The LetsRun webpage

This is another website that I would recommend to you. LetsRun is, according to the site's authors, the Running's Homepage. And it does indeed a great job at that.

I knew about the site and I went back to it recently when I was looking for articles concerning the Tokyo Marathon. You know, the 2021 Tokyo Marathon which was held in March 2022 still keeping the 2021 moniker while the 2022 Tokyo Marathon was cancelled (!). I was following Eliud Kipchoge's attempt to win another one of the World Marathon Majors. Which he did with flying colours, and the 4th best time in history, confirming the fact that he is the best marathoner ever. (By the way, Brigid Kosgei won the women's race with the 3rd best time ever).

LetsRun is a great site, perhaps a tad US-centric, but still of very high quality. I wouldn't hesitate to recommend it. They have a paid tier, what they call the supporter's club, which gives access to some members-only content. But most of the site is available to everybody.

While perusing the homepage I found, in the message board discussions, the announcement that Iowa has banned transgender athletes from competing in women's sports. The regular readers of the blog know that I am a great fan of women's athletics and I cannot stand the cheating by men who decide to participate in women's competitions. I have a great respect for transgender individuals but when it comes to competitive sports a line must be drawn. Women have struggled for decades in order to be allowed to compete in athletics (and sports in general). It would be really sad to see everything collapse in the name of gender self-determination freedom.

Weight management and exercise

I am often shocked when I meet former champions and realise that they have put on dozens of excess kilos. Admittedly this is not the rule: most ex-athletes manage to keep their weight under control but I have met too many overweight ex-champions to ignore a definite tendency to gain weight once one stops heavy training. This got me thinking on the relation of exercise to weight control and I decided to dig deeper.

The widespread belief is that exercise plays an important role in weight management. Just pick up jogging (or any similar moderately strenuous exercise) and you are going to shed your extra kilos. Alas, the situation is not that simple. When you count the extra calories burnt during the exercise, you realise that they do not amount to much. And thus the mantra of nutritionists became "if you wish to lose weight go on a diet". And they are essentially correct. But does that mean that exercise is useless when it come to controlling ones weight? Far from it!

To put it in a nutshell, the best way to prevent weight gain is a combination of diet with exercise. Exercise alone will not suffice for diminishing ones weight (but it becomes more and more effective when its intensity increases). However it does prevent a weight gain and, what is perhaps even more important, it prevents regaining the weight that has been previously lost (perhaps by a combination of diet with exercise).

But how does exercise manage to help in weight control? Most people believe that exercise increases one's appetite and as a result one eats more and gains weight. It turns out that this belief is plainly wrong. Already in the 50s a study on Bengal workers obtained some very interesting results. Active people ate more, in proportion to the intensity of their work, but remained in a calorie balance, with no excess body weight. Sedentary people on the other hand ate more and were overweight.

As one can imagine similar studies have been performed many times since and the initial results were fully confirmed. The relation between physical activity and appetite follows the curve shown in the diagram below. Sedentary people eat more than they should while active people adapt their intake to their levels of activity.

So the question is, what does cause this difference? A recent study has addressed this question and the conclusion is that exercise facilitates weight control, partly through its effect on appetite regulation. The belief that exercise does increase the appetite is well-founded. However this effect is counterbalanced by an improvement of the sensitivity to signals of satiety. One may feel hungrier after exercise but thanks to the latter one is less prone to overeat. Exercise enhanced appetite control is most beneficial to losing weight, or maintaining lost weight, since one is more sensitive to feelings of hunger and satiety.

These are solid conclusions based on observations but what is lacking is a deep explanation of how exercise regulates appetite. Physiological factors, like levels of insulin, have been proposed as a possible explanation. A theory that I ran across in an article that largely inspired mine is that of the "hungry ape". Humans evolved in a food-poor environment and had thus the tendency to gorge themselves when they encounter abundant food. On the other hand the body resists gaining weight, since becoming fat and slow diminishes the chances of survival. Physical exercise maintains the mind-body link while a sedentary life leads to a dissociation of the two and thus an unnatural increase of body weight. 

Another theory which I find appealing is one based on the relation of exercise to anxiety and depression. It has been known for years that exercise is just as effective as antidepressants. And the same is true as far as anxiety is concerned. Sedentary people often use food as a source of comfort, while active people turn to exercise as a source of feel-good sensations.

So while diet is incontrovertible if one wishes to lose weight, an exercise regime does help to the point where dieting becomes almost a reflex. Faced with the cornucopia of food (I am referring here to our western societies, where food abundance is the rule) a physically active person is more prone to ask himself whether he is really hungry or whether it's the food that is overly tempting instead of blindly succumbing to the temptation. Thus, thanks to exercise, maintaining a calorie balance becomes a way of life and weight control is automatically taken care of.

On running velocities

A few years back I published a post entitled "On marathons and ultramarathons". The main aim of that post was to present the dependence of the mean velocity of a race on the distance. In that presentation I was focusing particularly on the transition between long and super-long races.

Recently I was discussing with my friend and collaborator G. Purdy and he pointed out that (many years ago) he had published an article where he was presenting this dependence together with a best fit by a simple analytical expression. 

I have always believed that there are different regimes in the running performance depending on the biomechanical and physiological processes involved and thus one should better separate the various ranges of distances. So I decided to revisit the question of the mean velocity as a function of the distance of the race. The figures below are all based on the current men's world records but similar results are obtained with the women's ones.

The first figure shows the mean velocity for distances from 50 to 1000 m. Distances up to 400 m are the sprint events. Here one observes first a purely biomechanical effect. The athlete must accelerate over a certain distance before reaching his maximum velocity. Once this velocity is attained it can be maintained for a short time before deceleration appears. 

For races not exceeding 800 m there is a complicated interplay between the various energy production mechanisms, but beyond this distance the main energy source is the aerobic mechanism. However an athlete cannot cover the total distance producing the maximum possible power under the aerobic mechanism. The main reason for this is that despite the aerobic character of the effort there is always a significant quantity of lactate produced which limits the aerobic power and thus the mean velocity. (I am aware that I am over-simplifying things here).

Once one reaches races where the duration exceeds 2-2.30 hours another physiological change appears: the glycogen stored in the muscles is depleted. So the athlete can pursue his effort only by burning lipids. The consequence of this is that the maximal aerobic power that can be sustained for these super-long races is diminished. Hence the transition observed in the figure above where one sees that the mean velocity for races from 50 to 300 km follows a curve different from the one obtained in the middle and long distance regime.

The curve above shows the mean velocities of ultra-long, multi-day, events. Here, although the mechanism is physiological it has not to do with energy production.  In multi-day events the athlete must take time to sleep, eat and take care of other bodily functions. This has a direct impact on the mean velocity. It is in fact interesting to remark that the lower curve, when back-extrapolated crosses the upper one at around the 12-hour performance. This means that for races with a duration above 12 hours a non-negligible amount of time would normally be devoted to non-running moments. The fact that despite this the 24-hour record follows the upper curve means that the athletes minimise these pauses for the sake of the record. However as the race duration becomes longer this is no more possible hence the transition to the lower curve.

The last curve is the analogue of Purdy's curve, now extended over the whole spectrum of distances. One can distinguish the four regimes identified above. And it is interesting to notice that the records of the Marathon and, to a lesser degree, that of the 100 km are better that what one would have expected by drawing naïvely a continuous curve. This is due to the fact that the former is a most popular distance while the latter is the longest distance over which a world record is homologated by World Athletics. 

An interview with G. Purdy

Before proceeding to the interview itself I would like to tell the story of how  I met with Gerry Purdy. It is worth telling. 

As the regular readers of my blog know, I am a fan of combined events and their scoring. I have worked on and off on scoring since my childhood (one of the first articles in the blog is telling that story), have developed scoring tables for finswimming, and in 2007 I published an article in New Studies in Athletics on the physical basis of scoring. Doing the bibliography for my article I became aware of the work of Purdy. He had, in 1972, presented a PhD thesis on scoring. An abridged version of the thesis was published in the form of three articles which appeared between 1974 and 1977 in the journal Medicine and Science in Sports. 

At the beginning of 2021 I decided to publish a series of articles on the theories of scoring. I was familiar with the approach of D. Harder (which was the basis of my article in NSA) and I had an idea of the evolution of scoring in athletics, mainly from Zarnowski's writings. However I felt that, if I wished to do justice to a century's efforts to produce adequate scoring tables, I had to delve in the older writings. I remembered the writings of Purdy and tried to find the articles quoted in his bibliography. Curiously many of those were available but for some I drew a blank. However I am not easily discouraged. Purdy had defended his thesis in 1972, so we had to be of the same age give or take a few years and, most probably, he was still alive and kicking. I searched for his email, found it and wrote to him. Gerry wrote back the next day and the contact was established. 

Concerning the old references this is what he wrote in his email:

As for the older copies of reference articles, I do not have any of them. But, there is (slight) hope: When I finished my Ph.D. at Stanford, I put a copy of the thesis along with a box of reference articles into the Computer Science Department Library. That box may still exist so if you are interested in continuing your detective work, you can contact them and see if it still exists and, if so, to get a copy of the articles in the box mailed to you.

And here starts the next interesting part in my quest. I went to the web page of the Terman Engineering Library of Stanford University and I decided to contact the Librarian, Ms. Linnea Shieh. (I do not know why I chose Ms. Shieh. It was probably because she had the most welcoming smile). 

I wrote to her and again I had an immediate response. She was able to locate the item Gerry was referring to, had it repatriated to the campus (from the archive storage) and had the papers I was looking for digitised. Moreover she did also scan the thesis of Dr. Purdy and thus this document (of which only two or three hardcopies did exist) is now safe. (I am greatly indebted to Ms. Shieh: without her precious help I would never be able to complete the "scoring theories" series). 

Once the contact with Gerry Purdy was established we started to have more technical discussions and, one thing leading to another, we decided that by combining our expertise there was something to be done in the domain of scoring. So now we are collaborating on an article that has been completed and submitetd  for publication. 

The "Theories of Scoring" series in the blog was put on hold in June 2021 in order to make way for the series "The long and arduous road of women to the Olympics". But as I was pointing out in that last post, a second season was in preparation. Articles will appear in the following weeks (months?) but I decided  that, given the capital role played by Gerry Purdy, it would be interesting to launch this second season by an interview. I suggested this in an email of mine (or was it in a zoom session?), Gerry accepted, I sent him a list of questions and here you have the interview.  

BG. What attracted you to T&F? 

GP. This is an interesting question. When I was in high school (Northside High, Atlanta), I was active in sports. I played Little League baseball and JV basketball. I also enjoyed running cross country. But my PE teacher (Coach Arthur Armstrong) had everyone participate in a decathlon. He used it to identify promising athletes for his Track & Field team. I played Center Field in Little League and could throw the baseball farther than most others and definitely more accurately. I threw the javelin farther than anyone else in the decathlon so Coach Armstrong told me to strop running and start lifting weights. I got fourth in the State Championship in the Javelin in the 11th grade (as a Junior) and then won the Georgia State Championship as a Senior (1961). I also set the Atlanta City and State record at 184’ 10” (56.34 m). See below.

An interesting byproduct of all this was that I wondered how someone won the decathlon and got to look at the scoring tables. That was more of an awareness and not research or studying about it. 

BG. Have you been an athlete yourself?

GP. Really answered above, but in addition, I did road running races after college including 10K, Half and Full Marathons. My best marathon time was 3:23:00.

Gerry (right) at the Western Hemisphere Marathon - LA 1966

BG. Why did you decide to work on scoring tables?

GP. While I was running with a friend (Jim Gardner), we were trying to figure out the appropriate pacing for a training run and looked at the Decathlon tables to get ratings up to the 1500m. I happened to plot the points vs. performance and noticed to my (shocking) surprise that the field events were regressive (sloped over) instead of progressive (sloped up). 

I was so shocked that I called the head of the IAAF (John Holt) in London and explained the problem to him. I told him they all had to be progressive. He then said, “Oh really? Why don’t you fix it?” And as they say, the rest is history.

Once I had finished my work I did a presentation to the IAAF Technical Committee. The reaction of the committee members was very favorable. Unfortunately the functioning of the IAAF Technical Committee is of highly political nature and, since many countries tried to bid on being author of the next scoring table, it  took 12 years for the IAAF Technical Committee to approve a new table. Mr. Holt confirmed back to me that my work was instrumental in creating the new table that would be progressive throughout and be based on my Ph.D. thesis at Stanford.

BG. How about RunningTrax? 

GP. I tried a number of times to make a business out of my research but it was much harder than I thought. At first, I tried to build a Web app, but mobile was growing so fast that we stopped that and got a mobile developer to do a joint venture. We got the RunningTrax iOS app built, but didn’t have any funds to promote it so I tried to license the software to others who already had apps, but there was too much NIH (“Not Invented Here”) from others. And, then, I tried to license the app to the various road races since they generated a lot of money (25,000 runners x $30 entry fee is almost a million dollars). They all wanted the app but wanted me to build a custom version for just them (with their branding) plus they wanted me to go find a sponsor that would pay me plus them!

I eventually had to back away as I had to make a living and my hopes and dreams for making millions off of RunningTrax faded away (🙁). 

BG. And the current HPM project? 

GP. Back when I finished my thesis and Ph.D. from Stanford, I knew there was a LOT more work to be done. First, I had a ‘back of envelope’ math formula from someone in the Stanford Operations Research Dept. (I didn’t know one formula from another and I thought it was likely ‘good enough’). Second, I had a small amount of data in the order of a few hundred data points for each of the three data points needed to solve the non-linear equality with three unknowns. The high end was easy but I had to have a realistic mid-level set of performance so I just arbitrary assigned points the average Master’s performances to be the 500-point level. It was all a hodgepodge of stuff thrown together with some least-squares curve fitting software on the Stanford 3600/65 mainframe. Please, don’t share this with the folks at Stanford. They might withdraw my Ph.D.! (😊). 

I believe we are going about this the right way now. We’re using percentiles for points along the curve (as Harder did) and good math (primarily to be developed by you) to bring the two together. I suspect we will NOT have to use least squares software. I am not positive, but I *think* that if we have many points along the percentile rankings (e.g. going at in .01% increments), we will then have the result of a differential formula that should provide a calculation of the points from performances and vice versa (just as Newton did when he created Calculus). Generating points from performances is always easy. It’s going the other way (points to performances) that’s been more challenging. 

So, I created the Human performance Modeling (HMP) project with professor Jill McNill on the Biomechanics depertment as USC. The HPM project allows us to solve the points-performance relationship for running as well as a number of other events in which a symbiotic relationship exists. And then, the real benefit to the world is to provide guidance on how to train to perform well (‘personal best’) while minimizing the risk of injury. Our APIs and data should be able to then be picked up and utilized everywhere. 

Here’s one good example: road races will be able to take our performance to points formulas and  display the point score (likely to .001 precision) for every runner in a race. With the point score, clubs will be able to run virtual races comparing some who run 5K with others that run 10K, etc. With Big Data analysis, we can determine handicaps of one race against the ‘flat, good weather’ standard (much like the way golf courses are rated with the slope compared to par). 

Here’s another: with the 100% velocity for all running events, we’ll be able to (first) estimate the training pace based on my past research of 87.5% to 92.5% velocity. You need it fast enough to achieve the training effect but not too fast to cause injury. With the HPM models and large data reference, we’ll be able to determine a much better estimate of % max velocity in which to train. But wait, there’s more! We can do much better when we utilize large data from runner’s training data (collected most commonly from devices like the Apple Watch).  

Later, we can collect training data and outcome data for tens of thousands if not millions of runners. Professor Rand Wilcox of USC can help us sort through the data to refine the % velocity min to max that will achieve the best result without injury. We may find that the % velocity min-max is different as you go up the performance point levels. We may find that those who train via running perform best if they run alternate days with cross training (or perhaps not) or how much rest is necessary to prevent injury. 

How would we get the training data? Simple, provide a training capture API that would run in the user’s smartphone and/or smartwatch. The user gives HPM permission to use their training data without identity. 

And one final example: we can use the training data with race data to then correlate starting capability, workout data along the way and then outcome data (performance and point level) to be able to give data-driven estimates of what is a realistic goal for runners – all personalized to their level of ability. No more saying “I want to break 3 hours in the marathon” when 4 hours is the realistic goal. Goals will simply ‘fall out’ of the Big Data and eliminate guess work. That alone will be a gigantic move forward as it will help runners not over estimate their goals and, as a result, not end up injured as many do now because of over training.

I am going to assume that Jill, Harper and Christian (BG: members of the HPM working group) will come up with a number of other examples. Thus, the HPM could have a profound impact on the entire world of training to race activity – not only for running but also for cycling and swimming and, perhaps other events as well.

Thanks for asking me these interesting questions!