Tuesday, January 12, 2016
Timeline adjustments
?This one is based on hockey, but the similar issues applies in baseball. And the author proposes something similar to what I've done in the past.
The main issue is "average", that it treats average as a constant throughout time. By forcing that as the centering point, everything revolves around it. However, once you've got average, you have to worry about the spread around the average. In baseball, Michael Shuckers I believe gets around that by transforming all the performances so that the spreads for each season are the same. (I think that's what he does.) So, now we've standardized the mean and the spread. Which of course means that we're forcing a constant across time.
Whether that is a time period with six teams or thirty teams. As you can see, you have to be careful with standardizing data, just because you want to standardize the data. It has to make sense. As Bill James noted 30 years ago: if you are going to multiply A by B, there better damn be a good reason to multiply A by B.
We talk about Sandy Koufax and his innings and Pedro and his innings. Koufax for example had the same number of pitches thrown in his 6 best year stretch as Randy Johnson did. Except Koufax did NOT lead his league. Randy Johnson led his league easily. As the blogger notes in discussing hockey in his link, the focus, instead of being the average and the spread of all players, that maybe it should be the top-end players.
I agree. This will give you a better baseline. Of course, you have to be careful as to how to select the quantity of players. This is what I did when Sosa reached 500 HR, I guess some 15 years ago. I compared Sosa to the 10th place in HR. I did that for every hitter. And I just baselined everyone so that the 10th place in HR in every year was the constant.
See, the issue always comes back to the centering point. Whether I chose the exact 10th place, or the 5th through 15th place, or as the blogger did 3rd through 12th, we're still taking a position as to what is the constant.
In any case, I do agree that the best way to do these comparisons is to use a much higher baseline than "average". The key though is figuring out this baseline, and how even that will change as the playing context changes.
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