We are twelve weeks away from the
2013 Kalahari Augrabies Extreme Marathon (KAEM). In this entry I will attempt
to address the question of training distance etc. at the hand of the 2013
Marathon Des Sables (MdS) research conducted by me.
As many of my readers will know,
the MdS and KAEM are similar races in terms of distance, extremeness of terrain
and environment, as well as the fact that they are both self-sufficiency desert
races. It is my opinion that they are both extremely tough races but for very
different reasons, but this I have addressed in a previous entry. The experience
and knowledge gained in the one race is, therefore, justifiably transferable to
the other.
In terms of the discussion below,
the statistical data is that of those individuals who have completed the 2013
MdS. The aim is to determine relationships between performance and specific
factors.
In my segmentation of data for
this discussion I approached the distribution of date along a strong
bell-curve. The primary segmentation is:
1. The
top 10% of finishers, (in my view this is the genetically gifted group),
2. The
next 20% of finishers, (in my view this is a highly competitive group),
3. The
next 40% of finishers, (in my view they represent the average runner),
4. The
next 20% of finishers, (in my view this group are social / non-competitive
runners), and
5. The
final 10% of finishers, (this group represents the slowest of the pack for
various reasons that range from age to injury).
In most
analytical cases I grouped 4 and 5 together.
Before looking at the
relationship between race performance and training distance, I think some other
performance factors are noteworthy as they may have a significant impact on
individual performance.
Finisher Weight (BMI)
I think that it is noteworthy
that of the participants who completed the MdS, not a single person had a BMI
that was below average, as a matter of fact a significant number (13%) would be
deemed as over weight in terms of the BMI scale (BMI is greater than 24,9).
This is an important point, as an excessively lean and ultra-skinny built (BMI
underweight – a BMI less than 18,5) lacks sufficient reserves to counteract the
physical stress and nutritional strain that an ultra-endurance race places on
the human body. An ultra-lean body (below a normal BMI) would probably point to
a number of very negative factors, amongst these would be (a) under developed
muscles, (b) a deficient nutrition regime and (c) possibly even a lack of adequate
training. However, the relationship of an “overweight” BMI to race performance
clearly demonstrates that, from a performance perspective a BMI within the
normal range of 18,5 to 24,9 is best. Delving deeper into the race performance
delivered the following:
·
Among the top 10% of the field, all finishers
fell within the normal BMI range,
·
The next 20% of the field comprised of 17%
finishers with an overweight BMI rating,
·
The next 40% of the field, which I consider to
be the average runner out there, had 7% finishers who had an overweight BMI
rating,
·
The most significant group, the last 30% of the
field comprised of 33% finishers who had an overweight BMI rating.
From this it is reasonable to
conclude that a BMI within the normal range is a critical factor that
determines race performance. It is, however, not the only factor.
Race Experience
Race experience is another
element that determines overall race performance.
·
Of the top 10%, 78-percent of all finishers had
6 or more years of running experience,
·
Of the next 20% of the field 56-percent of
finishers had 6 or more years of running experience,
·
The next 40% of the field showed a similar
trend,
·
The last 30% of the field, however, had a
significant shift with 65-percent of finishers indicating that they have 1 to 5
years running experience.
Another dimension of experience
relates to the age distribution of finishers. The oldest runner in the top 10%
falls within the 55 to 64 year age bracket while the youngest person in the
bottom 10% fell in the age bracket of 25 to 34 years of age, while the youngest
finisher in the age group 18 to 24 years finished within the 40% that
represents the average runners. From analysis there does not seem to be a
definitive performance indicator beyond the relationship between age and
experience, however, age on its own plays a lesser role in determining
performance.
Weekly Training Distances
There is a direct correlation
between weekly distances covered and race performance. From the data it is
clear that as the field positions increased the training distances over the
preceding three months prior to the race decreased. The higher and consistent
distance runners did accordingly better.
There is, however, another
important factor to consider and that relates to over training. Within the top
10% of the field the average weekly distance over the three months leading up
to the event came to 85km’s with a maximum distance that did not exceed
100km’s. The bottom 10% of the field, however, only averaged 53km’s per week.
This is good-news for those individuals having to deal with injuries in the
three month that leads up to an ultra-endurance event like the MdS or KAEM. If
a 50km week can be maintained a runner has the ability to complete the event.
From the data it is clear that an average runner needs to run around 70km’s per
week consistently over the twelve weeks leading up to the event.
In my view a manageable training
program derived from the MdS research data would consist of the following
weekly distances:
Weeks
12 to 8
|
Weeks
8 to 4
|
Weeks
4 to 0
|
|
Competitive
Runner
(Top 10%)
|
85km’s
|
90km’s
|
80km’s
|
Casual
Runners
(Majority
of the field)
|
70km’s
|
70km’s
|
60km’s
|
Complete
Runner
(Bottom
10%)
|
55km’s
|
55km’s
|
50km’s
|
The table below describes the
role of a long run during training and its impact on race performance.
Position
|
Long Runs
over 12 Weeks
|
Average
Long Run Distance
|
Top 10%
|
12 (one per week)
|
31km’s
|
Next 20%
|
8 (two every 3 weeks)
|
28km’s
|
Next 40%
|
6 (one every 2 weeks)
|
29km’s
|
Next 20%
|
8 (two every 3 weeks)
|
25km’s
|
Bottom 10%
|
4 (one per month)
|
30km’s
|
The average long run across all
performance groups seems to be around the 30km mark. What does seem to have a
more significant impact is not so much the distance but rather the regularity
of the long run. Although both the top and bottom 10% performers run an average
30km long run, the top 10% does so once a week while the bottom 10% does so only
once a month.
And finally let’s take a look at
the number of training days per week and its relationship to individual
performance.
Position
|
Training
Sessions per Week
|
Average
Weekly Distance
|
Average
Long Run Distance
|
Average
Run Distance per Training Session
|
Top 10%
|
5
|
85km’s
|
31km’s
|
13,5km’s
|
Next 20%
|
5
|
80km’s
|
28km’s
|
13,0km’s
|
Next 40%
|
6
|
70km’s
|
29km’s
|
8,2km’s
|
Next 20%
|
5
|
60km’s
|
25km’s
|
7,0km’s
|
Bottom 10%
|
6
|
55km’s
|
30km’s
|
5,0km’s
|
In closing, let’s look at three individuals
as well as his or her training profile, based upon three different performance
goals within the KAEM field.
Top 10%
|
Middle of the Field
|
Bottom 10%
|
|
BMI Range
|
Normal
|
Normal
|
Overweight
|
Running Experience
|
6 Years plus
|
6 Years plus
|
1 to 5 Years
|
Weekly Training Distance
|
85km’s
|
70km’s
|
55km’s
|
Long Run Distance
|
31km’s
|
27km’s
|
30km’s
|
Number of Long Run’s per Month
|
4
|
2
|
1
|
Number of Training Sessions per Week
|
5
|
5
|
6
|
Average Distance per Training Session
|
13,5km
|
9,4km
|
5km
|
Estimated Race Completion Time (Avr)
|
30 hours
|
48 hours
|
66 hours
|
Of the seven performance
indicators discussed, six can be manipulated by the individual to improve
personal race performance. An individual in the bottom 10% may make a
significant improvement as to his or her race performance by losing some
weight. There also seems to be some over-training on the side of the bottom
10%, it seems as if two rest days per seven day cycle delivers better results
than having only one rest day per seven day cycle. Discarding genetics, an
average runner can improve race ranking significantly by increasing weekly
training distances by 21%. The table above, hopefully, provides some guidance
to my readers on how to match your training to your race aspirations.
Thank you for visiting my blog,
any discussion on the issues touched on in this entry is welcomed.
Genis
