Ironman Qualification

Which Ironman event gives me the best chance of qualifying for Hawaii?

HAMMOND QUALIFICATION COEFFICIENT [HQC] 2

YOUR RACE SELECTION METHODOLOGY.. Error! Bookmark not defined.
AGE GROUP HQC TABLES
FORTHCOMING ENHANCEMENTS TO THIS ANALYSIS

INTRODUCTION

Is this a question that you have asked yourself? More than likely you have if you have aspirations for Hawaii. If you are an improving triathlete who is knocking on the door of a Hawaii spot then your race selection can be critical to your chances of getting there sooner rather than later. How much of a difference can race selection make? Well I calculate that for my age group there could be a difference of 6% in the overall performance required to qualify between the easiest event and the hardest. At this point I stress that this is a 6% true performance difference. Take a minute out here and calculate what 6% might mean for you if you make race selection a lottery. Put it another way, if you are confident of qualifying, how critical would 6% added on to your time be to your qualification chances? Feel confident now?

So is it that easy to sift through the available data and evaluate your choices? My own situation obviously prompted me to go through all this. I am a 40-44 Age Grouper who been in Triathlon for less than 2 years. But I’m dedicated and want to get to Kona as soon as I can. A rudimentary look through what kind of time it is going to take to get there throws up more questions than answers. For example, in my category, an 11h15 minute time at Wisconsin (2005) would have got me to Kona in 2006. But in Switzerland it would have taking an incredible 9h49 to secure the last qualification spot. Germany seems like a great bet because they had 25 slots in my age group for Kona 2006, much better than say Western Australia that just had 2 slots. Would it therefore make sense to go to Frankfurt instead of Busselton? In fact, the answer was no in 2006! How can I determine if I stand more chance of grabbing the last of the 8 slots at Lanzarotte (a comfortable 10h44 in 2006) versus having to push myself to do 10h06 in Brazil to get one of only 5 slots available? Incredibly my analysis shows that Brazil is the better option.

Before we go any further let’s have a look at the entire scope of Ironman Races in the 2007 schedule. Apologies to Race Directors of 70.3 events with qualifying slots, I’ll get that info included at some point. I began by creating an event profile for all the races including the inauguration year, the time zone, race date, average weather conditions, participation numbers, slot allocations, basic course info for the Swim, Bike and Run, cut-off times along with the course records. I also included an element I will explain later called the Elite Reference Time. This table is available on my Ironman Event Profile Table. I would be grateful to anyone who has any complementary information to add as I have not been able to garner 100% of the data elements. I am also particularly interested in the Bike and Run elevation numbers if anyone has done the courses with an altimeter. The majority of this information is not really required for my analysis; however at some point you will want to review this data so see if the course that offers you the best chance from a pure performance basis also meets your particular preferences in a host of other areas. If you can’t do hills, then don’t go to Lanzarote, if you don’t like swimming in salt water, then don’t go to Busselton, if you don’t like running in the heat then perhaps Malaysia is not your best option despite what the analysis kicks out.

So where do we start with all the analysis. There are two key variables between the races, the time and the number of slots. But if what I have claimed in the opening is true, then both of these factors can lead me in the wrong direction. It is patently obvious that courses like Lanzarotte, Nice and the Sherbourne (all with bike elevations over 2000 metres) are not going to produce comparable race times with courses like Austria, Germany and Arizona which are essentially flat. Salt water swims are likely faster than fresh water swims, doubly so when the organisers leave 400+ metres off the course (France 2006). What about the marathon elevation, Sherbourne has an incredible 800 meters of total climb versus nothing for Nice, it’s just that in Nice you are running in temperatures about 10c hotter. So you can do all the data analysis you like on the times recorded, but trying to keep track of the course changes the organisers make each year and then factoring in the weather conditions will soon have you ripping out your hair in large tufts. Even if the race directors change nothing from one year to the next, differences in the temperature, humidity and wind will still render the race times incomparable.

Now consider the second factor, the number of slots available. The 25 spots that will go to my age group in Germany is going to grab a lot of attention from Kona hopefuls, whereas the 3 spots offered in Malaysia may pass unnoticed leading to a significant disparity in both the quantity and quality of the field. Even that doesn’t tell the whole story. The 8^th and final slot offered in Lanzarotte was taken by the 10^th place finisher. But the 6^th and last spot in Austria rolled all the way down to 14^th place.

Have I sufficiently confused you now? Have I made something which you thought might be a 15 minute decision armed with a web browser and notepad into a confusing nightmare with your $500 entry fee and Kona dreams lying shredded along with your training plan? Good. Were I more mercenary than I am, I could now convince you that purchasing my analysis for 10% of your next IM entry fee would be a prudent insurance policy to make the most of your investment. However, good natured chap that I am, I’ll just go ahead and explain it to you.

Now I’ll preface all this by saying that even all the analysis I have done is not absolutely bullet proof even though it is pretty good. I will endeavour to point out the holes in the amour as we go. Finally as your financial broker will always tell you, “past performance is no guarantee of future results”.

ELITE REFERENCE TIME [ERT]

TABLE 1
Elite Reference Time	Season
Event	2002	2003	2004	2005	2006	2007	Grand Total
Austria		08:18:00	08:20:25	08:23:46	08:16:52		08:19:46
Germany				08:32:00	08:22:28		08:27:14
Western Australia				08:32:50	08:32:25	08:25:52	08:30:22
Arizona				08:43:11	08:26:39	08:33:54	08:34:35
Switzerland	08:42:43	08:38:38	08:31:22	08:34:27	08:27:21		08:34:40
Australia				08:35:32	08:35:15		08:35:23
Florida	08:31:35	08:37:09	08:37:08	08:39:22	08:38:26	08:34:31	08:36:29
Brazil		08:28:56	08:32:41	09:00:08	08:25:41		08:36:52
New Zealand	08:39:07	08:36:50	08:41:21	08:37:08		08:40:05	08:38:54
South Africa				08:37:21	08:39:05	08:45:11	08:40:32
Canada		08:43:55	08:38:22	08:40:33	08:48:45		08:42:54
Coeur D'alene		09:03:31	08:54:39	08:35:03	08:51:04		08:51:04
Malaysia					08:52:57	08:51:41	08:52:19
Lake Placid	09:00:13	08:57:15	08:45:55	08:51:35	08:47:52		08:52:36
France				08:59:04	08:46:42		08:52:53
United Kingdom				09:05:42	08:45:06		08:55:24
Japan		08:56:22	09:02:31	08:55:49	08:53:05		08:56:57
Lanzarote	09:00:00		09:04:22	09:07:49	09:00:35		09:03:11
Korea	08:55:34	09:19:09		09:07:14			09:07:19
Wisconsin		09:02:38	09:06:10	09:01:08	09:09:44	09:23:14	09:08:35
Grand Total	08:48:45	08:47:29	08:45:00	08:46:16	08:41:02	08:44:55	08:45:01

The first objective, and really the key to the whole exercise, is to neutralise the differences between the races. There have been studies done to achieve this by comparing the performance of the Kona participants at Kona versus their qualifying races in order to quantify the relative difficulties of the qualifying events. I think this approach certainly has its merits, but also has some pitfalls.

1. The condition of the athletes in the two races. Did they peak better for Kona than they did for their qualifying race?

2. The time difference between the qualifying event and Kona. Wisconsin is over a year away and the qualifiers have time to have an off season, improve and re-peak for Kona the next year. No such luck for the Korea qualifiers.

3. The differences in the courses. Qualifiers from courses with large bike elevations are typically less suited to Kona. Will a comparison of their respective performances lead to a conclusion that they qualified from a weaker event?

I intend to perform that analysis at a later date as a secondary validation; however my initial approach at determining the relative strengths of the race is via another route. I have taken the times of the top 5 finishers in each race (including age groupers that came in the top 5) and calculated the average of that time. This time I am calling the Elite Reference Time [ERT]. Why the top 5 finishers? Well just looking at the winning time would subject the ERT to large fluctuations based on the quality of the winner. Taking an average over more athletes statistically yields a more stable result. So why not the first 10 or 20 finishers? Well at some point if we go too deep then the strength in depth of the Male Pro field will end up being judged rather than the race itself. Somewhere between these numbers there lies a compromise and I have chosen five as the number. A future opportunity for study would be to see how the analysis changes based on that choice.

Another important point to make about the ERT is that it is specific to each race and not each event. What I mean here is that Ironman UK is the event and Ironman UK 2006 is the race. Because of a bike course change the ERT varied significantly between IMUK 2005 and IMUK 2006.

I have analysed all the course data available to me going back as far as 2002. I have had to make one or two judgement calls along the way and I’ll explain them here so that you understand what has been included and omitted.

1. Shortened races (NZ 2006, Malaysia 2005, Korea 2006 etc) have not been included

2. For events with a complete change of venue and management like Ironman France, only data from Nice 2005 and 2006 is included. Similarly Germany only includes the Frankfurt event and not Roth

3. The reference times of Coeur D’Alene and Lake Placid have only been included when the men’s professionals have competed. For the gap years the ERTs from the surrounding years were used.

What we then have is the reference table Table 1. (right) which shows the ERTs for each race for each year. I have calculated an average ERT for the event but for the reasons stated earlier, please treat this average with extreme caution. Also note that I have assigned the races by the qualification season. So the Florida, Wisconsin and Western Australia 2007 races were actually raced in the 2006 calendar year, but qualified for the 2007 Hawaii event. Just in case any of you thought I had some clairvoyant skills.

Now there is a huge assumption here that most of you will have noticed already. We are assuming that the strength of the men’s field is the same in each race. Obviously the strength of the men’s field will vary with the following factors.

1. Prize Money

2. Pro Slots Available

3. Travel And Expense to get to the location. Note now the value of my Grand Prix table to determine in which geographical areas the strength of the field lies.

4. Proximity in the calendar to Hawaii. Unlikely that Normann and Faris are ever going to be competing in Wisconsin and Florida is it?

5. Course type. There are “grimpeurs” and “rouleurs” and most of the really top pros are not climbers since only 3-4 courses really provide a significant elevation to be considered. So these could be argued as specialist courses. I’m not for a moment saying that Herve Faure, Marcel Zamora, Gilles Reboul are not top quality triathletes. They absolutely are, as Tim Deboom found out in 2005, however I would suggest that their prize was the “maillot a pois” rather than the “maillot jaune”.

6. Course history, conditions and organisation. Will the weather conditions in the courses that were shortened and nearly postponed in recent years weaken the field?

However that being said, the data show a good level of consistency year over year for the same events and the results do bear out the levels of difficulties of each course as anecdotal reports have indicated over the years. I would therefore advance that this table serves us well in determining a valid reference time for each race for each year.

HAMMOND QUALIFICATION COEFFICIENT [HQC]

M40 AG
HQC	Season
Event	2002	2003	2004	2005	2006	2007	Grand Total
Lanzarote	25.4%		20.6%	21.4%	19.2%		21.7%
France				22.5%	20.3%		21.4%
South Africa				19.0%	16.9%	27.4%	21.1%
Lake Placid	20.1%	25.1%	21.0%	20.4%	18.8%		21.1%
Malaysia					23.4%	18.5%	20.9%
Arizona				19.6%	22.1%	20.6%	20.8%
Canada		18.6%	23.0%	23.6%	17.8%		20.8%
Brazil		25.8%	21.1%	15.2%	20.0%		20.5%
New Zealand	19.7%	20.1%	23.0%	21.2%		16.5%	20.1%
Korea	20.5%	19.3%		20.1%			20.0%
Wisconsin		18.9%	21.0%	18.1%	22.9%	13.8%	18.9%
Austria		17.4%	23.6%	14.3%	19.6%		18.7%
Australia				18.2%	19.2%		18.7%
Japan		18.0%	20.5%	16.2%	19.4%		18.5%
Germany				17.3%	19.4%		18.4%
Coeur D'alene		16.8%	15.1%	18.0%	23.4%		18.3%
United Kingdom				19.4%	16.3%		17.9%
Switzerland	19.9%	8.1%	18.1%	20.4%	16.1%		16.5%
Florida	17.7%	17.9%	13.5%	13.4%	14.8%	14.4%	15.3%
Western Australia				13.1%	23.2%	6.0%	14.1%
Grand Total	20.6%	18.7%	20.0%	18.5%	19.6%	16.7%	19.0%

Now that we have established this the next step is simply to determine the time of the last qualifier in each age group for each race to determine the degree of difficulty for qualification. Let’s calculate this as follows. In IM France 2006 the time recorded by the last qualifier in the M40-44 age group was 10h33m42s by a M. Olivier Bianchi. This was 20.3% more time that the reference time for this event 8h46m42s.

I call this the number (20.3%) the Qualification Coefficient. Actually I take that back. I’m now going to call this the Hammond Qualification Coefficient [HQC] and since you’re all getting this for free you can jolly well call it that too. Can’t you?

So all you need to know in fact is which race gives you the highest HQC. i.e. which race allows you to run the slowest relative to the elite reference time and still qualify for Hawaii. Notice now that we have not even considered how many slots were available in the race, how many were allocated to each group, how many rolled down etc.

For my age group I was then able to create the table showing the HQC for each race each year (Table 2)

I am desperately trying to figure out how to publish my database in an interactive format so that you simply have to click your AG to get Table 2 for your own AG. However I’m still struggling with the technology for the moment. In the interim, please click on the following link to get the HRQ Table for your own AG

THE ROLL DOWN FACTOR

Slot Acceptance Rate	Season
Event	2002	2003	2004	2005	2006	2007	Grand Total
Malaysia					94.3%	94.0%	94.2%
Florida	95.4%	84.7%	90.4%	83.9%	91.1%	91.7%	89.4%
Korea	81.8%	92.0%		81.3%			85.0%
Wisconsin		87.8%	84.2%	91.8%	72.0%	89.0%	85.0%
Western Australia				81.9%	78.6%	86.1%	82.2%
Arizona				72.3%	79.3%	94.9%	82.2%
Japan		77.2%	76.1%	94.3%	80.6%		82.1%
South Africa				88.1%	77.1%	64.4%	76.6%
Lanzarote	61.4%		73.4%	85.6%	79.3%		74.9%
Coeur D'alene		74.4%	74.2%	77.5%	67.6%		73.6%
France				67.2%	76.6%		71.9%
Germany				70.3%	73.0%		71.6%
New Zealand	58.4%	76.3%	67.2%	64.9%		77.1%	68.8%
Brazil		54.3%	57.2%	82.4%	81.1%		68.8%
United Kingdom				56.5%	79.6%		68.0%
Lake Placid	61.9%	58.5%	70.5%	62.3%	67.4%		64.2%
Australia				67.4%	59.9%		63.7%
Switzerland	69.0%	29.5%	73.6%	66.8%	68.9%		61.4%
Canada		60.9%	56.7%	61.8%	63.7%		60.8%
Austria		59.6%	52.4%	60.0%	61.9%		58.5%
Grand Total	70.8%	68.6%	70.5%	74.6%	75.2%	85.3%	74.0%

Although I think the pure analysis does not really require it, I will address the roll down factor as the question is frequently asked.

Evidently not everyone who earns a qualification slot actually wants to go to Hawaii and thus the slots roll down. The roll down factor varies by year, by event and by Age Group. The more the slots roll down, the higher the HQC obviously. I have seen some wild roll downs in my short IM experience and typically they occur in the upper age groups and the women’s categories. Including those wild data events in my database would basically skew my overall averages significantly. So for that reason I have chosen to restrict my analysis to the M18, M25, M30, M35, M40 and M45 age groups. I sincerely apologise for appearing to be ageist and sexist in this most inclusive of sports, however I feel that not only would including the other categories skew some of the overall data, but the lower slots and participation in those categories would yield unreliable results.

Here are some interesting facts about the rolls downs.

1. Globally the acceptance rate of earned Hawaii Slots has changed as follows over recent years

a. 2003 56%

b. 2004 63%

c. 2005 68%

d. 2006 66%

2. Florida has the highest pick up rate at 94% and Austria is the lowest at 36%

3. North America has the highest slot acceptance rate although it did actually decrease in 2006. The acceptance rate in Europe has risen dramatically over the last few years and if the trend continues will surpass NAM’s in 2007

Slot Acceptance Rate	Season
Area	2003	2004	2005	2006	Grand Total
NAM	68%	69%	79%	72%	73%
AFR			100%	40%	70%
ASA	67%	68%	61%	61%	63%
EUR	20%	50%	61%	70%	58%
LAM	50%	60%	57%	56%	56%
Grand Total	56%	63%	68%	66%	65%

So please note that whilst Austria has an extremely low acceptance rate of the Hawaii slots the qualification performance requirement was about average. This is why the number of slots and the pick up rate is really only an interesting aside to the true determinant which is the HRQ.

TABLE 3 Event	City	Inaguration Year	Total	Std Dev
Germany	Frankfurt	2002	120	1.26%
Coeur D'alene	Coeur d'alene	2003	80	2.83%
New Zealand	Taupo	1985	80	1.35%
Arizona	Tempe	2005	80	1.46%
Wisconsin	Maddison	2002	80	1.79%
Canada	Penticton	1983	80	2.68%
Lake Placid	Lake Placid	1999	80	2.42%
Florida	Panama City	1999	80	0.59%
Switzerland	Zurich	2002	75	5.00%
Australia	Port Macquarie	1985	70	0.59%
Lanzarote	Lanzarote	1992	60	2.45%
France	Nice	2005	50	1.27%
Japan	Goto	2001	50	1.81%
Brazil	Florianopolis Island	2001	50	4.38%
Austria	Klagenfurt	1999	50	3.90%
Korea	Seogwipo	2001	50	0.56%
Malaysia	Langkawi	2001	35	0.00%
South Africa	Port Elizabeth	2005	30	1.22%
United Kingdom	Sherborne	2005	30	1.79%
Western Australia	Busseltown	2004	30	5.84%

Your Race Selection Methodology

Now that we have the mechanism in place, please realise that this is just a tool and apply some common sense in using it. Also remember to apply the other factors that will weigh into the equation.

First of all I would treat this like I would if I were evaluating an investment. Remember that phrase “past performance is no guarantee of future results”. Well that is indeed true, but for some investments it is truer that others. This is no different. The key word is volatility. If this chart is going to play a large roll in my quest for Kona, then I’m going to study the history very closely. So whilst top of the pile with the highest HQC is Malaysia, guess what, I only have 1 year’s worth of data. So just how reliable is that? How long has the manager of that mutual fund been at the helm?

In the same vein another question is “how long has the race been in going on?” UK has only run for 2 years and in its inaugural year the HQC was 19.4% but dropped to 16.3% in its second year.

Ultimately a large number of slots available is a good thing, but not for the obvious reason. The reason that it is a good thing is that it will provide more consistency in the roll down factor and therefore allow a more solid projection of the next year’s required qualifying time. With 2 slots available at South Africa, you never know whether they will roll down to the 5^th place (2005) or if first 2 guys are going to grab it (2006). That roll down lowered the required performance by an effective 11 minutes.

Mathematically a good way to look at the volatility is to measure the Standard Deviation of the data, the lower the Standard deviation, the greater chance that future results will indeed reflect past performance. As an added tool the Table 3 shows the three factors to consider along with the HQC to validate your choice, the inauguration year, the Total slots available and the Std Deviation (for the M40-44Age group only)

My advice to your overall approach would be as follows.

1. Determine the order of events by HQC for your Age Group

2. Eliminate the events that fail to meet the required your required statistical certainly standards

3. Eliminate the events that do not meet your other constraints in terms of course type, calendar placement, cost, travel and registration availability.

4. From what is left, the event with the highest HQC should be your choice.

For the 2007 season I selected France as my A race. I eliminated Malaysia due to the variable weather conditions and lack of data and the next choice was between Lanzarotte and Nice with very similar HRQs. Well Nice is a little closer to home and I’ve also done the course twice already.

I added South Africa as an early season test of the winter training base. I was waiting to try China but the race didn’t get the go ahead and my first fall back plan Arizona (with a good HRQ) closed a few days before we heard the news about China. I then added Louisville as a fall back option if something goes wrong in Nice.

I sincerely hope that this helps you make your best choice in your personal journey to Kona. Good Luck and Hope to see you some day on the Big Island.

FORTHCOMING ENHANCEMENTS TO THIS ANALYSIS

I am actively working on the following items to enhance this analysis

1. Obtain more historical data from 2002-2004

2. Include data from 70.3 Events with Hawaii Qualifying slots

3. Conduct Kona performance versus qualifying race performances to validate Elite Reference Times

4. Add HQC tables for guaranteed qualification place. (i.e. if there were 5 slots available, what was the HQC for the time of the 5^th place person)

5. Add complete analysis for 70.3 Florida World Championship qualification

AGE GROUP HQC TABLES

M18 HQC	Season
Event	2002	2003	2004	2005	2006	2007	Average
Arizona				25.5%	24.8%	22.0%	24.1%
Canada		26.0%	20.8%	21.9%	13.2%		20.5%
Lake Placid	21.7%	25.6%	11.7%	22.9%	18.9%		20.2%
France				21.4%	17.5%		19.5%
Malaysia					24.9%	12.7%	18.8%
South Africa				21.8%	14.6%	17.0%	17.8%
Coeur D'alene		19.2%	13.7%	16.3%	20.7%		17.5%
New Zealand	21.1%	16.4%	13.8%	13.0%		22.2%	17.3%
Japan		15.7%	18.5%	17.2%	15.1%		16.6%
Korea	33.2%	7.3%		7.8%			16.1%
Wisconsin		14.7%	16.3%	15.8%	23.2%	9.8%	16.0%
Florida	21.1%	18.5%	10.1%	20.7%	14.1%	7.5%	15.3%
Germany				11.0%	18.2%		14.6%
Brazil		13.1%	13.3%	12.5%	13.4%		13.1%
Australia				12.6%	12.8%		12.7%
Western Australia				10.2%	9.7%	15.1%	11.7%
Switzerland		11.6%	6.8%	12.7%	12.6%		10.9%
Austria		11.5%	9.4%	11.4%	10.7%		10.7%
Lanzarote	13.7%		11.1%	10.7%	4.9%		10.1%
United Kingdom				8.2%	11.9%		10.1%
Grand Total	22.2%	16.3%	13.2%	15.4%	15.6%	15.2%	15.7%

M25 HQC	Season
Event	2002	2003	2004	2005	2006	2007	Average
Austria		8.5%	48.1%	9.9%	15.0%		20.4%
Arizona				13.5%	21.7%	16.6%	17.3%
Canada		15.6%	18.9%	21.5%	11.9%		17.0%
Korea	21.3%	13.8%		14.3%			16.5%
South Africa				21.0%	15.0%	13.3%	16.4%
Lake Placid	16.4%	16.2%	14.5%	18.8%	14.0%		16.0%
Brazil		21.1%	15.7%	11.5%	14.4%		15.6%
Wisconsin		14.4%	13.2%	15.0%	21.9%	9.8%	14.9%
New Zealand	13.7%	11.9%	13.2%	17.2%		15.3%	14.2%
Coeur D'alene		15.8%	13.8%	13.2%	14.1%		14.2%
Japan		14.3%	19.4%	10.2%	11.4%		13.8%
France				13.6%	13.1%		13.4%
Lanzarote	13.4%		18.6%	9.2%	11.7%		13.2%
Switzerland	16.7%	11.4%	12.5%	12.4%	10.5%		12.7%
Florida	14.2%	13.9%	10.5%	13.4%	8.0%	12.1%	12.0%
Germany				12.2%	11.4%		11.8%
Malaysia					15.7%	7.1%	11.4%
Australia				9.6%	12.5%		11.0%
United Kingdom				11.9%	9.7%		10.8%
Western Australia				6.7%	12.0%	7.5%	8.8%
Grand Total	15.9%	14.3%	18.0%	13.4%	13.6%	11.7%	14.3%

M30 HQC	Season
Event	2002	2003	2004	2005	2006	2007	Average
Brazil		19.4%	22.0%	15.0%	21.6%		19.5%
Arizona				13.7%	19.3%	17.1%	16.7%
Lanzarote	18.9%		11.3%	18.5%	16.0%		16.2%
Canada		14.2%	20.2%	16.2%	14.1%		16.2%
Lake Placid	15.9%	15.9%	16.7%	16.3%	15.8%		16.1%
New Zealand	16.8%	15.0%	14.9%	14.9%		16.4%	15.6%
Germany				13.0%	18.1%		15.5%
Malaysia					17.8%	12.6%	15.2%
Coeur D'alene		18.0%	11.8%	16.0%	14.2%		15.0%
Switzerland	20.0%	14.7%	14.0%	12.6%	12.3%		14.7%
South Africa				17.6%	11.4%	14.9%	14.6%
Wisconsin		13.0%	13.5%	14.3%	18.9%	10.5%	14.0%
United Kingdom				17.9%	8.0%		13.0%
Korea	15.4%	12.5%		10.7%			12.9%
Florida	15.3%	15.1%	12.1%	11.6%	10.0%	12.9%	12.8%
Japan		11.8%	12.9%	10.9%	15.5%		12.8%
France				12.6%	12.1%		12.3%
Western Australia				10.9%	13.3%	12.0%	12.1%
Austria		8.9%	15.6%	10.7%	12.5%		11.9%
Australia				11.3%	11.4%		11.4%
Grand Total	17.0%	14.4%	15.0%	13.9%	14.6%	13.8%	14.6%

M35 HQC	Season
Event	2002	2003	2004	2005	2006	2007	Grand Total
Lanzarote	17.7%		16.5%	23.1%	16.8%		18.5%
Lake Placid	16.3%	24.6%	18.7%	16.3%	15.5%		18.3%
Arizona				16.2%	20.4%	16.7%	17.7%
New Zealand	16.6%	18.7%	17.9%	17.4%		17.8%	17.7%
Malaysia					16.2%	18.5%	17.3%
Canada		14.5%	19.7%	19.2%	15.9%		17.3%
Brazil		19.4%	17.4%	14.7%	16.9%		17.1%
Coeur D'alene		15.7%	16.2%	17.3%	16.9%		16.5%
Wisconsin		14.1%	20.1%	16.5%	20.5%	10.6%	16.4%
Germany				15.4%	17.4%		16.4%
Korea	16.1%	15.4%		16.9%			16.1%
Switzerland	18.6%	13.2%	18.0%	15.3%	14.6%		15.9%
South Africa				18.7%	13.9%	15.2%	15.9%
France				16.3%	15.1%		15.7%
Japan		19.7%	17.3%	12.8%	10.7%		15.1%
United Kingdom				16.4%	12.9%		14.7%
Australia				13.8%	15.5%		14.6%
Austria		13.7%	17.1%	13.3%	14.4%		14.6%
Florida	15.8%	15.4%	13.9%	12.7%	12.7%	14.0%	14.1%
Western Australia				7.9%	16.3%	14.3%	12.8%
Grand Total	16.8%	16.8%	17.5%	15.8%	15.7%	15.3%	16.2%

M40 HQC	Season
Event	2002	2003	2004	2005	2006	2007	Average
Lanzarote	25.4%		20.6%	21.4%	19.2%		21.7%
France				22.5%	20.3%		21.4%
South Africa				19.0%	16.9%	27.4%	21.1%
Lake Placid	20.1%	25.1%	21.0%	20.4%	18.8%		21.1%
Malaysia					23.4%	18.5%	20.9%
Arizona				19.6%	22.1%	20.6%	20.8%
Canada		18.6%	23.0%	23.6%	17.8%		20.8%
Brazil		25.8%	21.1%	15.2%	20.0%		20.5%
New Zealand	19.7%	20.1%	23.0%	21.2%		16.5%	20.1%
Korea	20.5%	19.3%		20.1%			20.0%
Wisconsin		18.9%	21.0%	18.1%	22.9%	13.8%	18.9%
Austria		17.4%	23.6%	14.3%	19.6%		18.7%
Australia				18.2%	19.2%		18.7%
Japan		18.0%	20.5%	16.2%	19.4%		18.5%
Germany				17.3%	19.4%		18.4%
Coeur D'alene		16.8%	15.1%	18.0%	23.4%		18.3%
United Kingdom				19.4%	16.3%		17.9%
Switzerland	19.9%	8.1%	18.1%	20.4%	16.1%		16.5%
Florida	17.7%	17.9%	13.5%	13.4%	14.8%	14.4%	15.3%
Western Australia				13.1%	23.2%	6.0%	14.1%
Grand Total	20.6%	18.7%	20.0%	18.5%	19.6%	16.7%	19.0%

M45 HQC	Season
Event	2002	2003	2004	2005	2006	2007	Average
France				40.6%	23.0%		31.8%
New Zealand	25.3%	31.5%	22.4%	25.5%		22.6%	25.5%
Arizona				23.8%	27.4%	24.8%	25.3%
Malaysia					26.7%	22.8%	24.7%
Canada		20.3%	26.0%	32.2%	20.4%		24.7%
Lanzarote	30.1%		19.2%	24.0%	24.7%		24.5%
Korea	25.6%	21.2%		25.4%			24.1%
Germany				18.3%	29.0%		23.7%
Japan		20.6%	25.4%	22.0%	24.7%		23.2%
Lake Placid	22.9%	26.9%	23.3%	21.3%	20.7%		23.0%
Brazil		23.9%	26.7%	19.0%	21.1%		22.7%
Coeur D'alene		22.4%	18.8%	24.6%	19.8%		21.4%
Switzerland	19.4%	23.1%	19.5%	16.6%	24.4%		20.6%
United Kingdom				23.3%	17.6%		20.5%
Wisconsin		16.6%	23.6%	18.5%	24.9%	18.0%	20.3%
Australia				19.7%	20.7%		20.2%
South Africa				20.0%	17.8%	20.2%	19.3%
Florida	19.9%	22.0%	18.0%	17.1%	15.5%	21.2%	19.0%
Austria		16.9%	14.1%	20.0%	17.6%		17.2%
Western Australia				19.5%	18.3%	12.9%	16.9%
Grand Total	23.9%	22.3%	21.6%	22.7%	21.9%	20.3%	22.1%

Any comments corrections and critique of this analysis will be warmly received and considered for improvement. Please send to neil@neilhammond,com