Over the last two months, I have been working on quantifying which of the two battery mates deserves credit — or rather blame — for the running game and the passed ball and wild pitch. Note: It’s not dire that you read those articles to comprehend and enjoy this one.

The main take away from my research is that I have found a pitcher has more statistical correlation, with the caught stealing percentage, wild pitches, and passed balls of a battery, than the catcher. While none of this is revolutionary, it is important to note that neither the pitcher nor the catcher is solely to blame for any outcome in a battery, rather it is a combination of both. However, considering the strong correlations we discovered in the pitchers favor, we can now recognize that conventional wisdom underestimates the impact a pitcher can have on the outcomes of a battery — especially in regards to the running game.

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If you recall, last week, I talked about one approach that we can take for evaluating starting pitcher performance. Today, I’d like to continue on that vein, this time taking a look at relief pitching.

With regards to evaluating both player performance and player talent, relief pitching is one of the least understood aspects of baseball. There are a few factors that lead me to believe this, but the only one I’d like to talk about today is the problem of mid-inning pitching changes.

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Yesterday, it was reported Matt Harvey may need Tommy John surgery because of a torn UCL in his right elbow. Some people may say they saw the injury coming and the Mets were crazy to let him throw over 175 innings this season, but the evidence doesn’t really support those ideas. After looking over the history of other 24-year-olds, it appears that the pitcher’s ability to throw hard and recent small velocity drop were the only identifiable injury indicators.

Myself and others have looked at many indications of a pitchers chances of getting hurt. High increase in innings for a young pitcher (Verducci Effect). Velocity and Zone% drop (PAIN Index). Inconsistency in release points and velocity late in a game. High breaking ball usage. Bad Mechanics. High fastball velocity.

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I thought Matt Hunter’s FanGraphs debut article last week was really interesting.  So interesting, in fact, that I’m going to rip it off right now.  The difference is I’ll be using a Monte Carlo simulator I made for this sort of situation, which I’ll let you play with after you’re done reading (it’s at the bottom).

Matt posed the question of whether inconsistency could be a good thing for a pitcher.  He brought up the example of Jered Weaver vs. Matt Cain in 2012 — two pitchers with nearly identical overall stats, except that Weaver was a lot less consistent.  However, Weaver had a bit of an advantage in Win Probability Added (WPA), Matt points out.  WPA factors in a bunch of things, e.g. how close the game is and how many outs are left in the game when events occur.  Because of that, it’s a pretty noisy stat, heavily influenced by factors the pitcher doesn’t control much.  It’s not a predictive stat.  For that reason, I figured simulations might be fun and enlightening on the subject.  They sort of accomplish the same thing that WPA does, except that they allow you to base conclusions off of a lot more possible conditions and outcomes than you’d see in a handful of starts (i.e., they can help de-noise the situation).

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As some of you might remember from previous articles, velocity trends in July provide the strongest signal in terms of whether a pitcher is likely to experience “true” velocity loss over the course of a full season.

Yes, I know, we are more than halfway through August. However, between work, vacation, and Saber Seminar (which, if you didn’t attend you really missed out. You can still purchase posters and t-shirts, so get on that. It’s for a good cause) I’ve struggled to sit down and run the numbers. Better late than never.

Again, for reference, the table below 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 and odds ratios for each month — meaning, the increased likelihood (or odds) 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.

So while the overall rate of velocity loss based on a loss in June and July look pretty even, the relative risk and odds ratios increase by a solid amount in July.

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Today’s article will concern the “battery effect” and its far reaching influences on passed balls and wild pitches. However, before we delve in, I will fill you in on the details of my previous research as a reference point for today’s research.

The “Battery Effect”

The “battery effect” is most easily explained as the relationship between the pitcher and the catcher and how they affect each other. The effect is often subtle, but still significant in the big picture.

Let’s dive into the details. My previous study on battery combinations included investigating which of the two battery mates — the pitcher or the catcher — deserved the credit for catching a runner. The basic take away from this research was, surprisingly, that the pitcher had more of  a profound effect on the caught stealing percent of the battery. To measure this effect I ran a regression of the pitcher’s CS% — caught stealing percentage — on the battery’s CS%, and vice versa for catchers.  

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I’d like to talk to you today about pitcher evaluation.

I don’t mean evaluation in the sense of determining a pitcher’s talent level, or evaluation in the sense of determining a pitcher’s future value — or even evaluation in the sense of determining a pitcher’s market value. I mean a pitcher’s past value. Or, perhaps, because value is so often misunderstood and misinterpreted, we’d be better off speaking in terms of contribution. That’s how do we determine the extent to which a player contributed to his team’s success (or failure)?

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The FanGraphs staff made its obligatory preseason picks before the season (naturally), and I think it’s safe to say that none of us have psychic powers. My picks of the Angels and Blue Jays to win their divisions — they’re not looking so hot right now. In my defense, I was just blindly going along with what our preseason WAR estimates told me. OK, not the greatest defense, but I figured Steamer + ZiPS + FG-created depth charts could produce better guesses than I could on my own. Especially with the roster changes that have happened lately, I thought it would be a good time to revisit our projections. The Angels came up the series victors against the Blue Jays in their recent four-game Battle of the Disappointments, but both teams are still far below the expectations put on them.  However, let’s examine: could they actually be good teams who have just been unlucky?

Most teams have played somewhere around 110 games this season. That leaves plenty of room for unpredictability. If you flipped a coin 110 times, you’d expect to get about 55 heads, right? Well, the binomial distribution says there’s only about a 49.5% chance of the heads total being within even three of that (somewhere between 52 and 58 times). MLB teams are pretty different from coins — they’re a lot more expensive — but I think you can apply the same principle to them. The above calculation for the coin assumes the “true” rate of heads is 50%. What would we see if we were to presume our projections’ estimated preseason win totals are actually representative of the “true” win rates for each team? The following table will show you: Read the rest of this entry »

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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