Kona.

We shouldn’t forget the men.

Many more men race Ironman, so the equivalent chart for age group men shows a much denser scatter plot. Again, we can expect that the majority of athletes in the red diamonds are not blue crosses in that period. But the greater numbers helps ensure that there are many other athletes equally capable of hitting the same times. As I realised with the women’s chart, this isn’t the most useful way to view the data, at best it gives a sense of how distributed top performers are across the race season.

So, we come back to a distribution chart. With a large field the distributions become a bit more consistent. There’s only a 4 place difference in the median placing for each group with September-December coming last. At the 5th percentile there is very little difference, 2 places between early and mid-season and no difference between early and late season. We can clearly see the bump in the red group for the top ten places otherwise they are all much alike.

Let’s note here that for both men and women March through to May seems to produce a good number of the fastest results in Kona. I suspect this is those who raced Kona recovering and then qualifying for the next year as early as they can.

Summary

It’s been a journey. I’ve gone from believing there was no advantage to qualifying while Kona was happening to thinking there might be a small advantage for women, to thinking it’s so small that it’s not worth considering. While there was evidence that women’s times were slower in Kona for those who race in the early qualifying season, and more of the top 10 at Kona come from the March to May period, it’s not a huge difference. Men showed even less difference in times for the three groups, placings followed a similar trend.

Ultimately, man or woman, I’m not sure I would factor this into my thinking. Nor, were I planning an allocation system for slots, would I expect it to have a huge impact. Maybe a small uplift in slots during March to May, but back to the same from June onwards.

There’s a lot I still haven’t shown you. All of this data is based on races in 2015-2019. It’s a nice, consistent dataset, but is it representative of now? I have charts for 2021-2025, but I’m going to save those for another time. I’ve also put a lot of thought into athlete turn-over. We still see fast performances close to Kona because there are more fast age groupers than just those in Kona. New to the sport, returning athletes and those who’ve made clear progress in performance: the Kona podium includes them all. I’ve a lot of graphs to share on this too.

Finally, while I don’t think there’s any significant qualification advantage to racing early-season, one advantage to qualifying early is you get a full season to prepare for Kona.

Coach Cox