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.

^* Although they’re a bit more likely to encounter someone who makes top 10 in their age group between March and May. Otherwise. there are enough men to ensure fast athletes are available even when Kona is happening.
⁺ There may be a small difference for women, likely due to lower number of athletes in all. 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.

Kona Times and Placings

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? 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. As I’ll show in a moment racing Kona back-to-back like that is actually quite race, less than 20% of men and women race Kona in consecutive years. Although, for those who make the top 10 in their age group that number jumps closer to 40%.

Competitor Numbers

The numbers of athletes involved in Ironman was another area of consideration. For Kona to be influencing the competitiveness of other races it needs to have taken in sufficient fast athletes that there aren’t enough to be entering other events.

In the period I’ve focussed on we quite consistently have around 3.5% of the Male competitors in Kona and a touch over 5.5% of the female competitors there. Or consider it a pool of 45,000 men and 10,500 women who aren’t in Kona at that time. Has Kona cleanly snapped up the top 3.5% and top 5.5% of men and women respectively? It’s unlikely.

As I’ve already mentioned over 80% of men and women in Kona weren’t there the previous year. Or, based on the numbers in the table above around 300 men and 100 women in Kona were there the year before. You’re unlikely to have encountered those individuals at an early season qualifier, but are they the strongest athletes?

When we focus on those who manage to make the top 10 in their age group we see a shift in the numbers. Top 10 age group athletes raced the World Champs in the previous year about 35% of the time. The very fastest are more likely to race again and from our earlier charts are probably gaining their qualification in March to May if they can. But still, the majority of the age group top 10 were not in Kona the year before so could have easily qualified early season.

Just considering previous World Championship experience shows around 60% of athletes were first timers in Kona in any given year. Numbers rapidly decline beyond that point. The competitive-span of a Kona competitor is relatively short, a small percentage keep coming back. New Kona athletes are an important part of the makeup of the race.

Again focussing on those who make the top 10 in their Age Group in Kona we see slightly more longevity. Almost a third are new to the World Champs, that’s a good rate of change over. Almost a quarter have only raced once before. The fastest age groupers are a more stable group than Kona athletes as a whole.

But their numbers are small as a proportion of unique athletes racing Ironman in a qualification year. Not enough in my opinion to truly diminish the potential for faster athletes to be racing at early season events. At the very least there’s sufficient turnover to expect new names to be coming into the field.

What About Now?

What if we apply the same principles to more recent times? There are going to be issues. Covid had a huge impact on early numbers and then we alternated between two different Championship locations. I’ve only considered those racing in Kona for this analysis. Our trust in these numbers should consider the many other factors that influence athlete choice in these times.

In this recent period there’s a shift in male qualification with a clear gap between those racing early and those racing late in season. The median is fastest for the late season qualifiers, but as before the mid-season dominates the top 5%. Along with changes of locations and delays in championship races there was a huge increase in numbers with the gender split championships. A likely reason for the large difference in the middle of the field is the increased qualifying numbers in these years. At the front of pack we see no difference between those who raced early in the season or those who raced late.

As championship numbers are significantly reducing with the return to a joint race I would expect the time gap to tighten at the median.

If we check how placings have distributed based on prior race months the mid-season has a couple of places advantage at the front and the bigger field takes the medians outside the bounds of this chart. Were I choosing when to race, on this evidence I’d probably avoid March to May expecting the fastest athletes then.

But, does the larger championship field, varying timing and race location, and delayed racing from Covid allow us to trust these results in full? I’ll need a few more years of data to really be sure. With lower numbers and more stability we may see a return to 2015-2019 behaviour. The new allocation system could also reshape how qualification numbers look and raises the question of whether I should segregate on age group as well.

We have a similar pattern for women in this time range. June to August racers are fastest at the median, but not at the front 5%. September to December produces the slowest group of athletes, but the time range is slightly smaller, especially when compared with March to May. Greater numbers of women racing in a championship gives a smoother dataset. And, it should be noted, also reduces the skew of the finisher distribution in Kona.

Considering the distribution of finisher placings for women we again lose the median from the chart due to the high numbers, but like the men the top 5% are drawn most heavily from the March to May period with little difference between early and late season racers.

The very fastest tend to race March to May, by a small margin at least. In a system with very few slots for women that may well mean there are advantages to racing at other periods of the year. Not enough to account for a large discrepancy in the proportion qualifying over a season, but if only an age group winner gets a slot you’d be at risk in March to May.

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 probably 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.

More recent data could be taken to indicate a more significant difference, and in particular to avoid March to May as the most competitive period. But it comes with large caveats. A lot more variance in those years and factors that won’t apply to future racing in Kona. I’d not place too much weighting on these results.

Ultimately, man or woman, I’m not sure I’d factor any of 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. More analysis on athlete numbers and history, including how performance relates to past Ironman racing. I’ve many graphs to share, but if you’ve made it here you’ve seen enough.

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.

I’ll also take this opportunity to mention that maintaining the stats might feel like a job but I don’t directly earn anything from it. I hope they bring people to my coaching or to seeking consultancies on race selection. And I accept donations, if you find this site useful you can help out by clicking a donate button.

Let’s take a look at what I’ve got planned.

Basic Race Stats

This one is obvious: a page summarising competitor numbers and data for each race. Gender, age group, country – just basic demographics for each event. I have a stub of code in place ready for this but – if I’m honest – it hasn’t interest me enough to finish! It should be a straight forward build though, so I’ll get this one done soon.

Race Meta Data

Beyond the results there’s a lot of event information that could be useful. Course distances, elevation, weather etc. All have potential impacts on performance. In many respects this is a trivial addition, connecting a few new data points to a race isn’t hard. There’s a bit of labour gathering and sanitising data, initially at least.

The bigger project is utilising this new information. Going beyond just displaying a few extra race details and incorporating it into tools for analysing results. There’s lots of potential. Which brings me to my next couple of ideas.

Race Comparison

Before I took the data online, articles analysing and comparing results were a regular feature. I still write some, but it’s a laborious process. I’d like to incorporate comparison into the site so as you explore results you can easily add other race data to the charts.

While examining the finisher distribution for Ironman California you’d be able to add the finishers distribution from a previous year(s) or another race entirely. This could be apply to any chart or table where comparison makes sense.

This shouldn’t be too difficult to implement in principle but I can see myself getting sidelined into some needed code-refactoring in the process. That will add time.

Race Normalisation

Perhaps the most challenging concept – a way to normalise race results for better comparison. We’re all aware races vary in course, conditions and the athletes present. This makes direct comparison a challenge. So, how do we do it?

My conclusion is there’s no perfect answer and what might be effective for front-of-pack athletes won’t apply mid-pack (or vice-versa). That’s held me back. But I’ve an idea to build a framework for multiple methods of normalisation and comparative processes so that users can chooses the approach they like best. Or, models can be weighted and grouped to form aggregate comparisons for different performance brackets.

This one’s very much in the speculative, conceived-while-walking-the-dog phase of development. The framework itself is simple. Some of the normalisations methods will be more time consuming.

Qualification Analysis

I offer a race consultancy service where I advise athletes on race selection. This uses analysis tools I’ve developed but not made available through the site as they’re intensive and it maintains an advantage to consulting with me.

But I also see benefits for consultancy clients having ongoing access to current analyses for their race choices. I’ve a plan to take the data online for those who consult with me and those interested in these more individual stats. However, my intention is this would be a paid feature. This is the only area I might charge for, everything else will remain open to all.

Race Analysis Videos

You may have read one of my post-race analysis articles. I’ve been inconsistent with them lately because they’re often dull to write! Especially when a race is just average. Also, does anyone read on the internet anymore? Is anyone reading this now?

I’m considering the move to video. Walking through an analysis on screen, perhaps covering multiple events in a single video to focus on the interesting stats. This is less a development problem then a time problem problem. I’m going to explore the idea with some video guides to using the site.

Your ideas?

If I had a development timeline these five ideas would top it. But perhaps there are other features that would be much more useful? I’m open to suggestions.

I’ve not included simple one-offs either. Individual stats that crop up along the way. A current example is my comparison of old and new qualification methods. There’s scope for many more of these. So, if you have a question about Ironman let me know too.

I’ll continue adding new data, bug fixing and error correcting on existing data and growing the features on the site. If you’d like to support this work, you can make a donation below.

Result Distributions

Comparing recent results for women in Kona with this year’s race shows a similar overall distribution. This year’s swim was slightly slower, but the run slightly faster. The comparison includes a mix of 2019, a single day race, 2022, a partially split race where some male age groups raced the same day as the women, and 2023, when only women were racing.

For the second distribution I’ve compared the two (2023, 2025) fully independent women’s races in Kona with 5 years of women’s results from joint races. What we see here is a clearly slower race at the average, at least 30 minutes difference, but a more comparable race at the front. The major source of that difference is the run, with some contribution from the swim. Bike splits are more comparable.

The main factor is likely the increased numbers of competitors. This meant more women in older age groups present and also a deeper field selected from qualifying races.

DNF Rates

The DNF numbers above are not segregated on gender so use them as a guide to whether a race year was tougher/slower than others. With that in mind this year look typical for Kona, while 2023 appears to have been a particularly good year for the course.

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Median Splits

As we saw with the most recent race comparison there’s not much difference at the medians. Splits vary across age groups but by small margins of low significance.

Competitor Origins

There’s a broad spread of athletes racing this World Champs. Just over a quarter from the US, but spreading widely beyond that.

Performance Changes

Tracking the times for specific places over the years shows Kona to be one of the more consistent races when considering the front of larger groups. We can see how the more recent races with more slots available opened up numbers allowing the largest age groups to exceed 100 athletes. Also how 2023 was a good year for most age groups and this year’s results line up well with recent years.

Age Group Top 20

If we consider the top twenty of each age group then this year was consistently faster than the average and in some age groups pushing towards one of the fastest year. Look at F18-24 and those over 50 and you see times among the fastest for the course.

Conclusion

Overall this year’s championship compares well with recent years in Kona. The separate and larger women’s championship did see some decrease to the median finish time, but the front of pack remained as competitive with a number of age groups being near their fastest.

Note, that while 2021 falls in the last 5 years, due to changes to its swim its results have been excluded.

Result Distributions

Comparing this year’s results with the last 5 years shows it trends slightly faster overall. Most of this change appears to be down to a faster run. Differences on the bike or swim are too small to hold much significance. So a clearly faster run, and overall a slightly faster race.

DNF Rates

DNF rate are average for the course. There were a couple of higher years in 2022/2023, but otherwise the levels compare with most previous races. Run DNFs stand out at the lowest levels seen.

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Median Splits

The median split pattern matches the trends show in the distributions – that of a faster run average and slightly faster bike average.

Competitor Origins

European races always draw a wide range of athletes, mostly from around Europe. Barcelona is a little unusual for being dominated by British and French athletes, with Spanish in third. Although, the British don’t do quite as well in terms of qualifications.

Performance Changes

Finishing times in Barcelona have been quite stable with the exception of 2022/2023 which stand out as slower years. So, this year is largely inline with the course history.

Qualification

Applying the qualification adjustments to the results gives the above distributions of slots before roll downs. As has often been the case so far this season women’s age groups receive just a single slot each. The distribution among the men particularly favours 50-54 year olds with 35-39 coming in second.

Age Group Top 20

Tracking the top twenty in each age group we can see that someone’s age groups mostly trend around the average while men tended to be slightly ahead of the average. The success of an age group in gathering slots is likely reflected in how far they come ahead of the average and past results. This is certainly supported by M35-39 who outperform their averages by the largest margin. The difference is lower for M50-54 though.

Conclusion

While Barcelona 2025 looks a bit faster than the last 5 years of racing, this is likely due to 2022/2023 being slower, rather than this year being particularly fast. Ultimately, it was a normal year for the race.

Result Distributions

Comparing the distribution of finishers with those from the previous 5 years suggests this year’s race was average for the course. Not good news for writing an analysis as it leaves little of interest to say. The split data would suggest that the swim trended slightly slower while the run trended slightly faster this year. The bike, like the overall times was average.

DNF Rates

It’s a similar situation with DNF numbers. After last year’s high numbers we’re back to a level comparable with most recent years at the event. DNF rates in Wales sit towards the higher range for Ironman.

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Median Splits

Medians reflect the trends seen in the distributions. Meaning a slightly faster run and a slightly slower swim for this year’s race. There’s always some variation across age groups, but most stick with the trend.

Competitor Origins

The majority of athletes racing are from the UK, but about 10% come from the rest of the world. Slots are a little more broadly distributed with 60% likely to stay with UK athletes.

Performance Changes

For the largest Male age groups, those between 30 and 50, finishing times have been surprisingly stable in Wales. Conditions have quite an impact on this race, so we might expect more variability year-on-year. Smaller age groups deliver this – we can see far more variation in times for women’s age groups for example.

Qualification

I’ve applied the 2026 qualification adjustments to finishing times to determine how 2026 qualification slots will distribute. Numbers and times assume no roll-down of slots. You can review and adjust the slot allocation in more detail on my Ironman Wales qualification page. Women’s age group each receive 1 slot; the distribution of slots for men is quite evenly spread.

Age Group Top 20

This year’s top age group performers largely trend close to the course averages. There’s variation across age groups as to whether they’re just above, or below the average, but they don’t tend to stray far. This may help explain the largely even allocation of slots across age groups. With no age group’s relative performance standing out there isn’t a single group taking a bigger share of the slots.

Conclusion

This year’s race was an average year for Ironman Wales. That means much slower than the majority of Ironman courses, but nothing unusual for the Tenby course.

Except where noted the stats only consider male age groups at any race I’ve included. Be aware that the regular Ironman Nice course has a shorter bike, for the Championship extra distance was added to make the full 180km. So, when comparing times between this race and Ironman France they are not exactly like for like. On the other hand in September the temperatures on the course won’t hit the same peaks in June.

Result Distributions

Starting as always with the results distributions we can make comparisons with both previous men’s Kona results and previous men’s results at Ironman Nice. The championship Nice race is a faster event than the regular Ironman France despite having the longer course. A clear indication of the impact of selection over general entry. While there have been questions aroudn uptake and roll downs for Nice in the past, it’s clearly a more competitive field.

The Nice course is slower than the Kona course as a result of the bike which is significantly slower. Nice actually looks to have a slightly faster run – a simple flat course and lower heat and humidity likely being factors in that. We have similar championship skews to these distributions although the slower bike tries its best to hide them.

DNF Rates

For clarity the DNF rate data does include results from women either in their specific races or in the earlier years when the race was a one day event. The Nice Championship look to have higher DNF rates overall. Although if we look back at some of the earlier Kona results, the rates are higher.

For comparison Ironman France DNF rates are clearly higher than at the Championship. The main factor is likely to be the level of preparedness of a qualified field over a general entry. Lower heat levels may play a role too though.

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Median Splits

Median split reflect patterns highlighted in the distributions. So the slowest race is Ironman France and the fastest is the World championships in Kona. Our championship in Nice sits between them with only its run edging ahead of Kona.

Competitor Origins

Taking a brief look at the countries athletes come from the most notable difference between this year’s race in Nice and the previous year’s men’s race in Kona is a lower proportion of athletes from the US. I’ve not dived in deep enough to snider the exact number of qualification opportunities in different regions, but it looks like uptake in the US was reduced when the championship was in Nice.

Age Group Top 20

As a final comparison I’ve looked at the times for the top 20 in each age group and plotted them against times for men in Kona and at Ironman Nice to help give us context. Unsurprisingly the conclusion is the same as with the initial distributions. When compared to Kona the Nice championship is significantly slower. Then when we compare with Ironman France the times are faster. Again, we should remember this is despite a longer bike course for the Championship.

Conclusion

As an Ironman Championship Nice was a slower course overall, although this is fully down to its bike. While it may not have held quite the same appeal as Kona it clearly drew a competitive field as shown by the skew in race distributions and how many athletes outperformed Nice despite the longer course.

Result Distributions

Comparing this year’s results with those from the last 5 years the race trends faster across all splits. This is true for the medians, but appears to be particularly strong towards the front of pack. In this comparison 2025 comes out significantly ahead of the previous race data.

DNF Rates

We also get a much lower DNF rate than in recent years. In fact more in line with the period prior to 2019. The last 5 years stand out as particularly high, 2022 reaching the extreme level of over 25%.

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Median Splits

As medians reflect the trends shown in the distributions we see faster averages for every split quite consistently.

Competitor Origins

Wisconsin mainly draws athletes from the US with a small percentage coming from further afield. Kona slots look to mainly stay with the US too.

Performance Changes

Looking at how performances have changed over the history of Wisconsin (going all the way back to 2003) while times appear to have been quite stable for much of that time they are definitely more volatile in recent year. We can see the big bump for 2022 and the improvements in times since. Were I to compare this year’s results with those prior to 2019 it might not look quite as fast. 2022 has a big effect on that trend.

Qualification

Based on finishing times I’ve applied the appropriate adjustments and ranked athlete times in order to determine where slots went and the times that achieved qualification. This assumes there was no roll down – that will influence the numbers. You can examine this in more detail and play with the numbers on my Ironman Wisconsin qualification page.

Age Group Top 20

I mentioned earlier that the front of pack was fast and we can see that in the charts above. The top twenty in each age group often trending much faster than the average and for some age groups clearly the fastest in recent years. This is most pronounced among the male athletes but younger women’s age groups also perform well.

Conclusion

It’s definitely a fast year for Wisconsin, at least in the context of recent years of racing. If we go back further in the race’s history times may not stand out quite as much. Still front-of-pack age groupers raced quickly for the slots on offer.

Result Distributions

Overall this year’s race isn’t too different from the results for the previous few years in Thun. The major difference appears to be the swim, although I think there’s a shorter swim in one of the previous results to pull down the swim times in the older data. Bike and run are very similar, perhaps slightly faster at the median, but not significantly.

DNF Rates

DNF rates are low for the course. The majority of years have been over 10%, but here we’re just under 8%. That seems to be a good level of DNF for this race.

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Median Splits

Median splits are in line with the distributions. Meaning there’s no major patterns present in the comparison, times remain quite similar.

Competitor Origins

As with many of the European races there’s a wide mix of athletes competing; the majority of the field coming from outside of Switzerland. Slots are similarly widely dispersed under the new performance based system.

Performance Changes

Ironman Switzerland has a relatively short history in Thun. Looking over the data it’s quite consistent in timings with 2023 being the year that stands out (as it does in the DNFs).

Qualification

I’ve adjusted and ranked results and from that calculated the number of slots that would end up in each age group if no roll down takes place. Under the new system roll downs will directly affect the slots claimed by age groups. You can explore these numbers in more detail on the Ironman Switzerland qualification page.

Age Group Top 20

The majority of age groups have top twenty finishers close to the averages. A few stand out with some faster than usual times for the course. This tends to coincide with the age groups getting more sets due to higher performances.

Conclusion

Another race whose results are typical for the course. Not major differences from previous years and a low DNF rate for the race. The front of pack in this one also trend closer to the averages with only a few standing out.

Result Distributions

We have slightly faster distributions this year than in the last five, but not to a significant degree. Each stage differs by a matter of minutes so fully within the realms of normal variations.

DNF Rates

DNF rates are nice and low for the year. Not at the lowest end of the range, but comfortably below the last couple of years at least.

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Median Splits

Medians repeat the trend of the distributions meaning they’re near enough identical with some small variations among the age groups.

Competitor Origins

We have a very broad range of athletes at this race with the UK managing the biggest showing. Slots are likely to have been similarly dispersed among the athletes present with Finland taking a good share.

Performance Changes

Tracking performance changes over the years puts this race in line with some of the earlier years of racing in Tallinn. It’s definitely leaning towards a faster race for the men, but is a bit more variable for women. As with the distribution the differences are relatively small.

Qualification

I’ve applied the qualifying ranking process to the results and from that determined the number of slots each age group is likely to claim if there is no roll down. You can adjust these numbers and look at the impact on qualification on my Ironman Tallinn qualification page.

Age Group Top 20

As we’ve seen with the last couple of races the top twenty men are tending to perform better than average. It’s a less consistent pattern for the front of pack women though. Tallinn has a relatively small field and may not have the depth in smaller women’s age groups.

Conclusion

Overall a typical set of results for the Tallinn course. Some faster than average times at the front of the race, particularly for the men. As with the other Ironman races using the new qualification system men in their 50s do well in qualification, claiming 9 of the 40 available slots. Men in their thirties also do well with a further 8 slots going to them.

Result Distributions

Copenhagen is a consistently fast course. This year’s race might be a touch faster on bike and run, but the margins are small and unlikely to be significant. This carries into the overall distribution, again a small shift to faster times, but not one indicative of real change.

DNF Rates

DNF rates are low. Comparable with 2024 and better than any year prior to that. The course is as straight-forward as Ironman can be and DNF rates reflect that.

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Median Splits

Median splits mirror patterns in the distributions. Meaning slightly faster bike and run time as a broad rule. No significant patterns of note, just a year that trended towards the fastest for the course.

Competitor Origins

Copenhagen draws a wide field of athletes from around the world. Locals take half the slots, with the remainder spread among the nations present.

Performance Changes

The tracking of historic times demonstrates the consistency of this race. Variation is about as low as you get for an Ironman particularly for the largest age groups.

Qualification

I’ve applied the new qualifying adjustments to results for Copenhagen to determine how many would qualify in each age group for 2026. You can find stats for the 2025 qualification slots on the Ironman Copenhagen qualification page. Numbers assume no roll down takes place during the allocation process.

Age Group Top 20

This year’s top 20 times for age groups are mostly faster than average. Lending credence to this being a faster year in general. A few age groups are particularly fast and this tends to tally with the age groups that gain the most performance based slots.

Conclusion

As always Copenhagen was a fast race. There’s some indication this year was one of the faster ones, although the shift isn’t huge. The front of pack was fast with a few age groups putting out particular fast performances.