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Rest Duration and Race Performance – a closer look at IM Hawaii

This post continues the analysis of the data from the recent Ironman Hawaii race. One of the consequences of the new KPR qualifying scheme was that every Pro who wants to race Kona has to race at least one Ironman in the year before Kona. Some athletes were even forced to race more often than they might have liked in order to get enough KPR points to be ranked in the TOP 50 (males) or TOP 30 (female). One of the concerns this raises is if this is going to have an impact on the Kona race. Or in other words, the KPR system probably did a good job of picking “the best athletes” for Kona, but did the qualified athletes race in top form or were they still tired from recent races?

What I’m analyzing is the impact of the “rest duration” to race results. “Rest duration” is the time difference in months between Kona and the race the athlete qualified for Kona (i.e. the the last Ironman race the athlete did). One caveat: The athlete may have raced other, shorter races, for example Olympic distance or even 70.3 races such as the 70.3 world championships in Las Vegas four weeks before Kona. However, these races probably didn’t add significant fatigue for the Kona racers – most of them focused on Kona and probably raced accordingly.

Data: Impact of Rest Duration on Finishing

Here is a table that looks at the number of athletes who finished or DNF’d broken down by Rest Duration:

Rest Duration
in Months
DNFs Finishers DNF Rate
11   7 0%
7   1 0%
6   3 0%
5 4 10 29%
4 4 2 67%
3 12 23 34%
2 3 7 30%
1   1 0%
All 23 54 30%

There are a few differences between DNFs and Finisher, but they are not very easy to spot:

  • All Athletes with a large Rest Duration (6 months and longer) have finished the race.
    My interpretation is that these athletes didn’t carry any fatigue from their IM any more. Some of them had injuries (Pete Jacobs comes to mind), but they were able to take care of them before starting the race. If they had some serious issues that did not resolve in time, then they didn’t even attempt to start (e.g. Terenzo Bozzone).
  • On average, finishers have one more month of rest than athletes that DNF’d.
    Largely as a consequence of the first point, Finishers have an average Rest Duration of 4.4 months whereas DNFs have only 3.4 months.
  • For shorter Rest Durations, the DNF rate is pretty stable around 30%.
    The exception is 4 months, but the number of athletes is probably too small for it to be significant. If there is any difference at all, slightly longer Rest Periods (3 and 4 months) have a higher DNF rate than really short ones (2 months and shorter). I would think that athletes with short rest periods race a bit more cautiously.

When trying to decide when to qualify (or to validate a Kona spot), this could mean to race as early as possible, but the difference is probably too small to base a decision on this data.

Data: Impact of Rest Duration on Race Performance

There is some more interesting information when including the race performance into the analysis. I did this by comparing the “expected race time” (taking the athletes rating prior to Kona and conditions on race day into account) to the actual race time. This data is shown in the following graph:


On the x axis, it shows the Rest Duration in months (shorter rest before the race to the right), on the y axis it shows the difference between the expected and actual performance of the athlete (in minutes, positive values mean that the athlete was quicker than expected). The thin blue line shows the actual data, the thicker orange line an approximation.

The data indicates that athletes with short Rest Durations (3 months and less) are not racing all that well (the data point for one month is just one athlete, so the graph may not even rise but continue to drop further). If an athlete wants to race well in Kona, their Rest Durations should be longer than three months. The data is not totally conclusive on what the “best” Rest Duration is (actual data pointing to 7 or 4 months, but the approximation indicates some value in the middle of this range). The data from this year does not show any positive or negative impact of really long rest periods (11 months).

Conclusions: What does this mean for pros and WTC?

For an athlete that wants to perform well in Kona, this means that they should seriously consider skipping the big July races (IM Austria, IM Switzerland, IM Germany, IM Lake Placid) and should avoid the August races (most notably, IM Canada). Interestingly, WTC has scheduled their new races exactly in the “not so good for Kona” timeframe: IM New York and IM Quebec have both race dates in August.

Instead of these summer races, athletes should try to qualify in earlier races, say IM New Zealand (March), IM South Africa (April), IM St. George, IM Texas or IM Lanzarote (May), maybe as late as  IM Coeur d’Arlene (June).

This analysis also  raises questions about WTC’s tries of establishing regional “Championship races” without hurting Kona. Other than the new race in Australia (IM Melbourne in March), these races are in July (IM Germany) and August (IM New York). It almost seems that pro athletes have to choose between Kona and one of the regional championships as it seems very tricky to race well in the regional championship and in Kona. This is certainly not in the interest of WTC and the people organizing the regional championship races. However, WTC may not have too much of a choice in this matter: An ideal date for a great Kona race (say May) is too early for the bulk of the athletes starting in these races that are not focused on qualifying for Kona.

One thing that seems obvious to me: WTC should move the cutoff dates for Kona qualifying by at least one month. Currently, these cutoffs are at the end of July (40 males, 25 females) and end of August (10 additional males, 5 additional females). In order not to devalue the July races too much, they should decide all spots at the end of July (or change the relations between the first second batches to 25-15 for May qualifiers and the same number for July qualifiers). Races after these dates would then count for the following year’s Kona race (so points from IM Canada 2012 should count for Kona 2013). This avoids some hectic racing in August: People that had to race in August in order to qualify, didn’t do too well in Kona – they were just too tired to race really well (think Mary-Beth Ellis).

I’m not sure if WTC is considering changes to the KPR. As far as I know, the system remains pretty much unchanged for 2012, but maybe we’ll see some adjustments for 2013. Other than some general grumbling about the KPR, I haven’t seen many specific suggestions how the KPR could be made better. Hopefully, WTC is going to solicit feedback on improving the KPR.

How many people have completed 12 IMs and will now be able to get a slot for Ironman Hawaii? (Part 2)

My last post triggered some discussion on the TriTalk forum’s thread on the WTC Announcement.

A few people came up with other ideas for “guesstimating” the number of eligible athletes. One example was user Stengun who wrote the following:

I recently attended the Ironman Lanzarote "Special Achievement" Ceremony. For those of you who don’t know: They give a special award/medal for anyone that’s completed 5 Lanzas or more. I did go this year, and felt quite excited by it. However I left feeling a bit average and not very special by end. This was because of the numbers of people receiving this award. I assumed it would be me and hand full of others. But it was not. There must have been 40+ people all receiving this, and not just people with 5 Lanza finishes, there where plenty with 10+ and the one guy had 18! Remember this is just Lanza finishes. So almost everyone would most likely have other finishes at other event (as do I). I understand some events like Lanza have a very loyal following and that could skew the numbers a bit. But I’d estimate, there’s an equally loyal following at the other big and long standing events.

With similar qualifications as in my first post, this is certainly something I can look into! Here then is a list of the long standing Ironmen and the number of athletes who have raced in all six years that I have age group data for:

    • IM Canada: 50 athletes
    • IM Coeur d’Alene: 17 athletes
    • IM Lake Placid: 28 athletes
    • IM Wisconsin: 26 athletes
    • IM Lanzarote: 28 athletes (This number seems to be consistent with Stengun’s observation.)
    • IM France: 8 athletes
    • IM UK: 23 athletes
    • IM Switzerland: 4 athletes
    • IM Austria: 17 athletes
    • IM Western Australia: 23 athletes

This is a total of 224 athletes from 10 races. Trying to remove Kona qualifiers (again, using the crude 10-Hour-barrier as outlined in the first post) reduces this number to 207.

Then there are at least 6 more “long-standing” IMs (Arizona, Florida, New Zealand, South Africa, Australia, Germany, maybe Louisville) – which would give a total number of 331 athletes that have consistently race their “home race” in the last six years without having raced Kona.

It is a bit hard to estimate how many of these have completed 12 or more races overall. I’m guessing that not all of these would have raced the required number of “other” races (either at home or in another race), but that this number can be balanced by those athletes who have missed a year here or there but have done other races a few more times.

So this would give a slightly higher number than when estimating by “at least 8 races”. (That number was 273 which is in the same ballpark.)  If I had to give a number, I’d put it at around 300. But based on all this analysis, I’m very certain that the number can’t be close to “a thousand” that people have been throwing around.

How many people have completed 12 IMs and will now be able to get a slot for Ironman Hawaii?

Changes to the lottery system

The guys at IMTalk scored a real scoop this week. The day after the Kona race they interviewed Andrew Messick, the new CEO of WTC, the company who owns the Ironman brand and runs most of the Ironman races. In the interview (listen to the full interview here, the announcement regarding the lottery starts at 22:50) he announced that

“We’re changing how our lottery works. For athletes who have done 12 or more Ironman over their career – and who are still racing – we are going to guarantee that they have a chance to race Kona.

Andrew also announced that when you’ve entered the lottery and get not picked, you will get an extra chance when you enter the next lottery. In my opinion, these are very smart moves on their part, again encouraging people to continue to participate in Ironman races (or the lottery) and choosing one of their races instead of a Challenge or Rev3 race. (On a more personal note, this may give me a chance to get to Kona, but I would still have to finish 10 more IMs.)

Analyzing the data

This change in the lottery system prompted Thomas Peoples to send me an email with the following issue:

I’m interested to see how many people have completed more than 12 IM races for this new lottery system.

I don’t really have all the required data to do a proper analysis:

  • As I focused on PROs, I don’t have the agegroup results for all races (for about probably about 1/3 of races I only have PRO results).
  • My results only go back to 2005.
  • It is tricky to properly match results from different races, for example there are differences in spelling or the handling of special characters (technically “synonyms”). (Are Andi Boecherer and Andreas BÖCHERER the same athletes?) Especially on the female side, athletes change their names after marrying (two notable examples are Bella Comerford/Bayliss and Marilyn McDonald/MacDonald).
  • One other issue is “homonyms” – different athletes with the same name. For example, there are probably more than one “Peter Brown” or “David Smith”.

But even with these caveats, I did some analysis. I have 55 athletes that with my limited data have 12 or more races. When I look for athletes that have 8 or more races (as I only have age group data for 2/3 of the races), I get 339 athletes.

This number has to be reduced by subtracting the athletes that have already participated in Kona. As I currently don’t have age group results for Kona, I have resorted to looking at the finishing time for the athletes and remove the “fast” athletes. If I define “fast” as having at least one result of under 10 hours (probably allowing them to be in the mix for a Kona slot) and remove all of these athletes, I’m still coming up with 273 athletes.

With all the uncertainties mentioned, my guess is that the number of athletes eligible for an automatic slot is about 250.

How is WTC going to handle this?

It will be interesting to see how WTC is going to handle these issues. At least based on their public facing data, they do not have a “unified customer view” across all their different races. (Although it would be quite valuable information for them – how many “repeat customers” do they have?) Therefore, I assume that when you try to claim your “12 IM slot” you will have to submit a list of your IMs which they would manually cross-check against results in paper, HTML, PDF or maybe even in a database. They would also need to have a way to make sure that these athletes have not been on the IM Hawaii start list (or finished?). One can only hope that WTC has the necessary data available and that they diligently check the claims before assigning a “12 IM slot”. I would be very interested in a “look behind the scenes” on how they plan to handle this issue.

IM Hawaii 2011 – Analyzing Results

What an amazing race! I guess everyone interested in the Kona results has heard of Crowie’s and Chrissie’s great wins by now – so I’ll keep my regular look at race results pretty short.

Race Conditions

The conditions in Hawaii were the best that I have seen in the last years (my data goes back to 2005). The race adjustment was calculated at 2:09, resulting in a new course rating of –0:25.

Male Results

The common wisdom was that a fast race would not be good for Crowie who usually finishes in about 8:15. This might have been correct for the Crowie we had seen in the last years, but this year he hit one out of the park:

Rank Name Nation Actual Time Expected Time
1 Craig Alexander AUS 08:03:56 08:19:51
2 Pete Jacobs AUS 08:09:11 08:39:47
3 Andreas Raelert GER 08:11:07 08:11:58
4 Dirk Bockel LUX 08:12:58 08:28:23
5 Timo Bracht GER 08:20:12 08:22:34
6 Mike Aigroz SWI 08:21:07 08:49:53
7 Raynard Tissink SAF 08:22:15 08:33:27
8 Andi Boecherer GER 08:23:19 08:44:15
9 Luke McKenzie AUS 08:25:42 08:42:27
10 Faris Al-Sultan GER 08:27:18 08:25:04
11 Tom Lowe GBR 08:29:02 08:28:46
12 Daniel Fontana ITA 08:31:20 08:33:12
13 Marko Albert EST 08:35:18 08:31:47
14 Rasmus Henning DNK 08:35:53 08:23:16
15 Cyril Viennot FRA 08:37:00 08:44:12
16 Courtney Ogden AUS 08:38:11 08:44:54
17 Andy Potts USA 08:38:36 08:29:45
18 Michael Goehner GER 08:39:38 08:34:09
19 Jozsef Major HUN 08:39:52 08:47:28
20 Joe Gambles AUS 08:40:40 08:38:41
21 Michael Lovato USA 08:42:39 08:46:51
22 Maik Twelsiek GER 08:43:03 08:36:52
23 Matthew Russell USA 08:43:51 09:16:35
24 Ian Mikelson USA 08:48:40 09:09:31
25 Jan Raphael GER 08:48:44 08:35:40
26 Mike Schifferle SWI 08:49:01 09:11:42
27 Matty Reed USA 08:50:00 08:42:57
28 Axel Zeebroek BEL 08:58:13 08:44:29
29 Chris Lieto USA 09:10:26 08:44:02
30 Petr Vabrousek CZE 09:13:42 08:55:47
31 Georg Potrebitsch GER 09:15:08 08:31:03
32 Balazs Csoke HUN 09:16:44 09:04:07
33 Sergio Marques PRT 09:18:26 09:02:45
34 Hiroyuki Nishiuchi JPN 09:26:42 09:24:52
35 Mike Neill CAN 09:34:46 09:01:51

I find it very interesting that most of the TOP 11 (with the exceptions of Andreas Raelert and Faris) raced faster than expected (even considering the good race conditions), sometimes even by quite a large margin:

  • Crowie by 16 minutes
  • Pete Jacobs by 30 minutes
  • Dirk Bockel  by 16 minutes
  • Mike Aigroz by 28 minutes
  • Raynard Tissink by 11 minutes (even coming into the race with a recent virus infection)
  • Andi Boecherer by 21 minutes
  • Luke McKenzie by 17 minutes

These are also the names of the people who will probably think that they had a great race. Others were closer to their expected times (Andreas Raelert, Timo Bracht, Faris, Tom Lowe) were probably hoping for a little bit better results, even if they managed to finish in the top spots.

This goes to show that you can’t have a "ho-hum race” and still expect to finish in the TOP 10 for IM Hawaii.

Female Results

There is a similar “green color scheme” for the top spots on the women’s side. The exception is Chrissie Wellington who was few minutes slower than expected – but she still had to uncork a special performance to win the race after the beating she took from a bike accident just 10 days before the race.

Rank Name Nation Actual Time Expected Time
1 Chrissie Wellington GBR 08:55:08 08:51:37
2 Mirinda Carfrae AUS 08:57:57 09:13:42
3 Leanda Cave GBR 09:03:29 09:40:25
4 Rachel Joyce GBR 09:06:57 09:32:31
5 Caroline Steffen SWI 09:07:32 09:31:47
6 Karin Thuerig SWI 09:15:00 09:19:18
7 Sonja Tajsich GER 09:15:17 09:28:32
8 Heather Wurtele CAN 09:17:56 09:29:40
9 Caitlin Snow USA 09:18:11 09:40:42
10 Virginia Berasategui ESP 09:19:52 09:40:53
11 Catriona Morrison GBR 09:22:07 09:27:56
12 Tine Deckers BEL 09:28:21 09:36:32
13 Kelly Williamson USA 09:29:08 09:34:21
14 Natascha Badmann SWI 09:31:21 09:31:35
15 Mary Beth Ellis USA 09:34:06 09:14:24
16 Linsey Corbin USA 09:39:01 09:30:29
17 Samantha Warriner NZL 09:43:25 09:27:40
18 Amanda Stevens USA 09:50:11 09:36:21
19 Joanna Lawn NZL 10:02:33 09:30:59
20 Tyler Stewart USA 10:04:15 09:36:46
21 Uli Bromme USA 10:19:09 09:56:07
22 Jackie Arendt USA 10:21:02 09:56:52
23 Silvia Felt GER 10:31:10 09:35:55
24 Heleen Bij De Vaate NLD 10:35:58 09:38:27
25 Maki Nishiuchi JPN 10:36:33 10:18:55
26 Miranda Alldritt CAN 10:38:49 10:50:49
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