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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So, Yu Darvish is off to a pretty good start to 2013. Through eight starts this season, the Ranger’s right-hander currently sports the following statistics:

Darvish currently ranks first (or tied for first) among qualified starters in K% and SwgStr%, and he has posted the 6th best adjusted FIP in the league (56 FIP-). After a blazing start, his ERA- has dropped to 20th and his HR/FB now ranks 84th, but overall it’s clear Darvish has been a beast in 2013.

After watching this wonderful footage from Darvish’s dismantling of the Angels last night I was struck by how slow is curveball actually is.

Our own Carson Cistulli isolated his four slow curves from that night — check out the final bender to Mike Trout, resulting in a strikeout in the 6th inning. And, yes, that was 61 mph.

I wondered whether the differential between Darvish’s fastball and curveball was the largest in the league. And, so, to the data I went.

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The hard-throwing, 24-year-old Matt Harvey has quickly become a must watch when he toes the rubber for the Mets. Called up in late July of last year, Harvey and his blistering fastball (94.6 average velocity) currently sport a 31.3% strikeout rate and an ERA- of 25 — no, not 75, 25. In 2013, Harvey has made four starts, lasting at least seven innings in each appearance. He has only allowed one home run and a paltry 10 hits in 29 innings.

Harvey does feature a number of pitches, but he’s heavily reliant on his four-seam fastball, throwing that pitch 60% of the time. That ranks him fifth among all qualified pitchers in 2013. And that fastball has been deadly.

According to the PITCHf/x leader boards at Baseball Prospectus (powered by Brooks Baseball), Harvey has induced a .042 ISO (2nd best) and a .167 BA (3rd best) against when using his fastball. David Golebiewski from Baseball Analytics recently wrote about Harvey’s ability to win with the high fastball. The numbers were eye-popping. Harvey so far this year has induced whiffs on high fastballs 48.4% of the time, and he’s throwing upstairs over 50% of the time.

I was curious how this compared to others this year and in previous years. So I did some digging.

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Earlier this month I wrote about whether we should be concerned with when we see pitchers throwing slower in April, particularly with regards to CC Sabathia.

If we are trying to predict whether the pitcher has truly lost some zip on their fastball, the answer is somewhat. Pitchers who are down at least 1 mph compared to April of the previous year will go on to finish the season down at least 1 mph about 38% of the time. Essentially, they are over four times as likely to be truly losing velocity compared to those that are not down in April. However, the signal gains in strength as the season goes on. So, if a pitcher is down at least 1 mph in July compared to July of the previous year their likelihood of being down at season’s end jumps to 14 times more than pitchers that are not down in July.

But does being down in April predict an injury? This is something I had not yet investigated. Given the increased discussion about April velocity declines I thought I should take a quick look.

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A while back, Jeff Zimmerman and I introduced the concept of Edge% — a metric that attempted to quantify the extent to which a pitcher worked the edges of the strike zone. Jeff initially looked at how this applied to Tim Lincecum and how his performance depended to some extent on his ability to pitch to the edges of the plate. I followed up with a high-level piece that compared the performance of pitchers at an aggregate level depending on how extreme their Edge% was in a given season.

While the findings were interesting, they were also a little inconsistent. That’s because Jeff and I independently created two distinct metrics. We decided to combine our efforts (as we have been known to do) and settle on a single, consistent formula.

And that’s the focus of this article.

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Every year we hear stories about pitchers whose fastballs don’t seem to have the same life as last year. The most talked about are typically front-line starters that rely on their fastballs. In early 2013, the name that’s being discussed the most is Yankee ace CC Sabathia.

Throughout spring training, Sabathia’s velocity has been a point of concern. Coming off of elbow surgery during the offseason, Sabathia’s first regular season start did nothing to quell that concern. As The Star-Ledger’s Andy McCullough notes:

Sabathia’s fastball topped out at 91.7 mph on Monday, according to Pitch f/x data from Brooks Baseball. On Opening Day in 2012, his fastball hit 94.5 mph. On Opening Day in 2011, his fastball touched 94.7 mph.

(By the way, if you don’t read McCullough on a regular basis you are missing out.)

In the end, McCullough notes that while it’s reasonable to be concerned, Sabathia is likely to improve as the season wears on and has good enough secondary stuff to still be very good.

Overall McCullough is right, however, I think there is greater reason for concern than some may think.

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When Bill James speaks, many in the baseball committee listen intently–as they should. James, while certainly not always correct in his theories (and, really, who is), can always be counted on to provide the larger community with excellent food for thought.

In this most recent case, James claimed that ground ball pitchers have essentially been overrated. Per Rob Neyer:

Any analyst can give you a long list of reasons why ground ball pitchers should be the best pitchers. The problem is, they’re not.

Make a list of the best pitchers in baseball. Make a list of the best pitchers in baseball, in any era, and what you will find is that 80% of them are not ground ball pitchers. They’re fly ball pitchers.

…

What I have never understood about ground ball pitchers, and do not understand now, is why they always get hurt. Show me an extreme ground ball pitcher, a guy with a terrific ground ball rate, and I’ll show you a guy who is going to be good for two years and then get hurt. I’m not saying this about Chien-Ming Wang and Brandon Webb; I was saying this before Chien-Ming Wang and Brandon Webb. They’re just the latest examples. Mark Fidrych. Randy Jones. Ross Grimsley. Mike Caldwell. Rick Langford. Lary Sorensen. Clyde Wright. Fritz Peterson. Dave Roberts. They’re great for two years, and then they blow up. Always.

Now, there is a lot that can be teased out here, but I want to focus on the last part of James’ claim–that ground ball pitchers are more injury prone. Are ground ball pitchers (specifically, extreme ground ball pitchers) more injury prone?

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When I originally published my findings around CLIFFORD — my metric for predicting players that are at a higher risk of experiencing a collapse in their wOBA (defined as a drop of at least .30 points of wOBA) — I presented a limited number of players for 2013. The list only included six players that qualified under the criteria. As a reminder, players that experienced a significant decline in three out of four metrics (Z-Contact%, FA%, UBR, Spd) were tagged as CLIFFORD candidates. These players had 3.4 times the odds of collapse (53% versus 25% for non-CLIFFORD players).

The single largest driver of collapse was change in Z-Contact% — the percent of pitches in the strike zone that a batter swings and makes contact with. Hitters who saw their Z-Contact% decline by at least 1.4% had 1.68 times the odds of collapsing than those that did not experience such a decline. Since there were far more players that qualified with their Z-Contact% than the full CLIFFORD criteria I thought it would be helpful to share that data with everyone.

Behold!

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Last week, the Seattle Mariners inked their ace, Felix Hernandez, to a $175 million extension for the next seven years. The dominating righty will be entering his age-27 season this year, meaning the contract will through his age-33 season. That is, unless, he injures his right elbow.

Embedded within Hernandez’s contract is a clause that gives the Mariners a club option for an eighth season — at a paltry $1 million — should Hernandez miss at least 130 consecutive days due to any kind of procedure to his right elbow. The Mariners negotiated this clause after some concern over what their doctors saw in the pitcher’s MRI.

Apparently, the club was reassured enough by their medical staff to sign the mammoth deal, even though the track record for long-term pitcher extensions isn’t the greatest. But how confident should the team be?
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I recently wrote about my attempt to design an indicator that would predict when players were at a higher risk for having a collapse-type year. I named the metric CLIFFORD, referring to the fact that players identified by it were at risk of falling off a cliff offensively. My inspiration was Adam Dunn and his disastrous 2011, in which his wOBA declined by .113.

My initial research showed that 58% of collapse candidates identified by Marcel actually experience a wOBA decline of at least .03 (or 30 points)–2.43 times the likelihood of non-collapse candidates. Collapse candidates identified by CLIFFORD actually decreased by at least 30 points of wOBA 53% of the time–2.14 times the likelihood of non-collapse candidates.

Marcel initially appeared to do a better job identifying these candidates. If we knew nothing else outside of just the Marcel projection, our chances were better at identifying collapse candidates than if we used CLIFFORD (and, yes, the difference between the relative risk for both measures is statistically significant).

However, and here’s the bright spot, there was not much overlap between the two metrics.

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Like my work last year around pitcher aging and velocity decline, I am always looking for reliable indicators or signals of change in players. One thing I’ve been interested in trying to better understand are changes in performance that might signal or herald a large droop-off in performance in the following year.

Projection systems do a very good job of predicting performance, but my thought was there must be some way to better predict the 2011 Adam Dunns of the world.

So, one Saturday morning I decided to do some statistical fishing.

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Note: I found some errors in the data. Data below has been corrected, as well as some conclusions — BP

Yesterday, Jeff Zimmerman examined how Tim Lincecum‘s performance has depended to some extent on his ability to pitch to the edges of the plate. Last year, Lincecum was one of the worst starters in the game in terms of the percentage of his pitches thrown to the black. Coincidently (or not so coincidently), Lincecum suffered through his worst season as a professional.

As with many things, Jeff and I happened to be investigating this issue of the edge simultaneously. Of course, we were not the first to dabble in this area. Back in 2009, Dave Allen noted that differences in pitch location–specifically horizontal location–led to differences in BABIP.

Like Dave, I was curious about the overall impact that throwing to the edges–or the black–has on overall performance. My thinking about pitchers throwing to the edges naturally led to some hypotheses:

1. Throwing a higher percentage of pitches on the edges leads to lower FIP.
2. Throwing a higher percentage of pitches on the edges leads to lower ERA.
3. Throwing a higher percentage of pitches on the edges leads to lower BABIP.
4. Throwing a higher percentage of pitches on the edges is associated with lower four-seam fastball velocity.

I think the first three hypotheses are intuitive, but the last one stems from the idea that as a pitcher ages and loses zip on their fastball they cannot remain successful unless they increase their avoidance of the heart of the strike zone.

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Armed with a new measure for offensive volatility (VOL), I wanted to revisit research I conducted  last year about the value of a consistent offense.

In general, the literature has suggested if you’re comparing two similar offenses, the more consistent offense is preferable throughout the season. The reason has to do with the potential advantages a team can gain when they don’t “waste runs” in blow-out victories. The more evenly a team can distribute their runs, the better than chances of winning more games.

I decided to take my new volatility (VOL) metric and apply it to team-level offense to see if it conformed to this general consensus*.

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I suspect many researchers and writers have their own white whale or unicorn; an idea or concept that they are always chasing, regardless of how fruitless or costly that search may ultimately be.

My unicorn is the concept of volatility. I spent a large part of my tenure at Beyond the Box Score exploring the topic for both hitters and pitchers. I even looked at the concept in relation to team performance earlier this year at FanGraphs and other outlets.

Essentially, the idea is to understand whether there are appreciable differences in how players distribute their daily performances over the course of a season. For example, if you have two hitters that are roughly equal in terms of overall skill (i.e. both are 25% better offensively than the league average) is there a difference in terms of how much each is likely to vary from their overall performance on a game to game basis? Is one hitter more consistent day in and day out, while the other mixes in phenomenal performances with countless 0-4 days?

My initial work had some problematic issues (as most initial work does), but thanks to some great feedback from readers and colleagues alike I am ready to roll out the new and improved version of Volatility (VOL), starting with hitters.

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Of the many topics discussed in The Book is lineup optimization; essentially, the degree to which teams can extract extra runs throughout a season through better lineup construction.

The general consensus seems to be teams don’t do a great job at optimizing lineups. But the gains from proper optimization aren’t that great, anyway.

That being said, I was curious whether there’s evidence for league-wide changes in the ways players are deployed throughout lineups. Given the statistical research in the past few decades, is the league any more in line with setting lineups with the expressed idea of simply avoid outs?

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Multiple sources are reporting that David Wright and the New York Mets have reached agreement on a contract extension that essentially makes the third baseman a Met for life.

Initial reports have the deal at 7 years/$122 million. This is on top of next year’s $16 million team option, taking the total years and value of the contract  to 8/$138.

It always pays to be skeptical of long-term deals for players on the wrong side of 30, simply because we know — on average — that performance only declines from this point on.

Let’s take a look at how this might play out for the club.

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It’s the bottom of the eighth inning. Men are on first and third base, there’s one out and your team is down by one run. The opposing team has one of the best ground-ball pitchers on the hill, and the infield is playing back and is looking for a double play. All you need is a fly ball to tie the game and significantly swing your chances of winning.

So who do you want at the plate?

It’s likely that the opposing manager will either bring in a ground-ball specialist or just tell the pitcher to stay away from pitches that could be hit in the air to the outfield. Knowing who you’d want to hit requires an understanding of what pitches are the most likely to induce a ground ball — and what hitters manage to hit fly balls against those pitches most often.

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So much of what makes pitchers effective at the major league level is their ability to keep hitters off-balance. Sure, a 95 mph fastball with movement and a Lord Charles curveball help, but even these physical tools are only as effective as a pitcher’s ability to create uncertainly in the hitters mind from pitch to pitch.

One — admittedly crude — way of looking at this is whether a pitcher throws the type of pitch that’s expected in a given count. Does a pitcher throw fastballs in “fastball counts”, or do they throw off-speed pitches? Pitchers that throw counter to expectations are often said to “pitch backwards”. The Rays’ James Shields is someone that has been referenced as such a pitcher over the past few years.

But exactly how backwards does Shields pitch? And who are some other pitchers that fit into this category?

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David Wright experienced a resurgence of sorts in 2012. After four straight outstanding offense seasons, Wright’s offensive production dipped significantly in 2009 — from a 141 wRC+ to 125. In 2011, Wright’s wRC+ declined all the way to 116.

But this year, the old David Wright reappeared and the 29-year-old third basemen posted a 140 wRC+. The Mets, encouraged by Wright’s year at the plate, have not only picked up his 2013 option (which was predictable), but have also continued discussions for a long-term contract extension.

How likely we are to see Wright put up similar numbers in the future is debatable.

Regardless, one thing was clear: Wright was making better decisions at the plate in 2012. And while his plate discipline numbers were positive (e.g. -2.1% O-Swing), the overall change didn’t seem to capture how well Wright’s plate approach improved.

In an effort to tease this out beyond the basic plate discipline metrics, R. J. Anderson used Mike Fast’s “correct” decision-making approach to look at how Wright’s decision-making improved in the past three season. Anderson calculated the percentage of “correct” pitches Wright swung at in 2012, compared to the two previous seasons. He found Wright had improved his decision-making by 7%.

I decided to take an even narrower view than Anderson and focused only on the location of balls Wright swung at that were just off of the plate, or that were off the black.

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I’ve written quite a bit this year about pitcher aging — specifically, trends in velocity loss for pitchers. There are two general findings that I want to revisit today and apply to pitchers from 2012; the predictive power of velocity loss in July and end of season velocity, and the impact of losing velocity in one season on next season’s velocity.

First, a pitcher’s velocity will tend to vary throughout the year. Trying to get a read on whether a pitcher is having trouble velocity-wise during a season is difficult if you simply compare to last year’s overall velocity. So I compared a pitcher’s velocity in each month to their velocity the previous year in that same month and found that pitchers who lose at least 1 mph of velocity in July are 13.7 times more likely to finish the entire year down at least 1 mph.

Second, 91% of pitchers that do finish a season down at least 1 mph compared to the previous season will lose additional velocity the following season (average decline of 1.6 mph), with only 7% regaining some (but, likely, not all) of that velocity back.

With the close of the 2012 season, I checked back on how well July-over-July velocity trends predicted full season declines as well as which pitchers ended the season losing over 1 mph off of their fastball.

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