pitch results


I wrote a guest column for Rotojunkie, one of my favorite baseball discussion hangouts, analyzing the pitch repertoire of James Shields.

I have a new article at MVN analyzing Johan Santana’s pitches.

I have a new article at MVN analyzing Erik Bedard’s pitches.

If you’re interested in the raw data, you can download the Excel spreadsheet I used for the Bedard analysis.

I have a new article at MVN on Kansas City Royals closer Joakim Soria.

If you’re interested in the raw data, you can download the Excel spreadsheet I used for the Soria analysis.

I have a new article at MVN on Mariano Rivera and his cut fastball.

If you’re interested in the raw data, you can download the Excel spreadsheet I used for the Rivera analysis.

Once again building on pitch identification work I’ve done for a pitcher, here is Part 2 of the series on Joba Chamberlain. It’s not exactly all I hoped, for reasons I’ll get to in a moment, but there are some interesting things to be learned. This is similar to previous work I’ve done for Josh Beckett and Eric Gagne.

First, let’s look at which pitches Chamberlain uses in various ball-strike counts.

Count Fastball Slider Change Curve #Pitches
0-0 71% 24% 1% 4% 79
0-1 58% 26% 3% 13% 38
0-2 41% 59% 0% 0% 17
1-0 88% 12% 0% 0% 34
1-1 65% 32% 3% 0% 31
1-2 39% 61% 0% 0% 23
2-0 89% 11% 0% 0% 9
2-1 65% 29% 0% 6% 17
2-2 21% 68% 0% 11% 19
3-0 100% 0% 0% 0% 2
3-1 100% 0% 0% 0% 3
3-2 50% 50% 0% 0% 10
Ahead 49% 44% 1% 6% 78
Even 62% 33% 2% 4% 129
Behind 79% 20% 0% 1% 75
0 strikes 77% 19% 1% 2% 124
1 strike 63% 28% 2% 7% 89
2 strikes 36% 61% 0% 3% 69
Ball 0-1 65% 30% 1% 4% 222
Ball 2-3 55% 40% 0% 5% 60
All 63% 32% 1% 4% 282

Chamberlain Pitch Mix by Count

Joba Chamberlain definitely relies on his fastball, which is probably not unusual for a power pitcher out of the bullpen, but he throws his slider much more often with two strikes. In a 2-2 count, you can almost expect a slider (68%). I don’t think we have enough data on his use of the curveball to draw conclusions about that. You can compare my data to Josh Kalk’s, although my data set includes Chamberlain’s two divisional series appearances, and Josh’s algorithm classifies all of Chamberlain’s off-speed pitches as sliders, whereas I have identified his curveball and changeup separately.

Next, let’s look at the results split up by pitch type and batter handedness.

LHH Ball CS Foul SS IPO IPNO TB BABIP SLGBIP Strk% Con%
Fastball 38 22 14 3 13 7 10 0.350 0.500 61% 92%
Slider 13 3 5 14 3 0 0 0.000 0.000 66% 36%
Changeup 2 0 0 0 0 0 0 0%
Curveball 5 2 0 2 0 0 0 44% 0%
  58 27 19 19 16 7 10 0.304 0.435 60% 69%
                       
RHH Ball CS Foul SS IPO IPNO TB BABIP SLGBIP Strk% Con%
Fastball 27 15 18 7 9 4 7 0.308 0.538 66% 82%
Slider 18 8 5 18 4 0 0 0.000 0.000 66% 33%
Changeup 0 0 0 0 1 0 0 0.000 0.000 100% 100%
Curveball 1 1 0 0 0 0 0 50%
  46 24 23 25 14 4 7 0.222 0.389 66% 62%

CS=called strike, SS=swinging strike, IPO=in play (out), IPNO=in play (no out), TB=total bases, BABIP=batting average on balls in play (including home runs), SLGBIP=slugging average on balls in play (including home runs). For Strk% all pitches other than balls are counted as strikes. Con% = (Foul+IPO+IPNO)/(Foul+IPO+IPNO+SS).

The first thing that jumps out is, of course, the results for his slider. Wow! Just wow. In the PITCHf/x games, at least, nobody got a hit off of it, and hardly anybody managed to put it into play or even foul it off. The only real negative would be that it seemed like he had a little trouble throwing his curveball for strikes, but given that he only walked nine men in 27 and 2/3 innings, that doesn’t seem a big concern.

Next let’s look at the strike zone charts showing where Joba Chamberlain locates his pitches against left-handed hitters and right-handed hitters. I’m keeping the same formatting for these charts as I did in the Beckett and Gagne analyses. The strike zone is shown as a box, including one radius of a baseball on each side of the plate, and the top and bottom of the zone are a general average not adjusted per batter in these charts. The location is plotted where the pitch crossed the front of home plate.

Let’s begin with the fastball.

Chamberlain Fastball Results

Chamberlain works mostly on the outer half of the plate with the fastball to lefties, and he’s more in the zone to righties, although he also comes up and in to righties. Batters seem to be able to handle his fastball fairly well, not swinging and missing very often and having pretty good success when they do put the ball in play, similar to what we saw with Josh Beckett’s four-seam fastball. I don’t have a good idea yet how this compares to league-wide numbers for all pitchers’ fastballs or even to a significant number of other hard throwers.

Next, let’s look at the seldom-used curveball and changeup. I’ll present these without comment since there isn’t much data to discuss.

Chamberlain Results for Curveballs

Chamberlain Changeup Results

Finally, let’s move on to what you’ve all been waiting for: the famous Joba slider.

Chamberlain Slider Results

This is where this avenue of inquiry starts to go downhill. After looking at this graph, I wanted to talk about how Chamberlain gets a lot of swings and misses on his slider down and away to righties and down and in to lefties.

But I was bugged by the swinging strike that was recorded nearly at the lefty batter’s foot (x=1.83, z=0.35). Was a hitter so badly fooled by a slider that he swung at one at his shoe top? It’s certainly possible, but if so, I wanted to see it. So I brought up the MLB.tv footage for the game, September 23rd against Toronto, where Chamberlain entered with two on and two out in the 8th inning to face left-handed Adam Lind, trying to preserve a 7-5 Yankees’ lead. Jumping to the end of the story, Chamberlain throws Lind five straight sliders to strike him out and end the inning.

Unfortunately, however, the pitch locations recorded by PITCHf/x for these pitches were mistakenly attached to the wrong pitches in the Gameday XML data.

Chamberlain Lind PITCHf/x at bat

Chamberlain Lind Actual at bat

The first pitch of the at bat was a belt-high slider just inside that Lind swung at and missed, followed by a second pitch in almost the same location, with the same result. Next, Chamberlain threw two sliders at Lind’s feet; the second of these landed in the dirt. Lind laid off both of those pitches to even the count at 2-2. Finally, Chamberlain threw a slider down and in, labeled pitch #5 in the second graph, which Lind swung at and missed for strike three.

The XML pitch location data for this game seems to have missed the fourth pitch (the one in the dirt) altogether and added an extraneous pitch, labeled #3 in the first graph, that did not occur in the pitch sequence to Lind. Then the order of the other pitches is out of whack, too. The pitch labeled #1 should be #5, #2 should be #1, #4 should be #2, and #5 should be #3.

The conclusion is that, no, Chamberlain did not get Adam Lind to swing at slider at his shoe tops. He did get him to swing at a pitch down and in that would have been Ball 3 if he let it go by, and it was an impressive pitching performance by Chamberlain, but unfortunately it calls into question the integrity of our data set.

I don’t have any way to verify the integrity of the rest of the data without watching endless hours of games on MLB.tv. That may seem like a worthy endeavor to some, and I can’t argue too strenuously with them, but alas, the rest of my non-baseball life seems to think it has some importance, too.

I don’t intend my notation of this example in any way to disparage the incredible work that MLBAM and Sportvision have done in creating this data set and making it available to us. For free, no less. It’s an incredibly valuable resource, and some errors are to be expected during a season in which the system was being evaluated and debugged.

I just don’t know how prevalent these kinds of errors are and when they might call into question some of my conclusions. I do know that Eric Van spotted a similar error in Josh Beckett’s data from Game 1 of the division series, as detailed in this thread at Sons of Sam Horn, post #88. The PITCHf/x data in question for that game has since been removed from the data set altogether. Eric mentions plotting the human-generated x,y coordinates against the computer-generated PITCHf/x coordinates as a way to spot these errors, but in our case with the Chamberlain-Lind at bat, the human-generated coordinates look screwy to me, too. I haven’t applied Eric’s method to a larger data set, so it may still have merit.

While, we’re on this subject, I may as well put in a plug for Josh Kalk’s new PITCHf/x batter-pitcher matchup tool. You can look at the Chamberlain-Lind matchup there for yourself. It doesn’t tell you anything I didn’t show here, but I wanted to make sure all my readers were aware this great tool was available.

Update: Cory Schwartz from MLBAM addresses the PITCHf/x data error here.

Building on the pitch identification I did for Josh Beckett, I wanted to dig a little deeper into how he used his pitches and what results he got, similar to how I did with Eric Gagne.

First, let’s look at which pitches Beckett threw in various counts:

Count 4-seam 2-seam Cutter Change Curve #Pitches
0-0 48% 18% 1% 10% 22% 408
0-1 29% 26% 3% 11% 31% 197
0-2 33% 24% 2% 5% 36% 107
1-0 43% 18% 1% 16% 23% 160
1-1 34% 24% 3% 9% 31% 156
1-2 38% 20% 2% 3% 36% 161
2-0 52% 22% 0% 7% 20% 46
2-1 38% 30% 0% 16% 16% 74
2-2 33% 28% 2% 2% 35% 106
3-0 71% 29% 0% 0% 0% 14
3-1 65% 24% 9% 0% 3% 34
3-2 55% 24% 0% 7% 13% 67
Ahead 33% 24% 2% 7% 34% 465
Even 43% 21% 2% 9% 26% 670
Behind 48% 23% 1% 11% 17% 395
0 strikes 48% 19% 1% 11% 21% 628
1 strike 35% 26% 3% 10% 27% 461
2 strikes 38% 24% 2% 4% 32% 441
Ball 0-1 40% 21% 2% 10% 28% 1189
Ball 2-3 46% 26% 1% 6% 20% 341
All 41% 22% 2% 9% 26% 1527

Beckett Pitch Mix by Count

We can see that he used his curveball more often when he got ahead of hitters, and he leaned more on his four-seam fastball over his two-seam fastball when he got behind in the count. I should mention that I’m including post-season and All-Star game data, which is probably one reason my numbers differ a little from Josh Kalk’s.

Now, let’s look at results by pitch type. Here I’ve split the data up by handedness of the batter.

LHH Ball CS Foul SS IPO IPNO TB BABIP SLGBIP Strk% Con%
4-seam FB 137 71 79 26 27 16 30 0.372 0.698 62% 82%
2-seam FB 30 20 22 8 21 14 19 0.400 0.543 74% 88%
Cutter 4 1 5 2 1 2 2 0.667 0.667 73% 80%
Changeup 30 5 14 15 13 8 10 0.381 0.476 65% 70%
Curveball 59 45 16 19 16 4 6 0.200 0.300 63% 65%
  260 142 136 70 78 44 67 0.361 0.549 64% 79%
                       

RHH Ball CS Foul SS IPO IPNO TB BABIP SLGBIP Strk% Con%
4-seam FB 85 57 60 18 36 16 29 0.308 0.558 69% 86%
2-seam FB 69 36 51 10 41 15 19 0.268 0.339 69% 91%
Cutter 4 1 1 3 1 1 1 0.500 0.500 64% 50%
Changeup 17 12 7 5 5 4 5 0.444 0.556 66% 76%
Curveball 98 64 23 29 22 6 12 0.214 0.429 60% 64%
  273 170 142 65 105 42 66 0.286 0.449 66% 82%


CS=called strike, SS=swinging strike, IPO=in play (out), IPNO=in play (no out), TB=total bases, BABIP=batting average on balls in play (including home runs), SLGBIP=slugging average on balls in play (including home runs). For Strk% all pitches other than balls are counted as strikes. Con% = (Foul+IPO+IPNO)/(Foul+IPO+IPNO+SS).

Next are strike zone charts showing where he locates his pitches against left-handed hitters and right-handed hitters. I’m keeping the same formatting for these charts as I did in the Gagne analysis, but let me know if you have ideas for how I can improve them. The graphics are a little small, but I thought it was more important to contrast the general patterns of lefty versus righty than to see the exact result for a specific pitch.

The strike zone is shown as a box, including one radius of a baseball on each side of the plate, and the top and bottom of the zone are a general average not adjusted per batter in these charts. The location is plotted where the pitch crossed the front of home plate.

Let’s start with the fastballs. First the four-seamer. (As I mentioned in my previous analysis of Beckett, the line between the four-seamer and two-seamer is a hazy one; although I think my distinction is generally accurate, it is unlikely to be accurate for every specific pitch.)

Beckett 4-seam Fastball Strike Zone Chart

Beckett likes to work the 4-seamer away from lefties, and it looks like he gets a lot of foul balls at the edge or just off the edge of the plate. He also gets a lot of balls, mostly outside it looks, and other than the curveball it’s his pitch that gets the least strikes at 62%. Overall, lefties hit the pitch pretty well–a .372 batting average when they put it in play, and with plenty of power. I wish I knew how that compared to other pitchers’ fastballs, but I don’t have those numbers. Clearly, context is important for numbers like these.

Against righties I don’t see a clear inside/outside preference, although he seems to work up in the zone more than down. He’s also more effective at getting strikes with the pitch against righties (69%).

Moving on to the two-seamer…

Beckett 2-seam Fastball Strike Zone Chart

The first thing that jumps out is that he’s almost twice as likely to use the 2-seamer against righties than lefties. Against lefties, he’s in the zone with it a lot, and it gets hit fairly hard. Against righties, it looks to be his most effective pitch, generating a lot of ground balls when he gets it on the inner half of the plate. Against righties, he got 27 ground outs with his two-seamer compared to 14 outs in the air (pop outs, line outs, and fly outs). He also gave up 7 ground ball hits and 8 hits in the air from his two-seamer against righties. Again, I don’t know if those numbers are significant or how they compare to other pitchers.

Beckett’s least-used pitch is the cutter, so the graphs for it are not terribly interesting, but I’ll show them here.

Beckett Cut Fastball Strike Zone Chart

It looks like he mostly works the cut fastball down and in to lefties and up in the zone to righties, but it’s hard to find any meaningful trends in 26 pitches. He struck out Jay Gibbons on a cutter down and in, and…well, I don’t really have anything more to say about the cutter.

Now for a change of pace…

Beckett Changeup Strike Zone Chart

It’s obvious he likes to keep the changeup down and away from lefties, and he gets a lot of swings and misses that way, particularly when he keeps it down. Against righties, he keeps the ball down but works both sides of the plate. He gets quite a few called strikes on the outer half of the plate.

Finally, we come to Uncle Charlie, Beckett’s other favorite pitch and probably his most effective.

Beckett Curveball Strike Zone Chart

Against lefties, Beckett gets a lot of called strikes across the middle of the zone. He comes down and in a lot, and gets a fair number of swinging strikes when he keeps the curve low to lefties. I’m not sure what to think about the curveballs up and away. I thought those might be hanging breaking balls, but I don’t notice anything unusual when I look at how they moved relative to other curveballs. Maybe he was just hoping to drop those pitches into the top of the strike zone since the location data I’m graphing here was measured at the front of the plate.

Against righties, he’s out of the zone a little more, either down and away or up and in. Again, he gets a lot of called strikes in the zone and swinging strikes when he keeps the curveball down–it’s his most effective pitch for missing bats. Even when hitters put the curveball in play, they don’t have much success–a .208 batting average and a .375 slugging percentage.

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