Originally posted on Baseball Professor  |  Last updated 7/20/12

About a week ago I looked at which offensive statistics predicted others, and today I turned my attention to pitchers.

The same method was used; I looked at all qualifying pitchers over the last 10 seasons (at least 162 IP in a given season), which yielded 873 data points. The numbers are represented in the chart below.

Numbers closer to 1.0 and -1.0  indicate a stronger correlation, positive values indicate a direct relationship (i.e. a lower WHIP means a lower ERA), and negative values indicate an indirect relationship (i.e. more walks mean a higher ERA).

Just like last time, here are some thoughts I had on the data:

  • We can pretty much ignore the relationship between K and K/9. Obviously they have an extremely strong correlation. The only reason it isn’t a perfect 1.000 is because of those dominant 225-250 inning starters who strike out a lot of batters but do it with a K/9 of 7.5-8.5. I’m looking at you Jered Weaver.
  • We shouldn’t be surprised that a high strikeout rate correlates highly with a low OBA (R value of .662 for K/9 and OBA).
  • I’ll be honest, I kind of expected K/9 and BB/9 to have an inverse relationship (meaning a negative R value) because I thought high-strikeout pitchers would generally be higher-walk pitchers. With an R value of .120, this wasn’t the case, but such a low value indicates that there’s actually very little correlation between the two.
  • WHIP and BB/9 had a strong negative correlation. No surprise there.
  • I was a little surprised to see that ERA and GB/FB ratio had very little correlation (R value of .145), however the fact that this number is at least slightly positive means that a higher ground ball rate is generally desirable.
  • Interestingly, there was also almost no correlation between K/9 and GB/FB ratio (R value of -0.083). I expected the two to correlate a little more strongly because I always assumed ground ball pitchers generated fewer strikeouts. The slight negative R value indicates this is generally true, but really there’s not much tying the two together.

What did you notice in the data? Any surprises?

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