Statistical Skier

Earlier this week I introduced an idea I had for calculating a points-like measure for sprint racing that was applicable beyond the qualification round.  You can go back and review the methodology here.

In this post I’d like to revisit this idea and look at how these new points look when ranking athletes.  First, I forgot to give them a name in my last post, so I need to remedy that situation.  Since the method essentially slices up the points available between the 31st finisher’s qualification points and zero and distributes them among the top 30, I thought I’d call them Distributed Sprint Points (DSPs). Continue reading ›

Adam, over at NordicXplained, asks:

“Everyone knows how well the Canadian men’s team did at the Olympics, but the question that I wanna know is how well they stacked up against other countries. Even though they didn’t win a medal, their average placing in many races surely could have been higher than traditional powerhouses like Russia and even Norway in the opening 15km classic.”

Can do!

A few cautionary comments before we get going.  At the Olympics, each nation can only start four people in any given race.  So comparing team performances using only summary statistics may be dangerous here.  We’d be averaging four values!  It’s easy to get sucked into trying to invent some arcane numerical summary to “fix” this problem, but usually it’s just best to plot the data and look at it.  The answer is often fairly clear without having to do any fancy math.

Adam’s question deals with team performance, so that’s what I’ll look at.  That means that as I evaluate the data, I’m going to consider every result, top to bottom, for each nation.  What counts here is the performance of the “group”.  Socialism!  Yay! Continue reading ›