Steve already touched on Jeremy Greenhouse’s fantastic work over at Baseball Analysts of using linear weights on strike zone location for 2009 batters, and found a disturbing trend that outside of Pujols, Holliday and Schumaker, the Cardinals seemed to have done an awfully poor job on smacking a pitch down the middle when it comes. I thought it would be fun to put together some visualizations of the entire zone for the main members of the lineup and their run values per 100 swings for the 2009 season.

Here ya go -

Skip made his hay off of driving pitches down the middle, but seemed to sort of struggle with everything else, and was especially susceptible to high and inside pitches.

Rasmus liked low and in, high and away, but didn’t do much with anything else.

So there was a glitch in The Machine, and that’s pitches low and away, and low pitches in general. It’s not as if Pujols will be legging out a lot of ground balls. Pujols loved middle-up and high and away.

Luddy really struggled with pitches up in the zone, especially up and in.

Holliday handled pitches with low and inside and low and down the middle pitches, something most batters struggle with. He murdered a lot of pitched down the middle.

Yadi can handle himself on the inside of the plate, so long as the pitch isn’t up. He struggled mostly with pitches outside, which struck me as odd, because my general impression of Molina is that he’s pretty good taking the ball the other way. You’d think pitches on the outer half would be the type of pitches he could slap to the right side.

Now the Boogameister. It’s a little surprising to see a ground-ball hitter and a fast runner like Ryan to do so poorly with low pitches.

I’m going to pass on the more depressing cast-aways (DeRosa, Greene, Thurston), but I couldn’t resist putting together a zone for Ankiel. Ank handled pitches down the middle, but was helpless on just about everything else.

This was fun. Sometime soon we’ll have to look at pitchers.

I’m warm to the idea of putting McClellan in the rotation, and I’ll explain why I like it. First of all, McClellan does seem to have the repertoire of a starting pitcher. Here’s a look at some of his Pitch F/x data proving he has enough weapons to succeed as a starter. The data comes courtesy of

Type Count Selection Velocity (mph) Vertical (in) Horizontal (in) Spin Angle (deg) Spin Rate (rpm)
FF 483 44.40% 91.4 7.2 -7.79 227 2,136
CU 278 25.50% 75.8 -8.02 6.53 40 1,688
SL 153 14.00% 87.7 3.43 0.72 172 755
SI 95 8.70% 90.8 6.54 -9.15 235 2,220
FC 60 5.50% 88.5 5.3 0.19 180 1,090
CH 20 1.80% 84.6 5.54 -7.61 233 1,769

So what does this prove, exactly? Well, first of all it proves that I like making tables even though I stink at formatting them, that much you already knew. Getting on point…in order to succeed as a big league starter, there are some ingredients you must have, unless you’re a freak. Those ingredients are at least one “plus” pitch, two average pitches and average command. Looking at this chart, Mac has the pitches. And we’ve all seen him pitch dozens of times, I think our eyes tell us he has the goods.  (Some quick clarification  - sinker/fastball, same thing. Bad Pitch F/x algorithm! Bad! Same goes for his cutter/slider).

Anywho, his two-seam fastball is average. He doesn’t generate tons of sink, but the pitch has good “tail”. His cutter/slider and his curveball can both be very good pitches at times.

Let’s look at his results -

Type Strike Swing Whiff Foul In Play
FF 65.20% 45.30% 3.50% 21.50% 20.30%
CU 48.60% 32.00% 10.40% 10.40% 11.20%
SL 56.20% 45.10% 9.80% 17.00% 18.30%
SI 61.10% 41.10% 6.30% 14.70% 20.00%
FC 66.70% 50.00% 5.00% 20.00% 25.00%
CH 45.00% 35.00% 10.00% 5.00% 20.00%

A fair share of whiffs on the curve and slider. His command is fair enough, although he did walk a few too many hitters last year.

So I hope I’ve established that he has the pitches to start, how exactly would he do? Sean Smith did a study on pitchers from 1953 up to 2008, and found that when switching from starting to relief and vice-versa, a pitcher’s walk rate would stay static, while their hits went +/- 5%, their homers went +/- 15%, and their strikeouts went +/- 16%. Let’s apply those numbers to McClellan and see what we come up with. First, here’s his 50th percentile CHONE projection as a reliever:

McClellan 63 5 25 2 48 3.99

Now let’s see what we come up with for 150 innings for McClellan as a starter, take it for what it’s worth -

McClellan 150 14 61 5 102 4.42

150 innings, 4.42 FIP is a 1.6 WAR pitcher, which is a little over 3 times higher than what his projected WAR would be coming out of the bullpen with an average leverage index of 1.3; in other words him pitching as the primary set-up man. That’s Nick Blackburn/Jon Garland territory, which is serviceable. Let’s put it this way – if the Cardinals had Nick Blackburn, would you prefer they started him or put him in the bullpen? You’d want them to start him, of course.

But wait, to who does Mac’s innings as a set-up man fall to? Yo-yo and unproven arms like Motte, Hawksworth and Boggs pitching in the 8th inning is a scary proposition. Let’s just say for now that McClellan’s innings would fall to Jason Motte. His CHONE projection calls for a 4.4 FIP. The bad news is no non-LOOGY reliever is very likely to do better. And one of those pitchers would be taking Motte’s spot, and so forth. If you give his innings to Motte, that’s a loss of 0.4 WAR, and an increase in sales of garden tools…I mean angry mob supplies in the greater St. Louis area.

Let’s not also forget Jaime Garcia. He’s not projected to fare as well (4.69 FIP), but as a 5th starter, that’s fine and it’s feasible he plays better than projected. Garcia also is a talented arm. With the way the bullpen is set-up now, given the chaining, putting McClellan in the bullpen or in the rotation ends up being closer to a wash than I would have imagined, and that’s assuming he’d succeed according to the numbers I laid out. So is it worth it?

Speaking from a long-term perspective, I’d say heck yes. It would be more beneficial for the Cardinals to have a nice, cost-controlled pitcher in their rotation than one in their bullpen. If the Cardinals think Mac is even close to being the real deal, they need to upgrade the bullpen with someone available for cheap like Kiko Calero or (I can’t believe I’m about to type this) Chan Ho Park. It would be a worthy investment to develop a nice, young starter while saving your team some unnecessary angst in the late innings.  And hey, if he bombs as a starter, there’s no harm in having some extra depth in the bullpen, anyways.

Sign Kiko. Give Mac a long look.

As far as non-roster invitees go, you can’t get much more interesting than Rich Hill. In 2007, Hill struck out 183 in 195 innings pitched and was a three win pitcher. Since then, he’s contracted Steve Blass disease, also known as Ankielitis. Adding injury to insult, Hill is coming off labrum surgery. At least that might explain some of his badness.

CHONE, Marcel, ZiPS and the Fans are in harmony in their projections of Hill — 4.8ish FIP, lots of walks, a nice strikeout rate and somewhere between 90-100 innings; good for roughly around 1 WAR. Coming off labrum surgery, I think expecting much of anything feels optimistic. I’m still dealing with Mulder shell-shock, but at least they’re not paying Mulder money or anything near it. This is a no-risk, all-reward move. Yay Cards.

Stuff wise, Hill’s repertoire consists of an 88-90 MPH, the occasional change-up, and a droppifying curveball, the key to his whiffs. For fun, I thought it would be interesting to compare Hill’s curve to the average lefty and Barry Zito, the King of Lefty Curveballs.

Name Speed Horizontal Vertical Spin Rate Spin Direction
MLB LHP 74.7 -4.3 -6 1292 299.8
Rich Hill 71.5 -7.9 -8.4 1392 317
Barry Zito 72.9 -4.5 -10.4 1749 333.1

Not a true 12-6er, but that’s a lot of movement. It’s more of an 1-7 curve. Here’s a spin deflection graph, and then we’ll look at the results -

Name Strike% Whiff% Swing% Foul% In Play%
MLB LHP 58.4 10.1 38.8 13.7 14.9
Rich Hill 61.4 9.2 35.7 12.9 13.5
Barry Zito 65.5 7.5 42.5 19 16

I find it odd that two lefties with such great curves get less swings and misses than average. Huh. At any rate, Hill is flyer worthy.

If you caught my previews of the first two NLDS games you saw that I did a couple of heat graphs (aka heat maps).  I had what I thought was a decent idea for another version of that graph.  The attempt is to capture a pitches effectiveness by movement.  Without further ado, we have Carp’s curveball’s whiff rate.

The vertical axis is vertical movement in inches, and the horizontal axis is horizontal movement.  The picture is from the catcher’s perspective.  The basic takeaway is the more straight down the pitch broke, the higher percentage of whiffs he got.  The chart doesn’t break out for batter handedness, and I also removed some periphery data points where the sample size skewed the chart.  Anyway, what do ya’ll think?  is there some value here, or is it simply a pretty picture for the sake of pretty pictures.

A while back I went down the path of looking at similarity scores between pitches for different pitchers, but had since not really followed up.  Since they came up in a thread over at BtB, I thought it would be a good idea to revisit them now.  My basic methodology was the same that Josh Kalk used for pitchers, only removing the percentage thrown term.  In addition I also ran a set of scores where along with the three “physical” traits, I added whiff rate and GB%.

For my initial cut I only took pitches that I had over 150 instances of in the database in a hope that it wouldn’t skew the whiff and GB% distributions.  In other words if Joe Pitcher only threw 100 curveballs over the last 2 years, his curveball was not included for comparison. I’m not sure what I want to make the cutoff, or if I want one, but for this cut it was 150 instances.  The primary drawback is that high of a cutoff will eliminate a lot of the pitches where a comparison would prove useful, pitchers that are relatively new to the big leagues.  In retrospect I think I want to lower that bar substantially for “physical” only scores and keep it slightly higher when introducing results.

Anyway, on to results!!  I only ran the numbers on fastballs (both 2 and 4 seam) since I wanted to get a feel for how the methodology was going to work and what kind of numbers to expect.  The first table is the most similar fastballs based solely on their physical traits (movement and velocity)

Pitcher 1 Pitcher 2 Score
Joakim Soria T.J. Beam 0.008822813
Jim Johnson Jorge Julio 0.016173611
Armando Galarraga Trevor Cahill 0.029398104
Josh Geer Dan Giese 0.029431471
Tommy Hunter Brendan Donnelly 0.030507301
Brandon Lyon Jason Berken 0.030858617
Dan Wheeler Kris Benson 0.030926282
Brandon McCarthy James Parr 0.031952073
Jesse Crain Andrew Bailey 0.03290595
Steve Trachsel Keith Foulke 0.033642835

So what does the number in the score column represent? It is the sum of the differences between percentiles (as a decimal still so 90th percentile = 0.9) for each component. Clearly that number doesn’t look pretty, and I’m racking my brain to come up with a better way to present the number… any thoughts would be appreciated.

Moving on, the next table includes whiff and GB% to go along with the physical traits.

Pitcher 1 Pitcher 2 Score
Miguel Batista Luis Mendoza 0.088735311
Joe Blanton Leo Rosales 0.105227103
Sean Green Joe Smith 0.106274147
Kyle Lohse Adam Wainwright 0.107327243
Chris Sampson Shawn Camp 0.108540848
Braden Looper Carlos Silva 0.111769335
Max Scherzer Josh Roenicke 0.118732513
Francisco Cordero Chris Resop 0.122583619
Leo Nunez Jorge Julio 0.127953011
Pedro Martinez Matt Herges 0.136471458

For this set of scores all 5 components are equally weighted, which may or may not be valid. I’d like to put a little more thought into if I’d like to weight them differently or not. Anyway, that’s the first cut at getting some of this information out there. The to-do list with these is long. I’d like to re-run the fastball physical numbers with a broader net, run all the other pitch types, and look at some the top comps for various pitches that have good reputations/results (i.e. Mariano’s cutter, Brandon Webb’s sinker, Adam Wainwright’s curve).

In preparation for the debacle that was game 2 of the NLDS I did a quick survey of how Cardinal hitters hit high velocity LHP fastballs. With that data in hand I went down the path of looking at all hitters against all high velocity fastballs (for the purpose of this look >94 mph). For the study I only looked at players that had put 50 balls in play over the time frame I was looking at (2008-mid Sept 2009).  First the list of best SLGCON

SLG Rank Player SLGCON
1 Adam Dunn 0.984
2 J.D. Drew 0.962
3 Ryan Howard 0.896
4 Prince Fielder 0.893
5 Chase Utley 0.820
6 Joey Votto 0.808
7 Nick Swisher 0.806
8 B.J. Upton 0.779
9 Carlos Quentin 0.774
10 Torii Hunter 0.771

And the worst

SLG Rank Player SLGCON
177 Bobby Crosby 0.193
176 Juan Rivera 0.241
175 Kenji Johjima 0.250
174 Cesar Izturis 0.260
173 David Eckstein 0.278
172 Jason Kendall 0.278
171 Jeremy Hermida 0.280
170 Mark Kotsay 0.291
169 Yadier Molina 0.292
168 Magglio Ordonez 0.300

And the notable Cardinals

SLG Rank Player SLGCON
23 Ryan Ludwick 0.707
24 Albert Pujols 0.700
30 Matt Holliday 0.659
41 Mark DeRosa 0.616
67 Skip Schumaker 0.556
82 Troy Glaus 0.518
169 Yadier Molina 0.292
NA Colby Rasmus 0.556
NA Rick Ankiel 0.388

Just thought these might be interesting. For a point of reference MLB average over the sample was 0.510.  A more interesting look might be to do run values and expected run values, and I hope to have those incorporated into my database soon.

This post was written before the game finished last night, so hopefully it gives some insight into how we’ll try and take a 2-0 lead. The opposition in game 2 will be Clayton Kershaw. First, as usual, a summary table

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Hey folks, sorry I’ve been a little non-existant lately, but family and life in general was put first.  That being said, with the playoffs cranking up, I thought I’d try to dive into the opposition a little, starting with Randy Wolf.  One quick caveat, I haven’t updated my database in a while, so this is only up through mid September (the 14th I think). All the fun after the jump.

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I’m embarking on a fairly sizable project to see if pitch F/X can influence the world of player projections (in fact if my adviser will allow it I’m going to try and use it as my masters thesis).  The first step I’m taking is coming up with predictive models for batted ball type based on “stuff” (horizontal and vertical movement and velocity) and location.  My initial fooray into that has been GB%.  Here’s what I’ve got so far….  I’ll give you direct R output and then give a quick explanation.  NOTE this is for fastballs (all kinds) only, other pitch types still to come.

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Last week I posted some numbers on how the addition of Matt Holliday had impacted the way pitchers approach Albert Pujols, and the conclusion was not at all.  I also wanted to take a look at how Albert looming in the on deck circle impacts those hitting in front of him.  For this particular case study I looked at Colby Rasmus since he has a good amount of at-bats both before and after Pujols.  The next few tables summarize the findings, first the pitch distribution

Pitch Before Pujols Other
FB 53.2% 50.0%
CU/SL 28.7% 34.5%
CH 18.0% 15.4%

While Colby sees a few more fastballs hitting before Pujols, it’s not as big of a difference as the main stream media would have us believe. However, does he get more first pitch fastballs or fastball when the pitcher is behind in the count?

Before Pujols Other
0-0 57.4% 54.9%
1-0 52.5% 47.6%
2-0 44.0% 43.7%

So it appears the pitchers are a little more hesitant to fall too far behind in the count, but nothing too drastic. And finally are the pitches themselves “better”, meaning more strikes/fatter pitches

Before Pujols Other
In Zone 56.6% 54.1%
“Fat” 37.4% 34.2%
FB in zone 59.6% 61.9%

Overall he sees more strikes and more pitches in the fat part of the zone, but sees less fastballs for strikes when hitting in front of Albert. While there are advantages to hitting in front of Albert (usually on the order of 2-3% to the “good” side of the comparisons) those advantages appear to be minimal.

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