Not very long ago, I published a post titled Getting and Not Getting the Calls: Final 2012 Results. The post examined the differences between actual strikes and expected strikes for individual pitchers and teams, based on the PITCHf/x plate-discipline data available right here. The results were interesting, to me, and hopefully some of you. It makes sense that some pitchers might be more able to get bigger strike zones. It also makes sense that some catchers might be more and less able to get bigger strike zones. It wasn’t a huge surprise that the Brewers came out looking good, and that the Pirates and Mariners came out looking bad.
Well, as it happens, that same methodology can be applied to both pitchers and hitters, so we might as well check to see how the data looks for individual batsmen and groups of batsmen. It’s less obvious how a batter might end up with a bigger or smaller strike zone, relative to the expected strike zone, but that doesn’t mean there might not be anything there, and it only takes a few minutes to make all the calculations so why not just proceed, that’s what I say. Below there are tables of names and numbers.
As a quick refresher: we have raw strike counts and pitch counts. We also have zone rate, and out-of-zone swing rate, as determined by PITCHf/x. By putting the first numbers in one hand and the second numbers in the other hand and then clapping a bunch of times, we can figure out an “expected strike” count by adding zone pitches and out-of-zone swings. Then that can be compared to the actual strike count and, presto, desired results.
I’ll repeat that it’s far less clear how a batter could have an effect relative to pitchers and catchers. I’ll also note that this data might be fraught with complications since pitchers presumably work against a similar collection of strike zones over the course of a season, while batters have one PITCHf/x strike zone that might not be a whole lot like the given umpire’s strike zone. For these reasons and others, this is the less interesting of the two posts, but if we can look at the hitters then we might as well look at the hitters, and here we look at the hitters.
Note also, again, that the league average is not zero. It’s roughly five fewer actual strikes than expected strikes per 1,000 pitches. The key stat is listed as “Diff/1000″, and it refers to actual strikes minus expected strikes per 1,000 pitches. A positive number means a player or team saw more called strikes than expected, and a negative number means a player or team saw fewer called strikes than expected. Away we go now, beginning with the teams.
Table 1: Team Data
When looking at pitchers/catchers, the spread between the top and the bottom was about 30 strikes per 1,000 pitches. Here the spread is roughly half of that, which makes sense, because a larger spread implies more of an actual skill. I don’t know how to explain that the top 13 teams in this table are all in the National League. I also don’t know how to explain that the bottom four teams are all in the American League Central. These are just facts, presently without obvious reasons for being. Remember, a positive number here is worse for the hitters, so kudos to the Tigers for doing whatever they might have been doing. There is…there does not seem to be much here although you are free to interpret to your heart’s content.
Let’s look now at some individual players. I looked at all 459 players who batted at least 100 times this season. Our first table shows the ten top hitters who saw more strikes than expected.
Table 2: Top 10, More Strikes Than Expected
“Whoa!” you say, “poor Starling Marte!” Indeed, the numbers suggest the Pirates’ rookie outfielder kind of got the royal screwjob. But then, (A) we don’t know if this data is truly meaningful, and (B) Marte batted just 182 times and swung often, so the sample size is limited. Over more time, probably, Marte’s numbers would’ve looked a lot more normal. But this is enough to make you wonder at least just a little bit.
Now for the other end of the spectrum.
Table 3: Top 10, Fewer Strikes Than Expected
We’ve got a little dude ahead of the pack, which makes some intuitive sense, although Punto batted just 191 times. Marson batted just 235 times. Coghlan came in below 200 plate appearances, and Figgins also came in below 200 plate appearances. In this table, only Santana and Roberts were regulars or pseudo-regulars, meaning the other guys might well just be showing sample-size noise. There’ll be noise in the Santana and Roberts numbers, too, but just probably a little less of it. I don’t have explanations, and Chone Figgins is terrible.
That’s all I’ve got for now, although I’m curious to see if you guys find anything really meaningful. I’m a lot more interested in the pitcher/catcher numbers, myself. Click here for an Excel sheet of the individual hitter data. Or don’t, and make your own. You are your own person.