Author Archive

Yu Darvish, Defining “Change of Pace”

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:

8 39.0% 8.8% 13.9% 62 56 15.7%

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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Matt Harvey’s High Fastball Dominance

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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Does an April Drop in Velocity Predict An Arm Injury?

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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Edge%: It’s Baaaack

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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CC Sabathia’s Velocity Is Definitely Worth Watching

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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Is Bill James Right about Ground Ball Pitchers and Injuries?

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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Lesser CLIFFORD Candidates

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.


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Felix Hernandez’s Velocity

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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Cano, Granderson, and Other CLIFFORD Candidates for 2013

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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I Fought Marcel And Marcel Won

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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