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Tuesday, March 22, 2011
Win-scaling stats
Bill James had a cool concept a few months ago: convert strikeouts to wins and walks (I would do BB-IBB+HBP) to losses. How? Simply use league numbers to scale everything.
For example, in 2010: There were 34306 SO and 2430 wins. That’s 14.1 SO per win. There were 15778 BB (minus 1216 IBB) plus 1549 HB for a total of 16111 “earned” free passes and of course 2430 losses. That’s 6.6 BB per loss.
Take Jered Weaver. He had 233 SO and 54 walks. 233/14.1 = 16.5 “wins” and 54/6.6 = 8.2 “losses”. Felix: 232 and 77 becomes 16.5 W and 11.7 L. (Rounding the numbers makes it nicer to look at.). Works nice in-game as well. If you have 14 K and 0 walks, that’s 1 W and 0 L.
This is not to suggest that this is the entirety of everything they did, but rather express their K and BB numbers in terms of W and L.
So that got me to thinking. Can’t we do this with everything? In 1983, there were 3325 SB 1619 CS, with 2106 wins and losses. So, take each SB and multiply by 0.63 and each CS times 1.30. Tim Raines was 90-14 in SB-CS. His W/L record would come in at 57 wins and 18 losses. In that same season, the league had 53774 times on base and 105255 batting outs. Raines’ 292 and 426 turns into 11.4 wins and 8.5 losses.
Now, obviously a SB-based win is not worth the same as say an onbase-based win. But, it does show the extent to which he dominated on the basepaths, and it puts it on a lovely scale.
I kinda like this…
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