On the surface Mariano Rivera and Mark DiFelice should be two of the most similar pitchers in the game. They are both right-handed relief pitchers who throw a cutters almost exclusively, over 90% of the time. No other pitcher throws the cutter that often, and only a handful of pitchers throw any one pitch close to as often as Rivera and DiFelice throw their cutters. But there is a startling difference between the two: DiFelice has one of the largest platoon splits of any pitcher, while Rivera has a reverse platoon split. Two pitchers throwing the same type of pitch almost exclusively and they are at opposite ends of the platoon split spectrum. Obviously there must be something very different about their cutters.

The most obvious difference is velocity. Rivera throws one of the fastest cutters in the game working in the low 90s, while DiFelice one of the slowest working in the low 80s. I am not sure how velocity influences the platoon split.

The movement of the two cutters is also different.

Both cutters have positive horizontal movement (tailing away from RHBs), which is uncommon for cutters. There is some overlap, but, generally, Rivera’s cutter has more ‘rise’ and slightly more horizontal tail.

Here is a breakdown of their cutters by some different metrics against RHBs and LHBs.

+-----------------+--------+--------+
| Rivera Cutter   |    RHB |    LHB |
+-----------------+--------+--------+
| Run Value       | -0.018 | -0.032 |
| In Zone         |  0.513 |  0.508 |
| O-Swing Rate    |  0.360 |  0.373 |
| Whiff Rate      |  0.260 |  0.188 |
| Pop Out per BIP |  0.123 |  0.133 |
| BABIP           |  0.299 |  0.245 |
+-----------------+--------+--------+

Rivera’s cutter is great against lefties and righties, and here you can see its incredible reverse platoon split (the run value is the average change in run expectancy after the pitch, so negative is good for the pitcher). Interesting the reverse split does not come from strikeout or walk rates (Rivera strikes out more RHBs and walks about the same), but from balls in play. Somehow balls in play off his cutter from lefties have a higher pop out rate and much lower BABIP than from righties.

+-----------------+--------+--------+
| DiFelice Cutter |    RHB |    LHB |
+-----------------+--------+--------+
| Run Value       | -0.031 |  0.015 |
| In Zone         |  0.550 |  0.550 |
| O-Swing Rate    |  0.383 |  0.330 |
| Whiff Rate      |  0.373 |  0.216 |
| Pop Out per BIP |  0.188 |  0.077 |
| BABIP           |  0.200 |  0.333 |
+-----------------+--------+--------+

DiFelice’s cutter against righties is amazing: huge o-swing (percent of pitches out of the zone swung at), whiff (percent of swung at pitches that are missed) and pop-out rates. But unlike Rivera things fall apart against lefties, with each of these rates dropping dramatically.

Next I checked the location of their cutters in the strike zone versus right-handed and left-handed batters.

I think this is the key. Rivera works both edges of the strike zone against both lefties and righties. Since he can routinely place pitches on either edge his cutter can be successful against both. DiFelice, on the other hand, pounds his ‘sinking’ cutter down-and-away against righties. It seems that because of his cutter’s ‘sinking’ and tailing away movement (compared to a normal fastball) righties routinely swing at these pitches out of the zone. And even if they make contact the pitches are all down-and-away where most hitters can generate little power. Lefties, though, could generate tons of power from this down-and-in location and it looks like he cannot locate the pitch down-and-away against lefties. Instead most of his pitches to lefties are in the heart of the plate, not a good place for a low-80s cutter.

So it looks like the major difference is the ability to locate coupled with speed. Rivera can hit both edges routinely against both righties and lefties with his blazing cutter and somehow the pitch depresses lefties BABIP, which results in his reverse platoon splits. DiFelice can routinely locate the pitch down-and-away to righties, but has no place to go against lefties other than right down the middle.

A lot has been made of the large number of HRs at the new Yankee Stadium so far this year. AccuWeather speculated that the way wind traveled travelled through the new Stadium was responsible for the additional HRs. Greg Rybarczyk determined that the dimensions of the park are not, in fact, exactly the same as the old Yankee Stadium. AccuWeather since came to the same conclusions as Greg, that it was a change in outfield dimensions and not wind that was responsible for the change.

Greg determined that the biggest difference in outfield dimensions is in a portion of right field where the wall is between 2 and 9 feet closer to home plate and 2 feet shorter than in the old Yankee Stadium. This portion of the wall was already relatively close to home plate, the famous short porch in right.

The HR numbers are way up, and I wanted to see if they were up in this location that both Greg and AccuWeather identified as having the biggest difference in outfield dimensions between the two stadiums. So I looked at the HR per ball in air rate by angle for the old Yankee Stadium from 2005 to 2008 (I am using the GameDay data and that is the extent of it) and for Yankee Stadium so far this year. The thick lines is the estimate and the thin lines indicate the standard errors. An angle of -45° corresponds to the 3B line, 0° to right up the middle (second base) and 45° to the 1B line. Here is the rate for right handed batters.

In left field there is almost no difference in HR rate, but in right field there is a slight increase in 2009 compared to pre-2009. The confidence intervals overlap so the difference is not statistically significant. Along the right-field line there is actually a drop in HR rate, but since there have been so few balls hit there is no statistical confidence in this difference.

Here is the same image for left handed batters.

Here there is a real statistical difference. Between about 5° and 35° the HR rate has been statistically higher in 2009 than pre-2009. This data is for all hitters and is not corrected for level of hitter, as park factors are. So it could be that there have just been more power lefties hitting at Yankee Stadium this year compared to 2005-2008. But since the largest increase in HR rate is in the same area of largest outfield fence change I think it is that fence change that is responsible.

The new Yankee Stadium has an even shorter porch in right field, and, it seems, LHBs will be the primary beneficiary.

A couple weeks ago Other Dave noticed that Tim Wakefield has one of the best fastballs so far this year. He suggested that Wakefield’s fastball is so successful, despite working in the low-70s with average movement, because it is a good 7 or 8 mph faster than his knuckleball and keeps hitters off balance. I really liked this idea and wanted to see if Dave was correct.

So I went through and looked for at-bats in which Wakefield threw a fastball after throwing at least one knuckleball in that at-bat, and found the difference in speed between that fastball and the knuckleball that immediately preceded it. First let’s look at the run value of a fastball based on its speed, the black line is the average and the gray standard errors. The run value is the change in run expectancy after the pitch, so a negative number is good for Wakefield.

To begin with notice that his fastball is quite good, -0.02 runs per pitch is -2 runs over 100 pitches, which is great. Interestingly after Wakefield’s fastball gets up around 72 mph there is no increase in effectiveness with an increase in speed. This is pretty surprising, generally the faster a fastball the better the outcome. Now let’s look at the run value of a fastball based on how much faster it was than the preceding knuckleball.

Here you see a clear consistent, if noisy, trend. As the fastball gets faster compared to the previous knuckleball its success increases. These two graphs together tell us that it is not the absolute speed of Wakefield’s fastball that determines its success, but its speed relative to the previous knuckleball.

Just as Dave suggested the success of Wakefield’s fastball is indeed tied to how much faster it is than his knuckleball, and since his knucleball is so slow he can be effective with his low-70s fastball.

As Matthew told us about a couple of weeks ago a new age of baseball data is upon us. Sportsvision and MLBAM released the HITf/x data from April 2009, which gave us information on the speed and angle of the ball of the bat for all batted balls. One thing I was interested in is how the swings of high strikeout high home run hitters differ from those of non-home run low strikeout hitters. Since the data only covers one month we do not have enough data to analyze individual hitters in depth, so here I pooled two groups of hitter to get more data. I choose the most extreme strikeout/home run hitters and none home run/stikeout hitters to highlight the differences.

In the home run group I choose five hitters from last year with greater than 25% HR/FB and greater than 25% K/AB: Ryan Howard, Jack Cust, Adam Dunn, Jim Thome and Chris Davis. The non-home run group was five hitters with less than 5% HR/FB and under 10% K/AB: Placido Polanco, Yuniesky Betancourt, Jason Kendall,Ichiro Suzuki and Ryan Theriot. For each group I plotted the speed of the ball off the bat versus the vertical angle of the ball off the bat, the vertical angle ranges form -90, a ball hit straight into the ground, to 90, a ball popped straight up. With a 0 angle hit being parrallel to the ground.

The non-HR hitters hit balls with a below 0° vertical angle slightly harder than HR hitters, but for balls with above 0° vertical angle HR hitters hit the ball much harder, with the difference increasing as the vertical angle increases. I guess that is not terribly surprising, HR hitters hit balls in the air very hard and non-HR hitters don’t. Balls on the ground they hit about the same.

One interesting difference is the angle where the speed peaks. I think that you can roughly interpret this as the vertical angle of the swing of the bat as it hits the ball. The greatest speed of the ball off the bat happens when the ball is hit squarely and this should result in the ball coming off the bat at the same angle as the swing of the bat. If you believe this interpretation it looks like the angle of the non-HR hitter’s bat as they hit the ball is just above 0°, roughly parrellel to the ground. While for the HR-hitters the angle is around 10 or 15°, a slight upper-cut.

When data from more months are released we will be able to analyze individual hitters in the same manner.

So far this year Roy Halladay, Adam Wainwright and Javier Vazquez have each provided a win’s worth of value with their curveballs alone. They have saved over ten runs with their curveballs. On the other end of the spectrum is Brad Penny, whose curveball has cost the Red Sox about a win (9.4 runs).

There is a lot that goes into determining the success of a pitch (speed, location, sequencing, delivery), but for a curveball movement is of the utmost importance. The movement of a pitch is how much the spin of the pitch causes it to deviate from a spinless trajectory, and is commonly broken up into horizontal and vertical components. So the vertical movement is how much a pitch sinks or rises compared to expectation based on its velocity, its trajectory and gravity. The horizontal movement is how much a pitch tails horizontally compared to expectation. Positive horizontal movement indicates a pitch that tails away from a RHB and in to a LHB.

Here are the movements of Halladay’s, Wainwright’s and Penny’s curves
(including Vazquez’s would make the graph too cluttered, but his fit in between Halladay’s and Wainwright’s). The gray dots are the movement of all curves for reference.

Halladay’s curve has a wide range of movement, but generally lots of horizontal movement (tailing away from RHBs an average of about 6 inches) and not much vertical break. Wainwright’s curve has lots of vertical and horizontal movement, sinking and tailing away from RHBs by almost 10 inches in each direction. Penny’s curve has little movement what-so-ever (his curves are very close to 0,0).

The two best curves have more movement than the worst one. I wanted to know if generally the more a curveball broke the better. Is it true that ‘flat curveballs’ get crushed? So I found the run value of a curve based on its total movement (a negative run value is better for the pitcher indicating runs saved). The gray lines are the standard errors.

The relationship is quite strong. As the movement of a curve increases so does its success. Not unexpected, but nice to see. Still you cannot predict curve success entirely on movement, there are many pitchers with worse curves that move way more than Halladay’s. But as a general rule the more one moves the better you can expect it to be.

Kevin Youkilis? Evan Longoria? Mark Teixeira? All fair guesses and all up there, but the top spot belongs to Ben Zobrist. Ben Zobrist? What? Yeah Zobrist is riding a 15% walk rate and 0.351 ISO to the top of the list.

The walk rate is not totally unexpected. Last year it was over 11% and he has always had great walk numbers in the minors. His O-swing, Z-swing and contact numbers are all very good, but about where they were last year. So why the 4% jump in walk rate? Well he is seeing about 4% fewer pitches in the zone. Since he is so good at not swinging at those pitches his walk rate has jumped.

Why fewer pitches in the zone you ask? It probably has something to do with last year’s power surge, which has carried over to this year. The power is a surprise. He never hit over 10 HRs in any year in the minors, but then last year he hit 12 HRs in 227 PAs with a 17.4% HR/FB. This year he has hit 15 in 225 PAs with a 24.2% HR/FB.

The gameday fly ball distances back up the change, as his average fly ball went from 252 ft. and 253 ft. in 2006 and 2007 to 278 ft. and 290 ft. in 2008 and 2009. Zobrist is a switch hitter and although he has slightly more power as a lefty, he gets considerable power from both sides of the plate; his HR/FB rate this year is above 20% from both sides of the plate. His GB% and IFFB% have both decreased every year since 2006 adding to his power.

Zobrist isn’t the best hitter in the AL going forward, and he might not continue to hit just under a quarter of his fly balls for home runs. Still he is a very good hitter and one of a number great young players on the Rays.

David Ortiz had a horrible start to the season, going the first month and a half (154 PAs) without a home run. But recently he has turned it around, hitting five home runs since June 6th. Does this mean that Ortiz’s power is back, or has the recent HR outburst been a small sample size fluke build on a couple lucky shots?

One way we can answer this question is head over to Greg Rybarczyk’s Hit Tracker and see if his HRs were lucky or not. This shows Ortiz tied for the league lead with three lucky HRs, not encouraging. Greg’s information is great (providing the most accurate data available on every aspect of HRs we could ask for), but it only provide data about Ortiz’s HRs and there is another half to luck, maybe Ortiz has hit a number of long fly balls that just barely didn’t go for HRs.

I wanted to see a complete picture of Ortiz’s power including all of his fly balls, so I needed to look elsewhere for the distance on his non-HR fly balls. Back on June 5th when Ortiz was seemingly still in his slump John Dewan presented the average distance of Ortiz’s balls in the air using the Baseball Info Solution (BIS) data. It showed a serious drop in his average distance compared to 2007 and 2008. Unfortunately the BIS data are not publicly available so I could not use them to look at his fly balls during his recent power surge.

Batted ball location data are publicly available through MLB’s Gameday. They report the location x and y coordinate of every batted ball in pixel units, which are displayed on the field images in the MLB Gameday application and then stored in XML format. Peter Jensen came up with translation factors to convert Gameday provided pixels to feet for each ball park. Unfortunately Jensen found that the conversion factors change year to year and you need a whole year of data to come up to determine the factors. So I am going to have to use Jensen’s 2008 conversion factors to look at Ortiz’s fly ball distances and hope that we are not that far off. Luckily I can use Dewan’s BIS data to see how close they are.

 David Ortiz ball in air average distance (feet)
+------------------+---------------+---------------+
| Year             |     BIS Dist. | Gameday Dist. | 
+------------------+---------------+---------------+
| 2007             |           290 |           291 |
| 2008             |           273 |           279 |
| 2009 pre-June 5  |           254 |           256 |
| 2009 post-June 5 |            NA |           300 |
+------------------+---------------+---------------+

For 2007 and 2008 I used the appropriate conversion factors from Jensen and the BIS and Gameday average distances are surprisingly close. It is really an endorsement of the quality of data from both BIS and Gameday, and Jensen’s conversion factors. For 2009 I had to used the 2008 factors on the 2009 data, and luckily the BIS and Gameday numbers came out very close. So I am fairly confident going forward with the 2008 conversion factors on the 2009 data.

If you trust the 2008 conversion factors on the 2009 data, over the past two weeks Ortiz’s balls in the air have averaged 300 ft, higher than in 2007 and 2008. That looks good.

Here are the distances of all of his balls in the air by date with the home runs filled in. The straight line is his average 2008 distance and the wavy line a smoothed rolling average of his 2009 distance.

You can see he started out a good 20 ft below his 2008 average, but starting around the end of May his average distance has raised steadily. This graph also shows that in the past couple weeks in addition to his six homers he has had a number of other long fly balls. This is a small sample, but things look qualitatively different for Ortiz since the end of May, an encouraging sign for him and the Red Sox.

Yesterday David Appleman announced a new section at FanGraphs showing the Linear Weights Run Value for each pitcher and pitch type. He asked me to write a short explanation of how these values are calculated.

The run value of any event is the change in the expected number of runs scored over the rest of the inning from before and after the event happened. The expected number of runs scored is the average number scored from a given out and base-occupancy state. Let’s take Tuesday’s Oakland at Tampa Bay game as an example. At the top of the 1st inning with zero on and zero out the average team scores 0.55 runs. Orlando Cabrera hit a single off of Jamie Shields. Now with a runner on first and none out the average team scores 0.95 runs. So the run value of the single was 0.55-0.95=0.4.

You can do the same thing taking each pitch as as event rather than the outcome of each at-bat. To do this you need to know the run expectancy from each count, in other words, the average run value of all events from at-bats which pass through a given count. For example the run expectancy of a 3-0 count is 0.2, on average at-bats from that count are worth about half of a single.

Now we can run through Cabrera’s at-bat with Shields as an example of valuing each pitch in an at-bat.

0-0: Run Value 0.00

Pitch 1: Fastball for a ball

1-0: Run Value 0.03

So the value of that first pitch was 0.03 runs. On average the A’s will score 0.03 more runs than before Shields threw the pitch.

1-0: Run Value 0.03

Pitch 2: Fastball for a called strike

1-1: Run Value -0.02

The run value of that fastball was -0.02-0.03 =-0.05.

1-1: Run Value -0.02

Pitch 3: Fastball for a called strike

1-2: Run Value -0.08

The run value of that fastball was -0.06. You can see here that the run value of a strike (or any other event) is count-dependent.

1-2: Run Value -0.08

Pitch 4: Fastball for a ball

2-2: Run Value -0.04

The run value of that fastball was 0.04.

2-2: Run Value -0.04

Pitch 5: Change fouled off

2-2: Run Value -0.04

Since the count did not change the run expectancy did not, so the run value of this changeup was 0.

2-2: Run Value -0.04

Pitch 6: Fastball hit for a single

Runner on first no outs: Run Value 0.4

The run value of this fastball is the change in run expectancy, 0.4-(-0.04) = 0.44.

Shields threw five fastballs valued at 0.03, -0.05,-0.06,0.04 and 0.44. He threw one changeup that had a value of 0.00. These values are the change in run expectancy in the game, so a negative number is a good for the pitcher (fewer runs scored). On the player pages the numbers are flipped so a positive number indicates a good pitch, the number of runs saved by those pitches.