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.

Full historical WAR for all position players has been added to the site! It’s available in the career leaderboards and on the individual player pages currently and will work its way to some other sections of the site pretty soon. We’ll do full WAR for pitchers eventually, but right now that’s still only 2002 onward.

The other new feature are the WAR graphs where you can compare up to 4 players at a time in various ways:

Just a quick note that these two graphs in particular were inspired by work done over at Beyond the Box Score.

Some additional notes about our historical WAR:

– We’re using the best fielding metric publicly available at the time, so for anything 2002 onward, we’re using Mitchel Lichtman’s UZR and anything pre-2002, we’re using Sean Smith’s Total Zone. Total Zone prior to 2010 is also available in the fielding section of the site which has replaced Range Factors.

– The batting component is based on wRAA (based off wOBA / linear weights) and uses 5 year regressed park factors going all the way back to 1871.

– Positional adjustments prior to 2000 are based off Sean Smith’s positional adjustments by decade. 2000 onward are based on Tangotiger’s positional adjustments.

– Replacement levels are adjusted slightly by season. They’re all right around 20 runs with the exception of a few years and a couple leagues.

– The run to win converter is also adjusted by season, but it’s generally going to be right around 10.

If you want to know more about how WAR is calculated for position players, read the 7 part series.

Our advertising policy strictly prohibits any sort of non-prompted audio, popups, or takeover ads. Earlier today there was an obnoxious non-prompted audio ad that displayed on the site for which we received some concerned e-mails and tweets.

Our policy when we’re alerted to such an ad, or see one ourselves, is to remove every ad from the site until the offending ad can be tracked down and removed. We deal with a number of different advertising networks, and, while the majority of the time they provide ads that meet our standards, there are some rare instances where this is not the case.

We are heavy users of FanGraphs and it annoys us just as much as it annoys you to see obnoxious ads on the site, and we’re committed to keeping the site free of ads that are overly intrusive.

Ultimate Zone Ratings (UZR) home and away splits are now live in the splits sections for position players! And on the subject, I’ll add a quote from Mitchel Lichtman’s revised UZR primer, which we’ll be posting later today.

…if you still don’t trust a certain player’s UZR because of the park factors issue, you can check out his road numbers. Keep in mind that you will see lots of random differences between some players’ home and road numbers which have nothing to do with park effects – they are simply an artifact of small sample sizes. Remember also that even large sample sizes can have large random fluctuations as well.”