Statistical Skier

An old Dartmouth teammate of mine contacted me recently and asked if I’d be interested in looking at some triathlon data.  He has some ulterior motives here, as his sister is a very good triathlete.  Since he volunteered to gather the data himself (I believe from triathlon.org mostly) and send it to me, I just couldn’t say no.

My friend, Adam, had a very specific question, which I’ll get to in a second, but it turned out that there’s a bunch of interesting stuff to look at in these data, most of which I can’t get to in one post.  So I’ll be violating my nordic skiing theme some more with triathlon data.

First some background on triathlons in case you’re unfamiliar with the sport.  We’re discussing the Olympic distance triathlon (1.5km swim, 40km bike, 10km run).  Other than the distances, a major difference between these triathlons and the iconic Ironman variety (e.g. the one in Hawaii) is that drafting is legal during the bike.  This means that you are allowed to ride right behind people, which conserves a ton of energy.

An athlete’s time can be broken down into the five parts of the race: Swim, Transition 1 (T1), Bike, Transition 2 (T2) and Run.  The transitions are exactly what they sound like: you have to switch gear in a sort of pit stop area.[1. Sadly, it appears the times in the data have been rounded to the nearest second.  This means that when I add the five stage times I’m off by +/- 3 seconds from the recorded total time.  I doubt this will influence what I’m doing here drastically, but it obviously isn’t ideal.]

As I mentioned, Adam’s question was very specific: Suppose you finish the biking portion of the race just behind several other competitors.  Is it better to rush through T2 in order to start the run ahead of some of them, or should you “chill” during T2.

If you’ve never done triathlons, this might seem like a strange question.  Shouldn’t you always go as fast as you can?  I mean, it is a race after all.  I’ve never done triathlons, but based on what I know, switching activities can be pretty jarring both physically and mentally[1. Seriously, go try it sometime.  Bike 40km as hard as you possibly can and then immediately switch to running 10km.  Trust me, it’ll feel pretty awkward.].  So it seems reasonable that there might be a school of thought within the sport that it’s worth being 5-10 seconds slower through a transition if you feel like the added time helps you adjust to the new activity more quickly.

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As promised from long ago, we have the General Classification bumps plots for the first nine stages of the Vuelta a España. Both versions, all together and split by team are below the fold.

Don’t worry, more skiing content is in the works for the rest of this week…
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Back on Monday I posted the graph shown below and posed a question:

It turns out I didn’t ask my question very clearly, because I had to keep updating the post with clarifications.  So that’s my fault.  Maybe this time I’ll do better.  What I did was plot a part of the time series of FIS points for these five women.  The x axis is time, and the large white grid lines represent around five years.  So the “next race” for these five skiers will not be the same race.  They’re not all about to race against each other.

My question was for you to pick which of the five would have a top three result in a WC, WSC or OWG in their respective next races.  I provided a few more pieces of information: at least one of the women will succeed (so the answer isn’t “none of the above”) and the one(s) that do, it will be their very first career podium.

Here’s the answer in graph form:

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In one of the articles I wrote for FasterSkier.com, someone asked a question in the comments that I thought was interesting, so I dashed off a quick answer.  Sadly, as is common when I do something quickly, I made a mistake.  So I need to correct the record.

Commenter triguy mentioned that it would be interesting to look at the number of different skiers who land on the podium during each season.  I hacked out something really quick in SQL and slapped it up in a comment.  I had meant to return to that idea and look at turnover among top 10 finishers, top 30, etc.

When I did, I discovered a small error in the numbers I posted in that comment.  The general trend is roughly the same, but the ratios should all be shifted slightly.  So, my bad.  But now I get to elaborate on that idea with actual graphs! Continue reading ›