He has arrived.

Last night, the Detroit Tigers made a huge mistake. The last thing you want to do if you’re the Tigers is piss off Matt Wieters by getting him out four times (including once via strikeout). They won’t get away with it, you can count on that.

I have no real purpose here, other than to highlight one of the funnier sites on the ‘net. Matt Wieters Facts is a compilation of all the interesting facts about the greatest player to ever walk the earth. The best part: every single one is true! One of my favorites: “Matt Wieters’ greatness is actually visible from space.” Scratch that… the best part is clearly this picture:

matt-wieters-hof-postcard.jpg

(click to enlarge and read the ridiculousness)

Chien-Ming Wang was broken. Did they fix him?

Chien-Ming Wang returned to the major league mound on Friday night in relief of the struggling AJ Burnett. In three innings of work, he allowed 2 runs on 6 hits (one home run) and one walk. That outing lowered his season ERA by 9 1/2 points… to a cool 25.00. Yikes.

Wang had been rehabbing what was diagnosed as essentially weakness in his hips, which reportedly resulted from a foot injury he sustained last season in Houston while running the bases. He had been doing relatively well in rehab starts in AAA, and the Yankees needed a long reliever for the game after burning the bullpen the night before. I mentioned above Wang’s results for the night, but let’s take a look at how he got there, through the eyes of pitch f/x.

His average fastball was a strong 92.5 mph, averaging about 10 inches of horizontal movement and 6.8 inches of vertical “rise.” How does that compare to the beginning of this season, when he was getting bombed, and to 2008, when he was pretty good? Here’s a grid, for easier comparison:

Game    Avg. Velocity     Max. Velocity     Horiz. Movement (in.)   Vert. Movement (in.)
2008            91.8                  ~95                          11                                  4.9
2009            90.7                  ~93                         9.3                                  5.7
Tonight        92.5                    95                          10                                  6.8

Alright, so we see that his velocity tonight was back where it should be, and his horizontal movement is creeping back up. But what’s up with the vertical movement? He’s a sinker-ball specialist, remember. The lower the value of the vertical movement, the more the ball is “sinking.” Curveballs, which have a sharp downward break, are in the negatives. Tonight, 50% of his balls in play were groundballs, might that have been a fluke?

Let’s look at some individual games and find out. One of his best games in 2008 happened on May 2nd against the Mariners. He had 5 K’s, 2 walks, in 6 innings, allowing one run. More importantly (for our purposes), the M’s had 10 groundballs versus only 2 flyballs. In that game, Wang had 14.5 inches of horizontal movement, which is just fantastic, and 4.5 inches of vertical movement, which is also great. Going back a month earlier to opening day (which I was at), Wang threw 7 innings with 16 (!) groundballs, versus only 3 flyballs. In that game, the numbers were almost identical to the gem against the Mariners. He had 13 inches of horizontal movement and 4.5 inches of vertical movement, both of those of course being fantastic for a sinker/2-seamer.

So it would seem that it’s the vertical movement that’s causing him problems. here’s the part that completely threw me when looking into this: Chien-Ming Wang’s vertical movement on his 4/8 and 4/18 starts of this season were 5 inches and 3.5 inches, respectively. Remember, in those games he got absolutely bombed to the tune of 5 innings and 15 runs. His ball was sinking, but it was still getting rocked. Here was my reaction upon seeing this.

My next stop was the location graphs available on Brooks Baseball. If you look at Friday’s game, you’ll see that there were very few balls just below the strike zone. Compare that to his opening day start from 2008, and you see some difference. The Friday start isn’t nearly as down in the zone. These two starts, both from earlier this season, are even worse. Ok, so that was a lot of links all at once. If you got confused with keeping track of what’s what, or you just don’t want to click through, here’s what it means: Wang isn’t keeping the ball down this year nearly as well as he did in 2008. While his ball is sinking, it’s not being thrown down in the zone enough to be effective.

Finally, here is what I believe to be the main reason for his problems. There are 5 links below, all of them are from games already referenced in this post. What the images show is release point data, from pitch f/x. Simply put, the 2008 release points are from good games, the 2009 release points are from bad games. Try opening each one in a different tab and switch back and forth to more clearly see the difference.

4/1/2008   5/2/2008   4/8/2009   4/18/2009   snippet from beat writer Pete Abraham on April 14th, after Wang allowed 8 runs in an inning of work the day before: “Wang seemed stunned. He said the issue was where he released the ball, which was off to the side instead of over the top. A
sinkerball pitcher wants to stand tall on the mound and throw the ball
on a downward plane. Otherwise the ball floats over the strike zone and you see what happens
.” Despite having success in 2008 with a lower release point than he had in the early stages in 2009, he wanted to raise his release point even more.

To answer the question in the title, “did they fix him?” As it stands right now, I don’t think they did.

Chien-Ming Wang’s release point has been discussed previously at River Ave. Blues.
–Thanks to Brooks Baseball and FanGraphs for pitch f/x data.

What really happens in the clutch?

Maybe there’s something to this whole clutch hitting thing after all.  For the longest time, there’s been a maxim in the Sabermetric community that clutch hitting, as a repeatable skill, does not exist.  Recently, I’ve come to question that received wisdom that players aren’t really affected by pressure, even though in the past I’ve been one of the people spreading it.

Strictly speaking (and statistically speaking), over a one-year period, there is very little in the way of a repeatable clutch skill.  No matter how you measure it, whether it’s a leverage index based mathematical model of clutch or an intuitive “performance in ‘close and late’ situations” model, a player doesn’t seem to perform better or worse in the clutch than he does overall.  At least over a one year period.  But then, perhaps we’ve over-stated our case.  It’s one thing to say that a one year time period (or 700 PA or whatever) is not a sufficient sampling frame to attain proper reliability on a statistic.  It’s another to say that the skill does not exist.  It might just be covered by a lot of noise.

One of the problems with measuring clutch is the way in which it’s been operationalized.  The current best measure to be had is the method used at Fangraphs, which uses win probability added (WPA) and the Leverage Index (LI) to calculate a mathematically based clutch statistic.  The problem with WPA based versions of clutch is that WPA itself doesn’t stablize over a short period of time, (and here, even a full year is a “short” period of time).  Many of the usual one-number performance indicators (the slash stats) are unreliable in small sample sizes as well.  Perhaps we need a better indicator of what’s really going on in the clutch.

Let’s go back to what the idea of clutch is.  People react to stress differently.  Some people seem to thrive under pressure.  Others seem to crumble.  Anyone who’s ever had stage fright can attest to the latter.  The common thread is that performance suffers/improves under pressure.  But does behavior change under pressure?  There’s a difference.  Behavior is what players decide to do, over which they have a great deal of control.  Performance is the result, and as we’ve seen time and time again, is often the result of luck mixed with behavior.

Let’s take the decision on whether or not to swing at that pitch that’s currently hurtling toward the plate, perhaps the one thing over which the batter has total control.  (Swing percentage also stablizes at ridiculously low levels of PA.)  Do players swing more (or less) with the game on the line?  Doubtless, some will not be affected by the pressure, some will start swinging at everything, and some will politely keep their bat on their shoulder more than they otherwise would.  This is assuming, of course, that players in general are affected by pressure.  But are players consistent in the way that they are affected by pressure?

I took all player-seasons from 2005-2008.  I split all plate appearances into non-clutch and clutch.  For my definition of “clutch”, I went with a “close and late” definition (7th inning or later, score within 2 runs either way).  Why this and not a leverage-based method?  Because baseball players probably don’t know that leverage exists.  It’s a great mathematical tool, but it’s probably not how players decide whether this is a big situation or not.  The reason behind the 7th inning cutoff is that even if one is leading off the seventh inning, there is the very real chance that this may be my last chance to do something.  If the team is retired in order over the next three innings, the guy leading off the 7th will be standing in the on-deck circle as the game comes to an end.  Plus, those “close and late” situations have pretty high leverage values to begin with.  I fully realize that it’s not a perfect defintion of clutch, but I’m trying to model what goes on inside the head of a major league player.  I’m a psychologist after all.

I looked at each player’s swing percentage when in the ho-hum basket and in the “clutch” basket.  I set a 50 PA minimum for the clutch basket.  I found the difference between his swing percentage for the clutch and non-clutch situations.  Indeed, some players swung more, some less.  Some stayed pretty much the same.  Since I had four years worth of data, I ran an intra-class correlation (StatSpeak fans, take a shot!), and came up with .24.  (short explanation: think of ICC as a year-to-year correlation… only better.  For more details, go here.)

That may not seem very big.  Papers have been written about less.  The difference may not be stable at a minimum of 50 PA of clutch at bats, but perhaps at a slightly bigger sampling, it may approach respectablility.  Given that swing percentage is a very stable statistic, and is something that is almost entirely in the control of the batter, it may even be possible to determine from minor league data what a player will look like on this measure before he gets to the majors.

The other thing to consider is that swing percentage matters.  Not directly, but in concert with other diagnostics, it makes a difference.  Low contact hitters (generally, power hitters) who swing more actually have lower rates of HR and higher rates of K’s.  So, players who get nervous and start swinging more are much less likely to hit that homerun in the clutch.

Speed Score by Position

I’m not sure if this post will mean much, so there will be limited commentary on my part. What I did was export some leaderboards off FanGraphs and take some simple unweighted averages of speed scores. I used a 500 PA minimum from the last three years, since that’s the highest cutoff allowed. Without further ado, here is a chart of speed scores broken down by position, along with the range:

speedscores.jpg
Yea, there are probably more instructive ways to look at this, but there’s no real meaning behind speed scores so this is all you get. I find it interesting that second base and third base are so different. I’m not sure if that supports or refutes the articles on FanGraphs from this winter about second- and third-basemen having similar skillsets. Also, there must be a lot of slow guys that didn’t get a ton of playing time that make up these samples. The average speed score is supposed to be around 5, and the average here is clearly below that.

Anything else that you guys see here? Also, for an explanation of what comprises these speed scores, go here. 


The Return of Rich Hill

Orioles starter Rich Hill made his long-awaited return to the Major League mound on Saturday. Hill was last seen in the majors early last May when he was walking approximately everybody who came to the plate. OK, so maybe it was 20.2% of batters (8.24/9IP), but that’s still obviously unacceptable. This came as a surprise to everybody, as he had walked under 3 per 9 innings just one season before, and 3.53 per 9 in 2006.

There seemed to be two possible explanations. One line of thinking was that Hill had reverted to his pre-2006 walk-happy tendencies. In the minor leagues he had some problems throwing strikes, but seemed to solve them over time. The other theory was that he simply had contracted Steve Blass disease. So Hill was sent all the way down to the Arizona Rookie League at one point, but also spent time in the Single-A Florida State league, making 3 starts, and the Pacific Coast League (AAA), throwing 26 innings over 7 starts.

Regardless of where he went, though, he couldn’t find the plate, walking 28 in 26 innings in AAA, and 44 in 47.2 combined minor league innings. Rich Hill has had success so far this season after being traded to the Orioles and working in AAA. His control wasn’t spot on like it was in 2007 with the Cubs, but it was improving while he maintained his strong strikeout rate. When Hill returned to the major leagues on Saturday, he walked just two batters in 5.2 innings. So it would seem that Hill has improved somewhere. Usually, this is attributed to some kind of mechanical adjustment, either “staying closed,” or finding a consistent release point. I’ve been looking at his release points through pitch f/x to see if the latter is true. Take a look at the following two plots. One is his release point plot from his start on Saturday, the other is from his final MLB start in 2008 where he walked 4 batters in just two thirds of an inning.
release2.jpgrelease.jpgThe dots on graph on the bottom are pretty spread out compared to the graph on top. These are release points, remember, so it looks like his release point for that game was all over the place. That would seem to explain his control problems, right? Wrong. The graph on the bottom, which has the points plotted all over the place, is his game from this May 16th, in which he has just two walks and threw 61% of his pitches for strikes.

So what’s going on here? Has Rich Hill solved his control problems, or hasn’t he? This is where FanGraphs Plate Discipline comes in handy. Hill threw just 40% of his pitches in the strike zone, compared to 56% in the 2007 season. That’s just atrocious. His release point issues haven’t been fixed, and while that didn’t manifest itself in this start, I wouldn’t bet on him having sustained success this year unless he fixes it. Here’s his release point plot from a start against the Brewers in 2007 where he had 9 strikeouts, no walks, and threw 74% of his pitches for strikes:

release07.jpg
See how dense that plot looks around the center of his release point? There are a few outside the center of course, that will happen even with Greg Maddux pitching, but the vast majority fit inside a little 4×4 box in the center. He was extremely consistent that game and it obviously paid off with a dominant performance. I would be wary about picking him up in fantasy leagues until he shows improved consistency with his release points. And Orioles fans should reserve judgment for the time being–I’m just not sold that he’s back to being the very good and occasionally dominant Rich Hill that we saw two and three years ago.

Release point plots courtesy of Brooks Baseball.

Note: I put this up around 12:30 a.m. tonight (morning I guess). Within 90 minutes of me posting this, Harry Pavlidis of The Hardball Times (and Beyond the Box Score and Cubs f/x) posted an article with the same exact title. Great minds think alike. Harry focused on his stuff, while I focused on his control and release point. His article can be found here, I suggest reading that as well.

BP Idol

The ten finalists for BP Idol have been announced.  For those of you living under a Sabermetric rock for the last month or so, the people over at Baseball Prospectus are ripping off paying homage to that singing show where the British guy tells everyone that they’re dreadful.  I must confess that I’ve never actually watched American Idol.  (True story.)  But I will be watching BP Idol.  This will be fun.

Anyway, BP had their auditions and named their ten finalists, and whadaya know, there are some familiar names on that list.  Brian Cartwright, who not so long ago wrote for StatSpeak (and I think still has his login privileges… come back Brian!) is one of the chosen ten.  He’s joined by current StatSpeak writer Matt Swartz.  The two of them are clearly superior to the other eight finalists.  (Actually, I read through all the articles… there’s some really good stuff in there.  But Brian and Matt are better.  Vote for them.)

Throw in the fact that Eric Seidman made the transition from StatSpeak to BP a few months ago, and we’ve somehow managed to become the farm team for BP.

In any case, congrats go out to Brian and Matt.  You’ll do us proud.

Improving BABIP Projection by Batted Ball Types

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In my last article for StatSpeak, I tested the major projections systems’ abilities to accurately project a variety of statistics other than general production level measures such as OPS or wOBA.  One of the statistics that I tested thoroughly was Batting Average on Balls In Play (BABIP).  The projection systems were much worse at projecting BABIP than other statistics, like homeruns, walks, and strikeouts.  Presumably, that is because hitters vary far more in their abilities to achieve/avoid these outcomes, and BABIP is based largely on luck.  However, hitters do vary by their abilities to hit safely on balls in play, and seeing as 70% of plate appearances result in a ball in play (and not a homerun), it is important to project these.  As far as I know, none of the major projection systems use BABIP by batted ball type.  It is my belief that this would greatly improve projection overall.

In this article, I will continue my study of hitters’ BABIP, using more data than I previously had
access to.  Not only are the results
different, but I was able to test more variables than before since I had more
observations.  In my last
article
, I used data on hitters who had 300 PA from 2005-2008 to develop my
data.  In this article, I was able to
incorporate detailed data on hitters who had 300 PA from 2003 to 2008, which
allowed me to study far more observations. 
In trying to consider how to use three years of previous data to predict
a hitters’ BABIP, 2005-2008 only allowed me to look at 121 hitters.  However, adding in the four year ranges of
2003-2006 and 2004-2007 allowed me to use 381 hitters (or more accurately,
number of times a hitter got 300 PA in four consecutive seasons, as some
hitters were included more than once).  I
was also able to extend my study of predicting BABIP on groundballs, line
drives, and flyballs by using all of this previous data too. 

 

The regression lines changed in several ways.  For one thing, line drive percentage came out
significant in projecting BABIP again. 
It was surprising that it was so statistically insignificant while
looking at the 2005-2008 data, but now it does seem like this is a persistent
enough skill that it can be used in BABIP projection. 

 

Another particularly nice thing about using more data is
that you can minimize the effect of outliers. 
For instance, the coefficient on the natural log of contact rate (the
percentage of pitches that a hitter swings and either puts in play or fouls
off) was cut in half, and the reason is fascinating.  Coefficients in multiple regression equations
will tell you how to adjust the expected dependent variable (in this case,
BABIP) given what all the other variables were. 
In my regressions, I incorporated groundball percentage, BABIP on
groundballs, flyballs, and correlates with BABIP on line drives.  Additionally, I used the variable of natural
log of contact rate to help say which direction
BABIP should be expected to go given a hitter’s ability to make contact, since
the other coefficients served to regress BABIP back to the mean as far as
historical tendencies indicated it should. 

 

However, the strong negative coefficient on natural log of
contact rate indicated that hitters with poor contact skills would see their
BABIP fall more quickly.  That
coefficient remains significant and negative. 
However, Ryan Howard screwed with my data when I only looked at 121
observations.  His BABIP fell
dramatically after being strong in 2005-2007 (.358, .363, and .336), down to
only .289 in 2008.  The regression wants
to avoid the nasty error term that Howard would have left and the way that
Howard differed from the rest of the league is how low his contact rate is and
how high his homerun rate is.  Since the
natural log formulation served to expand the difference between his contact
rate and the rest of the league and to contract the difference between his
homerun rate and the rest of the league, the model used contact rate as the
cause of low BABIPs.  I realized this
error when I began doing BABIP projection for many other players and found that
guys like Adam Dunn and Mark Reynolds with poor contact rates would have BABIPs
around .240 or lower by my equation, which clearly did not sound right.

 

The reason that Howard’s BABIP fell so much in 2008 is not
that he is a poor contact hitter.  Being
a Phillies fan, I know from observation that defenses became better at shifting
against him.  While teams began to shift
against him towards the end of 2006 and throughout 2007, they had not yet determined
where exactly to position their players. 
They began playing the second baseman even deeper into right field and
closer to the line compared with other infield shifts, and this depressed his
BABIP a lot.  Initially, I only used
contact rate in my equation at all because it is correlated with a high BABIP
on groundballs, but that is probably because hitters who do not center the bat
on the ball often weakly tap balls off the bottom of the bat.  That does not describe Howard, who rips
groundballs into the same predictable locations.  As a result of this and a mixture of bad
luck, Howard saw his BABIP fall in 2008, and the model tried to correct for
that by crediting his low contact rate as the cause.  This is a pretty obvious warning to me to use
more data for regression equations. 
After seeing that the coefficient was much smaller when I removed Howard
from the equation, I thought it was smart to test how the model did with more
data. 

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