Fastest Times & Best Performances 2018 – Part 3: Run

This blog post looks at the best runs in 2018 IM-distance races. (Information about the best swims here and the best bike rides here.)

TOP 10 Run Times of 2018

Rank Name Actual Time Race
Matt Hanson (02:34:39) IM Texas on 2018-04-28
Ivan Tutukin (02:35:19) IM Texas on 2018-04-28
1 Jan Frodeno 02:39:06 IM Germany on 2018-07-08
2 Braden Currie 02:39:59 IM Cairns on 2018-06-10
3 Ivan Tutukin 02:40:00 IM Austria on 2018-07-01
Will Clarke (02:40:43) IM Texas on 2018-04-28
4 Javier Gomez 02:41:02 IM Cairns on 2018-06-10
5 Maxim Kuzmin 02:41:28 IM Barcelona on 2018-10-07
6 Patrick Lange 02:41:31 IM Hawaii on 2018-10-13
7 Cameron Brown 02:41:55 IM New Zealand on 2018-03-03
8 Joe Skipper 02:42:27 Challenge Roth on 2018-07-01
9 Miquel Blanchart Tinto 02:43:26 IM Barcelona on 2018-10-07
10 Jaroslav Kovacic 02:43:27 Challenge Almere on 2018-09-08

TOP 10 Run Times of 2018

Rank Name Actual Time Race
1 Laura Philipp 02:52:00 IM Barcelona on 2018-10-07
2 Sarah True 02:54:58 IM Germany on 2018-07-08
3 Anne Haug 02:55:20 IM Hawaii on 2018-10-13
4 Beth McKenzie 02:55:23 IM Cairns on 2018-06-10
5 Corinne Abraham 02:56:45 IM France on 2018-06-24
6 Daniela Ryf 02:57:05 IM Hawaii on 2018-10-13
Melissa Hauschildt (02:57:07) IM Texas on 2018-04-28
7 Sarah True 02:57:38 IM Hawaii on 2018-10-13
8 Linsey Corbin 02:58:16 IM South Africa on 2018-04-15
9 Sarah Piampiano 02:58:21 IM Brasil on 2018-05-27
Lesley Smith (02:58:47) IM Texas on 2018-04-28
10 Daniela Ryf 02:58:53 IM Germany on 2018-07-08

TOP 10 Run Performances of 2018

Rank Name Normalized Time Actual Time Race
Matt Hanson (02:37:37) (02:34:39) IM Texas on 2018-04-28
Ivan Tutukin (02:38:18) (02:35:19) IM Texas on 2018-04-28
1 Patrick Lange 02:38:39 02:41:31 IM Hawaii on 2018-10-13
2 Ivan Tutukin 02:41:43 02:40:00 IM Austria on 2018-07-01
3 Diego Van Looy 02:42:09 02:49:18 IM Wales on 2018-09-09
4 Jan Frodeno 02:42:21 02:39:06 IM Germany on 2018-07-08
5 Carlos Aznar Gallego 02:42:34 02:49:26 Challenge Madrid on 2018-09-23
6 Bart Aernouts 02:42:45 02:45:41 IM Hawaii on 2018-10-13
7 Braden Currie 02:42:51 02:39:59 IM Cairns on 2018-06-10
8 Miquel Blanchart Tinto 02:43:01 02:45:35 IM Lanzarote on 2018-05-26
9 David McNamee 02:43:07 02:46:03 IM Hawaii on 2018-10-13
Will Clarke (02:43:48) (02:40:43) IM Texas on 2018-04-28
10 Timothy Van Houtem 02:43:55 02:50:50 Challenge Madrid on 2018-09-23

TOP 10 Run Performances of 2018

Rank Name Normalized Time Actual Time Race
1 Anne Haug 02:52:14 02:55:20 IM Hawaii on 2018-10-13
2 Daniela Ryf 02:53:57 02:57:05 IM Hawaii on 2018-10-13
3 Sarah True 02:54:29 02:57:38 IM Hawaii on 2018-10-13
4 Lisa Roberts 02:54:48 03:02:11 Challenge Madrid on 2018-09-23
5 Sarah Piampiano 02:56:15 02:59:26 IM Hawaii on 2018-10-13
6 Alexandra Tondeur 02:56:55 03:04:23 Challenge Madrid on 2018-09-23
7 Laura Philipp 02:56:56 02:52:00 IM Barcelona on 2018-10-07
8 Mirinda Carfrae 02:58:28 03:01:41 IM Hawaii on 2018-10-13
9 Beth McKenzie 02:58:32 02:55:23 IM Cairns on 2018-06-10
10 Sarah True 02:58:33 02:54:58 IM Germany on 2018-07-08

Fastest Times & Best Performances 2018 – Part 2: Bike

This is the next installment of my series looking at the 2018 IM-distance races. (You can find part 1 about the swim here.)

There have been a lot of fast times at IM Texas, among them a sub-4 bike leg by Andrew Starykowicz and lots of sub 4-30 bike rides on the female side, the fastest time was a 4:25 by Jen Annett. But there’s also been a lot of controversy if all of these are legitimate results or were significantly influenced by short courses or lack of draft marshaling. I have decided to include the Texas times but not “count” them for the ranks and thereby also show the fastest times from other races. This approach also suggests that either a lot of athletes had fantastic days at Texas or that something wonky has been going on. While I think that most of the really fast times have to be considered invalid, Starky’s 3:54 bike split (the first sub-4 in an Ironman) is the “most legit” result of the day – there is very little chance to draft while riding at the front of the field and no motos on the course.

TOP 10 Bike Times of 2018

Rank Name Actual Time Race
Andrew Starykowicz (03:54:59) IM Texas on 2018-04-28
Johann Ackermann (04:03:19) IM Texas on 2018-04-28
1 Cameron Wurf 04:05:37 Challenge Roth on 2018-07-01
Matthew Russell (04:05:56) IM Texas on 2018-04-28
2 Marc Duelsen 04:06:21 IM UK on 2018-07-15
3 Cameron Wurf 04:07:13 IM Western Australia on 2018-12-02
Matt Hanson (04:07:27) IM Texas on 2018-04-28
4 Sebastian Kienle 04:07:29 Challenge Roth on 2018-07-01
5 Joe Skipper 04:08:30 IM UK on 2018-07-15
6 Henry Irvine 04:08:35 IM UK on 2018-07-15
7 Cameron Wurf 04:09:06 IM Hawaii on 2018-10-13
8 Bryan McCrystal 04:09:46 Challenge Roth on 2018-07-01
9 Sam Long 04:09:49 IM Boulder on 2018-06-10
10 Chris Leiferman 04:09:55 IM Boulder on 2018-06-10

TOP 10 Bike Times of 2018

Rank Name Actual Time Race
Jen Annett (04:25:10) IM Texas on 2018-04-28
1 Daniela Ryf 04:26:07 IM Hawaii on 2018-10-13
Jodie Robertson (04:27:30) IM Texas on 2018-04-28
Kimberley Morrison (04:27:45) IM Texas on 2018-04-28
2 Lucy Gossage 04:28:40 IM UK on 2018-07-15
Melissa Hauschildt (04:29:55) IM Texas on 2018-04-28
Sara Svensk (04:32:33) IM Texas on 2018-04-28
Michelle Vesterby (04:33:11) IM Texas on 2018-04-28
Meredith Kessler (04:34:32) IM Texas on 2018-04-28
3 Corinne Abraham 04:34:32 IM Sweden on 2018-08-18
Amanda Wendorff (04:34:46) IM Texas on 2018-04-28
Tine Deckers (04:35:01) IM Texas on 2018-04-28
4 Jen Annett 04:35:13 IM Arizona on 2018-11-18
5 Heather Jackson 04:35:25 IM Arizona on 2018-11-18
6 Angela Naeth 04:36:01 IM UK on 2018-07-15
7 Carrie Lester 04:37:42 IM Arizona on 2018-11-18
8 Lucy Charles 04:38:10 IM Hawaii on 2018-10-13
Lauren Brandon (04:38:15) IM Texas on 2018-04-28
9 Corinne Abraham 04:38:16 IM Hawaii on 2018-10-13
10 Camilla Lindholm Borg 04:38:47 IM UK on 2018-07-15

TOP 10 Bike Performances of 2018

Rank Name Normalized Time Actual Time Race
1 Cameron Wurf 04:17:23 04:05:37 Challenge Roth on 2018-07-01
2 Sebastian Kienle 04:19:20 04:07:29 Challenge Roth on 2018-07-01
3 Michael Weiss 04:21:14 04:16:09 IM Austria on 2018-07-01
4 Cameron Wurf 04:21:24 04:07:13 IM Western Australia on 2018-12-02
5 Bryan McCrystal 04:21:44 04:09:46 Challenge Roth on 2018-07-01
6 Cameron Wurf 04:22:18 04:09:06 IM Hawaii on 2018-10-13
7 Cameron Wurf 04:22:24 04:19:44 IM South Africa on 2018-04-15
8 Cameron Wurf 04:22:42 04:14:52 IM Switzerland on 2018-07-29
9 Michael Weiss 04:23:03 04:13:05 IM Cozumel on 2018-11-18
Andrew Starykowicz (04:23:41) (03:54:59) IM Texas on 2018-04-28
10 Andreas Dreitz 04:23:51 04:11:47 Challenge Roth on 2018-07-01

TOP 10 Bike Performances of 2018

Rank Name Normalized Time Actual Time Race
1 Daniela Ryf 04:40:13 04:26:07 IM Hawaii on 2018-10-13
2 Daniela Ryf 04:43:24 04:40:55 IM Germany on 2018-07-08
3 Corinne Abraham 04:52:49 04:34:32 IM Sweden on 2018-08-18
4 Lucy Charles 04:52:54 04:38:10 IM Hawaii on 2018-10-13
5 Corinne Abraham 04:53:00 04:38:16 IM Hawaii on 2018-10-13
6 Mareen Hufe 04:53:08 04:47:26 IM Austria on 2018-07-01
7 Lisa Huetthaler 04:53:12 04:47:30 IM Austria on 2018-07-01
8 Angela Naeth 04:54:35 04:43:25 IM Cozumel on 2018-11-18
9 Jen Annett 04:55:13 04:35:13 IM Arizona on 2018-11-18
10 Daniela Saemmler 04:55:14 04:41:44 Challenge Roth on 2018-07-01

Fastest Times & Best Performances 2018 – Part 1: Swim

This is the first part of a series of posts looking back at 2018 long-distance racing. It lists the best swims using two different measures:

  • Time: This is the actual time an athlete took to complete the leg.
  • Performance: Because of course conditions, comparing the actual time is not always meaningful. As part of my analysis I calculate a “Normalized Time” that as much as possible takes out the impact of the course into the time.

TOP 10 Swim Times of 2018

Rank Name Actual Time Race
1 Jesper Svensson 00:43:47 IM Brasil on 2018-05-27
2 Marcus Vinicius Fernandes 00:43:50 IM Brasil on 2018-05-27
3 Luiz Francisco Paiva Ferreira 00:43:51 IM Brasil on 2018-05-27
4 Ivan Rana 00:43:59 IM Cozumel on 2018-11-18
5 Lukasz Wojt 00:44:31 IM Austria on 2018-07-01
6 Igor Amorelli 00:44:45 IM Brasil on 2018-05-27
7 Lukasz Wojt 00:45:29 IM Italy on 2018-09-22
8 Guillem Rojas 00:45:34 IM Barcelona on 2018-10-07
9 Mark Bowstead 00:45:47 IM Australia on 2018-05-06
10 Dylan McNeice 00:45:53 IM New Zealand on 2018-03-03

TOP 10 Swim Times of 2018

Rank Name Actual Time Race
1 Lucy Charles 00:46:48 Challenge Roth on 2018-07-01
2 Haley Chura 00:47:29 IM Brasil on 2018-05-27
3 Lucy Charles 00:47:32 IM South Africa on 2018-04-15
4 Lucy Charles 00:48:14 IM Hawaii on 2018-10-13
Lauren Brandon (00:48:19) IM Texas on 2018-04-28
5 Lauren Brandon 00:48:39 IM Cairns on 2018-06-10
6 Kelsey Withrow 00:48:41 IM Cozumel on 2018-11-18
7 Kelsey Withrow 00:48:42 IM Australia on 2018-05-06
8 Teresa Adam 00:49:32 IM New Zealand on 2018-03-03
9 Teresa Adam 00:50:41 IM Cairns on 2018-06-10
10 Lauren Brandon 00:50:41 IM Mont Tremblant on 2018-08-19

TOP 10 Swim Performances of 2018

Rank Name Normalized Time Actual Time Race
1 Josh Amberger 00:45:19 00:46:53 IM Germany on 2018-07-08
2 Lukasz Wojt 00:45:25 00:44:31 IM Austria on 2018-07-01
3 Guillem Rojas 00:46:04 00:45:34 IM Barcelona on 2018-10-07
4 Lukasz Wojt 00:46:05 00:45:29 IM Italy on 2018-09-22
5 Marko Albert 00:46:09 00:46:12 IM Tallinn on 2018-08-04
6 Carlos Lopez Diaz 00:46:12 00:46:10 Challenge Madrid on 2018-09-23
7 Pablo Dapena Gonzalez 00:46:15 00:46:13 Challenge Madrid on 2018-09-23
8 Josh Amberger 00:46:32 00:46:24 IM South Africa on 2018-04-15
9 Jesper Svensson 00:46:36 00:43:47 IM Brasil on 2018-05-27
10 Marcus Vinicius Fernandes 00:46:40 00:43:50 IM Brasil on 2018-05-27

TOP 10 Swim Performances of 2018

Rank Name Normalized Time Actual Time Race
Lauren Brandon (00:47:26) (00:48:19) IM Texas on 2018-04-28
1 Lucy Charles 00:47:34 00:48:14 IM Hawaii on 2018-10-13
2 Lucy Charles 00:47:40 00:47:32 IM South Africa on 2018-04-15
3 Lucy Charles 00:48:24 00:46:48 Challenge Roth on 2018-07-01
4 Lauren Brandon 00:48:47 00:50:41 IM Mont Tremblant on 2018-08-19
5 Lauren Brandon 00:48:59 00:48:39 IM Cairns on 2018-06-10
6 Haley Chura 00:49:01 00:52:05 IM Netherlands on 2018-08-05
7 Haley Chura 00:50:33 00:47:29 IM Brasil on 2018-05-27
8 Teresa Adam 00:50:40 00:49:32 IM New Zealand on 2018-03-03
9 Teresa Adam 00:51:02 00:50:41 IM Cairns on 2018-06-10
10 Teresa Adam 00:51:17 00:52:00 IM Hawaii on 2018-10-13

Kona Pro Slots – Part 3: Opinion

In this series of blog posts on Kona Pro Slots, I’ve looked at Reverse Engineering The Assignment Algorithm and ideas for Different Approaches – both of these posts have been close to the facts. This post contains my views on the current algorithm, a bit of speculation on what Ironman was looking for and some bigger and smaller changes going forward. The following thoughts are my opinion, and I would love this to be a starting point for a broader discussion of how to get the best, most interesting races in Kona as possible.

On Its Own, the Assignment Algorithm Is Fair

I’ve had a detailed look at potential algorithms for assigning slots in Part 1 of this series. Here’s the graph summarizing the different approaches:

Algorithms4Slots

The algorithm that makes it hardest for the larger group (usually the men) to get both slots is “Hamilton Unassigned” – but it’s quite unfair as their share of slots would always be smaller than their share of starters. The next-best algorithm for the smaller group is the “Jefferson Unassigned”, and that’s the algorithm that Ironman has in all likelihood chosen to use (that’s why it’s highlighted).

I also find it reasonable that Ironman slightly tweaks the algorithm for Regional Championships as they have more fixed slots: If they used the unaltered Jefferson Unassigned, again you’d end up with a situation where the larger group’s share of slots is always smaller than their share of slots. The tweak is not very elegant, but the resulting slot assignment looks quite reasonable to me.

But Only If You Accept Proportional Assignment Based on Starters

Even if the slot assignment algorithm is fair, this doesn’t mean I like the overall system as it’s solely based on the number of starters. At first, this sounds reasonable (after all it has been used for assigning age group slots), but there should be other factors in order to determine the athletes that will likely have the biggest impact on the Kona races. (I have highlighted some of these factors such as Strength of Field or Race Performance in Part 2 of this series.)

I want to note that this discussion is a slippery slope as it can quickly deteriorate into an “X doesn’t deserve to be in Kona” type of argument that isn’t fair to anyone involved (or useful). For example, I think that Carrie Lester’s 8:44 in Arizona should have been good enough for a Kona slot – but that would have to come at the cost of TJ Tollakson who has been working hard to overcome his back problems and it’s great to see him qualify once again for Kona. But it’s hard to avoid this “men vs. women” discussion – after all the women will only get more slots if they “take” them from the men which is hardly the best way of developing our sport.

So Why Is Ironman Keeping the Algorithm Secret?

One of the criticisms leveled at Ironman is that they haven’t made much information about their slot assignment algorithm available – and the quick conclusion by some was that “Ironman must be hiding something”, even going so far as suggesting that there isn’t a real algorithm and that the unassigned slots “always go to the men”. As stated above, that is not the actual procedure (see Western Australia for a counter-example), but not being transparent has led to some confusion and frustration.

To be honest, I’m not really sure why Ironman isn’t making more information available. I can only guess that they want to avoid discussion at race sites about the slots (both for the Pros and the agegroupers) and that they want to be able to change some details whenever they see the need for it. But even in the absence of “official details”, I don’t really see “sinister motives” on Ironman’s side in keeping things private.

Has Ironman Just Been Unlucky With Arizona and Mar del Plata?

On one hand, Arizona and Mar del Plata have been very close to ending up with equal slots. (For Arizona, things shifted between the race meeting and race day, for Mar del Plata just one more woman or one less male would have made a difference.) Based on last year’s numbers Ironman probably expected equal slots: Arizona was 25–22 and Mar del Plata was 16-13, with would have clearly been equal slots.

I think that Ironman was hoping for a more equal distribution of Pro slots than in the past – maybe not providing equal slots but at least a lot closer than under the KPR system. And to a certain degree, that is what’s going to happen: Even if all the currently unassigned slots in the 2019 races go to the men, we will have at least the same number of WPROs with Kona slots as in the past.

On the other hand, the small number of starters pretty much assures that there will be some more “weirdness” in assigning the slots, similar to what we’ve seen in Arizona and Mar del Plata. This is a result of the small number of slots and the small number of racers. The “random” decisions of just one or two athletes can influence how the slots will be assigned, while that is extremely unlikely with around 50 agegroup slots and typically 2000 racers or the even larger numbers of seats and votes in context of elections.

Equal Slots Is the Cleanest Solution to This Conundrum

So what should be done moving forward? For a while there has been a push for equal slots for the Pro men and women in Kona, and I continue to believe that this is the cleanest solution on how to assign slots: When giving men and women the same number of slots we won’t have to debate the merits of this or another slot assignment algorithm. ‘Nuff said!

But I Also Have a Few More Realistic Suggestions

As “Equal Slots” would be a pretty big change for Ironman, I’m not holding my breath for this to happen in the next few seasons. (Though as Rachel Joyce has put it, I’m sure that “equality will prevail eventually”.) While I’m also not a fan of drastic changes without giving the current system to play out a bit more, here are a few ideas for minor “tweaks” intended to make the current system work a bit more smoothly:

  • Announce Slot Distribution at the Pro Meeting
    In order to minimize surprises on race day, the slot distribution could be fixed on the number of Pros that sign in at the Pro Meeting. This would also give Ironman a chance to announce the slots at the Pro Meeting.
    The difference between the number of athletes at the Pro Meeting and on race morning is almost always relatively small – and I’m not sure why someone getting sick in the last few days should have an impact on how the slots fall. I also don’t think that this leaves too much room for manipulation – when someone shows up to the Pro Meeting, the extra burden of putting their toes in the water on race morning is pretty small.
  • Fix the Total Number of Male and Female Slots for the Season
    Looking at the total number of Pro starters in the 2018 qualifying season (September 2017 to August 2018), I get 416 female and 772 male Pro starts. Applying those numbers to 20 unassigned slots (ten for the Regionals and another ten for Ironman races with unassigned slots) would lead to 7 female and 13 male Pro slots (pretty much regardless of the actual algorithm). When announcing the races for the upcoming season, Ironman could assign the slots to the races on the calendar, making it clear long before the races how many slots there will be for each gender.

While minimizing the surprises, implementing either of these suggestions would keep the base “proportional” system in place.  I hope that Ironman will at least discuss tweaking the system for 2020, maybe after we’ve had some more experience with the current system in the first half of 2019.

Kona Pro Slots – Part 2: Different Approaches

The current algorithm applied by Ironman to allocate Pro slots between and women (see my previous post “Kona Pro Slots – Part 1: Reverse Engineering The Assignment Algorithm“) is working based on the size of the field. This post describes two different approaches: Strength of the Field and Performance. I’ll discuss the Pros and Cons of these approaches in my next post.

Strength of Field

Measuring Strength of the Field

Measuring the quality of a field isn’t straightforward: I have tried a number of approaches that either didn’t work or were too complicated before settling on a relatively simple “points” system when showing in my seedings how good a field is. (The details can be found in my March 2017 “Strength of Field” post.) The base approach is to look at which starters have been racing Kona in the two previous years. As we’re looking for a system to determine how many athletes in each category to qualify for Kona, this seems to be a reasonable approach for assigning slots.

Basically, the points system determines how many “Kona athletes” are in the current race. Here’s how the system works:

  • 1 point for each athlete that has raced the previous Kona race (so for the current 2019 qualifying season athletes that have raced Kona 2018),
  • 0.5 points for each athlete that hasn’t raced the previous Kona race but the year before (athletes that haven’t raced Kona 2018 but Kona 2017),
  • 1 bonus point for each athlete that has won Kona in the past,
  • 0.5 bonus points for each athlete that has finished on the podium in Kona before

If you apply these numbers to the male and female fields, you get two numbers that you can then use as the base for the Jefferson Method as determined in the previous post. Please note that this system still favors the male athletes (as the male Pro field in Kona has been larger and there are therefore more male Kona Pros that can contribute points).

Example 1: IM Arizona

First, let’s have a look at the female field in Arizona:

  • Kona 2018 athletes: Annett, Jackson, Kessler, Lester, Robertson, Smith (6 points)
  • No Kona 2017 athletes that haven’t also raced in 2018 (no points)
  • No previous Kona winners (no points)
  • Kona podium: Jackson (0.5 bonus points)

This means the female fields “scores” 6.5 points. Next up, the men’s field:

  • Kona 2018 athletes: Dreitz, Plese, Skipper (3 points)
  • Kona 2017: Llanos (0.5 points)
  • No previous Kona winners (no points)
  • Kona podium: Llanos (0.5 bonus points)

The men’s field has a strength of 4 points. Running these number through the Jefferson Method (for unassigned slots), we get the following grid:

Arizona Strength 1 2
Male 4 4 2
Female 6.5 6.5 3.25
Total   2 slots

The result would have been an even split of slots.

Example 2: IM Mar del Plata

Here’s the strength of the female Pro field in Mar del Plata:

  • Kona 2018 athletes: Carfrae, Cheetham, Crowley, Lundstroem, Piampiano (5 points)
  • No Kona 2017 athletes that haven’t also raced in 2018 (no points)
  • Previous Kona winner: Carfrae (1 bonus point)
  • Kona podium: Carfrae, Crowley (1 bonus point)

This means the female fields “scores” 7 points. The men’s field:

  • Kona 2018 athletes: Chrabot, Hanson, O’Donnell, Potts, Weiss (5 points)
  • No additional Kona 2017 athletes (no points)
  • No previous Kona winners (no points)
  • Kona podium: O’Donnell (0.5 bonus points)

The men’s field has a strength of 5.5 points. Here’s the resulting grid for these numbers, taking into account the extra slots:

Mar del Plata Strength 1 2 3
Male 5.5 Auto 2.75 1.5
Female 7 Auto 3.5 2.33
Total   4 slots (1 each minimum)

Once again, we’d get even slots.

Performance

On the “If We Were Riding” podcast, Kelly O’Mara suggested to not “fix” the number of slot assignment before the race but to use the performance on race day. While her full blown version of “qualifying standards” is much more complicated and probably not workable given the differences between courses and conditions on race day, here is a suggestion on how to compare the performances between the male and female Pros in a given race.

Comparing Finishing Times

When looking at the best finishing times between the men and women, a conversion factor can be calculated. As a start, the average Top 10 finishing time of the male Pros in Kona this year was 8:04:02, the average Top 10 time for the females was 8:48:05. Dividing the female average by the male average, we get a factor of 91.66% that can be used to convert the female times into male equivalents and thus compare their performance. (Obviously, this factor will need to take more races into account if this approach is going to be used, but it seems to reasonable to calculate a factor after each Kona race and then use it for the remainder of the qualifying season.)

Example 1: IM Arizona

When applying the conversion factor to the female times, we get the following order of performances:

  1. 7:55:59 Heather Jackson (8:39:18 * 91.66%)
  2. 8:00:29 Carrie Lester
  3. 8:04:24 Eneko Llanos (no change to his finish time)
  4. 8:07:09 Jen Annett
  5. 8:08:41 Clemente Alonso

As we have four slots available, three would go to the women (Heather, Carrie and Jen) and only one to the men. (This would also “observe” the minimum of one slot for each gender.)

Example 2: IM Western Australia

For Western Australia we get the following performance order:

  1. 7:56:00 Terenzo Bozzone
  2. 7:57:40 Cameron Wurf
  3. 8:05:34 Caroline Steffen
  4. 8:07:18 Matt Burton
  5. 8:09:43 Luke McKenzie

IM WA also has four slots, in this case they would be assigned to three men and one woman.

Example 3: IM Mar del Plata

The conversion for Mar del Plata gives the following ranking:

  1. 7:30:23 Michael Weiss (already qualified)
  2. 7:38:33 Sarah Crowley
  3. 7:39:47 Matt Hanson
  4. 7:41:39 Susie Cheetham
  5. 7:42:22 Mario de Elias
  6. 7:46:30 Minna Koistinen
  7. 7:46:39 Jesper Svensson (already qualified)
  8. 7:48:09 Lukas Kraemer

With six available slots, we’d have an even split with three male and three female slots. (In addition, Mirinda Carfrae validates her Kona Winner slot.)

Powered by WordPress. Designed by Woo Themes

By continuing to use the site, you agree to the use of cookies. more information

The cookie settings on this website are set to "allow cookies" to give you the best browsing experience possible. If you continue to use this website without changing your cookie settings or you click "Accept" below then you are consenting to this.

Close