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Lucas Harrell and being mostly forgettable

Lucas Harrell's 2012 was rather good. Controlling for variables through standard deviations, how did he achieve that level of performance?

Lucas Harrell stares into your soul.
Lucas Harrell stares into your soul.
Kevin C. Cox

Note: I have finished the coding for my standard deviation analysis. There is so much data; until I figure out a good way to display all of it, which may never happen, I am going to break it up into chunks. I will examine something interesting within each chunk.

In 2012, Lucas Harrell, he of Houston Astros fame, posted a 2.6 fWAR season in 193 and 2/3 innings of operation. I was very nearly flabbergasted when I learned this, and probably a few readers almost spilled their drinks of choice after reading this information.

According to the research I've done regarding standard deviations in strikeout, walk, and ground ball rates, Harrell was in the group whose K% was between 0 and 1 standard deviations below the middle, whose BB% was between 0 and 1 standard deviations above the middle, and whose GB% was between 1 and 2 standard deviations above the middle. This may be abbreviated as (0-1, 0+1, 1).

If strikeouts and walks were all that we cared about, Lucas Harrell would have had a completely forgettable season in 2012. As it stands, Harrell's 2012 season was only MOSTLY forgettable. Look at the overall performance of the (0-1, 0+1, 1) group. There were 45 pitchers in this group (2002-2013), so the sample size is adequate.

4.23 4.43 4.30 1.6 1.5

The overall package brought by this group is, naturally, somewhat below average. Harrell's 2.4 RA9-WAR actually defined the boundary of the 3rd quartile, so Harrell himself was in the upper echelon of performance within this group. If he were to maintain his rates into 2013, it would be natural to expect some regression. He did not maintain his rates, and he fell off the top of Mt. Cook (located in New Zealand and ranked as the 39th tallest peak by prominence, matching Harrell's 39th rank by fWAR).

Having the complete set of standard deviation data, we can look at how the variation in ground ball rates affects the performance of pitchers whose K% and BB% match Harrell's. In this way, we might learn if the ground ball rate was indeed a factor in Harrell transitioning from forgettable to mostly forgettable. The following table shows the overall performance of each GB% standard deviation group within the (0-1, 0+1) group of K% and BB%.

GB St Dev Group ERA FIP xFIP RA9-WAR fWAR Sample Size
-2 6.50 6.83 5.41 -0.6 -0.5 4
-1 5.19 4.91 5.16 0.4 0.4 30
0-1 5.19 5.00 4.92 0.2 0.6 114
0+1 4.82 4.81 4.68 0.5 0.8 108
1 4.23 4.43 4.30 1.6 1.5 45
2 4.57 4.26 4.06 1.5 2.0 6

As the ground ball rate increases, performance improves. It appears that a high ground ball rate can overcome a combination deficiency in punch outs and free passes. Without taking into account the ground ball rate, the (0-1, 0+1) K%, BB% group had an RA9-WAR value of 0.5. Adding in Harrell's group's standard deviation, 1 above the middle, improves that value to 1.6. In essence, having a ground ball rate one standard deviation above the middle in this forgettable K% and BB% group added about a win. Adding wins is generally memorable.

. . .

All statistics courtesy of FanGraphs.

Kevin Ruprecht is a contributor for Beyond the Box Score. He also writes at Royal Stats for Everyone. You can follow him on Twitter at @KevinRuprecht.