Author Archive

Introducing the Interactive Spray Chart Tool

I’ve been working on an interactive tool that allows you to create spray charts using Game Day data from the past two years for a few weeks now. I’ve always loved the Katron Batted Ball tool, and it’s been a great resource of mine for years. However, I wanted to put something together that was a bit more interactive, allowed for more filtering, and made side-by-side comparisons easier.

Our writers here at FanGraphs have been kind enough to play around with it and offer suggestions. After some tweaks I am ready to officially release the tool into the wild so that anyone can use it.

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Matt Moore and Others Likely to Lose Velocity

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.

Month 1 mph Drop No 1 mph Drop Relative Risk Odds Ratios
April 38% 9% 4.2 6.2
May 47% 6% 7.8 13.9
June 55% 5% 11 23.2
July 56% 4% 14 30.6
August 53% 6% 8.8 17.7

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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More on Called Strikes on the Edge

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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Getting Strikes on the Edge

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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Velocity Decline Trends for June, 2012-13

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.

Month 1 mph Drop No 1 mph Drop Relative Risk
April 38% 9% 4.2
May 47% 6% 7.8
June 55% 5% 11.0
July 56% 4% 14.0
August 53% 6% 8.8

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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Putting Hitters Away with Heat

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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Hitter Volatility Through Mid-June


Velocity Trends Through May

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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Reliever Pitching Metric Correlations, Year-to-Year

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:

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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How the Rays Leverage the Edge

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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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:

GS K% BB% HR/FB ERA- FIP- SwgStr%
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.

Behold!

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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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