Tuesday, February 3, 2015

The predictive value of split differences

It seems that the format of the Knik 200 trail was not that much fun for teams, so we had a few mushers who didn't need qualifiers checking out early (that is to say, scratching).  But, the format also gave us an opportunity to look at some data that haven't been available in the past for distance racing.

The Knik had mushers running from Deshka Landing up to Yentna and back, twice.  That is to say, they passed over the same piece of trail 4 times.  This gave us an opportunity to look at whether or not the consistency of the speed with which they covered the trail had any predictive value, in terms of final placement.  If someone had pretty much the same times on each race leg, did they tend to finish higher or lower in the standings?

So, I created a spreadsheet sheet in which I took the differences between the two up times, the differences between the two back times, and the sum of the absolute values of the differences.  This gave me a handle on just how much variability there was in a team's runtimes.  I then ran correlations on the total differences, the run to Yentna differences, and the run back differences,  I found a fairly strong correlation between the summed runtime differences and final placement, but with also a fairly large standard error given the size of the field.  That is to say:
a team whose times remained consistent from split to split tended to finish better than a team whose times varied more from split to split
This should not be particularly surprising, since the source of speed variation tends to be slowing down due to tiredness, etc.  Another source of consistency, other than conditioning and fitness, could be the musher's expertise in managing their team's resources.  Steady speeds probably don't cause a good finish, but they can tell us something about the team's "quality" (for lack of a better word).

In the correlations I ran, the difference in splits was the independent variable, and finishing position the dependent variable.  Here's the table, with the first correlation being between the summed differences and the finish, the second being the summed differences on the run to Yentna, and the third beingn the summed differences on the run from Yentna back to Deshka:

correlation coefficient:0.7031
standard error:5.4138
correlation coefficient out splits:0.5768
standard error:6.2195
correlation coefficient in splits:0.5781
standard error:6.2121

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