Winning a Marathon

The proliferation and participation in the marathon has increased substantially in recent years.  No longer is the distance an event reserved for the super-athletic, but at least in the US one can from many vantage points on highways or streets see the infamous "26.2" sticker donning a rear windshield.  In a previous post I logged participation in marathons worldwide and as can be seen from the animation, certainly in the US this has increased over time.

As participation becomes more the norm we turn now to the question of actually winning a marathon.  The Association of Road Racing Statisticians (yes there is such a thing) maintains an excellent site with all sorts of data on the marathon event as well as other distances.  I created a large file from their data of all marathons each year in the world with their winners.  Marathon in this dataset is anything that is over or equal to 26.2 miles, so that includes trail races or ultra-marathons.  This will make sense when some of the finishing times are seen below.  I plan on talking more about this dataset in future posts but for now we'll look at winning a marathon in the USA.

According to this dataset, in 2013 there were 1,984 marathon events in the US (wow).  And seemingly Fall is the most popular time to host them (ya know before the Holidays).

So how fast do you need to run to win one of these or at least have a decent shot at winning?  Obviously lots of variance depending on which one - or as may be intuitive race purse/recognition is highly correlated with race speed*.  In general for the past several years in the US, the time needed for a male on average is about 3 hours.  As more races have been created giving opportunity to more people, the average time needed to win a marathon has decreased slightly.  In 2013 you "only" needed to run in the 3:30 range to win a marathon, that is on average across 1,984 races.

Interesting to note that to qualify for the Boston Marathon in 2013 as a male a time of 3 hours was needed (wonder if they based that on average winning times over the last 10 years).  Female winning times look similar in that they too have a slight bump in 2013/2014 in terms of "slower" winning times on average.

More recently across all the marathons in the USA, women are winning marathons at around the 4 hour mark.  Again, this all depends on the race one is entering.  But if you are like some of the people who run multiple marathons a year, hitting these averages gives you a decent chance at winning...especially as you heavily consider the number of participants and/or the purse involved ;-) 

For those interested, most of the code for pulling this data and the graph(s) will be on my github page.

*More challenging races (ultra-distance, trail, etc.) are included in the dataset (not all races were created equal) and perhaps more vetting on this dataset on individual races is needed to fully appreciate the finishing times.  A more vetted dataset would surely yield a lower finishing time for both male/female, however combing every race is beyond the scope of this post...maybe when I have a bit more time.

Networks and Their Importance

This is the first of hopefully more posts on social networks and how important they can be potentially to retrieve information about people.  The post that got me interested in networks themselves was a fictitious story of the government using social network analysis (SNA) to find Paul Revere.  It essentially shows how by knowing the memberships people have in certain groups we can infer as to peoples' relational connections (with each other) and amid these connections who would be a person that would be either influential in certain groups or across groups (hopefully that made sense).

Below is an excerpt from the blog post (link below):

"The analysis in this report is based on information gathered by our field agent Mr David Hackett Fischer and published in an Appendix to his lengthy report to the government. As you may be aware, Mr Fischer is an expert and respected field Agent with a broad and deep knowledge of the colonies. I, on the other hand, have made my way from Ireland with just a little quantitative training—I placed several hundred rungs below the Senior Wrangler during my time at Cambridge—and I am presently employed as a junior analytical scribe at ye olde National Security Administration. Sorry, I mean the Royal Security Administration. And I should emphasize again that I know nothing of current affairs in the colonies. However, our current Eighteenth Century beta of PRISM has been used to collect and analyze information on more than two hundred and sixty persons (of varying degrees of suspicion) belonging variously to seven different organizations in the Boston area."

Full Post on Using SNA to find Paul Revere

For those not interested in the numbers particularly but just in a humorous yet realistic use of SNA this blog post is worth a read.  It also provide a sobering/realistic perspective on how our digital trails could be used to find our associations (hopefully you read that without suspecting me to be too paranoid).  Understanding networks can be a powerful tool in somewhat understanding people without actually knowing them.  This may have been what Donald Rumsfeld was talking about when he mentioned "known unknowns".

Marathon Finishers Worldwide

This is an interactive timeline of the top 10 countries' participants who finished a marathon. The data was used/scraped from the Association of Road Racing Statisticians where this data is compiled (great site btw). Those interested in the code will find it on my github page.

Not too surprisingly, the US has the most Marathon finishers of any country. Interesting is Japan's increase in Marathon finishers in more recent years as well as the fluctuation in which countries occupy the top 10 with the most finishers. 2014 is incomplete because the year is not finished. The dip in 2012 is a result of the NYC Marathon being canceled.

Data: running2 • Chart ID: MotionChartID197c73527aa6 • googleVis-0.5.6
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