This is something I did several years ago and I think originally I just mentioned it on the SABR list.
Here is an example of how the error rate can affect things. The
error rate is 1 - fielding pct. The regression below shows runs per game
as a function of OBP & SLG for each season of the NL from 1920-2012
(I used the whole league instead of teams)
R/G = 22.77*OBP + 6.7*OBP - 5.68
Now what if we add in the error rate. The regression becomes
R/G = 15.75*OBP + 9.7*SLG + 15.69*ERATE - 4.94
The relative value of OBP & SLG changed quite a bit. But this is for a whole league. The ERATE applies to the whole league.
I
have used the ERATE and applied it to teams. That assumes that the rate
of errors made against each team is the same. Not totally realistic,
but that is what I have. Sof it the ERATE was .02 one year in a league,
every team got that rate.
I did all teams from 1920-1998. Here are the two regressions
R/G = 19.89*OBP + 9.79*SLG - 5.95
R/G = 17.63*OBP + 10.7*SLG + 13.51*ERATE - 5.87
So again the relative values of OBP & SLG change
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1 comment:
Can math like this be applied to daily fantasy sports as well? I am playing on https://www.dailylineups.com/ nowadays and I find it very interesting and fun to see how these kinds of games work. Do you know any strategies at how to improve your DFS teams?
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