Applying “Power Factor” to the 2010 Rays

I just finished reading a pretty nice piece from Wahoo Blues author Lewie Pollis.  He wrote a nice piece entitled A Better Way to Measure Power Basically, it’s a more pure version of Isolated Power (ISO) that takes BABIP fluctuations out of the equation.  I like it after viewing this:

Iso Ranking Chart

Power Factor Ranking Chart

The calculation is relatively easy as it’s just:

POWER FACTOR = ISO/BATTING AVERAGE

Doing this quick calculation yields this ranking sheet:

I don’t think too many fans would disagree with this ranking, though I’m a bit shocked that Dan Johnson graded out that strongly.  Boy’s got some power to him for sure.

Just wanted to share this real quick as I think  R^2 values demonstrated in  Mr. Pollis’s article speak for themselves as far as the predictive power of this tool.  As always, remember what we’re looking at.  This is strictly a measure of power and should be used in conjunction with other more well-rounded statistics like wOBA.

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About Jason Hanselman

Rays fan.
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3 Responses to Applying “Power Factor” to the 2010 Rays

  1. raysprof says:

    2 comments:

    1) Pena has a a strong power factor. Wonderful, but what is important is how it translates into runs scored or potential runs scored. Last year Pena was pretty much a replaceable player (and the Rays recognized it.)

    2) Johnson, who statistically should be a better bet for the great pumpkin-hood in the next year or 2 than Pena, is said to have a high R-squared (am I reading this correctly?) value. What does this mean? I understand coefficient of determination.

    • Jason Hanselman says:

      1) I agree as this is merely a measure of power. I’m not saying that someone that demonstrates a ton of power is necessarily a bad hitter, but if your concern is, “Where are the dingers going to come from?” Well these could be a few guys showing hints last year that they could be some untapped sources of power.

      2) In my haste, I wrote that pretty poorly. I’ve made some edits that address your points and I thank you for pointing out some things that I should have cleaned up. I was simply pointing to the R^2 figures that were in Lewie’s piece which showed very nice season-to-season correlation as well as better predictive power than Iso going into the next season.

      Thanks again for the comments, without feedback it’s hard to improve communication.

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