One of the most fascinating and influential parts of the book Moneyball was the chapter on run production, on-base and slugging percentages. In 1999 Billy Beane hired Paul DePodesta and DePodesta went to work figuring out a better method of combining OBP and SLG than just adding them together to form OPS.

DePodesta concluded that for the Oakland A’s purposes, "an additional point of OBP is worth 3 additional points of SLG." The same year Moneyball was published Sabermetrician Tangotiger penned a series of articles titled OPS: Begone! II in which he concluded that the best-fit multiplier falls somewhere between 1.5 and 2.0, with 1.7 or 1.8 now the accepted norm.

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For today's sabermetric primer I decided to take a look at IsoP, or Isolated Power.

Isolated Power can trace its roots in one form or another back to Branch Rickey and Al Roth in the 1950’s. The metric measures a batters power based on how many, and what type of, extra-base hits a player has amassed. Whereas slugging percentage counts all hits including singles, IsoP deals with only doubles, triples and home runs.

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[EDITOR'S NOTE: This is the first post in a series of primers to discuss some more "forward thinking" baseball statistics. We use these stats here at OTM, but we don't necessarily have time to explain them very well -- that's why I've asked redsoxstatscom to contribute.]

I started my website redsoxstats.com back in 2004 after a combination of reading Moneyball and getting my first copy of the Ron Shandler Baseball Forecaster books. There are other places on the web to find new and cutting edge sabermetric stats, those are some of my favorite places, but my site focuses on the huge second and third steps beyond evaluating players based on Batting Average, ERA, or RBI.

I was once told that the difference between a $1 HDMI TV cable and a $10 one is huge, but the difference between that $10 cable and a $50 one is very small. That’s the basic principal I use when running my site and thinking about sabermetrics. The difference between ERA and FIP is huge, but the difference between FIP and New-Random-Fancy-Pitching-Metric with a formula 50 variables long is not so huge.

Over the next few days I’ll be posting some brief primers on some of my favorite sabermetric stats and will be using Red Sox related examples of what they show.

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