I’m on a free hosting plan that allows for 25 usage hours per month. If they go over that they will just shut down until the month is over and my monthly allotment resets. So if they stop working, that might be why. I doubt they’ll see that much traffic though.
I’m planning on doing at least 1-2 more in the next week or two…
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.
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.
OPA Cup races (also known as Alpen Cup) and Scandinavian Cup races have acquired an informal reputation as a sort of “minor league” racing circuit, relative to the World Cup. One question this leads me to ask is exactly how much movement is there between these two circuits?
To answer this question I took data from skiers who had started in at least one World Cup and at least one OPA or Scandinavian Cup race since the 2004-2005 season. This yielded 543 men and 352 women. This is a rather wide net, since I’m including skiers who may have done one WC race in 2005 and then only OPA Cups, or vice versa.
Here is a graph that shows the number of starts of each type for men and women:
Each dot represents a different skier. The number of starts is the total (distance and sprint) over 6 seasons (04-05 to 09-10) and the points have been plotted with some alpha blending since many are directly on top of each other. Darker areas correspond to more points being plotted.
As you can see, the overwhelming majority of skiers who have done both types of races have done fewer than 15 of each type. Much of that racing is spread over 6 seasons. The skiers who’ve done a ton of WC’s tend not to have done many OPA or Scandinavian Cups, which isn’t terribly surprising.
As we move away from each axis, we get the skiers who’ve come the closest to splitting their time between these two racing circuits. As you can see, they exist, but not in huge numbers. Most of the OPA Cup skiers are probably getting their WC starts as part of Nation’s Group starts, rather than being rotated back and forth within or between seasons.
Like last time, we’re looking to quantify somehow the average difference in speed (in sec/km) between classic and skating. Only this time we’re focusing in on sprints. Organizing the times for the heats is a bit of a challenge, so I’m only going to consider the qualification round. So essentially we’re just looking at speed differences over much shorter distances.
Following the same general procedure to fit a model for the sprint races, we get the following results:
Again, happily, freestyle is faster than classic. Note that I’m not really considering the course length for sprints, so I’m essentially assuming that the differences between a 1km race and a 1.8km race aren’t going to be too meaningful. Recall from last time that the men saw a difference of between 8-14%, the women between 5-12% with the longer races tending to see a smaller difference between skating and classic, on average.
What’s interesting is that in this case we get a difference of around 8.4% for the men and 9.0% for the women. Given the differences we saw in 5-10km races on Monday we might have expected larger differences.
What I think is going on is that the speed advantage of skating is sort of non-linear in race length. For very, very short distances there isn’t enough time for skating’s efficiencies to have an effect. But when the races get really long, the increased efficiency in skating starts to be counteracted by physiological issues that will be more constant between techniques. So there may be a sort of sweet spot in between where skating makes a huge difference, but less so at the extreme race differences.
The strongest case for this I could make would be to consider truly absurd extremes. You’re not likely to see much difference between skating and classic in a 5 meter race. However, you also probably won’t see much of a difference in a 500km race. In the former, skating’s advantages never really get the chance to take effect, and in the latter the relative efficiencies of the two techniques probably may pale in comparison to the pacing demands of a race that long.
My skepticism stemmed from the fact that while Kikkan is arguably the best freestyle sprinter in the world right now, her classic skiing has traditionally lagged a bit behind. Based on the only experience I have, which is looking at lots and lots of results patterns over time, I was skeptical that she could improve her classic sprinting that dramatically.
So what has happened so far? Read more
I can’t keep up on the enormous number of good young skiers from every nation. I have a hard enough time with just those in my own backyard, the US and Canada. So it’s fun to look at some folks from other countries that I’m not so familiar with. For instance, here are some graphs looking at some good young male skiers from Finland. I’m using ‘cohort’ style graphs; they compare an athletes FIS points at a particular age to the range of FIS points achieved by skiers in the past that have gone on to achieve top ten results at WC, WSC or OWG races. (Remember, of course, that FIS points have plenty of limitations, and I calculate them slightly differently that FIS. For instance, I select people’s best results from a season, not from a calendar year.)
First, here are five young Finnish men who had strong distance results last season (click for full version):
Obviously, one wants to be at or below the shaded region, ideally. But those shaded regions don’t represent everyone who was ever successful, so it’s certainly possible to buck the trend and be a late bloomer. It just happens less frequently. Perttu Hyvarinen certainly seems promising. Sami Lahdemaki does as well, despite a slight uptick in his results last season.
To go along with this, here are five good young Finnish sprinters; again, FIS points for sprinting are only measuring qualification speed: Read more