Effect of start order on women’s WSC 10k

This topic has been covered elsewhere but I thought I’d add my two cents, and it turned out to be slightly longer than Twitter could accommodate.

A lot of wacky things went on that day, as you’d expect when the weather and waxing are tricky and change dramatically during the race. I haven’t watched the TV coverage of the race myself, so I’m at a bit of a disadvantage here since I don’t have any sense of how things progressed and how the athletes looked except for what I’ve read online.

Basically, it started snowing shortly after the race started, which changed the conditions dramatically. This both made the conditions for later starters inherently more challenging and additionally some nations (e.g. Norway) just flat out missed the wax and had terrible, terrible skis.

So naturally we’re interested in whether we can see direct evidence of this start order effect in the results. My approach is actually quite simple (from the perspective of all the machinery I’ve built up over the years in the form of code written to push skiing data around). I’m just going to take the basic data in the graph I Tweeted earlier and rework it a bit.

The idea in that original graph is that I’m just taking each skier’s percent behind the median skier and showing a rough “confidence interval” for perspective (it’s actually just the 25th and 75th percentile of their races over the previous 1-2 years). It already suggests strongly that a lot of the people at the top of the results sheet had “surprisingly good” races, relative to their prior results, as shown by the gap between the red dot and the horizontal bar. We can just take the difference (scaled by the racer’s inherent level of variability, i.e. the width of their bar) and then plot the results relative to start order.

Voila:

wom_10k_fr

On the x axis, positive values are better than expected results, negative values are worse than expected. There were 4-5 athletes (no one notable) that I dropped entirely since they had so few results for meaningful numbers. The red dashed line is my rough guess-timate (again, based only on this graph; I didn’t watch the race) on where things changed. My placement is rather aggressively toward the back of the field; you could arguably say that between starters 25-40 things had stabilized somewhat, and then finally the conditions had really nosedived after that.

And of course as you would expect the relationship isn’t perfect. There are certainly folks at the back of the field that had good races, for them. But this seems like very strong evidence to me that it was simply a good day to be at the front of the field. Virtually all of those people had good to excellent races compared to their personal past performances.

The usual caveats apply here: this suggests there was an effect, but it can’t tease out the magnitude of the effect on a skier-by-skier basis. Different folks were impacted differently based on the specific wax they had, and how they responded in race to having a great (or terrible) day, in addition to the regular “noise” in athletic performances.

Greatest Race Ever?

So the title probably gives this away, but bear with me. Consider a skier with the following distance results profile in major competitions:

olsson1

So by the end of the 2011-2012 season this guy has just turned 31. As you can see, he’s had quite a solid career. Things were good but fairly steady from 2004 to 2008. Then there’s an obvious peak in 2008-2009. Things tailed off slightly the following year, and then more significantly the year after that. Even so he never really dropped below his “baseline” level of performance from that 2004-2008 period. What does he follow this up with?

olsson

 

2011-2012 is by far his best season yet, essentially at age 32. Now, I’m by now means saying that once you turn 30 you’re doomed. But it is actually just more rare for people to improve dramatically at later ages. Not impossible. But certainly unusual.

And then, of course, he treats us to a stupendous performance in the 50k at World Champs.

Don’t quit, Johan Olsson! I want to keep watching you race for another few seasons.

Race Snapshot: WSC Men’s 50km Classic

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Race Snapshot: Women’s 30km Classic Mass Start

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An Unusual 15km Freestyle

Yesterday’s men’s 15km freestyle interval start race featured one of the most split fields we’ve seen in that event in a long time. The top three, Northug, Olsson and Gjerdalen had modest gaps between them, but then there was just over 30 seconds back to 4th place.

How big was this gap between the podium and 4th? It was the biggest in history (post-1992):

podium_gap

These are the gaps in median percent back between 3rd and 4th in every men’s 10/15km interval start WC, WSC, OWG or TdS race since 1992. I’ve separated the classic/freestyle races, as well as the WSC/OWG (Championships) and regular World Cups. Yesterday’s race was a bit of an outlier, as you can see.

However, it was only this past November that Johnsrud Sundby, Poltaranin and Hellner gapped the field in Sweden by around 20 seconds. Next is a classic 15km all the way back in 1996 (a regular World Cup). That was in February of 1996, in Russia, where Prokourorov, Smirnov and Daehlie (in that order) gapped the field by almost 31 seconds. That’s a larger raw interval, but as a classic race it was generally a lot slower, so the gap was smaller as a percentage of the total time.

Race Snapshot: WSC Men 15km Freestyle

Nice races from Ivan Babikov and Noah Hoffman:

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Race Snapshot: WSC 10k Freestyle

Just the women’s race today, men tomorrow:

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