The Relationship between Performance and Training Distance

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