New Additions

First off, I’d like to thank everybody who applied to join the StatSpeak team over the past week or so. It’s been a hectic week for us, but I’m happy with how it’s turning out. This process isn’t completely done, however, so if I haven’t gotten back to you yet about writing, don’t take this as a “no.” We’re still reviewing applicants to see if there’s anybody else we’d like to add, and I’ll be getting back to each person individually, regardless of how long it takes me.

For now though, I’d like to introduce the newest writers for Statistically Speaking. Some of the names might be familiar, others might not, but they are all quality baseball analysts that bring something to the table. The names are as follows: Zach Sanders, Daniel Jerison, Pat Andriola, and Adam Guttridge. Zach has been providing quality work at various places around the web, including MLB Notebook and Baseball Daily Digest. Daniel Jerison is making his blogging debut, but I think will impress you readers out there. Pat Andriola has been writing at Mets Geek, but won’t be joining StatSpeak until early August because of prior commitments. And finally, Adam Guttridge, whose work you may know from THT, will be contributing on a part-time basis.

I’ll let each person introduce themselves in more than ~5 words like I just did, but I wanted to get the news out there so we can get this ball rolling. If you haven’t heard back from me yet, you will be very shortly, so just be patient for a little while longer.

It hurts me to say this…

I was feeling a little like Eric Seidman today and decided I would check out the FanGraphs leaderboards for something interesting. I’ll get this out of the way now–the word “interesting” depends on who is reading.

I looked at the hitters who had the greatest percentage of fastballs thrown to them, minimum 120 plate appearances. Scanning the list, it’s clear that a distinct type of hitter is found on it. While these guys aren’t terrible, let’s just say you wouldn’t be building a team around them any time soon (unless Dusty Baker is building the team, in which case the lineup would likely not be your only problem).

A little ways down the list, you can find one Ken Griffey Junior, the proud distributor of over 600 baseballs into the seats of various stadiums. He has hit home runs off of 399 different pitchers in 43 different parks and now pitchers are pitching to him like he’s Scott Podsednik. Yeesh.

El Comedulce Getting Sweeter With Age

Bobby Abreu used to be known as a guy with one of the best power/speed combinations in the game. From 1999-2002, he slugged .500 or better in every season and routinely stole 25+ bases. He hit 41 home runs in the home run derby, including a then-record 24 in one round. Since that home run derby, he’s transformed into a below-average power hitter, but maintained the .300 batting average ability he had always possessed.

This year, at the age of 35, he’s taken that changed approach to a whole new level. With just four home runs on the season, he’s slugging only .426 as of this writing. His strikeouts have also been declining, which is a strange thing to happen to a player past his prime, and his groundball percentage has been increasing the last four years. It’s possible that he has recognized his decreased power potential and adjusted his swing to be more conservative.

Here’s what stands out the most: Abreu has stolen 16 bases this year and been caught only twice. Sixteen! That’s more than burners like Curtis Granderson, Brian Roberts, Shane Victorino, and Carlos Beltran, not to mention his higher success rate than all of those except for Beltran (who is one of the most successful base stealers ever).

Abreu has seemingly found the foutain of youth with an increased spring in his step. Even his UZR has improved, though it still remains below average and is subject to lots of noise. With all the talk these days about late-career resurgences being fueled by PED’s, Abreu’s transformation is a welcome sight.

A lonely link dump

Note: We’re still taking emails from people about joining the StatSpeak team. It’s been much more work than I imagined, but we appreciate the interest from everybody who has written in. And I literally just thought of this 30 seconds ago: If you don’t want to write full time, but have some research you’d like the world to see, email me about submitting a guest post and we’ll see if we can work something out.

Usually link dumps are a whole bunch of links that are just thrown together with no apparent connection. I don’t have a whole bunch of links to share with you at the moment, but this one warranted its own post. I present to you Flip Flop Fly Ball–a site dedicated to creating awesome graphics about baseball like this one:

info-greenmonster.jpg

Click the image to enlarge. There are all kinds of cool things to look at on the site, including one about The Wu-Tang Clan and the E-Street Band (yes, you read that correctly). Go check it out.

(h/t: RAB–link includes interview as well)

A Call to Arms

If you’ve been reading StatSpeak for a while now like the rest of the world, you’ve probably noticed some turnover in the past year or so. A blog that was at one point written only by Pizza Cutter expanded to include some of the bigger names in sabermetrics, such as Eric Seidman, Colin Wyers, Brian Cartwright, and Matt Swartz (vote for Matt and Brian!). Sometimes relative unknowns at the time they started here, these guys have since moved on to sites like The Hardball Times, Baseball Prospectus, FanGraphs, and even done consulting for Major League teams and players. While StatSpeak is proud to have such a strong alumni list, which you can read in full in Pizza’s Valedictory, we must constantly be searching for new people to fill the void left by talented writers moving on to other things. As you can see, I’m still here ;)

With that, I am putting out a call to the readers of Statistically Speaking, asking for your help. We would all like to keep this blog going full time, and it will be a difficult task to do once Matt undoubtedly wins BP Idol. So if you are interested in writing for StatSpeak, send me an email at dcn29@cornell.edu. Don’t post your interest in the comments section, because I have no way of contacting you if you do so. This isn’t a formal application or anything like that, and there aren’t any qualifications you must have, except for a passion for baseball. Also, as you can probably tell, the schedule here is pretty flexible, so don’t let that be a concern.

All you have to do is tell me your name and that you’re interested, but feel free to point me to any of the work you’ve done in the past, or even write a sample post (neither of these things are required in your email). I’ll get back to each person as soon as I can with further instructions.

I’ve been working on a list of writers that I have found to be interesting, but I’m 100% sure that there are talented people out there that haven’t been exposed yet. If you want to write for a respected blog with a wide readership of intelligent baseball fans, this could be your chance.

Another straight, effective fastball

If you don’t know him already, you should try and learn a thing or two about Mark DiFelice. In a nutshell, he’s a reliever for the Brewers who, after a long career in various levels of the minor leagues, has been mowing down hitters with nothing but an 82-mph fastball. No knuckleball or gyroball or anything like that, just batting practice fastballs that make guys like Hanley Ramirez look foolish.

In a post at FanGraphs, Dave Cameron presents this pitch f/x graph from one of DiFelice’s games:

difelice.jpg
Gameday classifies those pitches as sliders and changeups because, well, major league pitchers just don’t throw nothing but fastballs at 82 miles per hour and get away with it. DiFelice isn’t just getting away with it, he’s been more than 2 linear weights (LW) runs above average per 100 pitches with it despite throwing it almost every single time. The average horizontal movement of that “thing” is between +1.4 and -2.9 inches, so it’s pretty straight. But part of what makes it effective is that, compared to the average major league fastball, it’s not straight at all. Major leaguers have fastballs that, on average, tail about 5 inches to the arm side. Don’t believe me that it’s a fastball, DiFelice says so himself in that Yahoo link above.

I’m not going to go further in analyzing DiFelice, because my pitch f/x abilities are severely limited in that regard, and Dave Cameron already did a good job of it. What I noticed today was that there is another mostly unknown pitcher who has a similarly puzzling fastball.

That pitcher is David Robertson, a reliever for the Yankees. Robertson was known in the Yankees system as a guy with a devastating curveball and an average fastball. Radar guns confirmed this in the major leagues, when people saw the 90-91 mph fastball and his big looping curve racking up the strikeouts. I checked out his player card today and was surprised to see that 80% of his pitches this season have been fastballs. Not only that, his fastballs are registering 1.47 LW runs above average per 100 pitches. That puts him in the same company as Jonathan Broxton. Take another look at that graph above for DiFelice, and then look at this graph for Robertson’s game on June 12th against the Mets:

8241_P_0_200906120_game.jpg
Ignoring the colors, look at the cluster of dots in the middle of the graph. Yes, I realize that DiFelice’s ball drops a lot more than Robertson’s, but the horizontal movement is almost exactly the same. Robertson’s fastball is more like a cutter than anything else, and that’s probably why it has been so effective at around 90 mph, despite throwing it 4 out of every 5 pitches.

Ask any Yankee fan how Robertson has been able to have a strikeout rate of over 13 per 9 innings this year and over 11 per 9 innings in his career, and he’ll probably tell you it’s because of that curveball. And it might be because of it–after all, that huge curve might be in the back of a hitter’s mind, causing him to miss the fastball. If you want to surprise him, tell him just how effective Robertson’s “just average” fastball has been, and you’ll end up looking real smart.

Warning: Low content post

I just read a post on FanGraphs entitled, “The Greatness Of Joe Mauer,” and the box on the right side of the page caught my attention. Here it is:

mauer dos.jpg

Heh.

Mark Teahen is a turkey sandwich

This past week I was on a long bus ride sitting next to a (Canadian) friend I had just met about two weeks ago. Because my idle thoughts usually revolve around baseball, our conversation eventually shifted to that subject. As you probably expected, the average Canadian isn’t so knowledgeable about baseball, Tom Tango not withstanding. I was explaining different ideas to him, and he was asking a lot of good questions.

So we’re talking about what pitches different guys throw, and he asks about pitch counts, so I mentioned my post from a few days ago. I started to tell him about the whole statistics side of baseball, and player value and such. That’s when the “R” word slipped out. He looked at me blankly and said, “Do you expect me to know what a ‘replacement player’ is?” This friend of mine had only a vague notion of what the minor leagues consisted of, and now here I was being asked to explain a concept that not even JC Bradbury understands.

Here’s where the title of this post finally comes into play. I was searching for a way explain player value and the components of WAR (Wins Above Replacement)–batting, defense,
positional adjustments, and the replacement level (essentially playing
time) adjustment–to someone who didn’t know the difference between a curveball and a slider. As I mentioned before, I’m usually thinking about baseball. But after that, food is a close second. And food is what this post will be about.

I used the following analogies to explain to my friend the different concepts associated with WAR. Feel free to use this as a guide if a sabermetrics newbie ever asks you to explain wins above replacement, and/or the value section of a FanGraphs player card.

Batting

The first thing you have to realize in batting is that an average hitter has value. If you look at the batting section of a FanGraphs player card, you’ll see numbers that are both positive and negative in this section, depending on the player. Mark Teahen of the Kansas City Royals has both positive and negative numbers on his card, but is usually around zero batting runs. However, in every year since 2006, he has provided his team positive value, despite just an average glove. How is it possible for zero to be positive? Think of it like a plain turkey sandwich (I know, 5 paragraphs in I finally get to the title). If you had a plain turkey sandwich for lunch and dinner every single day of the year, you wouldn’t be saying, “wow that was fantastic!” after every single meal. Chances are, you’d feel that each meal, taken in isolation, was pretty decent, but nothing too special and nothing too bad. A turkey sandwich scores about a 5 out of 10 in terms of deliciousness, assuming you don’t get sick of having it every day. It will keep you from being hungry and dying of starvation, but let’s be honest…it’s not #1 on your list of things to eat before you die. The fact that a turkey sandwich will satisfy and sustain you is why it has value despite being just average. 

Replacement Level

What is replacement level? In baseball terms, it’s the AAA minor league scrub who you can get for the league minimum. In food, it’s the simple bread and water. You can’t get much worse than bread and water and expect to survive for very long. Essentially, this meal is the minimum level of food you can expect to have in your diet. The 2003 Detroit Tigers were the bread and water of baseball, and even they seemed to skip a few meals. Despite being horrendously bad, the Tigers were still considered major leaguers, just as bread and water would still be considered a diet.

Remember the plain turkey sandwich from before? An average turkey sandwich has value because it is more valuable than bread and water or a AAA scrub. Bread and water is the minimum, and every turkey sandwich you have instead of bread and water increases your level of satisfaction and overall health. The replacement level adjustment found on FanGraphs player cards accounts for playing time. A replacement player’s offense is expected to be around 20 runs below average for every 600 plate appearances. So for every 600 plate appearances, we add around 20 runs to a player’s contribution to measure value versus replacement level instead of versus average. This is why a player who is average on offense, average on defense, and doesn’t play any position especially well has positive value. One such player can be said to be Mark Teahen. And this is why Mark Teahen is like a turkey sandwich. A turkey sandwich won’t be anything special on offense or defense, and it can serve various purposes–at the beach, a picnic, dinner, etc.–but the more times you eat a turkey sandwich instead of bread and water, the more positive value you will have in your life.

Positional Adjustment

This one took some thought, but I eventually came up with something that I think works pretty well. Again, we’ll stick with Mark Teahen and the plain turkey sandwich as examples. Here’s why we actually use positional adjustments in baseball: Ultimate Zone Rating (UZR), the fielding metric used at FanGraphs, measures fielding versus the average player at the same position. Zero is average, plus-15 is very very good, minus-15 is very very bad, and it’s all measured in runs above or below average. Mark Teahen in 2006 played primarily at third base, and was about average there (zero runs above average). In 2007, Teahen moved to right field, which is an easier position to play, and was 8 runs above average. All UZR numbers are calculated specific to the position a player plays. So Teahen was zero runs better than the average third baseman in 2006, but 8 runs better than the average right fielder in 2007. The average player is expected to add about 10 runs to his UZR rating when moving from third base to right field. This makes sense, since it should be obvious that right field is easier to play than third base. That statement is easier to understand when looking at more similar positions, so think about it this way: most second basemen became second basemen because they weren’t good enough to play shortstop. Those players got a boost in their UZR ratings by moving to an easier position, and we have to account for that when determining player value. An average fielder at shortstop is more valuable than an average fielder in right field, despite both having the same UZR. All of the positional adjustments can be found at this link. Now let’s get back to food.

I said before that a turkey sandwich would rate about a 5 out of 10 for most people. But how would that rating change depending on who you asked? If you ask a world-class Italian chef what he thought of it, he’d probably give you a lower rating than, say, a homeless person desperate for food. Depending on the situation a turkey sandwich is eaten in, its rating would change; depending on the position a player plays, his UZR will change. It’s the same turkey sandwich, it’s just playing a different position. The positional adjustment accounts for this. Just as it’s easier to get a 7 out of 10 rating from someone who’s used to eating scraps than it is from a world-class chef, it is easier to save 5 runs in right field than it is in center.

Final Thoughts

I didn’t give defense its own section, because it’s pretty much explained throughout the rest of the article. I hope this can serve as a guide to anybody trying to explain the basics of win values to someone who doesn’t have a clue what they’re all about. While you’re busy doing that, I’m gonna go get something to eat. 

Valedictory

I don’t like long good byes, so I’ll keep this one short.  This is my official “retirement post” from StatSpeak.  StatSpeak has been a wonderful experience and I will miss having this lovely platform from which to yell my heretical notions about baseball.  It’s been a fun 2+ years, but as Kevin Federline taught us, life comes at you fast.  I’ve had a few life changes over the past month, including the Major League debut of my daughter, Narlie Cutter (mom and baby are both doing great!) and it’s time to step back from StatSpeak.

I promise that I’m not really going anywhere.  I’ll be around here and there doing Sabermetric stuff, sometimes in front of the camera and sometimes behind it.  Maybe I’ll pull a Michael Jordan (I can’t hit a curveball either) and re-appear here every now and again.  There are a few things up in the air right now, and I’m not sure where they will land.  And for right now, I kinda like things that way.

I owe a great deal of thanks to the other folks who have shared this space and collaborated with me behind the scenes: David Gassko, Sean Smith, Matt Souders, Michael Frain, Mike Fast, Eric Seidman, Brian Cartwright, Colin Wyers, Dan Novick, Jon Walsh, and Matt Swartz (not a bad list of alumni, with the exception of Seidman).  Plus there are all the folks who did roundtable last year before that had to be sacrificed to the time gods.  I also owe a big thank you to John Beamer, formerly of Chop-n-Change here on MVN, who recommended me for the gig here two years ago, and to MVN’s then baseball director, now content director Cory Humes and president Evan Brunell for giving me the platform to begin with.  I even forgive Evan for being a Red Sox fan.

If you’re reading this, thank you.  When I started doing my own Sabermetric work a few years ago, I didn’t think anyone would ever read it.  The internet is a funny place like that.  What I thought would just be a little hobby has turned into a chance to interact with some really cool people who get just as geeked up about regressions and home runs as I do.  It means a lot to me personally that people actually liked my stuff.  So, from the absolute bottom of my heart, thank you.

;-)

Strikeouts and Pitch Counts

Back in the days when men were men, nobody worried about pitch counts. If you tried to take Ed Walsh out of a game after 100 pitches, he’d probably tell you he had at least another 200 pitches in him. Despite the current efforts of Nolan Ryan to go back ye olden days of not counting pitches, the current way of the world is that every other team does it, so we might as well pay attention to it.

These days, a pitcher is usually taken out after around 100-110 pitches, give or take a few. Often times, this means taking out a guy throwing a shutout in the 6th inning because he had reached the 100 pitch limit. There are two ways around this: letting the pitcher throw more pitches (which could potentially increase the risk of injury), or becoming more efficient (i.e., throwing fewer pitches per at bat). I’m here to talk about the latter.
 
The conventional wisdom goes something like this: It takes at least three pitches to strike someone out, but only one is required to get a groundball out. Therefore, a pitcher could decrease his pitch count by not attempting to strike out as many hitters as he can. That seems good enough for most people, but the astute readers of StatSpeak know that this can’t end there. A strikeout results in an out 100% of the time (ignoring the rare dropped third strike), but a ball in play results in an out only 71% of the time. That “other 29%” results in more batters coming to the plate, which results in more pitches having to be thrown to those additional batters. On one hand, we have more strikeouts leading to more pitchers per at bat, but also leading to fewer batters coming to the plate. On the other, we have more balls in play (fewer strikeouts) leading to fewer pitches per at bat, but also leading to more batters coming to the plate. Which of the effects is stronger?

Thanks to the work of Tom Tango, it has been shown that the average strikeout requires 4.8 pitches, the average walk takes 5.5 pitches, and if the plate appearance results in batter contact then it takes an average of 3.3 pitches (the data he used are all publicly available, by the way). Before you say “but so and so is different,” these numbers have been tested against extreme pitchers here. So these averages apply very well to all pitchers, whether they follow the norm, or if they are unusual cases like Randy Johnson and Brad Radke. We can use these estimates to see how an increased strikeout rate affects a pitch count.

How about a real-life test of the estimator? Joba Chamberlain has received some criticism from mainstream media-types about needing to be more efficient with his pitches, so he’s as good an example as any (and since he’s a Yankee, I know this will get on Pizza Cutter’s nerves). Joba has faced 256 batters this year, striking out 55 and walking 28. Plug those numbers into the formula (remember to subtract K’s and BB’s from the total batters faced when using the formula), and you get just under 989 pitches thrown. How many has he actually thrown this year? 984. I hope that difference of only 5 pitches helps to ease your concerns about accuracy. 

Now back to the question at hand. Prorated to 9 innings, this is a fairly typical pitching line: 9 innings, 6 strikeouts, 4 walks, and one home run. If 30% of balls in pay fall in for hits, that also means that there are 10 hits allowed in those 9 innings. In that “typical” game, a pitcher is expected to throw 153.1 pitches in 9 innings. What about games that aren’t normal, like one where the pitcher racks up a ton of strikeouts?

Here’s an extreme example: Take the exact pitching line from above, but change strikeouts from 6 to 27. So the new pitching line is 9 IP, 27 strikeouts, 4 BB, 1 HR. Using the formula above, the pitcher would be expected to throw 154.9 pitches. The effect is actually smaller than that, and here’s why: If a pitcher strikes out 27 batters, would you really expect the ONLY guy to make contact to hit the ball over the fence? When a pitcher is that dominating, what are the chances that he’d give up a home run at a rate of one per 9 innings? I’d say very slim. Fewer balls in play means fewer fly balls, which in turn means fewer home runs, and fewer pitches. So the real pitch count would be lower than 154.9, but for simplicity’s sake I’m going to call it even.

Let’s look at the other extreme–a pitcher who doesn’t strike out a single batter all game. Such a pitcher would be expected to allow a little over 12 hits per game. His expected pitch count for a game that included 4 walks, no strikeouts, and 12 hits including one home run would be 151.2 pitches. The caveat above about home runs also applies here, but in the opposite direction–a pitcher who has ever batter put the ball in play on him would likely allow more than one home run per 9 innings, so he’d likely throw slightly more than 151.2 pitches.

So what did we learn from this exercise? Even in the most extreme cases, striking out lots of batters will not increase your pitch count by any noticeable effect. Even when comparing two pitchers with polar opposite strikeout tendencies, the difference comes out to fewer than four pitches per 9 innings, with the real-life effect likely being even smaller than that due to the home run issue mentioned above. Next time you hear someone saying that a pitcher needs to “pitch to contact” in order to decrease his pitch count, you’ll know that it makes no difference. 

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