A couple weeks ago Sean Smith released a revamped Total Zone system which included Gameday location data. You can read all about it on baseballprojection.com.

On FanGraphs you can now find the new Total Zone numbers for the 2005-2009 season in the player pages under the heading TZL in the Advanced Fielding sections. Here’s what Carl Crawford’s numbers look like:

In light of yesterday’s article on Infield Fly Balls and xFIP, there were some questions and debate about if popups are something under a pitcher’s control, or what you might call a skill. After reading the comments, I was somewhat doubting that my research might not have been thorough enough to essentially rule them out as a skill (which is what I more or less did).

If you look at popups per ball in play on a year-to-year basis, you get a correlation of about .52, which would highly suggest that there is some “skill” in inducing popups. However, there is a very strong positive correlation between popups and outfield-fly-balls (.64), and a very strong negative correlation between popups and groundballs (-.72).

In other words, as outfield-fly-balls increase, so do popups. As groundballs decrease, popups increase. For comparisons sake, line-drives have absolutely no correlation with popups.

Since the correlations are so high, you can basically come up with an expected popup rate based on a player’s groundball percentage. To me, it actually looks non-linear:

So it seems that each player has a dynamic expected popup rate based on his groundball percentage. Now the real question is, do players popup rates diverge from their expected popup rates consistently on a year to year basis?

If you look at the above chart, you’ll see that there’s not much consistency from year-to-year. The correlation is about .18, which pretty much agrees with Mitchel Lichtman’s findings of .14 as quoted in David Gassko’s Batted Ball DIPS article. For comparisons sake, BABIP has a year-to-year correlation of .15.

So what does this mean for popups as a “skill”? I’d say they are sort of a skill that is closely tied to groundball percentage, but from the findings above, that’s about as far as I’d go. While there may be certain pitchers that prove to be exceptions (just as there are exceptions with BABIP), popups in general do not seem to be much of an independent skill.

All batted ball data is from Baseball Info Solutions from 2006-2009 for pitchers with at least 100 innings pitched.

Today I saw a couple gripes around the Internets about xFIP and how infield fly balls are not taken into account. On FanGraphs, overall fly-ball percentage is used to calculated a pitcher’s “normalized” home run rate.

This got me thinking about David Gassko’s Batted Ball DIPS article from five years ago where he writes the following about infield fly balls:

Infield flies per ball in play actually have a slight negative correlation with outfield flies per ball in play. Inducing infield flies is a skill, and while it correlates somewhat weakly year-to-year (Lichtman found an “r” of .140), a small subset of pitchers exhibits clear control over the percentage of their fly balls that are infield pop ups. I would encourage studies looking into who those pitchers are—one thing I have noticed is that extreme ground ball pitchers allow fewer than expected infield fly balls.

What I believe is actually going on here is that fly-ball pitchers in general have higher infield fly-ball rates as measured by Baseball Info Solutions. The repeatability of infield fly balls is basically just a side effect of a pitcher’s total fly-ball rate. Looking at all pitchers from 2006-2009, here’s what you get when you bucket FB% in increments of 5%:

FB% Bucket     IFFB%    HR/FB%   HR/OFFB%
< 25%          7.1%     11.1%     11.9%
25% - 29%      7.8%     10.9%     11.7%
30% - 34%      8.9%     10.2%     11.2%
35% - 39%      9.7%     10.2%     11.3%
40% - 44%     10.5%     10.0%     11.2%
45% - 49%     11.6%      9.8%     11.0%
>= 50%        12.2%     10.0%     11.4%

So, while it’s pretty clear that overall FB% is impacting IFFB%, I’m not sure things are quite so obvious with home runs. It seems to me that home-runs-per-total-fly-ball plateaus at about 10% starting in the 30%-plus range. And for home-runs-per-outfield-fly-ball, things look pretty similar, except everything is about 1% higher because of the removed IFFBs.

So getting back to xFIP, does it really matter whether or not you exclude popups? The answer is, not really. You’re going to get almost the same results because HR/OFFB on average exhibits more or less the same issue as HR/FB. In fact, the correlation between using OFFB vs total FBs in xFIP is .996. The two, in practice, are virtually identical.

However, when you bucket the data like this, it seems that there is one thing made clear: When an extreme groundball pitcher induces a fly ball, there’s slightly greater chance it will end up a home run. I think it would be particularly interesting to look at the run values of different batted balls types for different buckets of fly-ball pitchers, but I’ll have to leave that for another time.

We realize the post: Ubaldo’s Unimpressive Start, caused a bit of a stir in the comments section with many people agreeing and disagreeing to various degrees.

Because of the strong opinions on both sides of the argument, we’d like to invite those with particularly strong thoughts and analysis on Ubaldo Jimenez’s continued success to submit posts to our community blog.

We will publish the two most well thought out and detailed posts on the agreeing side and the disagreeing side in our Community Blog as well as our homepage, as long as we receive submissions that are up to the high Community Blog standards.

To begin submitting an article, click here.

I would also like to take a moment to remind people to please be courteous in the comments section. Ad hominem or any sort of personal attacks on people will not be tolerated, so please keep it civil. We would like to continue to keep our comments completely open.

As I was poking around at Stephen Strasburg’s most recent start in our pitchf/x pages, I noticed that MLBAM was classifying one of his pitches as a two-seam fastball, which I recall was not the case in his first start a week ago. So I went back to check his first start and low and behold, a number of his four-seamers had been reclassified to two-seamers (and a couple to curveballs changeups).

This correction seems to agree with the this note from J-Doug over at Beyond the Box Score:

*Note: Several commenters and analysts (such as Tim Kurkjian) have noted that Strasburg throws both a four-seamer and a two-seamer (or what Strasburg calls a ‘one-seamer’). This makes sense considering the break on his fastballs. However, MLBAM doesn’t yet have enough data (I assume) to separately classify these two pitches, so they both came through as four-seamers. I’m going to rely on MLBAM’s estimation for now, since that’s where the data came from, but feel free to read everything that is labeled “four-seamer” as just plain “fastball.”

And it also seems to match up pretty well with Nick Steiner’s own pitch classifications.

I don’t have anything in particular to note about the pitches that changed in classification, but it is important to note that pitchf/x data is retroactively updated as the pitch classifying algorithms are adjusted for each individual pitcher.

Swinging Strike Percentage (SwStr%) is now available in all the leaderboards.

We’ve added new PitchFx charts to our game charts. They’re pitch location charts vs right and left handed batters from the catcher’s point of view. These are modeled after much of Dave Allen’s work:

FanGraphs now has a Facebook page where you can easily get a feed of the latest posts on FanGraphs. This will be especially useful for those of you who are Facebook users, but not necessarily Twitter users where we do pretty much the same thing.

Yesterday, Joe Pawlikowski noted that there seems to be an increase in the overall O-Swing% so far this season, which led to some questions about whether the strike zone was being measured consistently from season to season.

Over the course of the nine years Baseball Info Solutions has plotted pitches, there have been some not so small changes in the average O-Swing%. Over the past 3 years the numbers have been stable, but this season it seems O-Swing% is up about 3%. This can sometimes make raw O-Swing% a difficult stat to match up to year to year because the baselines can be somewhat different.

However, when looking at a player’s O-Swing% above average, there is a very strong correlation from year to year and this continues to be the case for the 2010 data. In other words, a typical player’s “plate discipline” does not end up changing that much season to season. Here are the more recent correlations for players with greater than 50 plate appearances compared to BB% or Pitches/PA.

O-Swing% Above Average / BB% / Pitches per PA
2009 – 2010 – .74 / .56 / .66
2008 – 2009 – .74 / .64 / .68
2008 – 2010 – .68 / .53 / .57

As you can see, even when compared to something as seemingly stable as Pitches/PA, O-Swing% is definitely more stable from year to year once you adjust for the baseline.

So the lesson here is that average O-Swing% is important to take into consideration. We’ll be adding O-Swing% Above Average (OSAA for short) to our repertoire of stats starting tomorrow, which will make life somewhat easier when comparing a player’s O-Swing% from season to season.

A couple months ago we released the “Dashboard”, which gave you quick access to the stats that we thought were some of the most useful. Now you can build your very own!

If you’re logged in and go to the new settings page, you’ll be able to select which stats and the ordering (and separators), you’d like to see first on any of the player pages.

There are also two other options.

– If you don’t like the Dashboard at all, you now have the option to remove it completely.

– You can now hide minor league stats on major league players pages by default.

If you create a custom dashboard, but would like to remove it, just clear all your custom stats selections and it will be gone.

It’s worth noting that you are not limited to the number of stats you’d like to put in your custom dashboard, but space is a bit limited right now and it’s probably best if you keep them around 17 individual stats. Here’s an example of the one I’m currently using:

Please let us know if you encounter any problems (this is still considered a beta product) or if you have any suggestions.

6/3 Update: There was a bug that I accidentally introduced around 10am this morning. Everything should be working again.