Category Archives: Research

“Slow Starts” in the Leyland Era

(the following was contributed by poster Jeff Molby)

There’s been a lot of talk about “slow starts” and “choking down the stretch” during the Leyland years. With 7 years of data points available, I figured it’s time to see if there’s anything to it.

Molby 2013-04-11_1236
There’s not. I didn’t bother to weight the numbers based on games played, so data isn’t perfectly accurate, but it’s close enough for our purposes. The most you could say is that June is a good month and August is a bad month, but really that’s just because of the volatility (mostly due to injuries) of the 2007 and 2008 teams.
Molby2 2013-04-11_1236
Pull those two years out of the sample and what you have is a team that’s consistently a bit above average. I know I’m viewed as one of the resident apologists, but I remember The Lost Decade all too well. If you had approached me in 2005 and offered a decade of “slightly above average”, I’d have kissed ya and invited ya over for dinner.

Charting Baseball Prospectus’ PECOTA

Last week I wrote about Baseball Prospectus’ 2012 PECOTA projections.  There was some great feedback from everyone, including Lee’s line which suggested that PECOTA isn’t so much projection for a specific player, but rather a projection for a similar player based on that player’s past performance and comparables.  (Lee runs Tiger Tales and his Beyond Batting Average is a great read for anyone trying to understand sabermetrics).   In light of this, what I wanted to do is take a little closer look at some of BPs 2012 projections, and discuss the accuracy of their 2011 projections for a few players. As Lee suggested, this may give us an idea on whether a regression or improvement is coming.

PECOTA projects quite a of information, but for consistency’s and brevity’s sake, I’m going to focus on OPS and WARP for hitters, and ERA and WARP for pitchers.  If there are other stats which you would like to compare for a player, let me know and I’ll pull them up.  Pitchers then Hitters, in no particular order.

Verlander – projected: 3.32 ERA, 5.5 WARP; actual: 2.40, 5.8.  BP projected a 3.05 ERA and 4.2 WARP for 2012, which would be his lowest WARP by a full run in the last 4 years.  I realize that Verlander’s 2011 was historic, but a full win less seems a bit extreme.

Scherzer – projected: 3.61 ERA, 3.6 WARP; actual: 4.43, 1.0.  I think that Scherzer falls nicely into the “due for a bounceback” bucket.

Valverde – projected: 3.37 ERA, 0.8 WARP; actual: 2.24, .7.  I don’t get why stud relievers have such low WARPs.  Valverde is currently at the top of my list of guys I’d like to share a beer or 10 with.

Porcello – projected: 4.40 ERA, 2.3 WARP; actual: 4.75, 1.1.  I think that everyone outside of Detroit still sees Porcello as a #2 starter.  I think that most of us think that this may be his last year to show it.

Cabrera – projected: .948 OPS, 4.2 WARP; actual: 1.034, 6.5.  I don’t know why anyone (including computers) bids low on Cabs.

Inge – projected: .694 OPS, 1.5 WARP; actual: .548 OPS, .1.  What I wouldn’t have given for a .694 OPS.

Jackson – projected: .704 OPS, 0.2 WARP; actual .681, 2.0.  PECOTA foretold doomsday for AJax mostly because of his outrageous 2010 BABIP of .396.  Well, he still had a .340 average on balls in play for 2011, so I don’t see that going away anytime soon.

Avila – projected: .720 OPS, 0.9 WARP; actual: .895/6.5.  I don’t know if PECOTA was more wrong about any player in 2011, definitely not any other player on the Tigers.  Avila’s tremendous season was buoyed by a .366 BABIP, so a return to normal there will probably lead to a regression in 2012 (PECOTA says .790/3.2).

Boesch – projected: .726 OPS, -0.1 WARP; actual: .799, 2.2.  Boesch’s 2011 projections were largely based on his atrocious 2nd half of 2010.  His improved eye coupled with a healthy season would give me hope that he could easily surpass the .765/1.4 2012 projections.

Fielder – projected: .922 OPS, 3.9 WARP; actual: .981, 5.3.  Fielder has a disappointing 2010 relative to 2009, which helps to explain why BP was aiming a little lower in 2011, but his 2012 projected (.952/5.5) is inline with a superstar who is 28.

Who do you guys see as over/under performing based on last year’s stats?

 

Playing in the spray – Curtis Granderson

Curtis Granderson’s 2009 season has received plenty of scrutiny, and this was even before trade rumors crept up. Granderson struggled at times during the season, and had a hard time sustaining success. His .249 batting average was the lowest of his career and it was a drag on his on base percentage and slugging percentage as well. We know batting average is volatile so did Granderson just suffer from some bad luck, or did something else change? Fortunately we have hit location data to help shed some light on these questions.

Granderson’s batting average was dragged down by a .276 batting average on balls in play. That is a number that should typically be in the .320ish range, especially for someone with Granderson’s speed. A shift like that would lead people to think he was largely unlucky. A closer look would show a shift in his balls in play from the harder to field grounders to the easier to field fly balls. Ask fans what they saw and many would say it looked like Granderson got overly concerned with the homers (a new career high) and that he pulled the ball to much. But what would the data say?

Continue reading Playing in the spray – Curtis Granderson

Justin Verlander’s New Slider

Justin Verlander has turned in 3 remarkable outings in a row amassing 31 strike outs as hitters can’t catch up with his heater or their knees buckle with the curve. But very quietly Verlander has added a slider to his repertoire.

This pitch received significant attention from Rod Allen and Mario Impemba last night when he picked up a swinging strike with it against Kelly Shoppach. But he actually began throwing it as early as the April 27th Yankees game. The pitch was first noticed by Eric Cioe (who comments here on occasion) and he posted about it at Motown Sports.

Continue reading Justin Verlander’s New Slider

What’s up with Verlander in the stretch?

Justin Verlander has gotten off to a rough start in 2009. People are starting to question his “ace-hood” and with an ERA of 9.00 after 4 starts it is probably justified. What makes his start so perplexing is that his “stuff” appears to be back. His fastball velocity is over 95mph and he’s fanning 10.7 batters per 9 innings. The numbers that really need a deeper examination though are those when runners are on base. Verlander is stranding only 39.6% of runners (a normal rate is 65-75%) and hitters post a 457 OBP with men on and only 296 with the bases empty. What’s up with that?

Continue reading What’s up with Verlander in the stretch?

Not pounding the zone

Way back in 2008 I started to run a series using pitch f/x data to look at strike throwing tendencies. Sadly this is the slowest moving “series” of posts ever. Nonetheless, it’s time for part 3 where we look at how teams do when they get strikes outside of the zone. For this exercise I’m not looking at those generous calls off the corners, but for those strikes when hitters go fishing.

The first table we turn to is the fish rate, or the percent of pitches outside of the strike zone that hitters swung at. This is presented by count. As for the pretty shading, red are lower numbers and green are higher numbers.

Continue reading Not pounding the zone

Strike Throwing – Part 1 – Lots of Tables

The Tigers walked a lot of people last year. Along the way they threw a lot of pitches, and many seemed to be ill advised. The performance cost Chuck Hernandez his job, jettisoned in favor of an instructor whose students have gone on to gain some renown as strike throwing machines. Armed with a season’s worth of pitch f/x data I’m ready to start delving into this whole strike throwing thing. We’ll start today with some general league wide information.

For those unfamiliar with pitch f/x I’ll have some additional links to more information at the end of this article. The short explanation is a couple of cameras measure the direction and speed a ball is moving shortly in front of the mound. From this the pitch’s path is calculated to within an inch of where it crosses the front of home plate. And it draws the trajectory in the MLB.com Gameday application. On to the data…
Continue reading Strike Throwing – Part 1 – Lots of Tables

Pitch f/x: Bonderman 4-3-08

From time to time this year (as time permits), I’ll delve in to MLB.com’s pitch f/x data to analyze a starters outing. Tonight we look at Jeremy Bonderman’s start against the Kansas City Royals on April 3rd.

Pitch Mix

This season MLB.com started classifying pitches. This seems pretty convenient, but from what I’ve seen so far the classifications don’t quite match. In the case of Jeremy Bonderman we know he throws both a 2 seam (sinker) and 4 seam fastball, a slider, and an occasional change. The data had Bonderman throwing a splitter, which looks to be a misclassification of his slider. Because of this, I did my own pitch classifications using K-means clustering and some judgment.

The table below shows his pitch mix and average velocity for the 87 pitches tracked by the system today.

	    n     mph
2seam       39   92.0 
4seam       25   92.6   
change       4   83.8  
slider      19   85.6   

Continue reading Pitch f/x: Bonderman 4-3-08

Do Bonderman’s pitches fool umpires?

An article at the Wall Street Journal delved into Jeremy Bonderman’s first inning struggles. Former pitching coach Bob Cluck wondered whether or not Bonderman’s struggles are attributable to umpires needing an inning to adjust to the movement on Bonderman’s pitches.

The stats seem to support this theory. The last seven times Mr. Bonderman faced an ump for a second or third time in a season, he allowed first-inning runs only once. On opening day last season — when the first three batters he faced all scored — the umpire behind the plate was Rick Reed, who hadn’t seen him in nearly a year.

Looking on a results basis probably isn’t the best way to determine this. But being able to check Bonderman’s called strike/ball rates in the first inning versus other innings, as well as factoring the first time an umpire sees him versus the second time, may be worth some effort. And then even expanding it beyond Bonderman to look for other pitchers who have similar movement on their pitches and if they have similar issues. The latter could be done with pitch f/x data and the former with retrosheet data. I’ll focus on the retrosheet piece for now.

Big View

The first thing I did was to look at Bonderman’s first inning ball and called strike rates compared to all other innings. I looked at all Bonderman data going back to 2003.
bondo1.JPG
The differences are pretty minimal, especially the ratio of balls to called strikes. In fact the ratio indicates that Bonderman gets more calls earlier in the game – if at all.

First Timers

Next, I took at all the times that an umpire was behind the plate for the first time against Bonderman. If the theory holds true, there should be a bigger disparity.

bondo2.jpg
We can see that a higher percentage of balls are called. We also see fewer called strikes in relation to the number of balls.

Return visits

Finally, a look at those who have called Bonderman games before.
bondo3.JPG
A somewhat interesting dynamic with this group. The ratio is more favorable in the first inning, but a higher a percentage of balls are called as well.

On another note, Brian Gorman is the umpire who has called the most of Bonderman’s starts with six. Larry Vanover has done 5 Bonderman games.

Taking familiarity one step further, I also pulled out the times when an umpire was seeing Bonderman multiple times in the same season. This isn’t a common phenomenon with it only happening 23 times in Bonderman’s 5 seasons. So it’s a situation that will present itself a handful of times a year.

bondo4.JPG
Things are certainly more favorable in the first inning for this situation. But that only seems to help in the first innings.

Conclusions

So is there anything to take from this data, does the specualtion hold up? I’d say that it is possible there is an effect for umpires seeing Bonderman for the first time ever. The rate of called balls, and overall rate of calling balls is highest for first timers in the first inning against Bonderman. The fact that the numbers in subsequent innings of that first start are in line with overall numbers does seem to indicate that the umpires do make an adjustment.

But otherwise the numbers are largely inconclusive. Given the in game variation for those seeing Bonderman repeatedly in a season seems to indicate that Bonderman has much more influence over these numbers than the umpires do – which really isn’t a shocker at all.

With pitch f/x data one could look for the frequency that pitches are called correctly by inning. But with only a partial season of data there isn’t enough to work with for the time being.

Scouting Bonderman with pitch f/x



Jim Leyland has come out on several occasions and said that Jeremy Bonderman is one of the keys to any success the Tigers might enjoy in 2008. Bonderman’s second half swoon, which I attribute largely to his elbow pain that he finally fessed up to, clouded what was starting out to be a phenomenal season. An ERA of 8.50 over his last 9 starts, combined with the arm troubles meant that Bonderman finished with the highest ERA and lowest innings total since his rookie season. Like with Dontrelle Willis, we’ll delve into the pitch f/x data and see what we can find out about the veteran 25 year old pitcher.
Continue reading Scouting Bonderman with pitch f/x

Scouting Dontrelle Willis

On Friday Lynn Henning wrote a detailed look at Dontrelle Willis with a heavy emphasis on scouting. I found the article fascinating from the stand point of getting a better understanding of Willis’s repertoire as well as the thought processes that went along with approving the deal for the lefty. He was after all coming off a pretty rough year. I also viewed it as a chance to dust off that pitch f/x database I’ve had sitting dormant and explore whether or not the reports meshed with what the system had reported.
Continue reading Scouting Dontrelle Willis

Trammell, grass, and the Hall of Fame

This year’s induction class for the Baseball Hall of Fame will be announced on Tuesday. And once again Alan Trammell will be on the outside looking in despite some compelling arguments that he should be in. I won’t make a case for him because quite frankly I’m fully aware of my bias. He was one of my favorite players growing up (behind only Lou Whitaker who was royally shafted) and so it’s probably best if more objective parties make their cases for Tram’s inclusion.

But I do want to briefly tackle one issue that Trammell dissenters have cited. And really it’s an argument that I’ve only seen from Joe Sheehan. Sheehan dismissed Trammell’s defense saying that he was helped by the notoriously long grass at Tiger Stadium. That’s premium content so I’ll just quote the most germane part of the article here for you:

I’m wary of the defensive numbers on him, as his home park was notorious for its high infield grass. With so much of Trammell’s statistical case built on very good defensive stats at his peak, the twinge of doubt I feel about their validity makes me nervous.

And to paint a fair picture, this was only of several reasons that Sheehan listed for doubting Trammell’s candidacy so this isn’t a make-or-break argument. It sounded reasonable enough to me that I didn’t think twice about this argument.

Rob Neyer called for further investigation of the point. Neyer stated:

Two, while I’m intrigued by the notion that Trammell’s solid defensive credentials — he won four Gold Gloves, and Bill James has him as a Grade B-minus shortstop over his entire career — are partly the result of the high grass in the Tiger Stadium infield, I’d sure like to see somebody do some actual work on this one. Yes, sinkerballer Walt Terrell’s home/road splits were massive when he pitched for the Tigers, particularly from 1985 through ’87. But did other sinkerball pitchers fare particularly well in Tiger Stadium during Trammell’s career? Were Trammell’s fielding stats significantly better at home than on the road? If the grass was long and did lead to more plays for Trammell, did it cost him anything as a hitter?

Inspired by Neyer I decided to at least take a very crude look at what effect the grass had on ground ball hit rates. This isn’t exactly answering Neyer’s question or refuting Sheehan’s claim, but at least it is another data point. My methodology was to look at all groundballs hit, and see at what rate they produced baserunners. I then converted those rates to park factors.

The park factors are over 7 seasons – from 1982-1988. Why those years? It was two fold. First, it corresponded reasonably well with the peak of Trammell’s career. Second, there was no change over in ballparks during that time making the analysis a little more convenient.

Here is the table with my results:

Team	PF
MIN	1.33
KCA	1.27
BOS	1.25
MON	1.21
DET	1.20
TEX	1.18
ATL	1.15
PHI	1.13
LAN	1.12
CHN	1.03
CIN	1.03
SDN	1.01
NYN	1.00
PIT	0.99
SLN	0.94
MIL	0.94
CHA	0.94
TOR	0.93
SEA	0.90
OAK	0.88
CAL	0.85
CLE	0.82
BAL	0.81
NYA	0.77
SFN	0.72
HOU	0.65

The higher values indicate parks where more grounders resulted in baserunners, and conversely the lower numbers would make the parks more favorable to the defenders. Tiger Stadium was one of the parks where more grounders resulted in baserunners – over 20% more – which would make Trammell’s defense more impressive, not less. Of course the same adjustment would have to be applied to Tram’s offense which could make his offensive numbers less impressive.

Explanations for this? Maybe the long grass slowed down balls too much meaning there were more infield hits. Perhaps the long grass, or bad infield dirt, led to more bad hops meaning more difficult plays or more errors. Or perhaps the grass wasn’t as long as it was reported, much like the 440ft dimension painted on the centerfield wall.

Caveats: I didn’t break it out and look at the impact by position. It could be that this is all the result of things being favorable down the lines. I don’t know. If Dan Fox continues working backward with SFR perhaps these types of issues can be uncovered. I have the data to do it, but the chances of me finishing it prior to Tuesday are slim. Maybe another day. Also, the Tigers had a great deal of stability at the time with their up the middle defenders meaning they are a large part of the sample. There was no regression or accounting for this – just straight arithmetic.

Still at a first glance it doesn’t appear that the long grass made the infielder’s jobs any easier at Tiger Stadium.

But this is all a moot point when it comes to Tram’s chances anyways. Tram has been hovering in the teens since being on the ballot and actually saw his numbers at their lowest in 2007 when he only had 13.4% of the vote. My hope was that with a weak ballot he could have maybe gained some steam and broken the 30% mark. However, Keith Law’s unofficial tally has him improving, but only to 22%.

I don’t view Tram’s exclusion as an egregious error. Even being a fan I don’t think it is a slam dunk case. Still, I don’t understand the voting disparity between Ozzie Smith and Trammell when you look at their entire body of work. That to me is the bigger injustice.

The information used here was obtained free of charge from and is copyrighted by Retrosheet. Interested parties may contact Retrosheet at 20 Sunset Rd., Newark, DE 19711.