Pikes Peak Marathon – a race of two dominant performers

The Pikes Peak Marathon and Ascent race has a long history having been first run in 1936 and then continuously since 1956 to the present. The race is an iconic “mountain” event and has been run by many high-level competitors over the years. It is also a “bucket list” race for many. I had focused training on the 1979 edition when I was living in New Mexico but a road cycling team invite scuttled those plans as I would be only cycling and traveling for the entire summer. As a climbing specialist then, and now, I still have plans to do the race, perhaps even this year.

The 7815 foot (2382 m) 13.1 mile Ascent followed by a return route down the same trail makes the Marathon an “ultramarathon” as the fastest finishing times are about the same as that for some of the faster 50 km trail races. The race regularly draws top talent from the mountain and ultramarathon running world as well some washed-up elite and sub-elite road marathoners looking for a new challenge. In 2014 the Ascent race (run the day before the Marathon) was the designated World Long Distance Mountain Running Championship of the World Mountain Running Association (WMRA). Since 1976 the race has been run on the same course. This allows for performances in this time period to be directly compared and aggregated for analysis.

Even though the race has a long history and has been run by thousands including a fair share of international elite runners, the best results are dominated by two performers- Matt Carpenter and Ricardo Mejia. Carpenter has completed the race 14 times and won it 12 times and Mejia has run 6 times and won it 5 times (I could not find any data on possible DNFs). These two competitors also account for 6 of the 11 performances that are within 10% of the record time as well as the record time (by Carpenter). Carpenter has played a central role in the race for many years and has taken it upon himself to provide a resource for all things “Pikes” at his website Pikes Peak Central. The website provides a comprehensive collection of data, history, and advice on running the race- either the Ascent, the Marathon, both, or both in the same year (known as a “double”) as Carpenter has done a couple of times. A “double” is possible since the Ascent is run on Saturday and the Marathon on Sunday. After Carpenter’s 6 in a row winning streak from 2006-20011, the winners have been international since (Jornet 2012, Miahara 2013, Lauenstein 2014).

The record for the Marathon is held by Carpenter- set in 1993 with a super-human time of 3:16:39. No one has come within 2.5% of this time and this result stands as one of the greatest fixed course mountain running performances ever. There are only four other performances that are within 5% of the record and 11 that are within 10%, an impressive record given the thousands of national and international elite mountain runners that have competed in the race over the years.


In a previous post, the Pikes Peak Marathon was compared to a number of other (mostly ultramarathon) races to gain an understanding of competitiveness in such long trail running races. The results indicate that Pikes exhibits a very wide range of competitiveness including both exponential and linear finishing time distributions. The “linear” years have competitiveness indices similar to other very competitive races. The “exponential” years have competitiveness indices ranging from values as high as some of the most competitive road marathons to as low as some of the least competitive ultramarathons. The competitiveness calculations utilize the 125% cohort from each year of the event. The 125% cohort is chosen because it allows for comparison of events ranging from 10 km road races to 100 mile trail ultramarathon races. Although a “true” elite population would be best described as those competitors within about 10% of the winning (or record) time, the 125% cohort allows for inclusion of enough ultramarathon results to allow for statistical significance in comparisons. In addition, because of the magnitude of multiplicative pace differentials, the finishing times are naturally more spread out in ultramarathons than for similar pace differentials in shorter races such as marathons and other shorter road races.

Examples of typical “exponential” and “linear” competitiveness plots for the Pikes Peak Marathon are presented below for the 2009 and 2010 Men’s events. Carpenter won both of these races, in 3:37:02 and 3:51:54, respectively. The 2010 event had a much deeper field (20 competitors in the 125% cohort) than the 2009 event (14 competitors in the 125% cohort) something that is partly due to the slower winning time in 2010, although there are likely other contributing factors (e.g. weather).



Presented below are the competitive indices (CI), coefficients of determination, sizes of the 125% cohort, winning times, winners, and winners age for the Men’s Pikes Peak Marathon from 1976-2014. As noted above, this event period is chosen because the race has been on the same course for the entire time and allows for direct comparisons of finishing times and other, derived, metrics.


Pikes Peak Parametrics Summary all years cropped

Competitiveness index (CI), coefficient of determination, size of the 125% cohort, winning time, winner, and winner age for the Men’s Pikes Peak Marathon 1976-2014. The red CI values are for the highly competitive “linear” years.

Within this dataset of 39 competitions, we see 5 highly competitive “linear” years, 6 very competitive “exponential” years (CI>0.130), and quite a number of years (16) with low competitiveness (CI<0.110). Clearly the competitive fields at Pikes are highly variable but can be very competitive. This is not unexpected from a niche event which draws from a relatively small population of competitive runners.

Pikes Peak Marathon and Age

In long events like marathons and ultramarathons the optimal age for top competitors tends to favor an older runner than for the shorter events such as the mile. In another post, an analysis of the optimal age for competition in the road Marathon was presented. What is found is that there exists evidence that the top road Marathon competitors are getting younger, particularly since the 2008 Olympics where 21 year old Samuel Wanjiru won the gold medal. It is hypothesized that Wanjiru’s result has spurred on many more younger athletes to pursue the marathon at a younger age than has typically been observed. A plot of the competitor age on race day versus the percentage back from the record Marathon time for the top 2500 Marathon times is presented below and shows the current optimal age to be about 30 years. There is a “bulge” in the data on the young side of the “whorl” centered on about 30 years, indicating that there is something of additional interest in the dataset.


Pursuant to understanding this “bulge” in the data, a more granular look dividing the data into two time periods (1967-2008 and 2009-2014) presented below, shows that as of about 2009, the best results in the marathon are getting faster and that the average competitor age of these fast results is tending to a younger age. This supports the “Wanjiru” effect but does not confirm it.


Looking at the same type of data for Pikes Peak Marathon shows a similar optimal age of about 30 years, but with a diffuse indication of increased performance for older competitors. In this graph the red points are Carpenter’s results, the green points are Mejia’s results, and the yellow points are Waquie’s results. Guidelines are included to help the eye detect the lack of symmetry in the data (similar to those provided in the Marathon graph above). Even removing the outlier results of Carpenter in his winning streak as he entered his 40’s, there is still a trend toward older competitors having superior times to younger competitors.


Age on race day versus percentage back from the record time for the Pikes Peak Marathon 1976-2014 for the 125% cohort of performances. The red points are Matt Carpenter’s performances, the green points are Ricard Mejia’s performances, and the orange points are Al Waquie’s performances. Even with removal of the outlier results of Carpenter in his 40’s, there is a tendency for older competitors to perform better than younger competitors.


So the Pikes Peak Marathon may be a race for the older competitor, at least when compared to the road Marathon. It is not unusual that an event that involves such a high level of endurance along with a high reliance on slow twitch muscle fibers (type I and to some extent type IIa) would favor the older athlete. What is interesting with Pikes however, is that the downhill is run at a very high speed where utilization of fast twitch (type IIb) muscle fibers is important. It may be that the uphill part of the race dominates the skillset needed to be competitive, although it is usually the case that a fast descent is required for a win- something that reinforces the subtitle of this blog- “the race is made on the ups and won on the downs”….. and also something to consider if one should be attempting to put up a good time at this event.


Analysis of finishing time data for the Pikes Peak Marathon over the period 1976-2014 reveals the following:

  1. The best performances have been dominated by two individuals- Matt Carpenter and Ricardo Mejia.
  2. The competitiveness of the race is highly variable- the competitiveness indices can be as high as some of the most competitive road marathons to as low as some of the least competitive ultramarathons.
  3. Analysis of the age distribution within the 125% cohort for all time performance indicates that the race favors an older competitor when compared to the road Marathon.
  4. It is pointed out that even though the ascent portion of the race likely dominates the skillset needed for high performance, a fast decent is required for a good time. This appears to necessitate that top competitors have an unusual mix of slow and fast twitch muscle fiber development.

Salomon S Lab Motion Fit Jacket, Tights, and Pants – Review

For F/W 2014 -2015 Salomon introduced a new line of S Lab garments specifically for cross country skiing. They are called “Motion Fit” and incorporate design features and materials that allow for high-output training on skis in just about any weather.

S Lab Motion Fit Jacket front

Salomon S Lab Motion Fit Jacket, in “Equipe” colorway. Note the slim fit and WindStopper fabric front.


I started using the jacket, tights, and pants at the beginning of the ski season in mid-November 2014 and I haven’t really used anything else (for skiing) since. With a total of about 1000 km (600 miles) of skiing with these garments, I can say without hesitation or qualification that each piece is outstanding and the performance is superior to anything I have ever used.

In the past I have used a system that consists of two sets of training clothing set-ups, one for ‘typical’ sunny days  (in the -12C/+10F to -0C/+32F range) and one for cold days (<-18C/0F) and found this to be sufficient to handle the spectrum of weather conditions we see here in the central Idaho mountains.

Last year I used the Salomon Elite Jacket and found it to be quite comfortable, well ventilated, and near optimal for training in the warmer winter conditions here. I would switch from the Elite Jacket and Momentum Warm tights to a warmer, less ventilated jacket (e.g. Momentum Warm Jacket) and full-zip Momentum Warm pants over the Momentum Warm tights. This system has worked well.

I expected that the S Lab Motion Fit Jacket would replace the Elite Jacket in the set-up, that the S Lab Motion Fit tights would replace the Momentum Warm tights that I have been using for the past four years, and that I would switch to the cold weather elements as described above for the cold days. As it turns out I have not used the cold weather pieces at all as I find the S Lab Motion Fit system to be sufficiently warm with the addition of a vest. More on this later.

S Lab Motion Fit Jacket

The jacket has a few different materials in the design. First, the front is made from WindStopper fabric and this ends up being a critical part of why this garment is so useful across the temperature range that I have experienced (40F to -10F). Second, the shoulders, sides, and back are made from a stretch fabric that gives plenty of stretch for even the highest intensity training sessions. Third, Salomon have applied their laser cut ventilation technology on the back panel along the spine. This ventilation is what makes this jacket so comfortable during interval and tempo workouts as well as long skis in warmer weather. Although many manufactures (including Salomon) have used stretch mesh fabrics in this area for ventilation, there is no better vent than a hole and I can attest to the great performance of this venting in controlling body temperature in hard workouts.

S Lab Motion Fit Jacket back vents

Laser cut ventilation holes along the back spine of the S Lab Motion Fit Jacket. There is no better vent than a hole.

As is clear in the image of the front of the jacket above, the fit is quite snug, however the articulation of the design and the stretch fabrics lead to no binding and free movement. There is a natural feeling of “just right” when wearing this jacket much like what has been achieved with the Sense line of running clothing from Salomon. Obviously there is a good bit of cross fertilization in the running and Nordic clothing lines.

The jacket has all of the other expected features such as a cinching waist, an upper torso clasp to allow opening the zipper without flopping of the jacket, and a nicely placed sizable rear pocket.

S Lab Motion Fit Jacket back pocket

The rear pocket of the S Lab Motion Fit Jacket is nicely places and easy to get things in and out of.

Although I expected that this jacket would not be sufficiently warm on very cold days, I have instead found that I can use this jacket right down to -10F by just adding a Momentum Warm Vest. The sleeves of the jacket are sufficiently warm as to not lead to excessive heat loss and still perform very well on warmer days. As a result I have not worn any other jacket this season, even on the coldest of days. With the laser cut ventilation the jacket is perfect right up to 32F. Salomon have found a very nice balance between performance for high output and comfort in cold.

Note: I have also used this jacket for winter running and found it to be a nice option. The ventilation is very effective for running where one tends to accumulate more sweat due to the lower speeds. As a result of a shoulder injury and reduced skiing, I have about 250 km of running in this jacket this winter.

S Lab Motion Fit Tights

The Salomon Momentum Warm Tights have been a fundamental workhorse in my Nordic clothing set-up since they were introduced four years ago. I have worn out a few pairs and still have a few more around. The only negatives that I experienced with these tights is that they eventually loose elasticity and begin to “bag” out and they are not the greatest in wind.

Enter the S Lab Motion Fit Tight and we have a refined, updated version of the Momentum Warm Tight. Salomon have added WindStopper fabric to the front, articulated the knees, and utilized a stretch fabric that, so far, holds it’s elasticity.

S Lab Motion Fit tight front

S Lab Motion Fit tight back

S Lab Motion Fit Tight front (top) and back (bottom).

The knees are particularly comfortable as they do not bind or limit motion at all.

S Lab Motion Fit tight knees

S Lab Motion Fit Tights knees showing articulation in design, a separate stretch fabric over the knees, and WindStopper fabric elsewhere on the front section.

The tights perform well right down to 0F and in any sort of windy condition- no need for another or different layer. Below 0F I add the S Lab Motion Fit Pant over these and find the combination to be very comfortably warm. The tights also perform well in high intensity sessions.

Note: I also use these tights for winter running- something that I have been doing more of this year as noted above. They are just as functional in winter running as they are in skiing.

S Lab Motion Fit Pant

For the coldest days I have been using a full zip Momentum Warm over pant over the Momentum Warm Tights. Although warm, it is sometimes too warm and the combination is quite bulky and would bind from time to time. I was glad to see that Salomon offered a warmer, slimmer fit pant with all of the articulation and wind protection in the S Lab Motion Fit Tight.

S lab Motion Fit Pant front

S Lab Motion Fit Pant with WindStopper front, articulated knees, and a slim fit.

I have used these pants alone on warmer days and in combination with the S Lab Motion Fit Tights on colder days. As mentioned above the combination performs very well in the cold and because of the slim fit the combination is not bulky. I have also had these out on some very windy days and they were very comfortable even when the temperature shot down to 5F. The knees, in addition to being articulated in the design also have a unique, totally separate section that eliminates binding.

S Lab Motion Fit pant knees


The jacket is $230.00 (US), the tights are $140.00 (US) and the pants are $175.00 (US). Pricey as usual for Salomon but, if my experience holds, these pieces will wear well and can be used for numerous seasons. Competitive products are all in a similar pricing range but many do not have the fit, articulated design, and carefully chosen materials that this system offers.

Bottom Line

Salomon have come up with a high performance clothing set-up for Nordic skiing that allows for comfortable skiing across the board from high intensity interval workouts to cold weather slogs. The articulating design and fabric choices are outstanding when compared to other offerings on the market. Highly recommended.

Review of “Fast After 50″ by Joe Friel

Fast after 50

Joe Friel is a well-known author of training books for triathletes, cyclists, and runners. He has also authored books on methodologies for using heart rate monitors for training. His training methods are loosely based on Lydiard-style progression and periodization but incorporate much new sports science-supported approaches and work-outs. I have read and regularly utilize much of the information presented in his book “Total Heart Rate Training” and have found it to be a great resource for both guidelines and specific work-outs (in fact in our household of two very competitive “old” athletes, we speak in “Friel-ese” when referring to various interval workouts, e.g. we might discuss whether a P2A workout or a A1A workout is best for the day’s work). So when this book was announced by Friel on his blog this past summer, and as a 58 year old competitor, I pre-ordered it on Amazon. The book arrived the day before Christmas. I started reading it right away but it was soon appropriated by my wife and I got the volume back about a week ago. I have now been through it twice and looked at a number of the critical references to ensure that I was comfortable with conclusive statements in the text (I am with those that I checked). The following is a review of the book, but suffice it to say that I can highly recommend this book as a resource for all ageing athletes regardless of sport. Note: I already subscribe to the type of training that Friel espouses so my comfort with those portions of the book are significant. Others may not be so comfortable with his approaches and one should keep this in mind when reading this review.


Ageing and the impact of ageing on the competitive athlete beyond age 50 is something that has not been written about in book form before. Friel has undertaken a substantial task and done a very good job with the subject matter. The physiologic changes that negatively affect athletic performance beyond age 50 (and to a lesser extent beyond age 40) are fairly drastic as any committed senior athlete can tell you. Friel develops a detailed framework to allow one to understand these changes and the ramifications on performance and then offers a training approach to slow down or possibly even delay the rate of decline. The current state of understanding is nicely summarized in a quote from page 108 of the book:

“This brings us back to the big three- the primary determiners of performance decline with age according to sport science. To refresh your memory, these are declining aerobic capacity, increasing body fat, and loss of muscle mass.”

Friel’s recipe for combating age-related performance decline therefore involves a primary focus on high-intensity workouts, methods for reduction of body fat, and heavy load strength workouts. It is proposed that these three areas are the keys to high performance as a senior athlete.

The book is structured in two parts where Part I (about 1/3 of the book) reviews the literature and describes Friel’s own experience with physiologic changes going on in the human body. This establishes a base-line of what we are up against. Part II describes the various ways that the changes discussed in Part I can be addressed from the perspective of a competitive athlete. Part II includes a substantial amount of guidance on training plans and suggested workouts (along with good appendices that elaborate on work outs in greater detail) as well as discussions of diet and recovery. It is quite comprehensive, if you subscribe to this style of training.

Friel has done a good job of dancing around the whole “diet” morass that is extant. Although he lauds a so-called Paleo diet (and has co-authored a book on the subject with one of the Paleo cult’s pseudo-scientific leaders) he is quick to point out that there is no one diet that works for everyone and that the task is to to determine what works for you. This is essentially what Matt Fitzgerald has covered in his book “Diet Cults“, a book that I recommend along with another of his books- “Racing Weight“.

As it happens, over the past year I have transformed my training structure to include increased volume of high-intensity workouts and max strength weight training, two of the primary messages in this book. Having had this past year to monitor progress, I can report that inclusion of these elements into my training program has been very successful and resulted in significant power development and associated performance increases. What Friel proposes works, at least for me.

Bottom Line

Friel has written a comprehensive and detailed guide to development of athletic excellence for the senior endurance athlete. Highly recommended.

2014 – Numbers for the Year, Training Recap, and 2015 Goals

My training “year” follows a December to December pattern due to a transition from trail running to nordic skiing right around late November- early December. It is a good time to tally the numbers, do some critical review, compare with 2012 and 2013,  and put together a training plan for the coming year.

Note: I am putting up this post primarily for my own use as an easy to access depository of the information and analysis. Writing such a post requires that one go through the exercise of analyzing, reviewing, summarizing, and deriving some sort of direction from the year of training and racing results. This analysis was typically in my training log journal but since there is a chance that others might find some value in this, I am putting it up here. If you have any questions/comments feel free to post such in the comment section.

This summarizes the third year of focused training and racing for ultra running and Nordic skiing. There is good progress in most areas with one major deficiency- fueling for ultramarathons. More on fueling later.

The Numbers

2014 Numbers cropped

I typically target about 700 hours as this volume has served me well for preparing for endurance sport competition over the past 35 years. At 962 hours, the year turned out significantly higher. This was not by accident. First, my coach insists that “active recovery” should be logged and accounted for in total training hours. I have not tracked active recovery in the past so that is a 180 hour adder to the total, and therefore for comparison to past years this needs to be recognized. Second, my coach also encouraged me to begin including separate specific strength workouts in my schedule and I have, to the tune of about 90 hours- another adder to the total. Excluding the “active recovery” and the strength workouts, I have a total of about 700 hours in sports-specific (nordic skiing and running) training. Of course, these volume data are only of value when viewed through a “time in zone” optic as will be reviewed below.

As for distance, it is remarkable that I have run almost exactly the same yearly total distance the past 3 years in a row- 2087 miles (2014), 2065 miles (2013), and 2067 miles (2012). The large differences have been in the zone distribution and the addition of significant (about 2 per week) interval sessions this past year (2014). In skiing the distance is up a bit 1848 miles (2014) vs. 1796 miles (2013) but still significantly less than 2012 (2120 miles). Once again the zone distribution has also changed as I added two interval sessions per week in skiing this season as well.

I decidedly went after increasing vertical ascention and succeeded in doing so and passed the 600,000 foot mark compared to a total of about 475,000 feet last year. The total this year is an average of about 1650 vertical feet per workout and 2500 more feet of vertical per week in 2014 vs. 2013, the largest increase being in the running season. This was planned as I chose to compete in very mountainous running races for the 2014 running season.

Once again I have extracted out the interval session data. These sessions represent a 3X increase in intensity work when compared to 2013. My coach insisted that I could handle the training load and that such intensity work was critical to performance and maintenance of  VO2 max. This approach has been supported by a recent book by Joe Friel- “Fast After 50″ , where he reiterates over and over how important the intensity work is, particularly for “senior” athletes (I will be posting a review of Friel’s new book soon). I can feel the very positive effects of committing to consistent inclusion of these sessions, both in running and in skiing. The intervals have a different impact (excuse the pun) in running than in skiing but it is all positive.


Presented below are the daily (blue) and 7-day rolling total (red) for distance (km for skiing and miles for running), time, vertical ascention, and TRIMP time series data for the skiing and running seasons, respectively.


I kept the volume in skiing at a fairly high level to ensure that my aerobic base was maintained whilst still keeping up the quality of the 2-per-week intensity sessions. This required an increase in sleep and recovery which meant making some hard choices as far as other activities were concerned. Overall I think that the focus was validated by the sense of accomplishment and the very good results in the three races that I competed in. At 58 I was able to stay at the front of the fields and finish in the top 10 in two of the three races- and, more importantly, less than 10% back from the winners in all of the races. Percentage back is a much better metric for performance than place.

Looking at “time in zone”, presented below is a comparison of zone distribution for 2014 and 2013. I successfully transitioned an overabundance of L3 into L2 and L4 as prescribed by my coach and the associated training plan- meaning I stuck to the plan and did not let passing skiers or other situations influence my pace as I have in the past. The increased L2 allowed for the 2-per-week intensity sessions to maintain quality and I felt the results, particularly in the last quater of my races where I had the depth to accelerate away and finish strong.


These data may represent close to an “ideal” skiing training season and will serve as a baseline going forward. I cannot emphasize enough the importance of the intensity sessions and suggest that they were the difference in my superior performances this yaer versus last year.

Moving on to the running season, the same daily (blue) and 7-day rolling total (red) time series data for distance (miles), time, vertical ascention, and TRIMP for the running season is presented below. The tapers and recovery for ultramarathons play havoc with any sort of smooth, consistent training once the season starts. The three ultramarathon races that I participated in can be seen as the three “spikes” in each of the time series plots. I did two very mountainous (17,000 vert and 12,500 vert) 100 km races and one very mountainous (11,000 vert) 60 km race. All ended in disappointment even though I felt great about the training. The first 100 km went very well through about 50 km where I nailed the bottom of my right foot on a sharp rock on a 6 mile downhill at sub-6:30 minute pace. I have only this year been able to increase my pace on the downhills due to the on-trail intensity sessions that provide both the stimulus for the turnover and the practice with going fast on trails. But I still have some technical improvements to make to ensure that I can avoid such rocks and other perturbations. In any case I hobbled for the next eight miles and finally decided to drop rather than ruin the rest of the running season with recovery for the injury. It still took 3 weeks for the bruise to subside and allow for regular running. Lesson learned- stay within your skill limits!

The 60 km mountain race also went well through about 40 km at which point I could not get any fuel down. This is a recurring problem and one that I have worked on and still have not figured out. My future in ultramarathons is dependent on getting the fueling figured- if I can’t then I will drop down to the Sky-distance where, if the past is any indication, I will have no issues. I took a break and let my stomach settle and then continued on to finish the race strong but not anywhere near what my potential is on that course if I can get the fueling down.

The last race, a high altitude (9500-12,500 feet) 100 km mountain race, was similar to the 60 km race in that I went through the 60 km mark in position to go sub-12:00 and then could no longer fuel for about 2 hours. I recovered and finished but it was disappointing.



The running training season also shows success with transitioning a bunch of L3 to L2 and L4 as prescribed. This was due, as in skiing, to the 2-per-week intensity sessions and the commitment to ensure that the intensity was always “quality”.


Fueling is the current barrier to any continued progress and this will be a focus for 2015- either figure it out or move on.


I added specific strength to the training program this year for the first time since racing pro/expert in MTB 15 years ago. Included is a broad spectrum of units that work maximum strength, core, and what I call “stability” micro-muscle groups in the knees, ankles, and arms. The biggest impact came from the maximum strength program- a modified version of the program outlined in “The New Alpinism” book I read and posted a review of earlier this year. This program, which is designed to recruit and synchronize a number of major motor units, has made a big difference in power in double poling and core stability on long runs. The protocol involves weight vested, max ability, low rep, pull-ups building to about 150% of body weight. I highly recommend such a program as it it a “strength not show” protocol to ensure that excessive muscle mass is not developed- an important consideration for endurance athletes where power to weight ratio is supreme. I also found that garhammers and weight vest step-ups to very effective. All of these exercises can easily be done at home in a minimum of space, no need to join or go to a gym facility.


2015 goals are still in an embryonic state but I am considering some combination of US Skyrunning ultras and Sky-distance races. Many are near (within a days drive) where I live and they all involve considerable vert and scenic courses- all of which are why I do this sport. If I can figure out fueling at the first (June) ultra then I will concentrate on the ultra distance, if not then I will go with the Sky-distance. As far as training, I think I have the basis now to iterate off of for whatever racing I do do. My coach is pleased with the fitness and specific strength gains and my success in skiing, but she is not pleased with my running performance. This is the work for 2015.


Competitiveness in Running Races – Part IV – Ultramarathons

Logical validity is not a guarantee of truth.

David Foster Wallace


At the outset of this series of posts, I indicated that many discussions involving assessments of the competitiveness of running races and, particularly, the competitiveness of ultramarathons, lack any sort of analytical context upon which one might rely to assert whether or not a race was “competitive”. In the prior three posts on this subject a methodology has been developed and successfully applied to known competitive races in the road marathon and road 10 km distances. The methodology utilizes the finishing time and finishing rank order as input data to define the normalized variables of percentage back from the winning time (from the finishing time data) and the cumulative probability/percentile rank (from the finishing rank order). These variables are plotted against one another for each race and this plot results in a graphical representation of the performance distribution for the race. Because the data are normalized, robust comparisons can be made with other races.

Analysis of the functionality of this performance distribution typically leads to a simple exponential function of the form:

y = a • exp (b • x)
x = percentage back from winning time for the cohort
y = cumulative probability of the result in the cohort
a = a pre-exponential factor inversely proportional to the excellence of the winning time relative to the cohort
b = the exponential factor directly proportional to the competitiveness of the cohort
It has been found that, with the exception of the Falmouth Road Race, an exponential performance distribution is extant in all marathon and 10 km road races analyzed. An exponential functionality is expected as the analysis is parameterizing the high performance tail of a normal distribution of competitors. The “steepness” of the exponential function (controlled by the magnitude of “b” in the equation above) is directly proportional to the competitiveness. This allows for the definition of a competitive index (CI) as equal to “b” in the functional form outlined above. Calculation of “b” and comparisons with other races and other race types allows for an analytical basis for assessing the competitiveness of a given race. An extensive comparison of marathon races is provided in Part II of this series and comparison of two well known 10 km races is provided in Part III.
The Falmouth Road Race typically exhibits a linear performance distribution and it is suggested that this is due to the presence of a “stacked field” of competitors assembled by the race directors. As a result, the performance distribution is not exponential since the field of competitors in the high performance tail is not representative of the tail of a normal distribution. Rather, this “non-normal” group of high performance competitors out-perform the expected exponential distribution. This is because the race has artificially assembled the high performance end of the population of competitors and “stacked” the field. Such races may represent the practical ultimate in competitiveness of a given race.
In this post an assessment of the competitiveness of ultramarathons is presented.


A selection of ultramarathon events have been chosen that represent the wide variety of such races. Presented here are analyses of:

  1. Western States Endurance Run- a mountainous, primarily trail 100 mile race
  2. Wasatch 100- a very mountainous 100 mile trail race
  3. JFK 50- a trail, towpath, and road 50 mile race
  4. Leadville 100- a high altitude, mountainous trail and dirt road 100 mile race
  5. Ultra Trail du Mont Blanc (UTMB)- a very mountainous, primarily trail 100 mile race
  6. Comrades Marathon- a road ultramarathon
  7. Pikes Peak Marathon- a marathon-length, mountainous trail “ultramarathon”
  8. The North Face Endurance Challenge 50 Mile Championship- a late season (December) trail 50 mile race that typically draws a large proportion of sponsored full-time athletes

The Comrades Marathon and Pikes Peak Marathon will be addressed separately as Comrades is a road ultramarathon (56 miles/ 90 km) and Pikes Peak marathon is a marathon-length “ultramarathon” (for the purposes of the analysis presented here, the Pikes Peak Marathon race is considered an ultramarathon because of the 7000 ft (2100 m) of both climbing and descending).

A great majority of the ultramarathon data fit well to the exponential performance distribution as is observed in an overwhelming majority of the other races analyzed in this study. However, in contrast to all of the road Marathon races analyzed, there are numerous ultramarathon races that exhibit a linear functionality in some years. Presented below is a summary of the data for the eight trail, hybrid, and road ultramarathons analyzed.

Ultramarathon Paramentrics All with Leadville via preview 2

Parametrics Com Pikes NFEC

Among these data are quite a few events that are best described by a linear relationship. As shown in Part III, such linear functionality can be the result of a “stacked field” of competitors where the expected normal distribution is perturbed at the high performance tail. The data for each ultramarathon will be discussed separately  below.

Western States

The Western States Endurance Run is a long-running (since 1973), very popular, and a generally accepted de-facto 100 mile trail ultrarunning championship race (although no “championship” award is given). The race is also one of the four races comprising the “grand slam of ultrarunning”. Due to the popularity of this race a lottery system for entry has been in place for quite some time. The field is limited to about 350 due to USFS Wilderness permit restrictions for a tiny portion of the course and, as of 2014, in excess of 2000 qualified applicants are in the lottery each year. Therefore entry into this race is highly unlikely from a probability perspective. As a result, the starting field of competitors is not necessarily representative of the ultramarathon population and may be skewed to some degree. One part of the ultramarathon population that can be compromised in such a system is the most competitive portion- the part of the population that is the subject of this series of posts. However, starting in 2007 Western States entered into an agreement with presenting sponsor Montrail that allows top finishers from a series of races known as the Montrail Ultra Cup to gain direct entry to Western States. This has greatly increased the probability for top competitors to get into the race.

Turning to the data on Western States presented above and singularly below, it is noted that there is a dramatic change in the functionality of the performance distribution that is directly aligned with the Montrail Ultra Cup entry process. Starting in 2007, the performance distribution of the 125% cohort reflects a linear relationship whereas prior to 2007 the expected exponential functionality is extant.

Western States Parametrics

As is seen in the Falmouth Road Race fields, this linear relationship is indicative of a “stacked field”. The temporal correlation of this change in functionality with the direct entry process from the Montrail Ultra Cup for top competitors, although not necessarily causal, has arguably produced a consistent crop of competitors that effectively “stack” the field. As an example, presented below is a plot of the cumulative probability versus the percentage back from the winning time for the 2014 Western States race. Both linear and exponential fits are shown; clearly the data are best fit to a linear relationship. Note also that, just as has been seen in the Falmouth Road race results, the competitors in the 2014 Western States race are out-performing the equivalent exponential distribution, meaning that this field of competitors is of a higher caliber than what would occur with a more random selection process.


Presented below is a plot of the same data as above as well as the data from the 2004 Western States race (pre-Montrail Ultra Cup direct entry process). This comparison is exemplary of the entire dataset. Here it is seen that the performance distribution comparison also shows that the 2014 field is substantially out-performing the 2004 field, meaning that the 2014 race is more competitive than the 2004 race.


Comparison of results from the Western States 100 2004 (pre-Montrail Ultra Cup entry process) and 2014 (post Montrail Ultra Cup entry process). The different functionality is indicative of a “stacked field” in the 2014 event.

Finally, presented below is a comparison of the 2014 Western States data and the 2009 Falmouth Road Race data showing a very similar performance distribution with essentially the same slope. The slope of the linear fit to the data provides the same competitiveness metric as the exponential factor “b” described above, i.e. the slope is the competitiveness index for these fields where a steeper slope indicates a more competitive field. What this means is that the 2014 Western States race was, within a reasonable error estimation, as competitive as the 2009 Falmouth Road Race, a race which is one of the most competitive 10 km road events in the world.


The calculated slopes for the 2007-2014 Western States races are 0.039, 0.036, 0.037, 0.040, 0.041, 0.033, and 0.039, respectively. With the exception of the very hot 2013 race, there has been a general increasing trend in the competitiveness of the Western States race, something which has been discussed anecdotally within the ultramarathon community for the past few years. This is confirmed by the observed continued decrease of about 8% in the average finishing time of the 125% cohort studied here- once again with the exception of the hot 2013 race.

Western Staes finishing times 2007-2014

Finishing times for the Western States Endurance Run, 2007-2014 (race was cancelled in 2008) showing reduction of about 8% in the average finishing time of the 125% cohort over the period.

It is noted that although a linear finishing time distribution is indicative of a “stacked field” of high performance competitors, such a linear relationship could obtain in the less probable instance of a disproportionate number of comparatively lower-performing competitors being in the 125% cohort. This would lead to a significantly lower slope and should therefore be identifiable. There is no evidence that such a low-performance linear distribution is extant in any of the data analyzed in this 4-part series.

Prior to 2007 and the inclusion of competitors from the Montrail Ultra Cup designated slots, the Western States race exhibits a uniform adherence to the expected exponential functionality as seen in competitor populations that are not “manipulated”. Also prior to 2007, the Western States competitor slots (with the exception of those competitors who were in the top ten the prior year and choose to compete) are filled via a lottery. The results of these lotteries seem to represent a random cross section of the competitor population, otherwise a non-exponential functionality would likely be in evidence. The pre-2007 races analyzed here have CIs in the 0.111-0.125 range with an average of about 0.118. When compared to the “big 5″ marathons and the 10 km road races analyzed in parts II and III, we see that the pre-2007 Western States races were, on average, significantly less competitive (about 20%-30% less competitive).

The introduction of competitor entry via the Montrail Ultra Cup events has significantly increased the competitiveness of Western States to a level that is on par with one of the most competitive 10 km-type road races (Falmouth Road race). In addition, during this time period the course record has been broken and reset two times, so not only has the race been very competitive in the post-2007 period, it is also a very fast race, once again similar to what is found in the fast and highly competitive Falmouth Road Race. Western States sets a high standard for competitiveness in ultramarathons.

Wasatch 100

The Wasatch 100 mile trail race was chosen for this study because it represents one of the more difficult mountain trail races (with over 25,000 feet (7600 m) of climbing and a similar amount of descending). In addition, Wasatch is one of the very few 100 mile mountain trail races that has been run on the same course for an extended period. This allows for transparent and robust aggregation of data from numerous years. Such aggregated data will be analyzed and presented in Part V (Syntopicon). Wasatch 100 is also one of the 4 “grand slam of ultrarunning” races.

Presented below are the competitiveness parametrics for the Wasatch 100 race for the study period.

Wasatch 100 parametrics

* the data for 2013 fit a linear and exponential functionality equally well with the exponential functionality giving a slightly better fit

The Wasatch 100 race has a course record time of 18:30:55 set by Geoff Roes in 2009. This compares to the record time for the Western States Endurance Run record of 14:46:44 set by Timothy Olson in 2012. An almost  4 hour difference in the record time for the nearly equivalent course distance reveals exactly how difficult the Wasatch race is in comparison to Western States. Wasatch is known to be much less of a “runners” race as there are a couple of miles more of vertical than at Western States. There are also substantial sections of steep power-hiking and equally difficult descents at Wasatch, both of which will slow the time of even the fastest competitors. Such a race will also have a different pool of competitors as the climbing, the attitude (+5500 feet (1700 m)), the heat (at times in excess of 100F (38C)), and the technical nature of portions of the course all filter out a good proportion of competitive runners who choose to participate in races more aligned with “runnable” courses. Wasatch still attracts many highly competitive ultrarunners given the underlying ethos of “challenge above all else” that is the fabric of ultrarunning as a sport.

The competitiveness of Wasatch is on par with pre-2007 Western States and includes a couple of more competitive “linear” years (2001 and 2008). The 2001 race winning time of 21:44:38 is the slowest time relative to other winning times in the study period. This time is about 5% slower than the next slowest time in the study period and over 17% slower than the record time of 18:30:55 from 2009. So although the 2001 race was competitive, it was not a particularly fast race. The 2008 winning time of 20:01:07 is a relatively fast time and, combined with the linear functionality of the percent back distribution, this race represents a fast and competitive example for the Wasatch 100. However, the Wasatch race is primarily a regional event and does not routinely draw a large group of known high-level competitors, so a highly competitive, linear finishing time distribution race is not necessarily a fast race as has been noted above. Of course, the weather can (and does) have significant negative effects on the finishing time, so that should be considered as well.

For the 12 “exponential” years in the study period, Wasatch has an average CI of about 0.110 compared to an average of about 0.118 in the pre-2007 (“exponential”) period for Western States. This makes Western States about 6% more competitive than Wasatch in these years. Neither the Wasatch nor the pre-2007 Western States races are as competitive as the “Big 5″ road marathons or other sub-elite road marathons analyzed where the “Big 5″ and the 2 sub-elite road marathons show a minimum average CI of 0.132 (New York) and 0.131 (Columbus). These road marathons are, on average, about 11% more competitive than Western States and about 16% more competitive than Wasatch. The two linear years 2001 and 2008 exhibit slopes of 0.035 and 0.039, respectively. These values are similar to, but generally lower than, those found with Western States although given the much smaller number of competitors in the 125% cohort in these years, the associated error is significantly greater, so it is best to keep this in mind when making comparisons.

Western States has an average of 19 finishers in the 125% cohort whereas Wasatch has an average of about 14. Western States  has a field size of about 375. The Wasatch 100 field size has grown during the study period from about 200 to about 325. Although the starting field size will play some role, the smaller number of finishers in the 125% cohort of Wasatch is at least partly due to the difficulty of the Wasatch course where the slower pace naturally leads to larger multiplicative time differentials.

JFK 50

The JFK 50 is a long-running 50 mile ultramarathon race first run in 1963 with 4 finishers and now with typically over 1000 finishers. The course is a hybrid of trail, gravel road (towpath), and road and is very “runnable”. This race was also chosen because of the longevity of the race on a stable course route thereby enabling aggregation of data.

Presented below are the competitiveness parametrics for the JFK 50 race for the study period.

Ultramarathon Parametrics JFK 50-2

As can be gleaned from the table, the JFK 50 has a similar level of competitiveness as Western States and Wasatch in the “exponential” years. The slopes in the linear years are  0.040, 0.040, 0.038, 0.038, and 0.041 for 2001, 2002, 2008, 2009, and 2011, respectively. All of these values are similar to those in the linear years of Western States. It is noted that although the JFK 50 exhibits similar competitiveness to Western States, the number of competitors in the 125% cohort is similar as well (particularly in the last few years) even though the total field size is about 3 times larger at the JFK 50. This indicates that Western States has a proportionally deeper field in the 125% cohort, likely as a result of the Montrail Cup entry process. However, the competitive depth in both races, in an absolute sense, is essentially the same.


Ultra Trail du Mont Blanc (UTMB) is a 100 mile, very mountainous (31,000+ feet (9600 m) of climbing), primarily trail race and is thought to be one of the most difficult 100 mile races. The race is also viewed as being very competitive as it attracts a large group of sponsored professional athletes from around the globe. UTMB, like Wasatch, is different from Western States and the JFK 50 as it is not considered to be a “runnable” race, meaning that there are significant portions of speed hiking, technical climbs, and slow, technical descents.

The competitiveness data for UTMB is presented below where the years of shortened races are excluded.

Ultramarathon Parametrics UTMB

UTMB exhibits a wide range of competitiveness from very low (0.077) to values on par with some of the most competitive “Big 5″ marathons (0.137). No linear years are observed. Why there is such a range in competitiveness and there are no highly competitive linear years, even with a high quality starting contingent, may have to do with the ruggedness of the course (lack of “runability”) and the highly variable weather playing havoc with even the fastest, most prepared athletes. The Alps is known for rapid weather changes that can test the mettle of anyone. Therefore the dynamics of the number of variables that play into a competitive time at UTMB is large enough to be seen in the results, independent of the quality of the field. The same might very well be the case for Wasatch and any other “difficult”, non-“runable” 100 mile race.

Leadville 100

The Leadville 100 is a high altitude (10,000+ feet (3200 m)), mountainous (about 11,000 feet (3300 m) of climbing and descending), trail and dirt road race. This race has become very popular and the race promoters have recently established a lottery for entrance into the race (starting with the 2015 event).

The parametric data for Leadville for each year the study period are presented below.

Leadville Parametrics

The Leadville 100 race is unique among the events studied here in that prior to about 2008 the number of finishers in the 125% cohort is very small. This means that the analysis for these years will have a high quantitative uncertainty, however, as will be explained below, some very solid conclusions can be made about the competitiveness of this race.

First we examine the study period of 2001-2007 as, with the exception of 2003, these races have similarly low CIs and very shallow fields in the 125% cohort. It should be pointed out that this period includes both Mat Carpenter’s record time of 15:42:59 (2005) and Anton Krupicka’s two attempts in 2006 and 2007 (17:01:56 and 16:14:35, respectively) to take down this record. At the time, these finishing times were much faster than those preceding and lead to very few competitors in the 125% cohort. In fact, in 2005 the second place finisher was over 20% back. This shows how fast Carpenter’s time was. Similarly for 2006 and 2007, the second place finishers were 10% and 20% back from Krupicka’s time. No other events studied here show winning times that are so much faster than the remainder of the field. This is indicative of the presence of a singular talent that super dominates the field which can be the result of the winner having a “perfect” day or that the winner is just that much better than everyone else. It is probably a mixture of these things in this case, however, Carpenter was clearly a “super” talent much like Jornet is today. Presented below are the percentage back versus cumulative probability plots for the 2005 and 2007 races and the 2004 race as a representative race from the 2001-2008 period. This plot shows just how superior these winning performances by Carpenter and Krupicka were.


Finishing time distributions for the Leadville 100 2005 (blue), 2007 (red), and 2004 (green) races showing how extraordinary the winning performances were in 2005 and 2007. Note, the cumulative probability values for the 0% percentage back performances (winners) for 2005 and 2007 are coincident.

Since the 2001-2007 period there have now been seven sub-17 hour finishes starting with Ryan Sandes 16:46:54 in 2011. In 2014 there were three finishers in sub-17 hours. The race is drawing a deeper field of high caliber competitors and is therefore becoming significantly more competitive. This is substantiated by the appearance of more competitive “linear” years. However, the CIs for the linear years are relatively low- 0.036, 0.037, and 0.029 for 2010, 2011, and 2014, respectively. Should the trends over the past few years continue it is likely that Leadville will ciontinue to become more competitive.

North Face Endurance Challenge Championship

The North Face Endurance Challenge Championship (NFECC) race is a late season 50 mile primarily trail race with significant prize money that started in 2007. This race also experiences little to no competition for runners from other similarly scheduled races and therefore this race regularly draws a high caliber, international field of professional runners and many upcoming elite runners.

The parametric data for the finishing time distributions for the 2008-2014 NFECC is presented below (I was unable to find the data for 2007).

Ultramarathon Parmetrics North Face EC

All of the years studied here exhibit linear finishing time distributions and the 125% cohort is as large as for any other trail ultramarathon in this study. In addition, the finishing times for this race are very fast for a trail race- for instance, Sage Canaday averaged a 7:12 mile pace for the 2014 race in muddy conditions.

The linear finishing time distributions are indicative that this race is very competitive. This combined with the consistently fast times and the deep fields means that the NFECC is arguably the most competitive ultramarathon studied here. A summary of the CIs for this event is presented in the following table along with the coefficient of and the size of the 125% cohort. Note that in this case the CI is the slope of the linear fit to the data as explained above.

Ultramarathon parmetrics NFECC slopes

The most competitive year for this race is 2012 but in that year the race course was changed and shortened due to torrential rainfall and a number of the top competitors got lost, inadvertently “shorted” the course, and were therefore DQ’d. So this race has an “asterisk”. However, a clear trend in increasing competitiveness is seen in the data and the magnitude of this competitiveness is on par with the highest values observed for Western States. The field is, however, much deeper than that of Western States. This is partly due to the fact that the race is 50 miles and late-race attrition is at a lower magnitude, but this is also due to the fact that entry into NFECC is essentially barrier-free for established elite runners.

“Other” Ultramarathons

Two “other” ultramarathons have been analyzed to provide additional context for comparisons:

  1. Pikes Peak Marathon- a trail marathon with 7000 feet (2100 m) of climbing and descending
  2. Comrades- a road ultramarathon

Pikes Peak Marathon

This race has a long history and has been run on the same course for many years. The 7000 foot (2100 m) climb followed by a return route down the same trail makes this marathon an “ultramarathon”. The race regularly draws top talent from the mountain and ultramarathon running world as well as a few washed-up elite marathoners looking for a new challenge. In 2014 the “ascent” race (run the day before the marathon) was the designated World Mountain Running Championship.

The parametric data for the Pikes Peak marathon during the study period is presented below.

Ultramarathon Parametrics Pikes Peak

The competitiveness of this race exhibits a very wide range and includes some more competitive “linear” years as well. This presents a very mixed bag with the depth of the field showing a decreasing trend. Comparisons of this race with others is best done on a year-by-year basis. The “linerar” years have CIs of 0.037, 0.036, 0.038, and 0.040 for 2005, 2010, 2012, and 2013, respectively. These values are all very similar to those found in the “linear” years of Western States and JFK 50, indicating that in these years Pikes Peak marathon is similarly competitive.  I plan to do a more extensive post on the Pikes Peak Marathon in the near future as there are numerous interesting results when the races from the 1990’s (i.e the “Matt Carpenter era”) are included in the analysis.


Comrades is a long running, very well known, highly popular 56 mile (93 km) road ultramarathon that draws an international field of high caliber athletes and is a good choice to make comparisons with trail and mountainous trail ultramarathons. The race is run in opposing directions in alternate years- one year “up” and the next year “down”.

The parametric data for the Comrades race during the study period is presented below.

Ultramarathon Parmetrics Comrades

As expected the Comrades race has very similar results to that of the “Big 5″ marathons, both with respect to the competitiveness and to the depth of the field in the 125% cohort. The competitiveness is on par with all of the “Big 5″ marathons and encompasses a range of CI values that are essentially the same.

There is no apparent difference in competitiveness on the alternate, opposing direction, years.

Comrades represents an ultramarathon that is as competitive as any standard marathon.


A substantial quantity of data and analysis has been presented here. Although the results are clear, additional insight can be gained with a few graphical comparative examples.

First we compare a very competitive road marathon (Berlin) to a very competitive road ultramarathon (Comrades). Presented below is a competitiveness plot with results from the 2008 Berlin Marathon and the 2010 Comrades Ultramarahon. The CIs are 0.158 and 0.155, respectively and therefore represent very similar races from a competitiveness perspective. They also have similarly deep fields of 106 and 179, respectively, in the 125% cohort and the finishing times for both races are very fast. In fact even the pre-exponentials are essentially the same which indicates that the finishing times for each of these races is similarly “fast” in relation to the 125% cohort.


Both races show an exponential relationship indicating that the fields are not “stacked” and represent a normal distribution of competitors. This comparison illustrates that the most competitive road ultramarathons are just as competitive as the most competitive road marathons. Part II provides more data on road marathons, all of which support the observations made here.

A question that arises is why there are no “linear” years in either the road marathons or the road ultramarathon studied here, yet the 10 km road, hybrid ultramarathon, and trail ultramarathons all show some “linear” years. One reason may be that, particularly in the “Big 5″ road marathons, the top competitors typically choose one or two marathons to run each year with the expectation that a win will have a big payoff from a remuneration perspective. So the top athletes are picking and choosing which marathons to enter, perhaps to best increase their odds of winning. As a result there are no truly “stacked” fields because the top competitors are being distributed among the numerous prestigious marathons rather than all showing up to just one event. In the case of trail and hybrid marathons, similar economics do not prevail so the competitors may not be primarily picking races in a way that would increase their odds of winning rather they are just engaging with the best competition that they can find and end up “stacking” the fields of certain races (like NFECC). In the case of the Falmouth Road Race, 10 km races do not require the same kind of recovery that marathons and ultramarathons do and therefore a competitor can race many more 10 km events and not be as concerned with recovery and injury. Certainly there are other possibilities that may explain the lack of “linear” years for the road marathons and ultramarathon.

Moving on to comparisons of trail ultramarathons with road marathons we will utilize the 2008 Berlin Marathon as an example of a highly competitive, deep, and fast exponential finishing time distribution as a basis. Presented below is a comparison plot of the 2014 Western States, the 2002 Western States, and the 2008 Berlin Marathon. Here it is clear how much more competitive a linear distribution is when one compares the 2014 Western States (linear) to the 2002 Western States (exponential). Even though the depth of the 125% cohort is the same (21 for both 2014 and 2002), only 30% of the of the cohort is less than 15% back in 2002 whereas this value rises to 60% for the 2014 race. That is a big difference in competitiveness.


It is also clear from this comparative graph that the depth in the Berlin Marathon is much higher and the competitiveness is very high as well (exp = 0.158) when compared to the Western States 2002 race (exp = 0.118) a difference of over 25%. The 2008 Berlin Marathon is significantly more competitive than the 2002 Western States. Note: when comparing these finishing time distributions one must take into account the pre-exponential values as in this case they are very different (0.0195 for 2008 Berlin Marathon and 0.0523 for the 2002 Western States race) and this leads to a displacement of the Western Sates 2002 graph to a position “above” 2008 Berlin Marathon in the plot. This is a result of the winning time for the 2002 Western States being comparatively slow in relation to the winning time for the 2008 Berlin Marathon, for the respective 125% cohorts. However, the 2002 Western States graph is clearly a shallower function, and therefore less competitive, compared to the 2008 Berlin Marathon. Prior to 2007 and the introduction of entry into Western States via the Montrail Ultracup, and with just four exceptions in the 67 “Big 5″ marathon races analyzed, none of the Western States races in this period were as competitive as the “Big 5″ road marathons. Certainly, on average the pre-2007 Western States races were much less competitive than the “Big 5″ road marathons in the same period.

It is difficult to analytically compare the 2014 Western States race to the 2008 Berlin Marathon as the finishing time distributions are functionally different and therefore yield different parametrics for assessment of competitiveness (i.e. slope of the linear fit for linear functions and the exponential factor for exponential functions). So, in the absence of any sort of normailzation approach, it is best to make comparisons of exponential finishing time distributions as a group and likewise for linear finishing time distributions. In the case of the 2008 Berlin Marathon, the CI is one of the highest measured in this study (only the 2013 and 2014 Boston Marathon exhibits a higher value of CI) and this value is higher than any of the ultramarthons with exponential finishing time distributions studied here.

A representative example for linearly distributed races presented below is a comparison of 2014 Western States with the 2009 Falmouth Road Race and the 2014 NFECC.


In this case the slope of the linear fit is the CI and we find about a 6% variation in CI for this comparison which is not statistically significant, meaning that, within the error of the fit, all of these races have about the same competitiveness. This is a significant finding as the comparison is between two “championship” ultramarathons and a known highly competitive, international road race. Based on this comparison it is clear that the most competitive ultramarathons are as competitive as the most competitive road races.

A summary of the data in tabular form is presented below in ranked format. For each event the associated competitive index (CI) (either the exponential factor for exponential functional years or the slope for linear functional years) is tabulated along with the standard deviation. Also provided is the size of the 125% cohort analyzed, the standard deviation of this value, and the number of event years that have been analyzed.


Among the races that exhibit exponential functionality the Boulder Boulder 10 km road race is the most competitive on average. However, the 2014 Boston Marathon was the most competitive race analyzed in the exponential functionality group. We see a distinct and significant drop in competitiveness for the trail ultramarathons where Western States 2001-2006 has the highest competitiveness and Leadville is the least competitive race among the ultramarathons studied. Also clear is a significant reduction in the size of the 125% cohort with the trail ultramarathons when compared to the road races. This is certainly due to the much smaller fields in the ultramarathons but may also be a reflection of a generally less deep competitive field in the 125% cohort.

In the linear functionality group, the North Face Endurance Challenge Championship is the most competitive with JFK 50, Western States 2007-2014, Falmouth, and Pikes Peak Marathon in close succession. Given the limited data, both the Wasatch and Leadville races that exhibit linear functionality should be considered individually and not as means, although the means are provided for completeness. Of the linear group it is best to limit any conclusive comments to the NFECC, Falmouth, and Western States where from a statistical perspective each have about the same level of competitiveness. Once again, as expected, the road races are much deeper in the 125% cohort.


At the outset of this series of articles, it was observed that much of any discussion of competitiveness lacked a fundamental analytical basis for comparisons and that such discussions were of limited value as a result. Provided here is a proposed methodology for development of competitiveness metrics including assessment of field depth and relative speed for the winning time. Such metrics can serve to quantify and “calibrate” competitiveness in a way that facilitates comparisons and can lead to constructive discussion of competitiveness in distance, marathon, and ultramarathon running races.

The primary conclusions from this work are:

  1. Generally speaking, finishing time distributions for distance, marathon, and ultramarathon races exhibit exponential functionality. Such functionality is expected from a normal distribution of competitors in the event.
  2. In the case of “manipulated” or “stacked” fields, a linear finishing time distribution can obtain. Such events are typically (although not always) very competitive due to the non-normal quantity of highly competitive runners in the 125% cohort. These linear functionality races are arguably more competitive than the exponential functionality races for the 125% cohort.
  3. The competitive road ultramarathon analyzed here (Comrades) is just as competitive as the most competitive road marathons and distance road races.
  4. The most competitive trail ultramarathons (Western States 2007-2014 and NFECC) are just as competitive as the most competitive distance road race analyzed here (Falmouth).
  5. 3 and 4 indicate that ultramarathons (either trail or road) can be just as competitive as any of the most competitive road races.

Addendum – 7 January 2015

There has been some discussion of the relative ranking of the most competitive 2014 ultramarathons over here. Although other races might also be considered, Lake Sonoma 50, Western States Endurance Run, and North Face Endurance Challenge Championship 50 are clearly among the most competitive trail ultras in the US for 2014. Presented below is a competitiveness plot comparing these three 2014 races all of which have a linear performance distribution for the 125% cohort.


Inset on the graph is a chart of the competitiveness index (CI), the coefficient of determination (R^2), and n (the number of finishers in the 125% cohort). From both a CI and depth perspective, NFECC is the most competitive race of this group with the highest CI and the deepest field. Western States 2014 is next, and Lake Sonoma is the least competitive of the group. One might argue that the NFECC race should be given some sort of proportional weighting to account for the highly competitive nature of this year’s race and so on with other races being considered when choosing an award like the UROY.

I will encourage others to conduct such analyses to found any position on the competitiveness of a given race. But, as it concerns UROY, in the end an award is an award and such will always have a degree of subjectivity that will, hopefully, nucleate some interesting and fruitful discussions.

Salomon Snowcross “Sense”

Having given up on Salomon offering a gaitered low drop winter running shoe with studs  for 2014/15 and after trying another brand without studs, I decided to “modify” the pair of Snowcross CS that I bought a couple of years ago. I stopped using these shoes soon after receiving them as the 11 mm drop was playing havoc with my Achilles and I felt like I was dragging my heel a bit more than was comfortable. While doing some work in my garage the other day I looked up and saw the Snowcross sitting on the shelf and with the imminent (and now, real) arrival of snow and ice it became abundantly clear that I should at least try to use these $200 running shoes.

Looking the shoe over and looking up the “official” drop as 29 mm / 18 mm or 11 mm it seemed possible to lower the heel sufficiently as to come close to a 4 mm total drop. This is accomplished by removing all of the heel lugs and leaving just the studs. Here are a few of images of the modification:


Starting point: essentially fresh sole. All of the heel lugs will be removed.


Finished product- heel lugs cut off and sanded smooth. Heel studs are anchored from the interior of the shoe so they are still quite stable; however only time and miles will tell.


Closer view of the hatchet job- finish work is not a strong suit.


With lugs removed, the studs are still at the same original height but they dig into the snow and ice. They are also ‘flexible” and the support structure deforms allowing for a much lower drop.


I have only had about 30 km of running on these shoes so this is preliminary but the lower drop is very apparent and very comfortable on snow. After a few years of running only in the Sense, these are now very comfortable for winter conditions. Note: I also switched out the original Snowcross insole with an insole from a pair of Sense 3s. I did this because the Snowcross insole has some structure at the instep and around the heel. The Sense insole is very minimal. The switch has eliminated some discomfort I initially felt.

As far as traction these shoes are about as grippy on snow and ice as is available. The gaiter keeps the feet warm but the use of the very inferior “ClimaShield” fabric is a negative. The CS is not waterproof or even water resistant and if you get near anything even approximating moist conditions your feet will quickly become wet and the CS does a great job of not letting any of that water out- exactly the opposite of what such a fabric should do. The net- your feet will become very cold very quickly. The Snowcross are best used in dry, snow and ice conditions, something that we, fortunately, commonly have here in the central Idaho mountains. I will put up an update after a couple hundred miles.

So… a stop-gap measure and hoping that Salomon will see fit to offer a true Snowcross Sense model soon…. with a GTX upper and gaiter please!! We know you can do it- look at the X-Alp.


Update 21 January 2015

Well after a freak fall whilst skiing, I injured my rotator cuff and have, unfortunately, been doing a lot more running this ski season. On the positive side I had forgotten how enjoyable winter running on packed trails can be. I now have about 300 km and 10,000 m of vert on the Snowcross “Sense” and can report that they are performing very well.


We have had a good bit of snow and lots of sunny days. This makes for conditions of packed powder alternated with icy conditions on those trails with southern exposure. I have also been out in 4-6″ of fresh powder on packed trails. All of these snow conditions have been relatively dry, so no experience to report with truly wet conditions. In all of the conditions I have experienced the Snowcross “Sense” have been outstanding- particularly w/r/t grip and stability. The shoes are warm and the trail performance gives one the confidence to stride out as if it were mid-summer. The removal of the heel lugs has not compromised grip and/or braking to any noticeable degree and the lower drop is quite comfortable for those who use low drop shoes.


Optimal Age for Elite Marathon Performance – Present and Future

A couple of recent posts have pursued an understanding of the concept of a potential “optimal” age for elite performance in the road marathon event.

Graydon uses top finishing times (times less than 2:11:00 for men) and “age on race day” to evaluate if there might be empirical support for determining an “optimal” age for excellence in this event and if there has been a change in the age distribution pre- and post-2000. He finds that there is essentially no change in average age between the groups and that this average age in the sub 2:11:00 population is about 28.3 years with a standard deviation of about 4 years. This result is essentially in agreement with the widely-held opinion that late 20’s to early 30’s is the peak performance period for elite marathon performance. No surprise here.

Alex finds that there is a statistically significant drop in the average age of the top 100 marathon times in each year since the 2008 Olympic Marathon 2:06:32 gold medal and Olympic record performance by 21 year old Samuel Wanjiru. It is suggested that Wanjiru inspired other younger runners to move to the marathon at an earlier stage of their careers. In another very nicely presented graphico-statistical piece by Alex and collaborators on what it will take to run a 2:00:00 marathon, it is argued that the person will be 5’6″, 120 lbs, very efficient, and in his early 20’s. The age part of this prediction is based on the same data as presented in Alex’s other post.

Presented here is an analysis of the same data that Graydon uses (the top 2500 men’s marathon times ever recorded, including individual repeat performances). The analysis is more granular in that rather than picking a “threshold” marathon time below which times are included, the same age correlated data are analyzed based on percentage back from the fastest ever time. It is argued elsewhere why “percentage back” is a more insightful way of looking at athletic performance in timed events, running in particular. A “percentage back” perspective on performance is used by many coaches to gauge progression for a developing athlete and in some sports (e.g. cross country skiing) “percentage back” is regularly used by National sports organizations to select competitive teams (e.g. Olympic, World Cup, and World Championship teams).

Graydon’s analysis approach utilizes a histogram count method to assess the statistics of the distribution of age for marathon performances below 2:10:00. Although insightful for such “elite” and “sub-elite” marathon performance, the analysis does not reveal any information on the shape of the distribution of performances within this “elite”-to-“sub-elite” category. Similarly, the analysis provided by Alex looks at the top 100 marathon performances for a given year with no upper time cutoff nor any granularity as to the shape of the distribution of finishing times (or “percentage back from the fastest ever time”) that are included in the analysis.

Analysis Approach

Here we will look at the fastest 2500 marathon times ever recorded as a function of the “age on race day” for each performance. The period for these data is from 1967-2014, in other words the oldest recorded marathon finishing time that is within the top 2500 times was in 1967 (2:09:37 by Derek Clayton (AUS) at Fukuoka on 12 December 1967). Similar to Graydon, we will also look at differences in two sub-periods contained in the dataset- 1967-2000 and 2001-2014 to attempt to detect any change in the age-correlated distribution of performances.

Presented below is a plot of all of the data (1967-2014) where the abscissa is the “age on race day” and the ordinate is percentage back from the fastest ever time (Dennis Kimetto, 2:02:57, Berlin 2014). Also provided are the finishing times associated with the percentage back values.


The data form a diffuse “whorl” terminating with an apex at about an age of 30 years for the fastest performances. Symmetric lines have been placed upon the data to guide the eye in looking at patterns in the data.  This “whorl” shape and apparent deviations lead to numerous observations including:

  1. The obvious “bulge” at younger ages (i.e. to the left of the left hand guideline on the core “whorl”) indicating that there are many more sub-3% back performances at ages less than 25 years than there are at ages greater than 30 years. This observation supports Alex’s prediction that the future sub-2:00:00 marathoner might be significantly younger than the current world record holder.
  2. That the higher proportion of younger performances becomes even more pronounced at the sub 2% back performance level. My personal experience has been that athletes that show results in the sub-2% back category are the most likely to become consistent race winners and potential record holders; I will argue that it is more likely that a record will be set from this group and since this group is “younger” we may very well see a sub 25 year old record holder.
  3. The non-linear decay in the number of performances as the world record is approached- in fact this decay is approximated by a simple exponential function as is expected in athletic performance of timed events.
  4. The significant number of outstanding “old guy” performances to the right of the right hand guideline- this gives old guys (35 years+) some level of hope….
  5. the “center of mass” of the “whorl” is, of course, right about where Graydon calculates the average age in the dataset (28.3 years).

Now let’s take a look at the same dataset split into two temporal populations- one population comprised of the performances from 1967-2000 and another population of performances from 2001-2014, the populations that Graydon analyzed. Presented below is the same plot as above with these two populations shown in red (1967-2000) and blue (2001-2014).


Here we have the following observations:

  1. None of the performances in 1967-2000 population are within 2% of the current world record, i.e the world record time has decreased by about 2% over the last 14 years.
  2. Nearly all of the sub-3% back performances are from the 2001-2014 population and this population is significantly younger than either the dataset taken in it’s entirety or of either of the sub-populations. The average age of the sub-3% back population is 26.95 years.
  3. The average age of the sub-2% back population is 27.1, about the same as the sub-3% back population, but the average age of the sub-1% population is significantly higher at 29.7 years.

Points 2 and 3 above indicate that Alex’s prediction of a younger than 30 year old sub-2 hour marathoner may not be entirely supported by the data. As the performance progression proceeds from sub-3% back to sub-1% back, the average age is increasing significantly and may be indicative of the maturing of those 21-25 year olds that show exceptional promise (sub-3% back or less) toward their peak somewhere around 30 years of age. Of course the size of the sub-1% back population is small (11) compared to the sub-2% back (62) and sub-3% back (192) populations. Statistically the error is inversely functional with the square root of n (sample size) so the error associated with the 1% back population is much larger than that of the other two comparison populations.

Now let’s see if the “Wanjiru effect” mentioned above is supported by a more granular look at the data. Presented below is, once again, the same plot as previously only now the populations are divided into the pre-2008 group and the post-2008 group as Alex has done for the top 100 marathon times in each year. Here we are aggregating all years for each group but at the same time attaining a higher statistical power.


We have the following observations:

  1. An overwhelming majority of the “young and fast” times have occurred since 2008- this is what Alex calls the “Wanjiru effect”- perhaps due to younger runners moving into the marathon event earlier than was typical prior to 2008.
  2. The average age of the sub-3% back group from the 2009-2014 population is 26.6 years whereas the average age of the sub-3% back group from the 1967-2008 population is 28.2 years. This is a significant (1.6 year) difference and supports the assertion that elite marathoners are getting both faster and younger; this is perhaps the strongest support for Alex’s “early twenties” future sub-2 hour marathoner.

Bottom Line

Aggregated data for elite-level marathon times indicates that a late 20’s to early 30’s age is currently “optimal”, however, since 2008, elite-level (sub-3% back from the world record) performances are showing a significant shift to younger ages when compared to the pre-2008 elite-level population. This trend, if it should continue, will clearly yield an increasing population of “young” marathoners, some of whom, on the right day, could take down the world record…. or go sub-2 hours?