In last episode’s thrilling cliffhanger, I left you with a formula that I brashly proclaimed “does a great job of explaining the trends” in strikeout rates for meetings between specific groups of batters and pitchers.  Coming up with a formula to explain what was going on wasn’t pure nerdiness — making formulas to predict these results is the point of this research project.  You see, the goal of my FanGraphs masters is to come up with a system by which we can look at a batter and a pitcher, and tell you, our loyal followers, some educated guesses of the chances of pretty much every conceivable outcome that could result from these two facing off against each other.  Getting a sense of the expected strikeout rate is merely the first step in what will likely be a long process of continuous improvement.

The idea of this matchup system is to not only give you estimates that are more free from the whims of randomness than “Batter A is 8-for-20 with 5 Ks and 1 HR in his career against Pitcher B,” but also to provide some evidence-based projections for matchups that have never even happened.  How do we propose this can be done?  By looking at the overall trends and seeing how players fit within them.  Can it really be done?  It definitely looks that way to me.  Today’s installment will be about attempting to convince you of that.

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“Riddle me this,” wrote editor Dave Cameron to me some time ago, “what happens when an unstoppable force meets an immovable object?”  OK, that’s not exactly how it went down.  What he actually did was to present me with the challenge of research, with the goal being to develop a model that would forecast the expected odds of an outcome of each match-up between a specific batter and a specific pitcher. Rather than talking about how players have done in small samples, can we use our understanding of player skillsets to develop an expected outcome matrix for each at-bat?

For example, such a tool might tell you that Adam Dunn has a 40% chance of striking out against Stephen Strasburg, a 10% chance of drawing a walk, a 5% chance of hitting a ground ball, etc… Forget I said those particular numbers — I completely made them up in my head just now.  You may be thinking “well, why should I care about that?  Rather than just being inundated with match-up data that is little more than randomness, such a tool might give you some idea of how much of a gain in expected strikeout rate a team would get by switching relief pitchers with a man on third base and less than two out. Or what the probability of getting a ground ball is in a double play situation, which might influence the decision of whether or not to bunt. Knowing the odds of potential outcomes could be quite beneficial in understanding the risks and rewards of various in-game decisions.

This project has been — and will continue to be — a major undertaking, as you can imagine.  This isn’t the kind of thing that can just be thrown together, but I really think the results could be great. Today, I’ll be sharing with you the findings of my research into perhaps the most important aspect of these matchups — K%, or strikeouts per plate appearance.  This will introduce the sort of process that will be involved in figuring out all of the other elements of the matchup tool. Read the rest of this entry »

We are a little more than two months into the season, and that means it’s time to check on early season velocity trends. As I’ve mentioned before, declines in velocity are a less reliable signal in April and May than in June and July, but nevertheless large declines can still be a solid predictor that a pitcher’s velocity has in fact truly declined and will remain lower at season’s end. Almost 40% of pitchers that experience a decline in April — and almost 50% in May — will finish the season down at least 1 mph. And while the signal gets much stronger in July, 40% is still a pretty sizable number.

So let’s take a quick look at the major decliners from April and May.

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One of the most interesting fields of study in baseball over the last few years has been that of pitch-framing, or pitch-receiving, or pitch-stealing, or whatever you want to call it. This is the stuff that’s made Jose Molina nerd-famous, and it’s drawing more attention with every passing month. Framing has been discussed on ESPN. It’s been discussed on MLB Network. It’s been the subject of countless player interviews, and what’s been revealed is that a great amount of thought and technique goes into how a catcher catches a pitch. Catchers don’t just catch the baseball. They catch the baseball with a purpose.

Research has uncovered a few outliers, like Molina and Jonathan Lucroy and, say, Jesus Montero and Ryan Doumit in the other direction. It’s interesting these guys can be given such different strike zones, since the strike zone is supposed to be consistent for everybody. And it’s interesting that, as much as people come up with run values in the dozens, it’s hard to identify the actual effect. For example, Rays pitchers this year have allowed a higher OPS throwing to Molina than when throwing to Jose Lobaton, the other guy. Last year, Molina again had the worst numbers. It reminds me too much of Catcher ERA for my tastes, but you’d think you’d see something. Instead, you see little. Where is the value going?

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If you are similar to me, then you spend more than a trivial amount of time on the teams leaderboard page. I find myself sorting the wRC+ column for my daily Ottoneu, The Game, and game preview needs. But, like a suspicious man at a bus stop, BABIP lurks just a few columns away. It haunts my well-crafted insults hurtled brazenly towards the Miami Marlins from the comfortable solitude of my home office.

I have spent the past year or two studying BABIP, in part because it has shown the power to unlock a fielding independent hitting metric I so cleverly and regrettably titled ShH or Should Hit. But other than confusing friends during spoken conversation, Should Hit can also regress offensive production based on four simple factors: walks, strikeouts, home runs, and BABIP.

We have previously employed ShH and its stepchild, the De-Lucker X (DLX), to regress players according to their previous performances. But now, let us throw whole teams into the De-Lucker vat. It will be great opportunity to kick the already over-kicked Marlins — as well as offer uncommon accolades for the San Francisco Giants and San Diego Padres lineups.
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Last week, Mike Trout hit for the cycle. When asked for a comment, coach Mike Scioscia said, “If I’m a betting man, I’ve got to believe there’s another cycle in his career somewhere.” That got me wondering.

Whenever I was in a math class where probability was being discussed, the question often in the back of my mind was, “How can this be applied to baseball?” One of the things I love the most about baseball is how well it lends itself to situations of probability, compared to most sports. I’m not sure what that says about me. Anyway, I figured this would be the perfect opportunity to refresh my memory (and hopefully some of yours) on how to crunch the numbers on situations like this. Don’t worry — the principles work on useful things other than just calculating the odds of that gimmicky achievement we call the cycle. Read the rest of this entry »

A little over a year ago I published the results of a study that examined which metrics were most consistent on a year-to-year basis for starting pitchers. My colleague, Matt Klaassen, followed up and expanded on that study recently here at FanGraphs. Matt’s study also focused on starting pitchers–those with a minimum of 140 innings pitched in consecutive years.

Recently I was asked the following on Twitter:

@billpetti have you ever done research on reliever year-to-year performance variability? I’d like to see the common wisdom tested

— Justin Overfelt (@j_felt08) May 21, 2013

I can’t speak specifically to what the common wisdom is Justin is referring to, but I can certainly run the correlations for relief pitchers and compare them to what I found for starters.

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Is there an ideal ground ball rate for hitters? Should they be thinking about how many grounders they hit? Armed with some spreadsheets and a couple conversations with some Royals’ hitters, let’s see what we can discover.

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In Sports Illustrated’s 2013 baseball preview, Tom Verducci wrote a great profile of the Tampa Bay Rays and their approach to optimizing the performance of their pitching staff.

One topic that was especially interesting to me was the apparent importance the Rays place on the 1-1 count. Verducci recounts how pitching coach Jim Hickey described the organization’s focus on getting opposing batters into 1-2 counts:

The Rays believe no pitch changes the course of that at bat more than the 1-and-1 delivery. “It’s almost a 200-point swing in on-base percentage with one ball and two strikes as opposed to two balls and one strike,” Hickey told the pitchers.

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“Stabilization” plate appearance levels of different statistics have been popular around these parts in recent years, thanks to the great work of “Pizza Cutter,” a.k.a. Russell Carleton.  Each stat is given a PA cutoff, each of which is supposed to be a guideline for the minimum number PAs a player needs before you can start to take their results in that stat seriously.  Today I’ll be looking at the issue of stabilization from a few different angles.  At the heart of the issue are mathy concepts like separating out a player’s “true skill level” from variation due to randomness.  I’ll do my best to keep the math as easily digestible as I can.

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