Statistical Skier

The third in the series, looking at differences in performance in skating vs. classic races for particular nations.  We’ve already done Japan and Italy.  Now, by request, we have Norway.

To avoid repeating myself, I’m going to skip much of the introduction and explanation for the methodology and graphs.  Go read the Italy post if you need a refresher.

Distance

First we’ll look at the estimates for each individual skier over their entire career:

There are a ton more skiers here, even after filtering out those with fewer than 2 skating and classic races, so I’m sorry that the names are a little hard to read.  As always, negative values (on the x axis) indicated better performance in skating races relative to the median WC skier.

As we might have expected, most Norwegians shown here are nominally better at classic skiing (positive value) and up near the top there are quite a few that even appear statistically significant (error bar doesn’t overlap zero).  Good old Odd-Bjoern, hanging out up there!

Unlike with Italy, we don’t see much of a difference between the men and women.

Here’s how things have changed over time for the entire team:

As before, there are some years that look unusual: 1996-1997 Women, 2007-2008 Men+Women, maybe 2004-2005 Men.  In a more “serious” analysis we’d go back and check those out to make sure our model isn’t doing something funky.  Perhaps I’ll get to that sometime…for now, though, I wouldn’t necessarily leap to the conclusion that the Norwegian women were suddenly way worse at skating (or better at classic) in 1996-1997 and then returned to normal the next year.  I’d be similarly cautious about interpreting 2007-2008.

And of course, the 2010-2011 estimate is preliminary.

Overall, though, we’re seeing a mild preference for classic (positive values) with the men that’s been mostly stable since the mid-90’s.  The women have seen more of an up and down, but only if we ignore the “problem seasons” of 1996-1997 and 2007-2008.  Ignoring those aberrations it looks like the Norwegian women went from preferring classic to being roughly even and then really preferring classic and then finally coming back down toward being roughly even in recent years. Continue reading ›

A pretty good day for Lowell Bailey and a decent race for Laura Spector. And of course more good racing from the Berger family.

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What can you say, except that today is a very good day?

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Honestly, I’m reluctant to wade into this topic, but since this blog is supposed to focus on the intersection of XC skiing and data, I feel like I need to say something. The USST announced their team for World Championships in Oslo, which inevitably led to a certain amount of griping about the choices made or the tools used to make those choices.

I spend a lot of time looking at XC skiing results data in far more detail than most people and I can’t see anything remotely questionable in the selections. What I mean by that is that if you compare the team they selected with a detailed look at all the available race results so far this season, nothing seems obviously wrong to me given the particular set of races on the schedule in Oslo.

Clearly, some judgement calls were made, but that’s inevitable. No ranking system is perfect. No matter how complex or sophisticated your points system and ranking method is, there will be instances where it fails to capture the “truth”. Data should be a guide, not an ironclad law.

As some food for thought, here are some slightly improved versions of the graphs I posted this morning, but with versions for sprinting as well. Once again, there are surely some “contenders” that I haven’t plotted; this isn’t meant to be an exhaustive look at everyone vying for a spot on the WSC team. The modification I made is to plot line segments rather than points. I recommend reading them by row: red lines are “good” for the athlete in the row, blue lines are “bad”. Only instances where each pair of athletes raced against each other are counted. The length of the line segment represents the difference in percent back for distance races and the difference in rank for sprint races. Some general observations follow: Continue reading ›