My latest post on Edward Mujica examined what may be causing his extremely high home run rate. To be honest, I’m not sure we can really conclude much on the issue – at least not from my analysis. Anyway, Larry and Mike made some good points in the comments, and I wanted to further investigate Mujica’s pitch location.

Conveniently enough, just yesterday Jeremy Greenhouse looked at pitchers who are able to locate their pitches on the corners of the plate, but avoid the middle. A home run – or hard hit ball – is most likely to be located somewhere in the middle portion of the plate, and probably slightly up.

Here is Mujica’s pitch location graph for 2010 (view from catcher’s perspective, measured in feet):

Mujica’s pitch location does not look that much like Rivera’s. It seems his pitches are in the middle of zone as often as they are on the corners.

Against righties, Mujica is very reluctant to go inside. Though he keeps it away from righties in general, he appears to miss off the plate quite often. And he’s still putting it in the middle of the plate at a pretty high rate. Against lefties, again, a lot of pitches are located too close to the middle of the zone.

The graphs above are just from 2010. Let’s look at his entire career. This time I’ll use the graphs created from TexasLeaguers.com:

Click for a larger image 

On the left is Mujica in his career vs. all batters, the middle graph is vs. righties, and the furthest right is vs. lefties. And I am pretty sure that changeups are actually splitters, as I discussed in the previous post. Check out the comparison between Mujica and Rivera:

You can see a pretty clear area where Rivera (on the right) doesn’t go – down the middle and up in the strike zone. He’s able to locate primarily on both sides of the plate, but avoid, largely, the dangerous section in the center. Mujica, on the other hand, does not appear to shy away from the middle and upper portions of the strike zone, and that’s a dangerous place to live.

Mujica has the stuff to be a quality reliever. Even with his home run problems, he has still been pretty solid. And this ‘analysis’ is not in any way conclusive. Home runs allowed involve a multitude of factors other than location – the hitter, the environment, pitch sequence, velocity, etc. That said, if Mujica is able to avoid the middle of the plate more often, I certainly think he may see a drop in home runs allowed.

Edward Mujica, right-handed pitcher for the San Diego Padres, is a pretty good middle reliever. Check out his numbers since coming to the Padres:

The strikeouts and walks look good. There is one thing I purposely left out, however, and that is home runs allowed. Mujica has given up 22 home runs in 124.7 innings as a San Diego reliever. That is a 1.6 HR/9 rate. He will have trouble staying in the majors if he continues to allow home runs at this pace. Of course, perhaps he has just been unlucky or in a slump.

We now have a variety of tools at our disposal to investigate Mujica’s home run troubles. First, let’s look at his ball-in-play rates (note that 2006-2008 with Cleveland is a pretty small sample, about 70 innings total):

IFFB: Infield Fly Balls
HR/FB: Home runs per fly ball

According to FanGraphs, the approximate 2010 averages are:

LD: 18.5%
GB: 44.6%
FB: 37.2%
IFFB: 9.5%
HR/FB: 9.3%

What we have in Mujica, then, is a pretty extreme fly ball pitcher. He has a career fly ball rate of about 47%, while the league average is 37%. He is pretty average on line drive%, IFFB%, and a bit over the league average on HR/FB%.  One interesting thing to note is that his infield fly ball percentage is at zero so far this season. So, apparently, every fly ball he has allowed has gone into the outfield. That is probably not helping his HR/FB rate.

Anyway, the sabermetric literature suggests that home runs are essentially a function of fly ball percentage. In other words, HR/FB should regress heavily toward the league average. Let’s take a look at Mujica’s actual home run rate and his home run rate after adjusting his HR/FB to the league average 9.3%.

Since 2009, if Mujica had allowed a league average HR/FB rate of 9.3%, his overall home run rate would be about average. While Mujica is a fly ball pitcher, his strikeout and walk rates are good enough that his propensity to allow fly balls should not be an issue, *if* he puts up league average HR/FB rates. Over these past two seasons, however, he has actually given up 22 homers on 135 fly balls, good for a 16.3 HR/FB%.16.3% is a different story.

Hit Tracker gives us another interesting tool to analyze Mujica’s home run struggles. Before looking at the data, the theory would go that, since Mujica has probably been “unlucky” so far in his Padres career (especially 2010), a lot of his home runs allowed should be unlucky home runs, homers that just barely got out.

Here are some definitions from Hit Tracker:

Std Distance (Standard Distance) - The estimated distance in feet the home run would have traveled if it flew uninterrupted all the way down to field level, and if the home run had been hit with no wind, in 70 degree air at sea level. Standard distance factors out the influence of wind, temperature and altitude, and is thus the best way of comparing home runs hit under a variety of different conditions.
“Just Enough” home run - Means the ball cleared the fence by less than 10 vertical feet, OR that it landed less than one fence height past the fence. These are the ones that barely made it over the fence.
“No Doubt” home run - Means the ball cleared the fence by at least 20 vertical feet AND landed at least 50 feet past the fence. These are the really deep blasts.
“Plenty” home run - Everything else, except for the 2 above Homerun types
Lucky Homer - A home run that would not have cleared the fence if it has been struck on a 70-degree, calm day.

Here are Mujica’s eight home runs allowed this season:

Mujica’s average Standard Distance is 418 feet (NL average: 395). The home run to Blake in Dodger Stadium is the only “lucky” home run Mujica has given up:

Here are the landing spots on all of Mujica’s home runs allowed this season:

He has given up some absolute bombs, some normal home runs, and a couple of borderline ones. It is tough to make any real conclusions based on this data because of the sample size. For a little additional context, last year Mujica’s Standard Distance on his 14 home runs allowed was 387 feet. He gave up four in Petco Park and 10 on the road. Six were classified as “plenty” and eight as “just enough.”

If anything, I think we can say that this season Mujica’s homers allowed have not been of a particular unlucky nature. For the most part, they have been hit hard. Of course, that is not to say that he hasn’t been unlucky. After all, it is still a small sample and there are a lot of variables that go into a home run: bad pitch selection, bad execution, the hitter’s swing, etc. Perhaps Mujica has thrown good pitches that hitters have handled, or maybe he’s just thrown a few bad pitches that have been hammered.

We have yet another tool at our disposal: PITCHf/x. I am just going to look at 2010 data, identify Mujica’s pitch types, usage, and then specifically look into his home run pitches.

According to PITCHf/x, here is what Mujica throws:

Here’s the pitch movement graph (x-axis: horizontal movement, y-axis: vertical movement, view from catcher’s perspective):

Mujica throws a split-finger fastball quite often (20% of the time for his career, according to BIS) that PITCHf/x apparently does not pick up on. It appears to me that the changeups classified by PITCHf/x are actually splitters. Here’s a good look at Mujica’s pitches from a bird’s eye view:

If you look closely enough, you can see the four and two seam fastball, along with the splitter (classified as a change), move in on a right-handed hitter. The cutter, slider, and curve all break in the opposite direction.

Anyway, the last thing I wanted to look at is the location and pitch type on Mujica’s home runs allowed this season:

Four homers on four-seamers, three on splitters (I reclassified the changes as splitters), and one on a slider. All located in prime home run territory, up and out over the middle of the plate.

If we’ve learned anything from this, it’s that we can analyze a player’s performance in a lot of different ways and, in the end, still be left with more questions than answers. Mujica is a flyball pitcher who is going to give up his share of home runs. However, if small sample size and regression mean anything to us, he won’t continue to give them up at this rate. At the same time, nobody wants to be testing any mathematical theories in the middle of a pennant race.

Stephen Strasburg made his debut Tuesday night against the Pittsburgh Pirates.

His line: 7 IP, 4 hits, 2 R, 1 HR, 0 BB, 14 K

Unreal. His lone blemish was a changeup to Delywn Young that caught a little too much of the plate, allowing Young to yank it into the right field stands. For most of the game, however, Strasburg toyed with the Pirates’ hitters, blowing 98 mile an hour fastballs by them, dropping unhittable curveballs, and occasionally resorting to his 90+ MPH change.

Watching Strasburg’s performance, I couldn’t help but think that he has the most dominating stuff I have seen since, I don’t know, Pedro Martinez. Sure, I’m probably caught up in the hype. There are plenty of great pitchers in the game today, and one start against the Pirates doesn’t make you the best in the game.

The highest drama came after the 6th, when Strasburg was at 80 pitches and the Nats, behind home runs by Adam Dunn and Josh Willingham, took a two run lead. There was much speculation from the broadcast booth that the Nationals phenom would be taken out. However, not only did Strasburg come up for the seventh, he struck out the side and left to an electric ovation.

You knew it was coming – let’s take a look at some PITCHf/x data on his debut.

That is simply Strasburg’s pitches, in order, accompanied by MPH. He did not lose much on the fastball as the game went on. Here’s his movement chart (from the catcher’s perspective):

Horizontal movement is on the X-axis and vertical movement is on the Y-axis, and the data points are color coded by pitch type. You can see that his pitches cluster into three pretty distinct groups; the fastball in the upper left, the change up just below it, and the curve in the lower right.

I think if we studied his pitches more closely, we’d find two fastballs and perhaps a variation of the curve (a true, knee buckling curve and more of a sharp breaking slider/curve). For now, though, the chart gives us a good indication of his pitch movement.

Using the data from Brooks Baseball, here’s his pitched usage breakdown (with average and max speed):

According to PITCHf/x, Strasburg hit 100.1 and 99.9, both in the second inning. He hit 99 or faster five times and at least 98 a remarkable 28 times. It is completely accurate to say this guys *sits* in the upper-90s. At least he did on Tuesday night. I can’t wait until the next time he takes the mound.

The general consensus is that a four-seam fastball is thrown with less movement and more velocity than a two-seamer. Check the San Diego Padres rotation, using 2010 PITCHf/x data from FanGraphs:

Yeah, check that rotation. Prior to looking up the data, my theory was that the four non-Clayton Richard (since we’ve already looked into the data on him) Padres starters would have four-seam fastballs that registered a few MPH faster than their two-seamers. Instead, we see not only Richard averaging the same velocity with each pitch, but also Garland (a faster two-seamer, in fact), Correia, and Latos (within one MPH). LeBlanc, the only one who has a clear difference between the two pitches, uses his two-seamer sparingly (less than 2% of the time).

What gives? Well, we have (at least) three possible explanations:

1. The Padres employ a staff that just so happens to throw both of their fastballs (ignoring the cutter, for now) at similar speeds. Perhaps they just happen to be on the same staff together or maybe it has something to do with pitching coach Darren Balsley or the Padres organization in general.
2. We are again encountering problems in classification, by Gameday, and are not witnessing real differences in the two pitches, rather difficulties in properly classifying them.
3. I’m crazy, and four-seamers and two-seamers are generally thrown at the same speed.

I think we can rule out choice three, as Dave Allen says here that four-seamers are indeed thrown faster — “about 1.5 mph faster than two-seam fastballs and 3.5 mph faster than cutters.”

My guess is that it is some combination between one and two. But, frankly, I have no idea what the correct answer is and whether or not it is significant. Any thoughts?

Our friend Zach did an analysis of Clayton Richard’s 2010 success over at Gaslamp Ball. He says:

In 2009, Richard threw his 4-seamer 51.3% of the time, and the 2-seamer only 19% of the time. In 2010, he’s dropped his usage of the 4-seamer, throwing it only 23.8% of the time, and upped the use of the 2-seamer, now throwing it 35.6% of the time.
Richard has also increased the velocity of his 2-seamer. Where in 2009 he averaged 87.6 mph, this season he’s throwing it at an average of 91.2 mph.

Indeed, if you look at the PITCHf/x data, located on FanGraphs, that’s what comes out. Check out his usage, in 2009 vs. 2010:

It struck me a bit that he’d change his repertoire so much in one-offseason. What’s also striking is the speed of his two-seamer; it has jumped almost four miles per hour in one season, while his four-seamer – and basically the rest of his pitches – stayed at the same mph.

As I thought about this, it started to click that perhaps Richard’s pitch selection hasn’t changed that much, maybe the PITCHf/x algorithm has. I wanted to look at the raw PITCHf/x data to see if I could find anything.

The first graphs I’m going to show has horizontal movement on the x-axis and vertical movement on the y-axis, and the pitches are a different color based on their pitch type (as classified by MLBAM). Remember, the view is from the catcher’s perspective:

2010

2009

You can see the two and four-seamers clustered in the top right of the graph. In 2010 you can see the two-seamers have slightly more horizontal movement and slightly less vertical movement. What is revealing is if we remove two-seamers to get a better view.

2010

2009

Notice the range of the four-seamer, the circled purple triangles, is much wider in 2009. It looks like in 2009, the algorithm was very reluctant in calling a pitch a two-seamer. Most everything in that upper right hand corner was identified as a four-seamer. In 2010, however, it is much more equal – in fact, as you can see by the graph and the chart above, the two-seamer is now being identified more often than the four-seamer.

If we take the 2010 version of the pitch algorithm as the more accurate one, then it appears what happened in 2009 is that a lot of two-seamers were actually classified as four-seamers. So, in reality, Richard’s pitch selection has probably not changed that much – the pitch algorithm has.

And, indeed, it appears that is what has happened. In an email exchange – and here on the Hardball Times – Mike Fast explained to me that Ross Paul, the guy doing some of the behind the scenes work at MLBAM, is now using a neural networks to classify pitches. A neural net is specifically trained to identify pitches from each pitcher, and should theoretically be a more accurate way to classify a pitcher’s arsenal.

****

I also created spin graphs for Richard, and while they may not add to the point of this article, I think they are still worth sharing.

2010

2009

Interestingly, it appears that the same thing may be going on with sliders and cutters. It appears that a lot of Richard’s pitches that were being classified as sliders last year are now being classified as cutters.

“At some point, ownership has to lead the revenue,” Gerut said. “It doesn’t always occur where your players first start to win and then you start to build around it. Sometimes the ownership has to lead that first. There’s ancillary stuff that I probably know nothing about. I know sometimes that if you build it, fans will come. It doesn’t always have to work out the other way. You don’t always have to get guys at absolute steals in order to keep them.” – Jody Gerut, in a FanHouse article by Tom Krasovic

As the San Diego Padres have continued to surprise the baseball world with a National League best 28-18 record, the near constant questions of Adrian Gonzalez’s future have calmed. He’s a Padre, for now, and not many people are thinking about a deadline deal at this point. Gonzalez is still working on a fantastic – from San Diego’s perspective — four year, $9.5 Million deal, and is signed for $4.5M this year. The San Diego Padres have a $5.7M option for next year, which is the biggest no-brainer in all of baseball.

So, there is no rush. However, in terms of trade value, as each day passes, Gonzalez loses some. It all about surplus value – the value that a player brings in above and beyond what he is being paid. The two aspects that determine surplus value, then, are performance and salary. Take a look at the chart, an estimation of Gonzalez’s surplus value through the remainder of his contract:

I’m estimating Gonzalez as a 4.8 WAR player, currently, based on his performance since 2007. One could argue either way, but I think it is good enough for blog-work. Using $3.5M per marginal win in 2010 and $4M in 2011, we can estimate Gonzalez’s value on the free agent market ($10.9M for the rest of this season and $19.2 for all of next year). His surplus value is the difference between his estimated free agent worth and his actual contract, which comes out to $21.3M.

Read the rest of this entry »

The San Diego Padres starting rotation has been excellent so far. The numbers do not lie (all courtesy of FanGraphs):

FIP has been used here on countless occasions, but as a refresher it is a stat that uses three aspects of pitching that pitchers have a lot of control over – strikeouts, walks (plus hit batters), and home runs – to churn out a number that is “fielding independent” and corresponds to ERA.

xFIP is essentially the same thing, except for one very important factor. It takes a pitcher’s fly ball rate and then uses the league average homers/fly ball to determine how many home runs a pitcher “should” have allowed, because research has shown that home run rates are generally a product of fly balls allowed.

Alright, let’s take a closer look at each starter.

Jon Garland is off to the hottest start, value-wise, with a 2.38 ERA. He has also, arguably, been the least effective pitcher on the staff so far – by fielding independent metrics. He’s striking out a staff low 5.3 per nine, and he’s walking almost 5 every nine. Garland has been helped so far by his solid .51 hr/9 (career: 1.10) and a .261 BABiP (career: .289).

Garland’s been a league-average type starter throughout his career, and frankly he has pitched just like one so far. That’s not a bad thing; I just don’t see any strong reasons to suggest that he won’t revert to his old form (as measured by ERA) as the season progresses.

Mat Latos has been excellent all-around, striking out nearly 7 per nine, and allowing just 2 BB/9. He’s been solid by FIP (3.90), xFIP (3.81), and tRA (3.10). The only real “luck-factor” that stands out with Latos is his .240 BABiP. That should creep its way up towards .300 and Latos’ ERA should climb a little bit as well, but overall he should be fine.

Clayton Richard has also been great so far. His .16 HR/9 rate is glaring, though. For one, it’s great. Two, it just isn’t going to stay that low. He’s allowed 51 fly balls so far. If we assume that 11% of them leave the park, he should have 5-6 home runs allowed at this point. He’s only given up one. That’s not a knock on him. He’s pitched well. But home run rates that low simply don’t last. His ERA should come up some, but as his other indicators show, it shouldn’t be a catastrophic decline.

Wade LeBlanc has seen his BABiP go from .224 last year to .321 this season. His ERA has declined, though, because he’s improved his strikeout and home run rates. Despite giving up 42 fly balls, he’s only allowed two home runs. All of the projection systems pegged his HR/9 to be somewhere around 1.3-1.4 before the season. That might not be the case, but LeBlanc should see a pretty large increase in his HR rate as the season progresses.

Kevin Correia, unlike the other four starters, should see his ERA fall if he keeps pitching as he has so far. Correia’s 4.57 ERA is higher than his FIP (4.00), xFIP (3.68), and tERA (4.34). His home run rate may fall a bit, as he’s allowed homers on almost 14% of his fly balls (career:9.4%).

This is of course just a cursory look at these five guys, by no means conclusive research. But what we see here is a pitching staff that is obviously performing excellent. Four of the guys should regress a bit as the season progresses, and Correia should improve slightly. If that happens, the Padres staff will not be the devastating weapon that it has been so far, but it can still be a strength.

Baseball Prospectus publishes adjusted standings, based on a team’s Pythagorean record, equivalent runs, and a strength of schedule adjustment. Here’s the NL West:

AEQR is equivalent runs, adjusted for SOS. AEQRA is equivalent runs allowed (also adjusted for SOS). W3-L3 is win-loss record, based on AEQR and AEQRA.

The San Francisco Giants and San Diego Padres have been the class of the NL West so far. The Rockies are going to be dangerous, as they are underperforming their actual record by quite a bit. The Dodgers have been a .500 team all-around. Arizona has struggled, allowing 211 adjusted runs already (221 real ones; most in MLB).

This is more positive reinforcement for the Padres good start. Not only are they 22-13, but they are not really getting lucky – just a little bit. Now, how about some early playoff odds? Here’s PECOTA’s version:

Average Wins is the average number of wins each team accumulates over one million season iterations. Playoff% is the percentage of times each team makes the playoffs. At this point the Padres are fourth in the NL in Avg. Wins (and playoff%), behind only St. Louis, Philadelphia, and San Fran. If we look at the original report, which does not regress back toward PECOTA’s projections, we get an even better prognosis for SD – 61% playoff odds.

One of the tenets of sabermetrics is that strikeouts, for hitters, are not as detrimental to an offensive attack as many think. The idea is that a strikeout, while easily the least desirable outcome for the hitter (except, of course, for the double play), is not really that much worse than any other out. Any type of out is, on average, not going to help your ball club. Over the long haul, striking out more than another player with similar offensive stats is not going to hurt a player’s overall value too much.

With that said, the strikeout can become dangerous for the hitter when it happens too frequently. Enter Kyle Blanks. So far this season he’s leading all of the majors, amongst qualifiers, with a 45% K rate. In fact, nobody is really close to him (Justin Upton is second at 37%). Last year, Diamondback Mark Reynolds led the league with a 39% K rate – only he and Jack Cust were over 35%. For additional context, Adam Dunn’s career strikeout rate is about 32% and he’s never had a season above 35%. The 40% range and upwards is rarified air.

However, a hitter can function, productively even, with a ridiculously high amount of strikeouts. Take Dunn, for example, who has been a tremendous offensive force his entire career. With the strikeouts, Dunn has  provided a lot of patience (17% BB rate) and power (22% HR/FB). Mark Reynolds, MLB’s current strikeout king, is also a productive hitter. Despite whiffing 37% of the time, he’s put up a .256/.340/.504 line, thanks to power similar to Dunn’s and a great BABiP.

Blanks so far has struck out in 40% of his major league at bats (222 of them). What is encouraging is that his body of work, at the major league level, is not even half a season. His K rate should go down, due to regression to the mean alone. However, strikeouts are definitely more stable than, say, average on balls in play, so the concern is real.

Blanks has been a relatively productive player, putting up a .350 wOBA in his brief major league career. He’s done it with a lot of power, a decent enough average on balls in play, and an acceptable walk rate. He has shown that he can be a productive major leaguer with the high strikeout totals. The million dollar question, of course, is can he keep it up? His numbers have already fallen off this year, with the increased strikeouts. It is possible that he could be okay while striking out in 40% of his abs, but it is definitely unlikely. And it severely limits his upside and his long-term ability to remain a starter in the majors.

I don’t want to make too much out of too little here early in the season. It is never too wise to get too excited either way about such a relatively small amount of data. That said, Kyle Blanks’ K rate is alarmingly high enough to warrant some early concern. Hopefully, Blanks will work hard to make more contact, while at the same time becoming more patient and continuing to provide good pop. If everything comes together, he has a chance to become a staple in the Padres lineup.

by Daniel Gettinger

I was not impressed by San Diego Padres’ pitcher Clayton Richard yesterday.  Sure his results were adequate (5.1 IP, 5 K, 1 BB, 3 R), but he seemed to rely heavily on his all too hittable fastballs.

Pitch f/x shows that Richard threw some type of fastball nearly 83 percent of the time.  He threw 16 four-seamers, 23 cutters, and 28 two-seamers, compared to only 14 off-speed pitches.  I am not entirely positive, but just from scanning the game log, it does not appear that Richard was forced to throw his fastball because he was behind in the count.

Richard’s fastball speed is nothing spectacular, but it is not terrible either.  He throws his four-seamer and two-seamer between 90 mph and 94 mph, averaging approximately 91 mph.  His cutter is a bit slower, averaging 87 mph, with a max around 91 mph.  Generally speaking, he held his speed throughout the start.

The problem is Richard’s fastballs all that much movement.  With the exception of the two-seamer, they are pretty straight.

To illustrate this point, I put together some flight maps using the pitch f/x data.  The first map is the “birds-eye” view, and the second is the “first base” view..

For comparison, lets look at the fastball flight maps from Jake Peavy’s April 7 start against the Cleveland Indians.  Peavy’s fastball speed was approximately the same as Richard’s and like Richard, he threw a large number of four-seamers, two-seamers, and cutters..

A quick comparison of the flight maps shows that Peavy gets  more movement on his pitches than Richard. Of course, this is not surprising.  A large reason for Peavy’s success is that his pitches–even his fastballs–have a lot of movement.  What this comparison confirms is that Richard, as is, is unlikely to become more than a back of the rotation starter. His fastball has adequate speed, but it does not move enough to consistently fool major league hitters if he is throwing it 70 percent-80 percent of the time.  To improve, Richard needs to develop better off-speed stuff, or figure out a way to get more movement on his fastballs.