Author Archive

Carl Pavano’s 2010: Trading Whiffs for Grounders

Although Carl Pavano had nearly identical FIPs in 2009 and 2010 (4.00 versus 4.02), he achieved them in quite different ways. Pavano, who re-signed with the Twins yesterday, did a great job of limiting walks in both years. But in 2009 he combined that command with average-ish strikeout and ground-ball rates. In 2010, however, had just 4.8 Ks per nine (two per nine fewer than in 2009 and sixth lowest in baseball), but induced grounders on over 50% of balls in play.

Usually such a change in strikeout and ground-ball numbers is the result of a drastic change in pitch usage, see Joel Pineiro‘s 2009. But it looks to me like Pavano’s pitch usage has not changed that much from 2009 to 2010:

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Chris Capuano’s Strange Platoon Split

Chris Capuano, the Mets’ new left-handed pitcher, has a strange platoon split. Against right-handed batters he gets grounders like Jered Weaver (37%), but against left-handed batters he gets them like Felix Hernandez (54%). The average pitcher has a fairly large difference in strikeout and walk rates by batter-handedness (Capuano’s strikeout and walk splits are standard for a lefty), but a small difference in ground-ball rate. Dave Cameron found that left-handed pitchers get only marginally more grounders against left-handed batters (46%) than against right-handed batters (44%). Capuano’s ground-ball split is out of the ordinary.

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“Does Bill James Even Like to Watch Baseball?”

Projecting players is a tough business. Because of the natural tendency to ignore injury risk and reversion to the mean, our instincts lead us to over project. We have seen this with the fans who are about half a win high (and a full win high projecting players on their favorite time) and who are generous with projected playing time.

So it is easy to look at projections and think they look low, and so we should give people a pass when they say so. But when they say so and in the process disparage the projector, I think it’s fair game to call them out. Here is a video of Bob Ryan and Joe Sullivan discussing Bill James’ projections for some Boston Red Sox players (h/t Repoz). Ryan and Sullivan were aghast that Bill James would project Jon Lester for just 14 wins in 2011, joking that their colleague Dan Shaughnessy would say “Bill James doesn’t even like to watch baseball!” and that if James actually watched Lester pitch he might think differently. Ryan claimed that Lester is a 19-game winner for the foreseeable future.

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Why Fans Do a Worse Job Projecting Their Favorite Team?

Yesterday in my Fan Projections 2010 recap I reported that fans do a worse job projecting players on their favorite team than other fans do at projecting those players. This is an interesting finding: these fans probably have more information about those players, but in spite of that do a worse job projecting them. Why is that?
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Looking at the 2010 Fan Projections: Part 2

Yesterday I looked at the 2010 Fan Projections for position players, and specifically how much higher fans of a team projected players on that team compared to non-fans. It turned out to be by about half a win. Commenters to that post wondered which group did a better job projecting the actual performance of the players.

Tango found that the Fan Projections were in the middle of the pack compared to other projection systems: a respectable 10 out of 21, up against the big hitters like CHONE, CAIRO, and Bloomberg. But that was with the Fans as a whole, not split out by the fans’ favorite teams.

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Looking at the 2010 Fan Projections: Part 1

With the opening of the 2011 Fan Projection ballots I thought it would be interesting to look back at the 2010 Fan Projections. The ballots ask fans to identify their favorite team; this allows us to see how differently players are projected by fans of their team than by the fan community as a whole.

Here I will look at this question for the position players (I will look at pitchers in my next post) who had at least 10 ballots by fans of their team. This left 206 position players. I assumed that players would be projected more optimistically by fans of their team than by other fans, and this was the case. On average players were projected half a win higher by fans of their team, with 1.5 runs coming from higher fielding projections and 3.5 runs coming from higher batting projections. Of the 206 players, only 34 were projected worse by fans of their team.

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Network of Baseball Players’ Twitter Accounts

The offseason offers an opportunity to reflect on the pressing sabremeteric questions of the times: free agent value, fielding metrics, pitchf/x release points. All worthy and important pursuits. Here, though, I hope you will indulge me as I address something much more trivial: the network (or “graph,” as Zuck would say) of baseball players’ twitter accounts.

You see, some baseball players tweet, and, not surprisingly, they follow other baseball players. But what does the network of these connections look like? Are teammates more likely to follow one another? Surely, but how much more likely? If player A follows player B, how likely is it that player B follows player A? And, more generally, how connected is the network — in other words how likely is it that any one player follows another?

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Batter Pace

Many commenters to my post yesterday and to David’s original post asked about batter pace. Obviously batters can control the time between pitches (pace) by stepping out of the box often and by spending lots of time out of the box when they do (i.e. step out). Also, based on the results from yesterday’s post, the pace slows for hitters in two-strike counts and when there are runners on, who have a high strikeout rate (because they face more two-strike counts), and who bat with men on base. So I wanted to see how much variability there is in batter pace and take a quick look at the leader board.

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Some Observations on Pace

I was excited to see that David Appelman added Pace to the stats pages based on don’t_bring_in_the_lefty‘s post at BtBS. It was something I had thought about before when Carson emailed me about including it in NERD. Nothing came of it, but I had the code lying around on my computer and I thought this would be good opportunity to share some of my observations.

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View Fans from World Series Games with TagOramic

MLB has a crazy new feature that let’s you view the entire crowd of every World Series game and two games from each of the other playoff series. During the game a single camera takes hundreds of photographs of the crowd, which are spliced together into a gigantic panoramic photograph. The photograph is of such high resolution that you can zoom in to see individual faces. It is linked up with Facebook so that you can tag yourself or your friend. It is called TagOramic. This is the full picture from last night’s game.

The boxes indicate how many fans in each section have been tagged. Those tagged have a blue indicator, but only their friends can see who they are. If you are friends with a person the indicator is red, and, if it is you, yellow. Here is a picture zoomed in on a section of the crowd.

Pretty good resolution, MLB gives the details on the photograph:

Panoramic image from the third inning of Game 4 of the World Series at Rangers Ballpark in Arlington, TX. The image is made up of 360 photos (30 across by 12 down) stitched together taken over a 19-minute span. The final hi-res file is 83,287 X 19,158 pixels or 1,596 megapixels. Photos by David Bergman.

I imagine it would be fun to see myself or a friend captured at some random point in the game, but none of my friends have been to any of the games. Even so it is cool to scan around the field and find people mid-hotdog bite, cheering, talking on their phone (it is surprising how many people are talking on or checking their phone), or whatever.

It does feel strange, and a little voyeuristic, to see this one moment in time for 50 thousand-ish people. From a privacy perspective I guess going to a game leaves you open to being projected on the jumbotron, or even being on the TV broadcast, and this isn’t any more of an invasion.

I was on the lookout for a particular group of fans at the game last night and found them in the first row on the first-base line.

Anyway a pretty cool application of high-resolution photography, photo-splicing software and Facebook tagging.


Cain’s Pitch Type Usage in Game 2

Matt Cain‘s seven-and-two-thirds inning, four-hit performance last night leaves him with 21.1 innings of playoff ball having allowed just a single run (and it was unearned). With all the other amazing playoff performances, Cain has flown a little bit under the radar. Part of Cain’s inconspicuousness might be because he hasn’t done it with overpowering stuff – just 13 strikeouts – but instead by inducing weak contact. This is a skill Cain has shown throughout his career, with a BABIP of 0.274.

I was mostly interested in last night’s game because, looking at the pitchf/x numbers, Cain was throwing a drastically different mix of pitches than he usually does. Cain is a four-pitch pitcher, and his fastball, slider, curve and change distinctly cluster in horizontal movement vs velocity space — making them easy to classify. Here are his pitches for 2010 and last night.

You can see how clearly Cain’s pitches cluster out, so there is little ambiguity in classifying them. Last night, compared to the season as a whole, Cain threw more changeups (26% versus 15%), more sliders (20% versus 10%), fewer fastballs (52% versus 62%) and many fewer curveballs (under 2% versus 13%).

I wanted to know whether the difference from his average pitch usage was anything out of the ordinary compared to other games (i.e., was it just standard fluctuation between games or a real shift). Here are the fractions of Cain’s non-fastballs over the course of the 2010 season for each game, with the same color-code: purple for curves, red for sliders, and yellow for changeups.

It looks like last night’s game was the continuation of a trend in decreasing curveball use, throwing the fewest curves of any game this season. On the other hand he threw the greatest fraction of changeups of any game this season, and there have been few games where he has thrown as high a fraction of sliders.

It is interesting that Cain would so drastically change his pitch usage during the World Series — and the playoffs in general, where we see the decrease in curves — but, obviously, the results have been good for him.


Adrian Beltre’s Fly Balls

Yesterday, Jack posted about Adrian Beltre‘s amazing year. I must admit that I was not aware just how amazing he has been, as Betlre’s wOBA of 0.396 puts him in the top ten in all of MLB. I wanted to see what was behind this offensive explosion from a guy who was a well-below-average offensive player last year (0.305 wOBA).

Beltre has never been much of a walker, and this year is no different, so his increased success is mainly based on his balls in play. Looking at his splits by ball in play type, the biggest increase in performance has been on his fly balls. To delve deeper into this better performance I plotted Beltre’s slugging on balls hit in the air by the angle he hit them into the field (with -45 the third-base line, 0 the line between home and second base, and 45 the first-base line). The curves estimate his slugging with standard errors indicated and the data are split by 2010 and 2005-2009 (the years covered by the GameDay hit information).

Beltre is a typical right-handed batter, getting most of his power to left field, his pull field. But in 2010 he has gotten even more power to left, starting at about center and going almost all the way to the third-base line. Beltre gets almost half a base more per ball in the air in 2010 compared to 2005-2009. To right field there is no real difference.

This profile, most of the change coming in left field, could be the result of Beltre’s change in home park: from Safeco, with a deep porch in left, to Fenway, with the Green Monster in left. Interestingly, though, Beltre is actually hitting better away from Fenway (0.416 wOBA away compared to 0.376 at home). Doing the same analysis above but for 2010 split by home/away:

So Beltre has done pretty much the same at Fenway and away to left, and a little better when away to right. Beltre has undoubtedly benefited from playing away from Safeco, but it looks like a big chunk of his success is just that he is pounding ball the a lot farther — with the help of the Green Monster or not.


Jered Weaver’s High Curves

Last night, Jered Weaver continued his breakout season with a seven-inning, seven-strikeout, one-hit, no-walk win against Cleveland. Weaver has career-high strikeout rate (9.6) while maintaing a career low walk rate (2.2). His great season may have flown a bit under the radar because the Angles have been out of playoff contention for so much of the season, so I wanted to make sure to look into it a bit before his season ended.

Looking at pitch-type numbers, his curveball percentage has gone up a bit, and that prompted me to poke around regarding his curves. Just checking one game’s location charts, I noticed that a number of these curves were up in the zone, and that those high curves induced some whiffs. Usually curves are thrown lower in the zone, where they get the most whiffs.

Were these high curves (and whiffs on them) a one-day fluke or a pattern for Weaver? I plotted a histogram of the heights of his curves to see.

Looks like Weaver does consistently throw higher curves than the average pitcher. Does he get a fair number of swinging strikes on them? (Bands around the line are standard errors of the estimate.)

It looks like that is also the case. His curves up in the zone, and above the zone, induce about 10% swinging strikes while the average pitcher’s curves up there induce under 5%. On the other hand, Weaver’s curves below the zone induce less than 15% swinging strikes, while the average pitcher can induce around 20% swinging strikes.

All of Weaver’s pitches tend to be up in the zone — he is routinely among the league leaders in FB% — so maybe he succeeds with a high curveball because of how it looks to the batter relative to his high fastball. Josh Kalk has an interesting post on curveballs following high fastballs. It would be cool to repeat such an analysis based on the height of that curve.


A Pitchf/x Look at Cliff Lee’s Command

Everyone knows that Cliff Lee is having an extraordinary season. Through 174 innings he has just 11 unintentional base on balls. That works out to 0.57 BB per nine innings. Even with regression it is very likely that Lee will average fewer than one walk per nine on the season. The last time a pitcher did that was Carlos Silva in 2005. That year Silva struck out 3.39 per nine; this year Lee is striking out 7.78 per nine.

Matthew pointed out that Lee’s season is even more amazing when you consider his tiny rate on other free passes (just two IBBs and one HBP). Matthew also showed how this lack of free passes allows Lee to get deeper into games than anyone since 1994 Greg Maddux.

I wanted to take a more micro-scale look and see what this amazing control looks like on a per-pitch basis. Not surprisingly, Lee leads the league in Zone% (BIS’s zone) with 58.7%. Ted Lilly is next at 54.5% and after that no other pitcher is above 53%; Lee is a major outlier, as expected.

Next I was interested in where all these extra pitches in the zone were ending up, and where those out of the zone didn’t end up. So I broke pitch locations (as the ball crosses the front edge of the plate) into a number of bins and color coded each bin based on the proportion of Lee’s fastballs in that bin compared to the proportion of all LHPs’ fastballs in that bin. Bins where Lee had a higher proportion of fastballs are red, and bins where all LHPs had a higher proportion are blue. The intensity of the color shows the size of the difference.

The pattern is not surprising. The bins in the zone are red (Lee throws the ball there more often) and those out of the zone are blue (Lee throws the ball less often). But I think how strength and consistency of that pattern is surprising. There are just a handful of red bins out of the zone and they are very pale. Within the zone Lee tends to locate on the outer proportion of the zone, a good place to be.

It is not like we needed any further evidence that Lee has amazing command, but it is interesting to see what Lee’s command looks like on a per-pitch basis.


Halladay’s New Changeup

Yesterday I read that Roy Halladay had changed the grip on his changeup and was throwing it more often (hat tip to Calcaterra on this one). Before this year Halladay held the ball in his palm when throwing a changeup, but during the offseason he worked with pitching coach Rich Dubee and changed to using a split-finger grip. Let’s see whether we can pick up the difference in the pitchf/x data. I think it is clearest in velocity-vertical spin deflection (vertical movement) space.

Definitely a difference: the changeup has about the same velocity but instead of rising about five inches it now drops a couple of inches. The horizontal movement on the pitch is also different.

So the different grip imparts different movement on the pitch, so I think it is fair to say it is a qualitatively different pitch. And Halladay is throwing the pitch more often. As is noted in the article, before 2010 Halladay was pretty much a three-pitch pitcher (cutter, sinker and curve — three great pitches), rarely throwing his change more than 5% of the time. But now he is throwing the change almost 12% of the time. It seems he is much more comfortable with it.

The results show that Halladay has good reason to be more comfortable with the pitch. By our linear weights it is worth 1.5 runs per 100, much better than his changeups in any full year before. Looking at components, the pitch is wildly more successful, getting 19% swinging strikes per pitch in 2010 compared to 6% in 2009-2007 (pitchf/x years). It also gets more ground balls (57% GB/BIP versus 55%) and a lower slugging on contact ( 0.452 bases/contact versus 0.505).

Looking at Halladay’s pitch-count splits, he uses the pitch often when he is ahead in the count. So it looks like he has developed a second out pitch — along with his curve — to put batters away when he is ahead in the count. As Calcaterra noted, it is not like Halladay needed another weapon, but he has one.


If Pittsburgh Were in Game 7 of the World Series Would You Pay $400 Million to Be There?

Last week, MLB announced their Postseason Ticket Reservation program. This program allows fans to buy the option to later buy a postseason ticket at face value should their team reach the playoffs. You choose the team and game, and then pay $10 for the Division Series, $15 for the Championship Series or $20 for the World Series. If the team you chose gets to that series you have the right to buy a ticket for the chosen game at face value. If not, you get nothing (i.e. your money is not refunded). For the DS you can choose Game One, Two or Three, which are the first, second and third home game for your chosen team, not necessarily the first, second and third game of the series.

So if you choose Game One, you have the right to buy a ticket for the first game of the series (if you have the home team for the series) or the third (if you have the away team for the series). If you choose Game Two you go to Game Two if your team is home and game four if your team is away and the series lasts that long. If you choose Game Three the option is worthless unless your team makes the playoffs, and is home, and the series goes to five games. You get the picture. It works similarly for the CS and WS.

Ok, so should you take part in this program? First, answer these questions: would you like to see a playoff game? Is it worth it to pay above the face value of the ticket to ensure you can go? If so, by how much? That is how much more than face value would you be willing to pay for a given game, assuming that game were to take place. Let’s call that amount, x. Now assuming the probability of the game taking place is p, the the value of the option is:

Value = (1-p)*0 + p*x
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Ordonez’s Contact Skills

Magglio Ordonez is having a great year for the Tigers; his .402 wOBA sits just outside the top ten in baseball. Ordonez has always been able to maintain a relatively low strikeout rate for a hitter with his power and walks, and this year it is even better with a career low strikeout rate (9.5%), a career high walk rate (11.7%) and solid power (ISO of .185). The great BB:K numbers are caused by his best O-Contact%, Z-Contact%, and Contact% in the FanGrapgs Era (since 2002). His 91% contact rate is ninth best in the bigs and of those players better than him only Dustin Pedroia and Victor Martinez have anywhere near his power.

Here I will quickly look at where that extra contact is coming from. I use the contact contours that I introduced in this post. These contours are estimates of Ordonez’s contact rate.  A swung-at pitch inside the solid line is contacted over 92.5% of the time. A pitch around the solid line that Ordonez swings at (92.5% contour) is contacted 92.5% of the time.  A swung-at pitch between the solid and dotted is contacted between 92.5% and 87.5% of the time.  And so on. I compare his contact rate for 2010 to his contact rate on pitches between 2007 and 2009 (those in the pitchf/x data set) and the image is from the catcher’s persepctive.

Because Ordonez hasn’t swung at many pitches outside of the zone, inside the curves are unresovled there, but throughout the strike zone he has a much higher conatct rate. His 92.5% contour covers much of the zone in 2010 compared to perviously where it was just a small part middle-in. He is also making contact at a higher rate on pitches up-and-away. Previously, he made contact on those pitches less than 87.5% of the time, but he is making contact at a high rate (higher than 92.5%) on these pitches.

That extra contact has led to fewer strikeouts and is partially responsible for Ordonez’s success so far. On the face of it, this success may partially vindicate the Tigers’ decision to allow Ordonez’s option to vest (he has already been worth nearly 11 million dollars, and more if you think wins are more valuable than average to the Tigers who have a shot at the AL Central). Still, decisions need to be evaluated based on the information that was available when they were made. And even the Tigers could not have predicted a .400 wOBA first half from Ordonez.


Max Scherzer’s Big Strikeout Numbers

Max Scherzer‘s four starts since returning from Toledo have been quite good. Although he has given up a number of runs, he is striking out lots of batters while not giving up a huge number of walks — a recipe for success. Here are his numbers broken down before and after his time in AAA.

Before:
8 starts, 42 innings, 26 Ks (5.6 per 9), 16 BB (3.4 per 9), 9 HRs, 5.89 FIP

After:
4 starts, 24 innings, 33 Ks (12.4 per 9), 10 BB (3.8 per 9), 3 HRs, 3.33 FIP

Scherzer claims to have made some mechanical adjusments in AAA and the results bear this out, as he is throwing his fastball a solid two mph faster (91.8 mph before heading down, 93.9 since). His change is 1.5 mph faster and his slider is the same speed. One would think that given this jump in fastball speed Scherzer’s increased strikeouts would be the result of more swinging strikes against the fastball. Interestingly, this is not really the case.

Swinging Strike Rate
          Before   After
Fastball  0.053   0.057
Change    0.113   0.191
Slider    0.140   0.229

Breaking and off-speed picthes almost always have a much higher swinging-strike rate than fastball, so that should not be a big surprise. But I am surprsied that Scherzer’s recent jump in strikeouts is largely the result of an increase in swinging strikes on his breaking and off-speed pitches rather than his fastball. I am sure that the work he did on his mechanics improved his slider and change, but it is also possible that the increased swinging-strike rate on those pitches is due to the increase in speed of his fastball. The effect one pitch has on subsequent pitches in an at-bat is complicated, interesting, and not well understood, but Scherzer may offer an interesting case study in which the results from an improvement to one pitch type (increased fastball speed) is seen in other pitch types (change and slider swinging-strike rate).


Tommy Hunter’s High Curves

Over the weekend R.J. Anderson, Zach Sanders and others tweeted about Tommy Hunter‘s curveball. R.J. noted that Hunter’s curve was way up in the zone during Hunter’s start on Saturday. I was not watching the game, but I thought it was an interesting observation. I pulled up the pitchf/x data for the start and they were right:

His curveballs were very high: most in the upper half of or above the zone. Those curves got him a good number of called strikes, a handful of whiffs, and a couple balls in play (all outs).

Next I wanted to see whether his curves were always that high and how they compared curves as a whole. So I plotted a histogram of the height of all of his curves (his one start in 2010, a couple from 2008 and 19 from last year) and those for all curves in the pitchf/x data.

Hunter’s curves are, on average, a good six inches higher than the average pitcher’s. While curves as a whole peak in the bottom third of the zone and there are a good fraction below the zone, Hunter’s curves peak above the middle of the zone and a good number spill out above the zone.

Do all these high curves hurt Hunter? It doesn’t seem so. He throws the curve a lot, over 25% of the time, and by linear weights the pitch it is a very good pitch. Digging into why that is we can compare some of its components to Harry Pavlidis’ benchmarks for the curve:
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John Ely’s Changeup

John Ely, acquired from the White Sox in the Juan Pierre deal this offseason, has been a helpful addition to a Dodgers’ rotation dealing with an injury to Vicente Padilla. Although Ely’s ERA probably will not be under three at the end of the year — his BABIP is 0.274, he has yet to allow a HR in spite of his slightly below average GB%, and I don’t think he can maintain that 1.57 BB/9 rate (in the minors he never posted one below two) — his performance has been very encouraging, capped by last night’s no-run seven-inning start. (For a fantasy breakdown of Ely check out David Golebiewski’s recent piece.)

Ely’s fastball averages just 87 mph and the pitch is far from overwhelming, causing just 3% swinging strikes (whiffs per pitch). But he can get it in and around the zone enough to avoid walks and get ahead in the count. While the average pitcher goes with a slider or curve with two strikes to finish off an at-bat Ely goes to his change. Even against RHBs he throws the change 20% of the time in two-strike counts (against LHBs 45%).

And the results are very good, the pitch has a 28% swinging strike rate (whiffs per pitch). Part of the success of his changeup against lefties is its location; he keeps it perfectly located on the outer half of the plate, where the results are best. Here are the locations of his changeups to LHBs from the catcher’s perspective.

Pitchers whose best pitch is a changeup tend to have small-to-no-to-reverse platoon splits and his minor league numbers bear this out (4.08 FIP against LHBs and 3.97 against RHBs). In his 46 MLB innings so far (very small sample) he has posted a ridiculous reverse platoon split of 0.96 FIP against LHBs and 2.80 against RHBs.

As I said at the beginning, regression is coming for Ely, but, all the same, the Dodgers could do a lot worse for a starter after Clayton Kershaw, Chad Billingsley, and Hiroki Kuroda.