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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.)

0

Kona Pro Slots – Part 1: Reverse Engineering The Assignment Algorithm

Late in 2017 Ironman announced a new system for Kona 2019 Pro Qualifying, moving to a slot-based system almost equal to the agegroup qualifying system. One aspect of the system is “unassigned slots” for some races that will be “assigned according to the ratio of starting Pro Athletes” (as stated in the official “Ironman World Championship Profession Athlete Qualification“). The specific details of the assignment algorithm is considered private by Ironman. Based on the first races and the resulting slot assignments Russell Cox (who is focused on the agegroup side, his data can be found at http://coachcox.co.uk) and I have done our best to reverse engineer this algorithm. This post looks at the available data, potential algorithms and the conclusions that can be drawn. This “algorithm post” will be followed in the next days by one looking at alternative approaches and an “opinion post”.

Data on Races With Unassigned Pro Slots

Here’s a quick look at the Pro races with unassigned slots so far:

  • IM Arizona – 2 MPRO slots (based on 15 female and 32 male Pros starting the race) plus 1m+1f base slot
  • IM Western Australia – 1 Pro slot each (based on 10 female and 13 male Pro starters)  plus 1m+1f base slot
  • IM Mar del Plata (South American Regional Championship) – 2 MPRO slots (based on 15 female and 23 male Pro starters) plus 2m+2f base slots

In addition, Ironman has stated that they use the same algorithm for determining the slot assignment for the agegroups, so we can also cross-reference if the “suspected” algorithm also fits the agegroup slots.

Assignment Algorithms

There are a number of algorithms dealing with a similar problem to slot assignment. Typically, they come from a voting context, where a small number of indivisible “seats” (usually tens to hundreds) has to be assigned based on “votes” (usually thousands). Even though the US voting system is typically majority-based, it also has to deal with a number of “representational” issues. One example is assigning a fair number of seats in the House of Representatives (capped at 435 seats) to the States in relation to their population (total US population based on the 2010 census 308.7 million, with state populations between 37.25 Million and 0.56 Million).

This post looks in detail at the two most widely used approaches, the Hamilton and Jefferson methods using the size of the field as the basis for the slot assignment. Different approaches such as depth of field will be discussed in a follow-up post. When working off the size of the field, it seems best to apply the algorithm to the number of athletes starting the race. The number of registered athletes is often quite different from the number of athletes actually racing, especially on the Pro side. And the number of finishers isn’t finalized for some time during and after the race (especially considering DQs that might be contested for days or weeks), and DNFs often contain an element of bad mechanical luck.

Hamilton Method

The Hamilton Method, also know as Hare-Niemeyer or “Largest Remainder”, is one of the oldest systems of assigning seats. It calculates the number of votes required for a seat by dividing the total number of votes by the number of seats available and then divides the votes a party has received by this number. Another way to put this is that it multiplies the number of available seats with the fraction of votes a party has received. This calculation results in a number with an integer part and a fractional part. According to Wikipedia:

Each party is first allocated a number of seats equal to their integer. This will generally leave some seats unallocated: the parties are then ranked on the basis of the fractional remainders, and the parties with the largest remainders are each allocated one additional seat until all the seats have been allocated.

In the context of Kona slots, the Hamilton Method multiplies the number of slots with the number of starters in a group divided by the total number of starters. The algorithm is probably easier to understand with a few examples.

Hamilton Method on Unassigned Slots

The first suggested algorithm applies the Hamilton method to the number of unassigned slots. (Russ and I believe that this is the “old” method of assigning agegroup slots that was used until the summer of 2018.)

For the Pros, there are two unassigned slots. Using IM Arizona as an example, we get the following calculations:

Arizona Starters Quota
Men 32 1.36 (2* 32/47)
Women 15 0.64 (2* 15/47)
Total 47 2 slots

This means that the men get one slot (the integer part of their ratio), while the second slot would go to the females (as their fractional part of 0.64 is larger than 0.36). As both unassigned slots at IM Arizona went to the men, this is obviously not the algorithm that is used for the 2019 qualifying season.

If you look at this type of calculation, then the larger agegroup will have to be at least three times as large as the smaller one to get both slots (i.e. 75% of the whole Pro field):

HamiltonUnassigned

Obviously this is a very tough requirement, and therefore not very useful to achieve “proportional slots” for the Pros when assigning only two slots. It’s also not fair that the men will always have a smaller fraction of slots than their fraction of the Pro field. It’s a bit of speculation, but I think that Ironman also felt that the system they have been using so far for assigning agegroup slots doesn’t work well for the small number of Pro slots, and that’s why they decided to change their algorithm going into the Kona 2019 qualifying season.

Hamilton Method on All Slots

Another approach would be to apply the Hamilton method on all slots while observing “minimum” slots. Again using Arizona as an example:

Arizona Starters Quota
Men 32 2.72 (4* 32/47)
Women 15 1.28 (4* 15/47)
Total 47 4 slots

This would result in the men getting three slots: two from the integer part of their ratio, and another one because 0.72 is larger than 0.28) – the minimums are already observed in this example. In order for the larger agegroup to get three slots, they would need more than 5/3 of the smaller agegroup or at least 62.5% of the field:

HamiltonAll

While this method gives the observed slot assignment in Arizona, IM has stated that their assignment process is based on the number of unassigned slots and not all slots. It’s also tricky to extend this algorithm to include minimum slots for the bigger number of agegoups for all cases. (For the technically minded: The minimum slots plus the integer parts may already assign more slots than available.) It’s very unlikely that this is the method used by Ironman.

Jefferson Method

The Jefferson Method (also known as D’Hondt method) uses a larger number of operations to determine the slots:

The total votes cast for each party is divided, first by 1, then by 2, then 3, up to s, the total number of seats. The winning entries are the s highest numbers in the whole grid; each party is given as many seats as there are winning entries in its row.

Similar to the Hamilton Method, it can be applied to all slots or only those that are unassigned.

Jefferson Method on Unassigned Slots

Based on the unassigned slots, here’s the resulting Jefferson grid for IM Arizona:

Arizona Starters 1 2
Male 32 32 16
Female 15 15 7.5
Total 47 2 slots

There are two unassigned slots, and as the male starters divided by 2 is larger than the number of females starters, both “winning entries” are from the men and both slots would get assigned to the MPROs. This fits the slot assignment in Arizona.

In general, to get both unassigned slots the larger agegroup needs to have at least twice as many starters as the smaller one, i.e. at least two thirds or 66.7% of the whole Pro field:

JeffersonUnassigned

Jefferson Method on All Slots

As for the Hamilton Method, we can also apply Jefferson Method for all available slots while observing minimums.

Arizona Starters 1 2 3
Male 32 Auto 16 10.34
Female 15 Auto 7.5 5
Total 47 4 slots

Observing minimums is relatively straightforward in the Jefferson Method – instead of starting with the divisor 1, you start with the first divisor that is larger than the minimum. As there is one minimum slot for each, the Divisors start with 2, and again the two unassigned slots would go to the men. For Arizona, this approach would also yield the “observed” 3:1 slots, but as we know that two more WPRO starters would have changed the slots, this can’t be the actual algorithm.

In order to get both slots using this approach, the larger agegroup needs to have at least 60% of the starters:

JeffersonAll

Conclusion .. For Now

Here’s an overview of the different approaches so far:

Algorithms4Slots

Based on the text in the Pro Qualifying Rules (referencing “the ratio of starters to Unassigned Slots”) and the fact that the correct Arizona distribution is yielded by the Jefferson Method on Unassigned slots, I thought that I had identified the method used – that’s why it’s highlighted in the graph shown above. Russ provided further evidence from the age-group side that was supposedly using the same algorithm. (More on the agegroup side of things in Russell’s post on Age Group Kona Slot Allocation.) It also correctly predicts an even split of Pro slots for IM Western Australia. But we have not reached the end of the story yet …

Slot Assignment For Regional Championships

Regional Championships have a different number of slots: While they also have two unassigned slots, they offer two base slots each for the men and women. (IM Arizona has one slot each, plus two unassigned slots.) But as the Pro Qualifying Rules state that the slots are assigned based on “the ratio of starters to Unassigned Slots”), I was confident that there would be even slots in Mar del Plata. Here’s Jefferson Grid for Mar del Plata:

Mar del Plata Starters 1 2
Male 23 23 11.5
Female 15 15 7.5
Total 38 2 slots

However, the actual slot assignment was that both slots went to the men, resulting in the final numbers of four slots for the men and two for the women. So we need another twist to the algorithm.

It seems reasonable that the distribution for the unassigned slots is slightly different when there are two base slots as the resulting “uneven distribution” is 3:1 (or 75%) in case of the normal races and 4:2 (or 66.7%) for the Regional Championships. If the Jefferson Method on Unassigned Slots were used, then the fraction of slots for the larger agegroup would always be lower their fraction of starters.

Jefferson Method On All Minus 2 Slots

As the Jefferson Method has been working remarkably well for the Ironman races with just one base slot each, I was looking for slight tweaks to get the right results for Mar del Plata and some of the variations. (Apparently, one more WPRO or one less MPRO would have changed things for Mar del Plata.) This “tweaking” results in the “Jefferson All Minus 2” method:

Mar del Plata Starters 1 2 3
Male 23 Auto 11.5 7.67
Female 15 Auto 7.5 5
Total 38 4 slots (1 each minimum)

This method is equivalent to the Jefferson Method on Unassigned Slots except for the Regional Championships that offer two base slots each. (In addition, it’s the same for agegroups as there is always a minimum of one slot there.)

For the Regional Championships, there is a minimum of 60% of the field needed in order to get both unassigned slots:

JeffersonMinus

Conclusion

Going forward, the “Jefferson Method on All Minus 2 Slots” will be the algorithm I’ll be using to predict how the slot assignments will look like. New results will either send me back to the drawing board, but hopefully they will strengthen the evidence that this is indeed the algorithm Ironman currently uses.

Based on this algorithm, the “inflection points” for the slot assignments are 66.7% of the Pro starters for “regular” Ironman races with unassigned slots and 60% for the Regional Championships:

CurrentSlotAssignment

0

Ironman Mar del Plata 2018 (South American Championships) – Analyzing Results

IMMarDelPlataCourse Conditions

The swim at IM Mar del Plata was shortened to 1.9k (or 1.2 miles, possibly even a bit shorter) due to low temperatures. Obviously, this makes this years results ineligible for course records and comparing the results a bit tricky. Even though the fastest athletes were quicker than last year’s athletes (and new course records would have been set), it seems that the bike and the run were a bit slower than last year.

Male Race Results

Rank Name Nation Swim Bike Run Time Prize Money
1 Michael Weiss AUT 00:22:24 04:14:19 02:49:11 07:30:23 US$ 20,000
2 Matt Hanson USA 00:22:18 04:28:46 02:45:07 07:39:47 US$ 10,000
3 Mario De Elias ARG 00:22:15 04:28:45 02:47:24 07:42:22 US$ 6,500
4 Jesper Svensson SWE 00:19:09 04:32:04 02:51:49 07:46:39 US$ 4,000
5 Lukas Kraemer GER 00:22:02 04:30:20 02:51:42 07:48:09 US$ 3,000
6 Stefan Schumacher GER 00:26:05 04:17:12 02:58:37 07:48:40 US$ 2,000
7 Andrej Vistica CRO 00:22:28 04:38:05 02:50:42 07:55:53 US$ 1,500
8 Pedro Jose Andujar ESP 00:22:26 04:28:37 03:05:49 08:01:12 US$ 1,250
9 Felipe De Oliveira Manente BRA 00:22:16 04:47:14 02:52:46 08:08:17 US$ 1,000
10 Matt Chrabot USA 00:19:05 04:34:24 03:15:23 08:13:02 US$ 750
11 Felipe Van de Wyngard CHI 00:19:18 04:36:34 03:16:33 08:16:47  
12 Andy Potts USA 00:19:21 04:30:10 03:27:27 08:21:11  
13 Ivan Risti ITA 00:19:42 04:57:01 03:02:01 08:22:31  
14 Diego Vasquez ECU 00:22:40 04:48:01 03:09:32 08:25:35  
15 Marcus Hultgren SWE 00:23:35 04:56:00 03:07:45 08:32:37  
16 Andres Chirinos PER 00:23:29 04:56:53 03:14:02 08:39:25  
17 Nacho Villarruel ESP 00:22:30 05:14:27 03:12:34 08:56:59  
18 Giovanny Marmol Ruiz ECU 00:23:19 05:21:52 03:14:20 09:04:20  
19 Andres Darricau ARG 00:22:12 05:11:51 03:38:08 09:16:53  
  Dylan McNeice NZL 00:19:18 04:48:39   DNF  
  Christian Carletto ARG 00:22:21 09:58:55   DNF  
  Timothy O’Donnell USA 00:19:14     DNF  
  Frank Silvestrin BRA 00:20:51     DNF  

Female Race Results

Kona QualifyingWith 23 men and 15 women starting the race, I initially thought that there would be even Kona slots. But apparently the two “base slots” each for the men and women played a role, and Ironman announced that there would be four slots for the men and two for the women.For the men, Michael Weiss and Jesper Svensson had secured Kona slots in earlier races, and the slots rolled down to sixth place. Matt Hanson, Mario de Elias, Lukas Kraemer and Stefan Schumacher received the four slots. The two female slots went to Sarah Crowley and Susie Cheetham, while Mirinda Carfrae validated her Kona Champion slot.

Rank Name Nation Swim Bike Run Time Prize Money
1 Sarah Crowley AUS 00:22:23 04:51:10 03:01:54 08:20:17 US$ 20,000
2 Susie Cheetham GBR 00:23:18 04:56:01 03:00:02 08:23:40 US$ 10,000
3 Minna Koistinen FIN 00:25:06 04:53:25 03:06:04 08:28:57 US$ 6,500
4 Sarah Piampiano USA 00:25:29 05:00:53 03:00:48 08:32:18 US$ 4,000
5 Asa Lundstroem SWE 00:24:59 04:56:43 03:07:06 08:32:59 US$ 3,000
6 Mirinda Carfrae AUS 00:23:02 04:59:22 03:11:37 08:39:31 US$ 2,000
7 Dede Griesbauer USA 00:22:08 04:55:40 03:33:46 08:56:52 US$ 1,500
8 Bruna Mahn BRA 00:24:07 05:18:48 03:10:04 08:57:26 US$ 1,250
9 Ashley Paulson USA 00:29:03 05:26:00 03:00:16 09:00:22 US$ 1,000
10 Caroline Livesey GBR 00:24:20 05:13:57 03:41:52 09:25:33 US$ 750
11 Barbara Buenahora ARG 00:27:52 05:32:36 03:20:19 09:28:19  
12 Jennie Hansen USA 00:28:15 05:23:25 03:40:46 09:39:37  
13 Erika Simon ARG 00:27:55 05:46:33 03:53:49 10:15:48  
  Kimberley Morrison GBR 00:23:12 04:54:53   DNF  
  Pamela Tastets CHI 00:23:13 05:22:54   DNF  

Kona Qualifying

With 23 men and 15 women starting the race, I initially thought that there would be even Kona slots. But apparently the two “base slots” each for the men and women played a role, and Ironman announced that there would be four slots for the men and two for the women.

For the men, Michael Weiss and Jesper Svensson had secured Kona slots in earlier races, and the slots rolled down to sixth place. Matt Hanson, Mario de Elias, Lukas Kraemer and Stefan Schumacher received the four slots. The two female slots went to Sarah Crowley and Susie Cheetham, while Mirinda Carfrae validated her Kona Champion slot.

3

Ironman Western Australia 2018 – Analyzing Results

IMWA15_Logo

Course Conditions

After last year’s canceled swim, this year saw a “regular” Ironman in Busselton. The race was quick as usual (adjustment of 13:26), but there have been quicker conditions before and overall pretty much in line with the course rating of 16:39. It’s been mainly the bike that has been fast, as evidenced by a new bike record by Cam Wurf (his 4:07 was just a minute quicker than Luke McKenzie’s time from 2015) and Caroline Steffen missing Mareen Hufe’s bike record from 2016 by just three minutes even with a slow flat in the last miles before T2. Caroline’s overall time was good enough for a female course record, with an 8:49 she improved Mel Hauschildt’s time from 2016 by five minutes.

Male Race Results

Rank Name Nation Swim Bike Run Time Diff to exp. Prize Money
1 Terenzo Bozzone NZL 00:48:47 04:12:11 02:51:12 07:56:00 -09:11 US$ 10,000
2 Cameron Wurf AUS 00:49:54 04:07:13 02:56:29 07:57:40 -15:06 US$ 5,000
3 Matt Burton AUS 00:52:16 04:16:20 02:53:40 08:07:18 -33:48 US$ 3,250
4 Luke McKenzie AUS 00:49:49 04:19:28 02:56:01 08:09:43 -07:03 US$ 2,500
5 Patrick Dirksmeier GER 00:49:48 04:23:35 02:55:16 08:13:28 n/a US$ 1,750
6 Lachlan Kerin AUS 00:52:12 04:25:35 03:00:19 08:23:17 -1:02:22 US$ 1,250
7 Blake Kappler AUS 00:52:10 04:28:54 03:09:33 08:35:11 n/a US$ 750
8 Simon Billeau FRA 00:56:51 04:24:57 03:14:40 08:40:55 -06:30 US$ 500
9 Konstantin Bachor GER 00:52:17 04:26:32 03:35:41 08:59:35 19:52
10 Ryan Palazzi AUS 00:52:10 04:46:06 03:50:53 09:35:44 -19:32
James Cunnama ZAF 00:49:46 04:27:09 DNF
Esben Hovgaard DEN 00:52:16 04:25:51 DNF
Nathan Groch NZL 00:52:10 DNF

Female Race Results

Rank Name Nation Swim Bike Run Time Diff to exp. Prize Money
1 Caroline Steffen SUI 00:53:53 04:44:51 03:06:13 08:49:45 -10:09 US$ 10,000
2 Barbara Riveros CHI 00:53:59 04:58:46 03:10:55 09:08:08 n/a US$ 5,000
3 Dimity-Lee Duke AUS 01:00:31 04:59:08 03:10:41 09:15:36 -09:27 US$ 3,250
4 Emily Loughnan AUS 00:55:38 05:07:24 03:13:02 09:20:53 12:13 US$ 2,500
5 Beth McKenzie USA 01:02:47 05:09:41 03:06:02 09:23:27 15:47 US$ 1,750
6 Anna Eberhardt HUN 01:10:21 05:06:40 03:20:40 09:42:59 -10:11 US$ 1,250
7 Jessica Mitchell AUS 01:02:52 05:13:31 03:35:23 09:58:26 07:54 US$ 750
8 Melanie Baumann SUI 01:19:08 05:04:15 03:50:41 10:21:40 n/a US$ 500

Kona Qualifying

IM Western Australia had one base slot for each gender plus two unassigned slots. Based on my understanding of the assignment process and the number of athletes starting the race (8 females and 13 males), one slot should go to each gender. This means that both of the top two finishers in each gender get a Kona slot, and Terenzo Bozzone, Cameron Wurf, Caroline Steffen and Barbara Riveros have secured their Kona 2019 start.

0

Kona 2018 – How the Male Race Unfolded

Here are the results of the top finishers and the athletes that had an influence on the outcome of the MPRO race:

Rank Name Nation Swim Bike Run Time Diff to exp. Prize Money
1 Patrick Lange GER 00:50:37 04:16:04 02:41:31 07:52:39 -08:47 US$ 120,000
2 Bart Aernouts BEL 00:54:07 04:12:25 02:45:41 07:56:41 -18:09 US$ 60,000
3 David McNamee GBR 00:49:31 04:21:18 02:46:03 08:01:09 -20:07 US$ 40,000
4 Timothy O’Donnell USA 00:47:45 04:18:45 02:52:33 08:03:17 -11:45 US$ 22,500
5 Braden Currie NZL 00:49:28 04:17:17 02:53:38 08:04:41 -15:03 US$ 19,000
6 Matthew Russell USA 00:54:02 04:12:58 02:52:56 08:04:45 -18:35 US$ 16,000
7 Joe Skipper GBR 00:50:53 04:15:41 02:54:15 08:05:54 -13:30 US$ 14,000
8 Andy Potts USA 00:49:33 04:18:51 02:56:27 08:09:34 -04:48 US$ 12,500
9 Cameron Wurf AUS 00:50:51 04:09:06 03:06:18 08:10:32 -11:16 US$ 11,000
10 Michael Weiss AUT 00:54:14 04:11:27 03:00:02 08:11:04 -07:22 US$ 10,000
11 Javier Gomez ESP 00:47:46 04:19:44 02:59:24 08:11:41 06:42
13 Andreas Dreitz GER 00:50:56 04:15:17 03:02:50 08:14:02 07:54
28 Lionel Sanders CAN 00:53:59 04:16:58 03:15:26 08:30:34 18:45
40 Andrew Starykowicz USA 00:49:34 04:12:18 03:44:48 08:52:41 28:14
Josh Amberger AUS 00:47:39 04:18:42 DNF
Sebastian Kienle GER 00:50:42 04:20:07 DNF

Here’s the Race Development Graph for these athletes (click for a hi-res version):

Kona 2018 Men

Many thanks to Xavier Marjou for extracting the detailed splits for the Kona race.

Kona Champion: Patrick Lange

On a fast day, Patrick was the fastest athlete and won in convincing fashion:

Kona Patrick

Patrick’s day didn’t start well: Usually he is a front-pack swimmer, but this year he lost contact with the lead group and was more than three minutes behind the lead in T1. Aside from Sebastian Kienle’s win in 2014, the winners have always started the bike in the Top 10 group – this year Patrick started the bike in 19th place and well back from the Top 10. But when his team-mate Andi Dreitz was able to ride up to the front of his bike group, Patrick had a strong ally who was keeping the pace high but even. This worked extremely well for Patrick: They quickly rode up to athletes such as Tim O’Donnell, Braden Currie or Javier Gomez,  faster swimmers that weren’t keeping up with the fast pace at the front. As the race progressed they weren’t losing much time to the leaders, and more and more athletes were not able to follow Andi’s pace. Both Patrick and Andi said after the race that this wasn’t pre-planned, but comments by Braden Currie indicate that Andi was setting exactly the right pace for Patrick and that they were working extremely well together. Patrick’s improved Kona bike times show his progress on the bike: He rode 4:37 in 2016 (when he was third), then 4:29 last year and 4:16 this year. The fast conditions certainly played a role as well, but even his normalized bike times improved from 4:38 via 4:33 to 4:29 this year.

By the time the group reached T2, Patrick was in a great position: Last year he was more than ten minutes back from the leader, this year less than seven, and – probably even more important – he had no one in front of him that could be expected to run well. After T2, he was running his own pace even when Tim O’Donnell or Braden Currie were a few seconds in front of him. After climbing Palani he was slowly but steadily running away from them, taking the lead at about ten miles from Cam Wurf. He continued to extend his gap over the other runners and ended up posting the fastest marathon of the day. A 2:41 was slower than his 2:39s in the last two years, but after a fast bike his final time was 7:52, the first sub-8 time in Kona and a clear win by four minutes.

Patrick Win

Second Place: Bart Aernouts

Bart raced his best Kona race so far, running his way into second place with a strong marathon:

Kona Bart

Bart had finished in the Top 10 a few times before (last year he was 12th), usually he loses some time in the swim and on the bike, but then a good marathon allows him to move up into the money slots. In the last two years he has stepped up his game, allowing him to win Lanzarote, Roth and Hamburg – still aided by a fast run, but he was starting the run in a better position than before after he was able to step it up on the bike, and this year’s Kona race is another indication of his faster bike. Together with a couple of other strong bike riders, he was able to do something very unusual in Kona: Usually if you’re behind the group in Hawi, you’re only losing more time on the way back to Kona, but Bart made up time to the main bike group after the turn. At 60 miles he was still three minutes behind the Dreitz/Lange group and riding in 34th place, but then together with Michael Weiss, Matt Russell and Joe Skipper he was able to ride up and reach the main group about ten miles from T2.

Bart started the run in sixth place but was running a similar pace to Patrick Lange. When Patrick stepped up the pace, he wasn’t quite able to stay with Patrick, but he moved into second place not too far behind. When Patrick continued to run well, Bart lost a total of four minutes but he was able to distance all the other athletes that started the run with him and to hold on to his second-place finish. He also finished under eight hours.

Bart Palani

Third Place: David McNamee

David was quite a surprise to finish third in 2017, after a disappointing 2018 season it was maybe an even bigger surprise to see him run his way into third again:

Kona David

At a cursory glance, David’s 2018 race was pretty similar to last year: After a good swim he lost just over ten minutes to the leaders on the bike, but then a good marathon (last year a second-best 2:45 marathon, this year a third-best 2:46) allowed him to pass most of the athletes that were in front of him in T2.

Looking closer, for most of the race another podium result was even more unlikely this year. As usual David was just two minutes behind after the swim, but then the pace at the front (mainly set by Andrew Starykowicz and Cam Wurf) quickly saw him drop back further. And while last year he was able to ride with Patrick Lange into T2, this year Patrick and his group quickly passed him after less than 20 miles on the bike. After the turn in Hawi, David was riding for a while with the strong bike riders such as Bart or Matt Russell, but then lost contact to them when they increased their effort in order to close the gap to Patrick. David rode the last part of the bike with Sebi Kienle and Lionel Sanders who didn’t have a good day, losing three more minutes to the front in the last 20 miles of the bike. All this meant that David started the run in 19th place (compared to tenth last year), five minutes behind Patrick Lange and many other good runners that had been able to ride up to Patrick.

At the start of the run the focus of the coverage was on the group around Patrick, but David matched their pace on Ali’i Drive and quickly started his climb in the ranks far away from any cameras. By Palani (about 8 miles into the run), he had overtaken Joe Skipper and Matt Russel (see photo below), and he was already in the Top 10. He passed the duo of Tim O’Donnell and Braden Currie in the Energy Lab and had moved up into third place. Patrick and Bart were too far ahead of him and running too well for any further improvements, but he could enjoy the last section before crossing the line, claiming the third podium spot for the second year in a row.

David Run

Fourth and Fifth Place: Tim O’Donnell and Braden Currie

Tim and Braden were racing together for most of the day, in the final miles of the run TO had a bit more left in the tank to claim fourth place in front of Braden:

Kona TO Braden

Both Time (who was in the lead group) and Braden swam faster than Patrick, but when the Dreitz/Lange train got rolling, they were swallowed up after 20 miles (Braden) and 40 miles (TO). Apparently they were content to stay in that group – Andi Dreitz reported after the race that no one was willing to take the lead in the group from him even when he was sitting up. Consequently, both Braden and Tim reached T2 with Patrick.

Tim had the quickest transition and started the run a few seconds in front of Braden and Patrick, but the three of them were quickly running together, closely followed by Bart Aernouts. When Patrick slowly ran away after the climb on Palani, Tim struggled a bit, but Braden never managed to build a lead of more than 15 seconds to Tim and by mile 15 they were back running shoulder to shoulder. It wasn’t until the final hill before town that Tim was able to build a gap to Braden, and he turned himself inside out to claim fourth place. Braden was totally spent as well and crossed the line just a few seconds in front of sixth place Matt Russell.

Both Tim and Braden are probably satisfied with this year’s results, but I’m sure that they will be looking to step it up next year. This year showed that Patrick continues to be the strongest runner in Kona, so they will have to build a gap on the bike. This year they might have been able to capitalize on their lead after the swim, but usually Patrick is a better swimmer and it’s unlikely that such a situation will happen again next year. This leaves the second half of the bike. With the good conditions and the speed of the bike leg this year, maybe they thought that Patrick was biking too hard and wouldn’t be able to run well. Who will be able to put time into Patrick on the bike next year?

TO Braden Run

Sixth and Seventh: Matt Russell and Joe Skipper

Matt and Joe are another pair that had a close fight for most of the day, ending up in sixth and seventh place:

Kona Matt Joe

Both lost some time in the swim, but as they are typically slower swimmers they can be quite happy with staying with the main group (Joe) or losing just three minutes to them (Matt). They were part of a strong bike group that formed in the climb to Hawi and on the way back into town was able to close the gap to the main contenders around Patrick. After the race Joe mentioned that his focus in the early part of the run was to make sure he was running his own pace, giving him a chance not to overheat. Even then, he and Matt were running close to each other, slowly gaining one position after another. On the Queen K they moved into the Top 10, and even the heat in the Energy Lab didn’t slow them down. Matt was able to build a small gap to Joe, finished in sixth place and was almost able to run down Braden Currie. Joe took some more time to enjoy the last mile and finished seventh, a minute behind Matt but well ahead of the next athletes.

Around Tenth Place: Andy Potts, Cameron Wurf, Michael Weiss, Javier Gomez, and Andreas Dreitz

For the athletes around tenth place race day developed in different ways:

Kona Around Top10

Andy Potts (dark red line) was racing his own race for most of the day: He didn’t follow the strong swimmers and started the bike in the second group about two minutes behind. When the big bike group overtook him, he didn’t follow them and for a while settled in the group behind them. When that group split up (part of the chasing the Dreitz/Lange group), he once again didn’t chase and continued to ride his own pace. At the end of the bike he was 18th, taking his time in T2 and again continuing to run at his own pace. It wasn’t until the end of the Energy Lab that he moved into the Top 10, passing some more athletes in the final miles and finishing in eighth place.

The athlete probably getting the most camera time was Cameron Wurf (green line). After a great swim in the main group (three minutes behind Josh after almost seven last year), he quickly worked his way to the front, breaking away from the rest of the field with Josh Amberger and Andrew Starykowicz. Starky was doing most of the work leading up to Hawi, but when he and Josh started to struggle at around 75 miles, Cam was powering away, building a lead into T2 of over two minutes to Starky and about six minutes to Josh who was almost swallowed up by the Dreitz/Lange group. By his own account he did not push the pace but was focused on an even output and putting himself in a position for a good run. After posting a new bike course record of 4:09:06 (another three minutes quicker than last year), he was leading the run similar to last year. But while his lead in 2017 was very short-lived (Lionel Sanders overtook him after less than two miles), this year he was running much better and was able to hold on to his lead for much longer. But by the time he hit Palani at about eight miles, his lead had already shrunk to less than two minutes. When Patrick Lange stormed by him on the Queen K, the two exchanged a quick first bump. Cam was quickly overtaken by more runners and he fell out of the Top 5 in the Energy Lab. He started to run with Javier Gomez who wasn’t able to run at the level he was hoping for before the race. Both were losing a few more spots, and in the end Cam was even able to run away from Javier, claiming ninth place with a marathon time of 3:06, a huge improvement over his 3:19 when he finished 17th last year.

Cam Bike CR

Tenth place was claimed by Michael Weiss (violet line) who had worked hard on the bike to close the gap to the front group with the second-best bike split of the day. Though his run improved this season, he wasn’t able to run sub-3 and to capitalize on his fourth position at the end of the bike. Javier Gomez (blue line) was one of the favorites to at least finish on the podium, but he was pretty much a non-factor this year. After a good swim he was content to ride with the Dreitz/Lange group and then lost some time at the end of the bike with a mechanical. But the bike was probably a bit too hard for him – he was never able to make up any time to the fast runners, and a lot of people expected him to run faster than a 2:59 and claim eleventh place in the end.

Next to Camron Wurf, Andi Dreitz (orange line) got the most camera time on the bike: He was leading the group that included his teammate Patrick Lange for about 90 miles, keeping the pace solid. He admitted after the race that he probably worked harder than what was prudent for a good final result, and he slowly fell back on the run. Nonetheless, he didn’t fall apart and was able to run a solid 3:02 marathon, allowing him to claim 13th place just behind Tim Van Berkel.

Pre-Race Favorites Not Having Their Best Days: Lionel Sanders, Andrew Starykowicz, Josh Amberger, and Sebastian Kienle

There are a few more athletes who affected the race or had high hopes for a good Kona finish:

Kona Others

After his second place last year, Lionel Sanders (red line) was one of the pre-race favorites for this year, but after a rocky summer he didn’t have a good race in Kona. While last year he had a great swim, he was one of the last athletes after the swim this year and was never able to make up solid ground. He was working hard in the first half of the bike and around the turn at Hawi it looked as if he was bridging up to the Dreitz/Lange group. But then he fell back to the chase group and wasn’t able to match their pace when they worked to bridge up. He was in 20th pace in T2, starting the run with David McNamee but also didn’t have a strong run. After a 3:15 run he finished in 28th place.

After the swim Andrew Starykowicz (light blue line) was less than two minutes behind the lead, and as promised before the race he quickly stormed to the front of the race and building a solid lead with Josh Amberger and Cam Wurf. But at around 80 miles he started to struggle and was dropping back from Cameron Wurf. At the end of the bike he was more just two minutes back, but then took some extra time in T2 and then obviously wasn’t running comfortably. In the end, he ran a 3:44 marathon and finished in 40th place.

Josh Amberger (violet line) was also at the front of the race for a good while. This year he had no intention to swim away from everyone else, but he probably dragged a few more athletes than intended into T1: There were eight athletes within 20 seconds of him, including Tim O’Donnell and Javier Gomez. But these athletes quickly started to drop back and when Andrew and Cam worked their way to the front of the race, he was able to ride with them building a nice lead. But then he was forced to drop back after about 80 miles and almost chased down by the Dreitz/Lange group. At the start of the run he lost some more time and then dropped out before climbing Palani.

For Sebastian Kienle (green line) Kona 2018 also ended in a DNF. Race day started well for Sebi: After a great swim he was running through T1 with Cam Wurf and Patrick Lange. But right after starting the bike, he had some technical issues and had to get a new rear wheel – in his own words it was like “winning the lottery and then losing the ticket”. After that blow he never seemed to find back into the race – riding with the chase group and then losing more and more time when he would have needed to step it up. He called it a day shortly after T2 when problems with his Achilles made it impossible to continue.

Sebi

Credit: All photos by Ingo Kutsche

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