Ironman Qualification: Analysing race results to pick a qualifier

Why does anyone come to this website? Statistically it’s to look at Ironman results, most likely to search for an athlete – themself or a competitor. Potentially to look for specific race statistics, determine how fast it is, what they might be able to do, what might qualify them for the Ironman World Champs. That was the motivation for analysing all these results when I started over a decade ago. In that time I’ve pottered away building tools to explore the data, trying to better categorise and rank races.

A simple solution doesn’t exist but I’ve found a range of stats that are useful. Some you see on the site: slot allocations, qualifying times and finisher rates. Quite a lot of them I develop behind the scenes. Too complex and cumbersome to put on public view, they form the basis of my Qualification Reviews. Something that you’re probably unaware of as I used to bury it behind an ugly link at the bottom of each page.

You’ll now find the above link sitting in the middle of the page across much of the site. (If you’re on a big enough screen you’ll also see links for my coaching and training camps). What’s hard to convey in a small piece of promotion and a short page detailing services is exactly what’s contained in a Qualification Review. Which leads to this post: a look through the Qualification Review and what it can show.

I’ll be using screenshots taken from an example review to highlight the most important elements. You can also download a full PDF of the example data here.

The starting point of any analysis are the qualification slots. We normally know slot allocations for the coming year but that doesn’t rule out useful analyses beyond that. I’ve recently completed a couple of reviews for the next male Kona Championship in 2026. I can estimate likely allocations based on previous years and can always make multiple passes to check alternate allocations.

However these numbers are derived, age group allocations can be calculated from the average athlete distribution for each race. Which leads to their average qualification times, one of the key components of the data. For this example I’ve looked at 2025 qualification numbers for the M45-49 age group. This is a Nice year so slot numbers are higher.

While it’s possible to look at qualification stats just from the perspective of an age group, throwing in some personal results helps build context. I can use an existing result for comparison or any arbitrary target time. Often I’ll mix the two, running mutliple passes with different comparison settings. For this example I’m using one of my old results from Ironman New Zealand 2010, a performance I definitely couldn’t match now.

Slot Numbers

The first section of summary data is relatively simple. We’ve the average number of athletes in the age group and the expected slot allocation they’ll receive. From this I’ve derived the percentage of the field who will qualify. Sometimes races gain a surprising number of slots for their size – a tenth of the age group could qualify with this many slots in Malaysia or Taiwan.

To try to assess competition I’ve added a secondary percentage: the proportion of the age group that have finished 10 minutes after the final qualifying time. This gives us a sense of how many athletes are close to qualifying. The bigger the difference between the qualifying percentage and the chase percentage the more competitive the race. Tallinn has slots for 8% of the age group, but 16% finish close to a qualifying time.

The final column shows the chance that a slot rolls down from another division. Roll down is unpredictable but when an age group has no finishers or has unclaimed slots they are rolled to other age groups based on the size of each age group. Using the slot allocation method this number indicates how many slots would have to roll for this age group to gain another slot. Lower numbers indicate a better chance of gaining additional slots.

Qualification Times

The Final Qualifications Splits table shows the average final qualifying splits for each race and also shows the difference between those splits and the comparison result. The good news for me (were I still at that level of fitness) is my splits are faster than a lot of qualifying times. That’s not necessarily hard when you consider races like Wales, Lanzarote, Taiwan and Malaysia. I’m clearly not going to deliver the same time on each of these courses.

I don’t have the maths to convert a result on one course to an equivalent on another, so we need to use a range of comparison points to gain more insight. I produce similar tables for a variety of positions: overall winner, age group winner, the same position and percentile placing as the comparison result and the average for the age group.

Race Comparison

To simplify this comparison I’ve also compiled this data into a single summary table. As well as comparing my particular result I compare a variety of other results to build a picture of how each race differs. My performance being faster than the final qualifying time for another race is only notable if the exact same isn’t true for other placings.

An example might help. If I’m considering Ironman Lanzarote my time is over an hour faster than the final qualifying time but I’m also confident that New Zealand is a faster course than Lanza. Scanning across the other columns I can see that New Zealand is indeed faster at every placing. Perhaps it’s not a good choice. But there’s potential, as the difference between my time and the final qualifying time is much bigger than those other data points. I’m further ahead than the race averages so even allowing for differences in courses I may still have a chance.

Race Details

I like to be thorough, so following the high level tables are a series of pages covering each race in more detail. I calculate fastest and slowest times for each race alongside the averages used in the summaries. This gives a better sense of the range of results. Some courses are a lot more variable than others and their average may hide a wide disparity in qualification time.

There’s also another useful cross-race analysis (shown above): a comparison of times from athletes who’ve raced both events. Averaging the difference in splits for these athletes builds a matrix comparing performances across the last 5 years of racing. It’s another way to give some indication of the differences between courses. As we’d expect, in this example, New Zealand is consistently faster than Lanzarote. Looks like 2019 was quite a slow year in Lanza too.

This comprises the core analysis. As a basic option I’ll produce your own individual analysis PDF covering all of the above areas. You’ll find qualification information much faster than browsing the site and also access deeper statistics. It’s still a lot of data to work through.

For a little more I’ll produce a detailed report on your analysis or arrange an hours Zoom consult to discuss the information in full. I’ll also dive a little deeper into the data for prominent races in the season producing more data on qualification times at these events.

Diving Deeper

A report and a consultation draw on over 10 years experience analysing results data and 15 years coaching Ironman. I’ll assess the main data in full, taking on board your input and preferences when identifying races best suited to your goals. Once I’ve picked a few key races that offer good qualification prospects I’ll produce some more detailed data on the individual events.

You can download a PDF of example data for Ironman Lanzarote here.

There’s some repetition in this file: we’ve seen fastest, average and slowest splits for qualification already. But I build on this with a more detailed look that lists these splits for every place within qualification range. The aim is to throughly examine variance. No two years of racing are the same; sometimes even courses aren’t the same.

At its heart this race analysis inverts the process up to now. Rather than looking at typical splits for particular rankings I examine typical rankings for particular splits. A finish time of T achieves at least a ranking of R in P percentage of races.

An example should make this clearer: in Lanzarote a finishing time of 9:25 would be enough to win my age group in every year of results (unsurprsingly). If you’re curious you can scroll down the file and find that this year a 9:27 won the age group. More relevant to qualification a finish time of 10:15 is enough to place 11th or better – and qualify – in every year of results. Add 15 minutes and a finish of 10:30 will be good to qualify in 88% of races. As the time increases beyond this the odds continue to drop.

For the sake of completeness I also include every year of results for qualifying places in case we want to pick out particular outliers or carefully check the data.

By this stage I’ve a huge amount of data to inform race decisions. I’ll summarise this all in a written report that will cover the options and look at pros and cons for individual races. Often these reports will confirm options or steer away from others. It might be a race you thought was a good fit, but looks to be highly competitive and close on qualifying time. Or an event that has more slots than expected and offers slower qualifying times with it.

It’s also important to recognise what can’t be achieved. A review won’t find a surprise, easy qualifier. It will show differences in difficulty, but no race will give you an easy ride – particularly if you’re targeting Kona. We can find races with the better odds for those who are closing in on qualification. We can identify splits to target and feed that information back into training to help set process goals.

You’ll find details on available options and costs on the Qualification Review page.