When I originally calculated the run value of a ball to a strike, I looked at all plate appearances for a given count. That came out to a run value of .161 runs Iblemetrician pointed out that certain counts will have more pitches taken - and even potentially have more pitches on the edge, which might be more likely to be miscalled.

With those suggestions (and after a little bit of programming), I went back and calculated a run value weighting by both called pitches and called pitches on the edges of the strike zone.

Let’s look at the same sort of table as in the first post - this time weighted by called pitches.

Run Value of Switching a Ball to a Strike - Weighted by Called Pitches

Now let’s look at the same breakdown, but considering called pitches on the edge. These are pitches within 2 ball radii of any side of the strike zone (and within the strike zone on the other dimension). That means we’ll count the pitch if it’s one ball width outside the strike zone on the left and middle of the strike zone in height, but we won’t count it if it’s one ball width outside the strike zone on the left and three balls widths outside the zone high.

Run Value of Switching a Ball to a Strike - Weighted by Called Pitches on the Edges

I’m a little worried about sample size here - at least comparatively. The results back up the expected behavior though. Players are less likely to take a close pitch with two strikes than otherwise - which lowers the relative run value of those events and therefore the overall run value of “mistakes”.

We’ll start with a table:

Remember that positive numbers are good for the pitchers (fewer runs), while negatives indicate more scoring.  The innings that jump out are the first, sixth and extras.  I have no idea what to attribute the sixth inning to (perhaps starters are tiring and getting more wild in general - which contributes to umpires being less lenient).  I also don’t really know why umpires help the starters so much in the first - but I’m guessing it has something to do with an unconscious desire to give the pitcher the benefit of the doubt at first, or to start the game off fast.

But I think I understand why the extras (and understand the sample size for all the extra innings combined is about 1/9 of any other inning) are so favorable to the batters.  Without someone scoring, the game can’t end.  If the game doesn’t end, I can’t go home.  I’m sure there’s no conscious reason why the umpires would behave this way, but I’m not sure I’d blame them if there was.  After 3.5 hours of calling pitches, I’d probably want to do everything in my power just to be allowed to sit down.

I’m still working on putting together a longer article combining all this information together.  Unfortunately some of the analysis is taking longer than I was hoping.  Look for it early next week, though.

This time I looked at whether better pitches got more close calls than bad pitchers.  I broke all pitchers who pitched in 2006 and 2007 into three groups - those who had career runs averages (runs allowed per 9 innings) of less than 4.00 up through 2006, those with a RA between 4.00 and 6.00 and those with over 6.00.

My hypothesis was that the better pitchers got more calls in 2007, as umpires looked more favorably on better pitchers.  Surprisingly, this doesn’t appear to be the case.  Good pitchers actually received .01 runs per game less than average from umpires’ calls.  Bad pitchers were hurt by close calls to the tune of .25 runs per game.  The middle group came out at .05 runs per game above average as a whole.

That’s a result I wasn’t expecting.

I thought of three possible “biases” that could change how a pitcher is judged by an umpire: age, reputation (by which I mean success) and early-game wildness. I’m sure there are probably others, and I’m willing to take requests for study.

Anyway, these will be probably be combined into a longer post for Beyond the Box Score later, but I thought I’d post the results as I got them here.

The first test I looked at was by age. I broke down all the pitchers from 2007 into three categories - under 25, between 26 and 35, and older than 35. The age breakdown was somewhat arbitrary, but I wanted to get a young group and an old group, with the hypothesis that umpires were kinder to the older pitchers.

Turns out that young pitchers saw missed calls cost them .37 runs per game, while older pitchers benefited by .23 runs per game. The middle age bracket gained .14 runs per game.

There are a couple of potential flaws to this study that I’ll point out, but I don’t think they’re too serious. First, the release of the Lahman database doesn’t include the Retrosheet ids for rookies from 2007, so I didn’t have a birth year for them - although my presumption is that most will fall in the bottom group. I eliminated them from the study - which will lower both the number of opportunities and the number of missed calls from the young pitchers. If anything, this dampens the actual effect and younger pitchers were hurt a lot more.

Second, there’s definitely a selection bias in play. Older pitchers are those who have pitched well enough to stay around, while younger pitchers may be drummed out of the league under 25. I’m not sure how to correct for that yet, but I might have a better idea when I try to measure how big a role reputation plays.

Long story short, younger pitchers appear to lose at least .6 runs per games to older pitchers based on umpires calls. This may not be solely related to age, but to a combination of factors that are correlated to age, which I’ll try to examine over the next few days.

The goal is to use the PITCH f/x data to automatically classify each pitch as a fastball, curveball, whatever.  Besides the obvious usefulness of this data for evaluating pitchers, I think it can be used to enhance some of my work with catchers.

Anyway, this could be a fairly long process (and there may be some other interesting things coming along with it).  I figure I’ll post progress updates here.  After all, what is a blog but a place for me to blather on about things no one is interested in?

UPDATE: MGL let me know about what turned out to be a rounding error that was pushing the numbers off a bit. I’ve updated the charts to fix the issue, but things might look a little different than you remember.

For those of you who haven’t been following this series, I’m using the GameDay data to measure how each catcher performed in blocking pitches by looking at their misses (wild pitches + passed balls) and their opportunities (balls in the dirt with runners on base).

Before we get to the results, a couple words of warning. There’s definitely something up with the scoring of balls in the dirt. Each year had widely varying counts for opportunities, as you can see below. If we assume that these scoring differences affected every catcher equally, then we’re ok if compare the runs saved columns since those are based on average numbers. I think this might be a reasonable assumption because the opportunities look to be somewhat depressed for all the catchers in 2006.

Opportunities and Block Percentage By Season

Also, I’m still wary of the effect of pitching staffs and of course random variation. One of the next steps is to try a With or Without You (WOWY) analysis to see if I can tease out the impact of the pitchers.

Enough of the warnings, let’s look at the results. I’ll go year by year, including 2007 for those who don’t want to look back at other posts, and then look at the leaders and trailers over the three seasons (by summing runs per season). Remember, I’m calculating an average block percentage for each season individually. I’m still limiting this to those catchers with more than 100 opportunities and assuming a run value of .27 runs per miss. I’ve changed the sort order to sort by actual runs saved rather than runs per 120, since I think the actual value is more interesting.

2005

Catchers With More than 100 Opportunities in 2005
One big surprise in 2005: Jason Varitek, who the top catcher in 2007, is near the bottom here. He had 44 misses this season, while never having more than 24 in the other two seasons. I thought maybe he had been catching Tim Wakefield more in 2005 than in the other seasons. Turns out that Wake wasn’t the biggest cause of Varitek’s misfortune. Matt Clement actually led to 13 misses for Varitek that season, by far his highest number. This seems to be somewhat of a trend for Clement. Just looking at his WP numbers over the previous seasons he often has at least 10 wild pitches (and 23! in 2000), which puts him near the league leaders. Obviously Clement missed the large majority of 2006 and 2007, reducing Varitek’s misses. This quick look definitely highlights the need for a WOWY study.

Other than that, nothing too surprising. Mike Matheny, long considered a top defensive catcher, is number one. Pudge and Pierzynski are near the bottom. I’m happy to see the range of runs is roughly 12-15, right where it was for 2007.

2006

Catchers With More than 100 Opportunities in 2006
Looking at this list makes me question even more the validity of the raw data from 2006. The range from top to bottom is still the same 12-15 runs, but the relative ranking is very different from 2005 and 2007. Of course that could just imply there’s a whole lot of random variation built into this measure, but I think there’s some systematic scoring issue that’s changing things.

2007

Catchers With More than 100 Opportunities in 2007
I’ll present 2007 without much comment except to say that I feel more confident in the raw data than in either 2005 or 2006 because of the improvements to the GameDay system. Of course most of the improvements were technological and the scoring decision that leads to a measured opportunity is in the hands of the human stringer.

Leaders and Trailers

As a close, I’ll leave you with the top and bottom three in actual runs saved over the last three seasons.. I’ll admit I eyeballed this a little, so I may have missed someone. Also note that there were a bunch of catchers who might have made these lists but didn’t receive enough opportunities in all three seasons.

The Best:

The Worst: