A Hard Goodbye – Part One

Finesse, intelligence, and technical expertise are areas in all walks of life that have always struck my fancy; using words and intellect to navigate oneself towards a positive outcome rather than strength or brute force. Give me Bret “The Hitman” Hart over Steve Austin, George Clooney over Arnold Schwarzenager (except in Junior), and Fox Mulder over Detective Vic Mackey. It should then come as no surprise that my favorite pitcher, the one I have idolized since becoming a fan in 1993 (at the age of eight), is Greg Maddux.
He has been the only individual player I have exclusively followed and would often stay home on a Friday or Saturday night if it meant being able to watch him go to work. Boy was that fun – growing up a Phillies fan with my idol playing for a direct rival.
In a recent interview Greg expressed a sense of certainty, albeit small, that 2008 would mark his final season as a professional baseball player. Upon reading the interview I went into a short-lived state of shock; it was one of those situations where the end was known to be coming soon but it still hits you like a ton of bricks.
Maddux has not been the same insanely dominant pitcher he was in the early-mid nineties, but knowing that he is still in the game, mentoring youngsters with his own brand of theory and strategy, brings with it a sense of comfort; a sense of normalcy; one of the final ties to an era those my age grew up with. The idea that, this time next year, he might not be gearing up to report for spring training is a tough one to swallow.
With the potential for this season being his last I decided to write a four-part series of articles revolving around one of the greatest careers ever had and, with no disrespect meant towards Pedro Martinez, the single greatest pitcher I have ever had the privilege of watching. Part One will focus on his oft-misunderstood usage of personal catchers.
Greg and Javy
Poll numerous fans on the relationship between Greg Maddux and Javy Lopez and the most common response will involve some variation of how the former “didn’t like” the latter. After all, Lopez and Maddux were teammates from 1994-2003 and yet Javy was the starting catcher for just 50 of Greg’s 327 starts in that span. It has been well-documented that Greg preferred not to pitch to Javy but the idea of the two disliking each other really sprouted out of some media speculation and the ever-fun jumping to conclusions (..it’s a mat… and you jump to different conclusions!).
Hal Bodley wrote a great USA Today article on September 19th, 2003, about this very subject. Around that time, Lopez had just served as the starting catcher during a Maddux start for the first time since September 8th, 1998 – a little over five years. Though I will be cherry-picking tidbits from the article, to read it in its entirety, click here.
When asked about not pitching to Lopez, Maddux replied – “It’s nothing personal. I won a Cy Young with Javy. I like throwing to him.”
Wow. What an arrogant snob. He clearly had a distaste for Lopez. I bet all of the skeptics will assert that Greg’s pants caught fire after that statement. He extended his reasoning on not throwing to Javy –

“If a catcher catches 120 games in a season, that’s a lot. So subtract my 35 starts from 162 games. Pitchers don’t like to change catchers during the season, and by Javy not catching my games, Bobby only has to find another ten or twelve games to rest Javy.”

Theoretical personal differences aside, Maddux apparently did this in order to ensure he reached a comfort level with a certain catcher for a large majority of a season and to help Bobby Cox have specific times in which he knew his star catcher could rest. On situations prior to 2003, the breakout year (cough, contract year, cough) of Javy Lopez, Bodley writes –

“In the past, Cox could justify not matching Lopez with Maddux. There wasn’t that much of an offensive drop-off. This year [2003], though, the Braves’ attack is dramatically weakened without Lopez in the lineup.”

Tim McCarver also added that the “controversy” arose because Greg’s personal catchers did not enhance the offense as he did while serving as Steve Carlton’s personal catcher.
The Personal Catchers
Intrigued by McCarver and Bodley’s statements I decided to scan the gamelogs at Baseball Reference in order to compile the offensive statistics of those who served as the starting catcher during games in which Maddux pitched. First, a breakdown of catchers games started during his starts, 1994-2003:

Eddie Perez 96-00 120
Henry Blanco 02-03 64
Javy Lopez 94-03 50
Paul Bako 00-01 48
Charlie O’ Brien 94-95 25

In case you are noticing how Lopez is said to have caught Maddux for 72 games on the BR-Splits, I am looking at games in which the catchers started. According to the gamelogs Lopez was the starting catcher for Maddux only 50 times. Here are some offensive statistics of these catchers, during games in which they were the starting catcher for Maddux, ranked by OPS:

Javy Lopez 45-193 6-27 .694
Eddie Perez 96-403 8-43 .623
Paul Bako 27-135 3-9 .622
Charlie O’ Brien 16-83 3-7 .581
Henry Blanco 40-213 2-14 .502

While Lopez’ numbers outshone the rest, they were still fairly terrible. These same numbers were expected from the others but probably not from him.  He retrospectively did not perform that much better than the others during Maddux’ starts. No conclusion can be drawn from this, due to the small sample sizes, but it can be said that Lopez ranked higher than the rest.
Personal Catchers = Better Defense = Better Results?
The common belief is that Maddux figured he would perform better with stronger defensive catchers. Lopez was known more for his stick than his glove and strategy while the others were more along the lines of defensive specialists. Everyone steals bases on Maddux due to his everlasting creed of focusing attention on the batter and not letting the runner serve as a distraction. Due to this he would probably be best-served by someone capable of throwing out a high percentage of baserunners:

Paul Bako 28 85 32.9
Eddie Perez 30 109 27.5
Henry Blanco 18 77 23.4
Javy Lopez 11 53 20.8
Charlie O’ Brien 6 33 18.2

As the heading of this segment indicates, the theory is that a defensive-minded catcher will aid a pitcher in making better decisions, therefore producing better results. Here are the OPPONENTS statistics from 1994-2003, by catcher:

Charlie O’ Brien .203 .230 .262 53
Javy Lopez .229 .261 .311 77
Eddie Perez .242 .272 .329 86
Paul Bako .253 .282 .386 105
Henry Blanco .260 .298 .394 113

Keep in mind that O’ Brien caught Maddux in 1994-95 when even Pizza Cutter could have caught him and produced tremendous results. If the minimum is set to 35 games, the equivalent of one full Maddux season, the results that rank the highest (or lowest, in this case) belong to the battery of Greg and Javy.
Maddux is not an idiot. In fact, he’s probably the smartest (or one of the smartest) players to ever step foot on a professional field. To think he does not know information related to the statistics and tables in this article would be an insult to his intelligence. He had to know that teaming with Javy previously produced great results. According to his quote he felt it would be best for the team if the backup catcher caught all of his games, not because of a distaste for Lopez, but rather for the betterment of his own mindset. Who knows when Lopez may have needed days off? By planning when Javy would receive his rest Maddux was able to know that he needn’t worry who the second half of his battery would be in a given game.
If he says the reason for not using Lopez was entirely born out of strategy, and a way for him to reach a comfort level with a catcher for an entire season, then why doubt him? A man with the nickname “The Professor” clearly knows what he is doing.
Next week, in Part Two, I am going to take a look at his strike-shortened 1994 and 1995 seasons. These had the potential to be two of the greatest seasons ever recorded by a pitcher. Even with the strike they still rank highly but people always love knowing “what if’s” so we will explore those possibilities. Isn’t it funny how the 1994 strike derailed Tony Gwynn’s quest for .400, Matt Williams’ quest for 61+ HR, the Expos quest for first place, and Maddux’s quest for one of the top seasons ever?

Winning with an 89-mph fastball: an analysis of Brian Bannister (Part 3)

In Part 1 of this series, we examined Brian Bannister’s suggestions for why he has been able to beat the league BABIP. He indicated that it was probably due to pitching more often in favorable pitcher’s counts and inducing balls in play with two strikes, when the hitter is against the ropes. However, the evidence didn’t show much advantage for Bannister. We noted that he did pitch a little more often in favorable counts, but this led to him avoiding walks more than anything; it had little salutary effect on his BABIP.
In Part 2 of this series, we learned about the pitches that Bannister threw during 2007 and how he used them. We saw that the fastball and curveball were good pitches against right-handed hitters, and the slider was a good pitch against left-handed hitters.
Part 1
Part 2
Part 3
In this final part of the series, we’re going to marry those two approaches to see if we can uncover any patterns that might explain Bannister’s BABIP performance. In this portion, I’m not concentrating so much on evaluating Bannister’s own statements, as I did on Part 1. Rather, I’m thinking more about what we can expect from Bannister in the future. I’m also interested in investigating techniques that could prove useful for evaluating DIPS theory on a component basis as we accumulate more PITCHf/x data in the coming seasons.
Should we expect Bannister to maintain any of his BABIP edge and thus his 3.87 ERA from 2007? Or are the projection systems like PECOTA (subscribers only) and CHONE more reasonable when they project an ERA of 5.19 or 4.74?
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Roster is here!

Roster, MVN’s new online magazine is now live on the internet.  In addition to finally bringing about world peace, Roster will bring you a full fantasy baseball prep pack for 2008.  That means team previews, player rankings, downloadable cheatsheets, and some fantastic features, including articles by StatSpeak’s Eric Seidman on the constant search for good starting pitchers and by yours truly, Pizza Cutter, on psychological warfare in the fantasy draft.  Plus, a bunch of content from the rest of the MVN crew.  So, if you came here today for your daily dose of StatSpeak, fear not.  We have it… it’s just hiding somewhere else.
A reminder that if you do not read Roster, nuclear war will break out all over the world, and it will be your fault.  I’m just saying…

Winning with an 89-mph fastball: an analysis of Brian Bannister (Part 2)

In Part 1 of this analysis, we examined the league numbers for batting average on balls in play (BABIP) and whether Bannister was able to beat the league BABIP by pitching in favorable counts. We found that he did not gain any particular advantage by inducing more balls in play on two-strike counts, so we turn elsewhere to seek an explanation for his 2007 performance.
Part 1
Part 2
Part 3
What pitches does Brian Bannister throw? The scouting reports tell an interesting tale, especially if you follow them back a couple years. In the minor leagues, the cut fastball was reputed to be his best pitch. His four-seam fastball was thrown in the high 80’s, touching 90, although he was able to locate it well, his curveball was a big breaker that was considered a plus pitch, his changeup was a work in progress, and his slider was regarded as a pitch likely to be scrapped. But in the fall of 2006 in the Mexican League, Bannister worked on a two-seam fastball, and after joining the Royals in trade for Ambiorix Burgos, he scrapped his cutter, experimented with different speeds on his curveball, and started throwing a slider again.
What can we see in the PITCHf/x data regarding his pitch repertoire in 2007?
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Do hitters get more jumpy during a slump?

One of the criticisms thrown at Sabermetricians is that we are analysts who do not appreciate the richness of the psychology that goes into the game and its players.  And for what it’s worth, the charge isn’t completely without merit.  Many of our models look at baseball as simple agglomerations of probabilities without any sense of what’s going on inside the players’ heads.  The place where this particular argument has gotten the most play is in the clutch hitting debate.  After all, say the doubters, some people have a psychological ability to perform in the clutch while others freak out.  And they’re actually right, at least generally.  The problem is that over the course of a baseball season, the actual effect of this “clutch” ability is fairly minimal.  Whether Bill James’s fog is to blame or whether it’s Mike Stadler’s, author of The Psychology of Baseball, theory that while this ability exists in the general public, baseball players make it to the Majors in part because they all have this particular “clutch” ability, clutch hitting ability has consistently shown itself to be a (very) minor player in explaining the variance in actual outcomes.
But, clutch hitting isn’t the only place where a player’s mental state might affect an individual at-bat and make him something of a different man from one at-bat to another.  Consider the slumping batter.  He’s had a bad couple of days (weeks?) and he just can’t seem to get a hit.  He might be feeling a little desperate.  Will he ever get on base again?  Perhaps he should swing a little bit more or a little bit harder to try to break out?  Sportscasters like to call this “pressing.”  But does it really happen?
In The Book — Playing the Percentages in Baseball (which if you haven’t read, you are a horrible human being), StatSpeak friends Tangotiger, MGL, and Andrew Dolphin laid out the case pretty convincingly that as far as the actual outcome of the at-bat, a hot streak or in a slump has very little predictive power over what will happen next.  You’re better off betting that a player will do what he normally does over the course of the season.  But, that doesn’t mean that our esrtwhile batter gets to that outcome in the same way as usual.  A slumping player’s outcome may be the same as might be expected were he not slumping, but he may go up to the plate with the mindset that he needs to do something different in this plate appearance.  Perhaps he might take a few more chances on some pitches and swing a little bit more.  It makes sense that he might try this strategy.  Let’s look at the data.
First, let’s define a slump.  I took the 2006 season and eliminated all the pitchers batting.  I then set up to look at each plate appearance and the ten that came immediately before it.  A player was in a slump if during the last ten plate appearances he had made an out in at least nine of them.  That’s a really rough definition of “slump”, but it keeps things manageable. 
Now, how to tell if a player is pressing or not.  At first, I looked at pitches per at bat.  Do players who are in slumps have shorter at bats (they poke at the first ball near the strike zone) than when they aren’t slumping?  The answer is no.  I took everyone in baseball with at least 50 PA in 2006 and calculated their average pitches per PA when they were slumping and when they were not.  Then, I ran a paired samples t-test to see whether there was a significant difference between the two groups.  A paired-samples t-test has the advantage of comparing people to themselves, so that there’s not the confound that batters who have longer at bats might be better hitters overall and thus less likely to go into slumps.  Players saw an average of 3.70 pitches when not slumping and 3.69 when in a slump.  There’s a problem though: number of pitches doesn’t tell you what the player did on those pitches.  For example, a player could take two balls and a strike, then put the ball in play (1 swing in 4 pitches), or swing at four pitches (and foul one off) for a strike out.
I needed a better idea for how to measure a player’s willingness to swing at the plate.  His jumpiness factor, if you will.  Thankfully, a brilliant researcher named Russell A. Carleton published a paper (pdf warning) last year in SABR’s Statistical Analysis newsletter, By The Numbers.  In it, he uses signal detection theory to measure whether a player’s willingness to swing, correcting for the fact that some players don’t see a lot of swing worthy pitches, comparatively.  A strike may be a strike, but whether it was a called strike or a swinging strike tells us something about a player’s attitude toward swinging.  He called the stat “response bias.” 
So, I calculated the response bias for all players, both when they were in a slump and when they weren’t and compared the two, again with a paired-samples t-test.  Most players in baseball have a response bias around 1.0, which is ideal.  Greater than 1.0 means that they swing too much, less than 1.0 means they don’t swing enough, but a higher number means a greater likelihood of swinging.  Players when they were not in a slump had an average response bias of .965.  When slumping, it jumped to .990.  That difference was significant.  There’s no units to put on those numbers, so you can’t interpret them as .990 somethings, only that it indicates a little bit more of a willingness to swing.  The effect isn’t huge.  Players don’t turn into Vlad Guerrero-like free swingers when in a slump (Vlad was overall a 1.671 in 2006), but they do seem to go up to the plate with a little more urgency.  A little.
I wanted to rule out one possible alternate hypothesis.  Perhaps players who like to swing a lot compensate for a slump by swinging more, but those who are more reserved about their swings actually go to the plate even more reluctant to swing.  I split the group into halves and looked only at those who were above 1.0 in response bias (the free-swingers) when not slumping and then those below 1.0 (the takers) when not slumping.  Both groups increased their overall response bias when in a slump.  Looks like everyone gets a little jumpy from time to time.
So sure, psychology is in play in baseball.  How could it not be?  Players are human beings.  Now, are the effects on actual behavior and outcomes that big?  No.
This is what happens when people practice psychology without a license.  People assume that most (other) people crack under pressure and thus, are unable to come through in the clutch.  In fact, when there’s an actual emergency situation (and here I’m talking about something actually important, where people might get hurt), most people report that while they felt a little afraid, they were able to put it aside and do what had to be done.  Are there people who freeze?  Sure, but they are actually fairly rare breeds.  And I agree with Mike Stadler’s explanation that none of them make it to the Majors. 
Why then do we believe that there’s this amazing performance-to-mental toughness link in baseball?  Because we get most of our information from media members who get paid to lay on the drama and romance.  Dramatic sells, and “mental toughness” is a wonderfully romantic concept, because anyone can be “mentally tough.”  Not everyone can hit a 95 mph fastball 400 feet, no matter what his mental state at the time.  But where’s the fun in that explanation?

Winning with an 89-mph fastball: an analysis of Brian Bannister (Part 1)

I’ll warn you from the start that the title is a tad ambitious. I don’t know exactly how Brian Bannister wins in the major leagues with a below-average fastball speed, but I hope to share some of what I have learned on the topic. This article will take the form of a three-part series.
Part 1
Part 2
Part 3
In case you’ve been hiding under the proverbial sabermetric rock the last few weeks–maybe you’re one of those weirdos who believe players are human or you’ve been out of your garage recently to look at the sky–Brian Bannister gave a fascinating three-part interview to Tim Dierkes at MLB Trade Rumors last month.
In Part 3 of the interview, Bannister talked about his opponents’ batting average on balls in play (BABIP).

I think a lot of fans underestimate how much time I spend working with statistics to improve my performance on the field. For those that don’t know, the typical BABIP for starting pitchers in Major League Baseball is around .300 give or take a few points. The common (and valid) argument is that over the course of a pitcher’s career, he can not control his BABIP from year-to-year (because it is random), but over a period of time it will settle into the median range of roughly .300 (the peak of the bell curve). Therefore, pitchers that have a BABIP of under .300 are due to regress in subsequent years and pitchers with a BABIP above .300 should see some improvement (assuming they are a Major League Average pitcher).
Because I don’t have enough of a sample size yet (service time), I don’t claim to be able to beat the .300 average year in and year out at the Major League level. However, I also don’t feel that every pitcher is hopelessly bound to that .300 number for his career if he takes some steps to improve his odds – which is what pitching is all about.

In the interview, Bannister postulated a reason for his success on BABIP.

So, to finally answer the question about BABIP, if we look at the numbers above, how can a Major League pitcher try and beat the .300 BABIP average? By pitching in 0-2, 1-2, & 2-2 counts more often than the historical averages of pitchers in the Major Leagues. Until a pitcher reaches two strikes, he has no historical statistical advantage over the hitter. In fact, my batting averages against in 0-1, 1-0, & 1-1 counts are .297/.295/.311 respectively, very close to the roughly .300 average.
My explanation for why I have beat the average so far is that in my career I have been able to get a Major League hitter to put the ball in play in a 1-2 or 0-2 count 155 times, and in a 2-0 or 2-1 count 78 times. That’s twice as often in my favor, & I’ll take those odds.

This interview has gotten a lot of buzz in sabermetric cyberspace. Several people have taken a look at BABIP at different ball-strike counts, including my colleague at StatSpeak, Pizza Cutter. There seems to be some ability for the pitcher to control the count on which hitters put balls into play, but it looks like a fairly small effect on average. (Pizza, correct me if I’m summarizing your conclusions incorrectly.)
Bannister also mentioned to Dierkes that getting two strikes on the hitter gives him the strategic advantage in terms of pitch selection.

It is obvious that hitters, even at the Major League level, do not perform as well when the count is in the pitcher’s favor, and vice-versa. This is because with two strikes, a hitter HAS to swing at a pitch in the strike zone or he is out, and he must also make a split-second decision on whether a borderline pitch is a strike or not, reducing his ability to put a good swing on the ball. What this does is take away a hitter’s choice. If I throw a curveball with two strikes, the hitter has to swing if the pitch is in the strike zone, whether he is good at hitting a curveball or not. He also does not have a choice on location. We are all familiar with Ted Williams‘ famous strike zone averages at the Baseball Hall of Fame. It is well-known that a pitch knee-high on the outside corner will not have the same batting average or OBP/SLG/OPS as one waist-high right down the middle. Here is a comparison of the batting averages and slugging percentage on my fastball vs. my curveball:
Fastball: .246/.404
Curveball: .184/.265

We do know from John Walsh’s work something about batting average and slugging percentage against the typical major-league fastball (.330/.521) and curveball (.310/.471). If Bannister is correct in his numbers, he’s doing quite a bit better than the league with both the fastball and curveball. But is Bannister correct in the numbers he quotes and assertions he makes?
So far, most people are accepting what Bannister said at face value. Let’s take a closer look and see if we should believe his numbers and conclusions. We’ll draw on two data sets from the 2007 season. One is the standard pitch-by-pitch result data for all of Bannister’s 2603 pitches in 2007. With this data set we can examine results on balls in play and how Bannister performed in various ball-strike counts. The second data set is the detailed PITCHf/x trajectory data recorded for 1304 of Bannister’s pitches, or about half of his starts. With this data set we can identify pitch types and reliable strike zone location information in order to gain a greater understanding of Bannister’s pitching strategies.
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A sneak peek at MVN's new magazine: Roster

On Tuesday, February 26th, MVN will proudly launch it’s brand new online magazine, Roster.