Last month, I did a two-part analysis that showed what happens — strike out-wise — when, say, a pitcher who strikes out 15% of batters faces a batter who strikes out 20% of the time. As a special bonus for you all, I included a few hundred other K%-matchup types too. I made handedness matchups central to the study, as I think it’s pretty well-established that you can expect a hitter to strike out more often against same-handed pitchers. That is, if I was trying to give an expected result for a righty batter against a lefty pitcher, I looked only at the hitter’s past performance rates against lefties and the pitcher’s history against righties. Before I moved on to performing a similar analysis on batted ball types (grounders, liners, outfield fly balls, and infield popups), I wanted to see whether handedness matchups mattered to these as well.

For this study, my sample was all non-switch-hitting batters from 2002-2012 with at least 300 PA against lefty pitchers plus at least 300 PA against righties. I’d have gone by number of batted balls, except I’m throwing some non-batted ball stats into the analysis.

Let’s get right to it — the following table shows the chances that handedness really makes no difference to each stat, according to paired t-tests:

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Hank Aaron. Barry Bonds. Albert Pujols. Edwin Encarnacion.

If one of those names doesn’t seem to fit, perhaps you haven’t been paying close enough attention in 2013. (And you probably didn’t read Dave Cameron’s trade value column.) The fact that Encarnacion is third in the majors in home runs and fourth in runs batted in doesn’t put him in elite company in historic terms, though.

No, it’s something Encarnacion is not doing that makes him unique.
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When we last left our discussion of Edge% we were looking at the differences in the rate of called strikes based on the count. Generally speaking, umpires were less likely to call strikes on the Edge in pitcher-friendly counts and more likely to give those calls in hitter-friendly counts.

While we learned a bit from that analysis, it was really just the tip of the iceberg. There are a number of additional ways to cut the data, and that is the focus of this article. Count is just one dimension when we are thinking about what might influence the likelihood of close called strikes. There are a number of additional dimensions we can layer onto count, and that’s precisely what I show in the (admittedly large) table below.

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Chris Davis, with 37 home runs so far this season, has been generating a lot of buzz lately — both on the field and more recently with some comments he made during the All-Star break. When he was asked about the all-time home run record, Davis said:

“In my opinion, 61 is the record, and I think most fans agree with me on that.”

I have no idea if most fans agree with him, but it probably shouldn’t be  surprising that a guy within spitting distance of a 61 home run season would view that as the mark to beat — rather than 73 home runs, which is essentially out of range. So, just for fun, let’s figure out what Davis’ chances are of reaching Roger Maris.

At Tom Tango’s website, there was a discussion that tried to put a number on Davis’ chances of reaching that mark. Tango performed a “quick back-of-envelope calculation” to do so, but today, I’ll be providing you with an interactive tool that might make it easy for you to perform a more sophisticated calculation for situations like this (and many other types of situations).

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If this were any other stat, it wouldn’t be worth a post. If this were instead “All-Star Break Home-Run Update,” it’d be a waste of your time, because you could simply just look up the stat on the FanGraphs leaderboards. They’re right up there! But, at the moment, FanGraphs doesn’t house and update any pitch-framing statistics, and while that could change in time, that’s the way things are today, meaning this post could have some substance. Most people can’t look this stuff up on their own, so I’m here to provide for you while crossing something off my weekly quota. Everybody wins.

FanGraphs is pretty selective for the intellectually curious. And, of course, baseball fans, and intellectually curious baseball fans have generally been interested in pitch-framing research. It’s just another thing that players can be good or bad at, so fans want to know where their catchers rank. Right now, we have a little break in regular-season action, so it seemed like a good time to post the latest numbers, through the middle of July. It was either this or a .gif post about Yasiel Puig and no there weren’t any other options. I’ll give a quick explanation, before the data.

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The last time I wrote about Edge% it was in the context of the Tampa Bay Rays using it to get their pitchers into more favorable counts on 1-1. But now I want to take that topic and drill a little deeper to understand how often edge pitches are taken for called strikes.

Overall, pitches taken on the edge are called strikes 69% of the time. But that aggregate measure hides some pretty substantial differences. Going further on that idea, I wanted to see how the count impacts the likelihood of a pitch on the edge being called a strike.

Here are the results:

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Well friends, we are now approaching that time of year where a significant drop in a pitcher’s velocity passes the 50% threshold in terms of signaling that they will finish the year down at least one full mph.

The table above breaks out the percent of pitchers who experience at least a 1 mph drop in their four-seam fastball velocity in a month relative to that same month a year ago and who also went on to finish the season down a full 1 mph. It also shows the relative risk — meaning, the increased likelihood that a pitcher will experience a true velocity loss at season’s end when compared to those pitchers that didn’t lose 1 mph in that month.

For example, pitchers that lost velocity in May finished the season down a full 1 mph 47% of the time, compared to just 6% that didn’t lose 1 mph in May — an increased likelihood of 7.8.

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In his Major League debut for the Mets, 23-year-old Zack Wheeler struck out seven hitters in his six innings of work. Of those seven strikeouts, six came on fastballs — and of those six, four came on whiffs induced by fastballs.

This got me wondering, what pitchers this year have generated the largest percentage of their strikeouts off of their fastball? And how many generated those strike outs on swings and misses on fastballs*?

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Jeff Sullivan’s recent enjoyable trot through San Diego Padres statistics and history led to a number of commentors thinking about San Diego’s park factors. The Padres changed the outfield dimensions of Petco Park in the off-season, and since park factors are backwards looking and rely on multiple years of data, changing dimensions can throw a bit of a monkey wrench into the calculations. So, it’s possible that our park factors are now somewhat behind the times, and we need to keep this in mind when looking at the park adjusted numbers (such as wRC+, ERA-/FIP-/xFIP-, WAR, etc…) for San Diego players, both hitters and pitchers.

It’s not quite so simple as noting that the changing dimensions have made the old park factors useless, however. Moving in the fences helps home runs, yes. This is undeniable. But it also can decrease triples and doubles, as well as effect the more odd elements of park factors, such as walk-rates, strikeout rates and pop-up rates.

It’s too early in the season to construct terribly useful park factors for the new dimensions, but we can do some harmless back-of-the-napkin mathematics to at least determine if the recent numbers suggest at least the early signs of serious run environment changes.
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Last year I reintroduced VOL, a custom metric that attempts to measure the relative volatility of a hitter’s day to day performance. It is far from a perfect metric, but at the moment it’s what we have.

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