WHEREAS with a flawed formula, you calculate an ISo of .200 and a SA of .400 for player A and an Iso of .400 and a SA of .800 for player B

]]>So, his true ISO is .33879, reflecting his average bases per hit of 2.016, his batting average of .342, his true SA of .504 ,etc, ….

]]>For instance, instead of Ruth having a SA of .690 of a possible 4.000, it is actually .504 of a possible 1.000.

Ruth had 5,793 bases negotiated of 33,596 baases possible from 2,873 in 8,399 at bats and a batting averge of .34206453. Correlating the components of that batting record we can determine Ruth’s correct SA. 5,793 bases negotiate divided by 33,596 bases possible; then divided by the batting average of .34206453 equals .5040898

That is an expression of an average hit bieng slightly greater than a double. A SA of 1.000 reflects an average hit being a home run. The traditional .690 SA expresses a batter having run down the first base line 62 feet before he was tagged out

Not only is SA flawed but ant other stat incorporating SA into its formula is also flawed — especially Isopower and OPS.

]]>Who knew that Fenway would make him into a singles hitter.

]]>I like this stat a lot (as someone interested in extra bases, as opposed to power); in your article, you mention Adrian Gonzalez. Has it changed appreciably in Fenway? If so, can you add park factors?

Good stuff…

]]>What the baseball definition of power?

If you ask baseball fans, who is the most powerful hitter are they going to name the guys with the most home runs or the most total bases?

To me, this is one area, where we probably don’t need a sabermetric stat.

]]>((1b*.9)+(2b*1.24)+(3b*1.56)+(HR*1.95))/(AB-SO)

As strictly a measure of power the linear weights do a good job of scaling the hits and since we’re only interested in balls in play the denominator also works nicely.

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