One of the most fun aspects of this blog (for me) is that I’m learning as I write. Digging further into the statistics on my own is exposing me to new things that I wasn’t expecting. Sometimes, this results in me getting an answer that isn’t as complete as it should be. In the comments in my last piece about Brian Wilson‘s strikeouts, Amy (@SpaceDodgers) pointed out that I did not account for the atbat continuing after a looking strike (that resulted in a looking strikeout) was called a ball. This is a good point, and I’ve adjusted my methods a bit as a result.
The first step was to find out what counts Wilson’s looking strikeouts occurred on. Thanks to baseball reference’s playbyplay data, this task was relatively simple:
Count 
Looking strikeouts 
02 
1 
12 
1 
22 
8 
32 
1 
Total 
11 
As I found in the last post, Wilson’s career looking strikeout rate is 31.7%. So, given that he got 10 strikeouts swinging, 11 strikeouts would become 4.65. The rest of the atbats would continue with one additional ball.
In order to determine what would happen if the atbat continued, I used baseball reference’s average count splits for Wilson:
Split 
PA 
HR 
BB 
HBP 
SO 
BABIP 
After 12 
473 
3 
28 
4 
209 
0.281 
After 22 
401 
4 
50 
2 
156 
0.323 
Full Count 
278 
3 
72 
1 
95 
0.393 
If a looking strikeout on 02 was called a ball (which would occur in the percentage of atbats that did not end in a looking strikeout), then I used the split for results after a 12 count. 12 became 22, 22 became 32, and 32 became a walk.
Here are the results of the adjusted numbers, by count:
Looking K count 
Stays looking K 
K later in atbat 
BB later 
HBP later 
HR later 
In play later 
In play, hit later 
02 
0.42 
0.26 
0.03 
0.00 
0.00 
0.28 
0.08 
12 
0.42 
0.22 
0.07 
0.00 
0.01 
0.27 
0.09 
22 
3.38 
1.58 
1.20 
0.02 
0.05 
1.78 
0.70 
32 
0.42 
0.00 
0.58 
0.00 
0.00 
0.00 
0.00 
Total 
4.65 
2.06 
1.88 
0.02 
0.06 
2.33 
0.87 
Given the total change that this adjustment has, I also adjusted Wilson’s IP total. Since the model had 0.87 more hits, 1.88 more walks, 0.02 more HBP, and 0.06 more HR, Wilson would record 2.83 fewer outs if facing the same number of batters. That would adjust his IP count to 18.72 (down from the 19.67 he actually pitched).
Here’s a summary of Wilson’s overall stats, showing the actual results, the old regression method, and the new regression method:

K% 
K/9 
BB% 
BB/9 
FIP 
FIP 
xFIP 
xFIP 
Actual 
28.8 
9.61 
8.2 
2.75 
2.02 
56 
2.82 
75 
Old regression 
20.1 
6.70 
8.2 
2.75 
2.47 
69 
3.24 
85 
New regression 
22.3 
8.03 
10.7 
3.60 
2.57 
72 
3.39 
89 
This new method isn’t quite as friendly to Wilson as the previous method. While his strikeouts go up, so do his walks. This results in a higher FIP and xFIP than he had previously. I also calculated FIP and xFIP (which I didn’t have before). Even with the new regression, Wilson is an above average pitcher, but not nearly as lightsout as his career peak.
There are still a few problems with these methods. The sample size of Wilson’s 2013 season is pretty small, so this is not an accurate way of projecting Wilson next year. The source data might represent a higher than average level of competition, since 1/3 of the data occured during the postseason. The samples on Wilson’s count splits are pretty small, too. There’s also the possibility that different counts lead to different rates of looking strikeouts (it is already known that different counts have different strike zone sizes). Using Wilson’s career average as opposed to the league average might be a mistake, given changes in run environment, Wilson’s injuries, and the uneven nature of his previous career.
Overall, though, this method gives a bit of a better picture of what happened with Wilson last year. He’s not going to continue getting 50% of his strikeouts looking, so it was fun to see what would have happened if he didn’t.