Result Distributions

This year’s race trends a little faster than the amalgamated results from the previous 5 races. Swim splits look to be largely identical in pattern, but the run clearly trends much faster. The median run is around 30 minutes faster than the amalgamated data and the top 5% around 10 minutes faster. This shift in the run overshadows the very slight change in bike distributions, which is also a touch faster. Overall, thanks to a fast run, we have a year that trends faster than recent results.

DNF Rates

While the event appears to be faster, DNF rates aren’t too far form previous races. Lower than a number of recent years, but not outstanding.

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Median Splits

Median splits follow the patterns of the distributions, so the stand out feature is the faster run times. Bike and swim times don’t vary enough to show major patterns.

Competitor Origins

The majority of athletes are unsurprisingly from the US, with Canada in second place. Qualification slots will mostly have been offered to them.

Performance Changes

Looking at the long race history of Lake Placid and times for specific race positions what’s most evident is how variable this race has been. Both year-on-year and between age groups at the race. This year is not universally faster across all age groups – we can find examples where it trends slower.

Qualification

Based on the start numbers for this race I’ve calculated the number of slots allocated to each age group and from that their final qualifying times. Roll down isn’t accounted for and with one age group having no finishers there will be at least one roll down slot.

You can explore qualification in detail and see how the new qualification system works on my Ironman Lake Placid Qualification page.

Age Group Top 20

The top 20 age group performances at this year’s race also reflect the variability of this course. Female age groups trend a bit faster than the average more often than not, male age groups lie closer to the averages for the most part. With a couple of exceptions there isn’t as large a deviation from averages as the run splits might imply. Going back to the distributions we did see a bigger change at the median than the top 5%.

Conclusion

As a whole Ironman Lake Placid 2025 was a faster year for the course. This isn’t played out consistently across all age groups and is more evident at the middle-of-pack than the front-of-pack. The fastest age groupers performed as might be expected, as we go further back in the field we see a bigger change.

Due to some irregularity in splits I’ve excluded my 2014 result set form some of this analysis

To start the analysis I’ll compare the distribution of split times at the 2019 race with the amalgamated results from the previous decade (2014 excluded). As stated this was a slower year for Lake Placid with bike and run distributions trending behind past results. The bike appears to have been 5-10 minutes slower at the median and the run around 20-30 minutes slower. This leads to a shift of around 30-40 minutes at the median for the race.

The DNF rate this year was unusually high for the course. At close to 15% this is heading into unusually high territory for DNF rates. Both bike and run DNFs were higher than usual for the course. You can compare this with other races and find out more about DNF rates here.

Individual age group medians generally follow the trends shown by their respective distributions. In this case median bike and run splits trend slower than in the previous decade of results (2014 excluded).

The majority of competitors come from the US or Canada with a small selection form elsewhere in the world. Kona slots appear to have mostly stayed in the US.

Tracking overall times for specific age group positions gives further indication of how this race compares with past events. This year’s results do appear to be towards the slower end of the spectrum for Lake Placid, although not too dissimilar to 2018 in many respects. This does vary with age group and position, but if we focus on the largest age groups (men 35 through to 50) then times are similar or slower to 2018 which puts this year as one of the slower years.

Based on the start list I’ve calculated the Kona slot allocation and from that automatic qualifying times in some age groups. As a number of divisions didn’t have any finishers there will be some roll downs on top of this so final numbers and times will have varied a little. You can compare this with other races on my Kona qualification page.

The top twenty charts are varied, but the majority show slower than average splits for the top twenty. As is often the case the smaller age groups are the ones that vary most significantly from this. Times for Kona qualifiers are often closer to the averages, but further back in the top twenty the gap tends to open up. This was certainly a slower year for qualification in the largest age groups.

You can access a spreadsheet of the full results from Ironman Lake Placid 2019 on my Google Drive.

I’ve spotted inconsistencies in my copy of the 2014 result set from Lake Placid, so have excluded that data from this analysis.

This year’s results very clearly trend much slower than the amalgamated results of the previous 10 years. The biggest shift appears on the bike, but the run is also slower and as a consequence we see quite a big difference in the overall distribution too. The shift appears to be consistent from front to mid and back of pack. There’s a lot of weight in numbers towards the right of the distributions.

DNF levels on the bike are on the high side for Lake Placid – higher than we’ve seen over the decade. run DNf figures are typical for the race, clearly the bike presented the biggest challenge of the day. Allowing for the fact that older data often includes DNS numbers as well I’d view this year’s race as having slightly higher than average DNF rates.

Age group medians largely follow the same pattern as the distributions. Bike splits are consistently slower, but run splits are more varied with instances where they trend faster.

Lake Placid doesn’t tend to draw too many competitors from outside North America and it keeps the slots there too.

Tracking times for specific placings across the age groups shows that in most cases there’s a slow down this year, pushing this race towards the slow end of the spectrum. That said, it’s not true of every division, for example M45-49 is comparable with previous results.From the distribution it’s clear that the overall pattern was slower, but within some age groups this is less evident.

Based on the start numbers I’ve estimated the slot allocation and from that the final qualifier times. These numbers may vary an the final qualification time will depend on roll down too. You can compare Lake Placid with other Ironman races on my Kona qualification page.

Finally the top twenty times within each age group. For the most part these are slower than average, but the change here is smaller than we saw at the median. It’s also inconsistent with some age groups trending closer to, or faster than, the average. Overall it does look like it was a slower year for the Lake Placid course, but some of the impact on the results distribution comes from a larger mid to back of pack.

You can access a spreadsheet of the full results from Ironman Lake Placid 2018 on my Google Drive.

A quick note, that the 2014 race is excluded from this analysis due to irregularities with the results set.

The split distributions from this year’s race are very similar to those from the previous decade. The bike median comes out slightly slower, although further up in the race there’s little difference in the splits. So, 2017 is very slightly slower at the median, but this seems to reflect slower performances in the mid to back of pack.

We’ve got good quality DNS/DNF numbers this year without confusion over who started. The main points to note here are that both bike and run DNF rates are on the lower end for this race. Drop outs were relatively low at this year’s event which suggests conditions were moderate.

Given the similarities between this year and past results there are no major patterns in the age group median data. Splits aren’t too dissimilar to the aggregated historical data and while they vary it’s not universal across divisions.

As with most US races the majority of athletes are North American, not a huge surprise. The US takes most of the slots, but Canada punches above its weight based on entry numbers.

There’s quite a lot of variance in times by age group and position over previous year’s results. Again, no clear pattern across all the age groups. That said, variations are generally quite small and mostly within the bounds of past races This puts this year’s race somewhere in the middle of Lake Placid results.

Based on the results list I’ve calculated how the slots might have been allocated, actual numbers may have varied slightly. From this I can estimate an automatic qualifying time assuming no roll downs. Again, final qualifying times may vary especially when roll downs take place. You can compare this with other races on my Kona qualification page.

To round up the analysis, performances among the top twenty of each age group. These largely trend close to previous averages with one or two notable exceptions. The F55-59 age group performed particularly strongly while the M25-29 division trended behind most consistently. Taken as a whole this again points to a typical year of racing for the Lake Placid course.

You can access a spreadsheet of the full results from Ironman Lake Placid 2017 on my Google Drive.

A quick glance at the distributions above is enough to show how this year’s race trended slower than the aggregated course results. It’s worth noting how in both bike and run peak split times are consistent with past results, but there’s much more weight in the rear half of each histogram. So although times appear slower across the field there’s a particularly heavy middle to back of pack really pulling the medians down.

Entry numbers are the highest at this year’s event and so too are the combined DNS/DNF statistics. In all three disciplines the DNF/DNS rate runs at the high end for this event. I can’t say what pushed the DNF/DNS rate up, the numbers are comparable with last year though. It may be conditions were tough at Lake Placid yesterday, but potentially we may also see greater numbers of DNF from the extra weight towards the back of pack.

As we’d expect after the distributions, median splits for age groups largely trend slower than before. It’s true across all 3 disciplines and most age groups.

The majority of athletes at Lake Placid are, unsurprisingly, from North America. It’s not often we see India among the 10 most numerous nationalities at an Ironman.

Tracking the finishing times for top 100 finishers at Lake Placid actually places this year’s race on a par with previous events. This may reflect the size of the field – some age groups will number over 400 athletes putting 100th place in top to middle of pack. These particular charts may miss the influence of that bulk of middle to back of pack athletes seen in the distribution charts.

Based on the athlete list I’ve calculated the slot allocation for Lake Placid and the automatic qualifying times from the results. Exact start numbers and roll downs will likely affect some of these times. For comparison with previous years and other races check my Kona Qualification page.

There’s no consistent pattern to the top twenty results in each age group – some came out slower this year, others faster. This was certainly not the slowest year for qualifying on the course, nor was it the fastest. The results fall within the range we’ve seen at Lake Placid, although not consistently enough to imply a trend. My feel is that the front of pack athletes were largely on par with previous years, but as we’ve seen in other races the growing number of competitors is expanding the middle to back of pack and pulling down averages.

You can access a spreadsheet of the full results and splits from Ironman Lake Placid 2016 on my Google Drive.

I’ve been informed of other sources for the Ironman Lake Placid 2014 results, but unfortunately my current tools can’t easily extract the details from them. To ensure a fairer comparison it’s easier to simply drop the 2014 results. Knowing times from 2014 were affected by this issue there was a chance that they were skewing some of the charts so I thought it best to produce new medians and distributions covering only 2002-2013 (excluding 2005).

Fortunately it appears that 2015 didn’t have much impact on the age group medians. Swim times are more comparable now and they were always going to be the most heavily affected part. Other than that the 2015 race still comes out slower on bike an run.

It’s the same with the distributions too. The swim comes out closer, but the rest of the race remains that much slower than usual.

So not really a huge change to my earlier analysis, but I wanted to be sure that the drop off in times wasn’t just the result of an anomalous results set. You can read the original analysis here.

Update: this analysis includes the 2014 results when a storm affected swim times (some athletes only completed a single lap). As this will influence the comparison I’ve produced some updated charts which can be viewed here. The 2015 race remains slower than usual, but the margin is smaller than implied by the medians and finish time distributions below.

Comparing the median splits from this year’s race with those from past events what’s quite apparent is that across the age groups this year’s race is slower. Unusually this is true in all three disciplines and largely consistent from youngest to oldest age groupers. This almost universal shift in times suggests conditions played a role in performance at the event.

This shift is perhaps better illustrated through the split distributions. Again comparing this year’s race against the past each distribution comes out slower. Removing the pros from the very front of the field will naturally have an affect, but the impact here is much larger than I’d expect. The median finish time falls back by at least 40 minutes on the previous averages.

Following finish times across a number of years shows that this year’s race – at least for those finishing in the top 100 of their age group – was much slower than 2014 and mostly slower than the previous 12 years of racing. I’ll note the picture is incomplete as 2005 results are missing from this data set. Even so this year’s result fall on the slower side for the race.

Looking at the front of the age group field (also the front of the race without the pros) most come out a little slower than the race averages and few come out faster. Where the 2014 race was much faster than usual 2015 sits heavily at the slower end of results. I’d note age group winners tend to fall much closer to the average, it’s really outside the top three where the gap opens, so Kona times aren’t too far off expectations.

A full spreadsheet of results and splits from Ironman Lake Placid 2015 can be accessed on my Google Drive. I’d be interested to know what race conditions were like on the day to help explain the shift in results.

Those who want more detailed statistics for individual age groups can download them at the bottom of this post.

Comparing the medians from a decade of racing in Lake Placid with the aggregated results from all the North American races of 2013 gives some indication of how it stands. Times aren’t too disimilar with the most notable differences coming on the run where Lake Placid trends a little faster. Otherwise in the age group ranks variations are generally small. In contrast professional times at Lake Placid come out a little slower than last year’s North American averages.

Comparing distributions gives a more detailed picture. Much of it is the same, but we can see how times vary across the front of the field. Swim times in Lake Placid are clearly faster. Bike times, while similar in the middle of the pack, are slower at the front of the pack (5-10% of athletes). Conversely run times are faster in the middle of the pack, but by the front of the pack they are largely the same as the other races. Lake Placid has fewer athletes filling out the tail end of the split distributions, but no additional weight at the front of the pack.

The above table contains my predictions for Kona slot allocations based on the start list available on the Ironman Lake Placid website. Actual numbers will likely vary depending on how many actually start. Lake Placid gets 60 slots, 10 more than most other races, otherwise the distribution is typical of Ironman with the biggest group being M40-44.

While there is year-on-year variation in top twenty times, generally the trend within each age group – the difference between first and tenth place – is consistent. It’s impossible to predict what time will make a Kona qualifying performance, but the average finish times (red line) gives a ball park figure. For male age groupers between 25 and 40 this likely means a 9:30-10:00 finish; for women in that age range the spread is wider 10:00-11:00.

There’s a lot more detail and information for individual years and age groups in the PDFs below.

Detailed Age Group Statistics

Age group specific Ironman Lake Placid results analysis. More charts and more detailed timings and splits for top age grouper and Kona qualifiers.

Select an age group to view or download PDF

M18-24
M25-29
M30-34
M35-39
M40-44
M45-49
M50-54
M55-59

F18-24
F25-29
F30-34
F35-39
F40-44
F45-49
F50-54
F55-59

Found these statistic useful? Support the stats, buy me a coffee.

A full analysis of results will follow the race, but may be delayed due to travel.

Comparing the finisher splits from this year’s race with the past it’s clear that while there are differences in individual disciplines the overall results aren’t that far off. If anything despite skews towards slower swim, bike and run distributions the overall finish times appear weighted fractionally faster. The biggest difference of note comes in the swim where the distribution is clearly slower than in previous years.

The median splits give a mixed message. For most divisions the swim times are slower although not to a level that should be considered too significant. Bike times are far more variable: women and younger men mostly slower, older men faster. The differences in median run times vary across the categories. However in the pro ranks median splits are similar or better than in previous years.

This was the second use of the SwimSmart rolling start. Lake Placid’s swim appears to have been slower than expected, while Coeur d’Alene was faster. How much the adjustment to the start influenced this, the dynamics of the bike and ultimately the run is another question. Did the rolling start reduce congestion – and hence the level of drafting – on the bike? Unfortunately without access to precise start times for all the athletes it’s impossible to analyse the impact it had on the race.

The front-of-pack age group is clearly faster this year. So while the distribution of results and their medians may differ, at the sharp end of the race times have improved in the majority of age groups. Generally they remain within the bounds of previous results, but to qualify your performance needed to be better than the average. Again it would be interesting to be able to examine the start times for these athletes and perhaps see if the rolling start offered them a better race.

While the front-of-pack went a little faster there appear to have been more athletes with marginally slower splits than expected. It’s hard to say whether this was due to conditions, a new kind of start or simply the diversity of athletes who’d entered. All three likely played a roll. For would be Kona qualifiers faster times were required across the board.

I’ve uploaded a spreadsheet version of the Ironman Lake Placid 2013 results and splits to Google Drive.

As the distribution of split times above show, like Coeur d’Alene, Ironman Lake Placid is a typical North American Ironman. There are a lot of similarities between the two charts each covering about 10 years of racing at their respective venues. The field size is large and the spread of abilities broad as is reflected by each of the graphs. Peak finishing times for each discipline are similar and tails are weighted towards slower times.

Comparing the median splits across divisions highlights a few potential differences between the two races. Faster swim and run at Lake Placid, but a slightly slower bike which results in the overall times being comparable. It’s hard to determine the impact a course has on results, perhaps more so when such a large and broad field of athletes are present. From a results set perspective both these events offer comparable challenges.

Will the change in swim start have an impact? The limited data offered by the athlete tracker makes it very hard to assess – it’s impossible to determine starting patterns or analyse course congestion. Swim times at Coeur D’Alene this year (under the new swim start) were faster than average, but the differences were small and not particularly significant. At this stage it’s hard to build a clear picture of the impact the rolling start has on the race.

Using the Bib list available on the race website I can estimate the Kona slot allocation (above). Ironman Lake Placid will have 60 slots available with 2,780 athletes registered to start. As usual the male 40-44 age group is the largest and takes the biggest portion of those slots. Averaging the finishing times within age groups allows me to estimate probably qualification times in the charts below.

Over the last 10 years there has been quite a wide variation in finishing times for each placing within most of the age groups. Conditions and competition on the day determine the exact times that will be required; how well prepared an athlete is determines their ability to actually deliver this. As a rough guide men under 45 should probably aim to break 10 hours to be confident of a slot. Women, as ever, will need to win or place on the podium and that’s likely to require a finishing time under 11 hours.

Another fairly standard North American race – a large field with a broad spread of abilities – but competition for the Kona slots remains tight though.