Archive for Player Analysis

A few weeks ago, Sportvision hosted the 3rd Annual Pitchf/x Summit.  Sportvision is the company behind the Pitchf/x system and has initiated Fieldf/x, which I’ll get into in a minute.  The goal of the summit was to share some of the research being done in baseball analysis, while also serving to explain the possibilities that exist with the new system.  Without further ado, here were the presentations:

Using Velocity Components to Evaluate Pitch Effectiveness (Matt Lentzner/Mike Fast): The purpose of this study was to change the reference point by which Pitchf/x data are measured.  Often, fastballs show more movement than breaking balls, but without the proper frame of reference, it means nothing.  Mike and Matt were able to demonstrate how to determine the horizontal and vertical velocities with respect to the batter’s eye and make the Pitchf/x data more meaningful.

Pitchf/x Application in Player Development and Evaluation (Dr. Glenn “Butch” Schoenhals): Dr. Schoenhals has a Pitchf/x system set up at his instructional school, which allows pupils (including some major leaguers) to see the their pitches broken down immediately and make adjustments.  In conjunction with three cameras set up around the pitcher, the Pitchf/x data provide benefit to both pitchers and instructors in learning/teaching how to pitch.

Okajima’s Mystery Pitch (Matt Lentzner): Hideki Okajima throws a pitch roughly 20% of the time that had previously been classified as a curveball, more specifically a “rainbow curveball.”  Actually, it didn’t really fit any of the known pitch types.  Using his research on pitch types and arm slots (“The Pitching Peanut”), we see that this pitch has almost no break, is faster than a curveball but slower than a slider, and falls at the exact center of the peanut.  His explanation: Okajima is the Boston pitcher who is actually throwing the gyroball, not his more famous teammate Daisuke Matsuzaka.

Leaving the No-Spin Zone (Alan Nathan): Dr. Nathan showed his experiments that relate the spin of the baseball just before and just after it is hit. The result? The two are almost totally independent of each other! I couldn’t believe that, but Dr. Nathan made a lot of sense.  This was a high-grade physics lesson, crashed into about 20 minutes.  He explained why balls tend to curve toward the foul lines; he showed that the bat actually “grips” the ball for a few nanoseconds or so before the ball explodes off the bat, which contrasts the earlier model of the ball “rolling” off the bat.  Really, really cool.

Fieldf/x System Overview (Vidya Elangovan): And the main event began.  Fieldf/x is a new tracking system that utilizes cameras attached to the light standards in baseball stadiums (for now, just AT&T Park) to track the movement of every person on the field 15 TIMES A SECOND.  As soon as I heard that, my mind started going crazy and I don’t think I paid attention for about 5 minutes.  The only issue at the time is that the system does not include the ball (but it will).  All ball events currently have to be added by someone watching the video.  The following presentations showed some of the things you can actually do with the data, and it’s fairly obvious that these data, particularly when connected to batted ball data through the Hitf/x database, are about to revolutionize how baseball players are evaluated.

Infield Defense with Fieldf/x (John Walsh): Actually the first presentation, thanks to being in Italy, (tough life), but it really would have been more helpful after the overview.  Either way, a lot of cool stuff.  First thing he said was that in tracking the different players, he noticed that an average centerfielder runs 8 miles per game, which stunned me and kept my attention.  Thanks to these new data, we can also see the effects of shifts and also what players away from the ball are doing while teammates are attempting to make plays.  Other questions John poses: can we see infielders cheating in a certain direction as the pitchers throws the ball? Do infielders lean in a certain direction before the pitch? Based on his initial investigations, he saw that third basemen step toward the line as the pitch is delivered and shortstops step directly at home plate.  Weird, but potentially important, and just a peak into what can be obtained.

From Raw Data to Analytical Database (Peter Jensen): As a baseball nerd and a programming dork, this was really cool.  Peter Jensen took the 400,000 lines of code that results from each game and wrote a macro to display what actually happened in the game in an Excel worksheet.  The simulation relates the position of each player as well as an approximation of where the ball is throughout the play.  His solution with regards to the reorganization of the data was very impressive for a first run, and it is absolutely vital to make the data useful for analysis.

Using Fieldf/x to Assess Fielders’ Routes to Fly Balls (Dave Allen): These next three were absolutely incredible to me (and I’m sure the last three would have joined them had I had the time to stay).  By using the data to reconstruct fielders’ routes to the ball, Allen surmises that the Fieldf/x data can be used to determine the speed of an outfielder as they pursue a ball, the starting points of each fielder at the time of the pitch (and hit), and how efficient each player is in getting to the ball (measuring the distance traveled against the shortest distance to the ball).  To me, this is something that teams can use to help players they already have by addressing alignment issues or noticing what is happening during the different points of pursuit.  Are outfielders getting good reads/jumps on the ball?  Are they running in straight lines or weaving?  Simply put, the data can confirm for us (and also measure exactly and more efficiently) what our eyes (and scouts’ eyes) have seen.

Measuring Base Running with Fieldf/x (Mike Fast): Mike’s presentation examined the different portions of base running and what the data can be used for.  Mike was able to track each base runner’s path around the bases, even what they were doing on pitches that weren’t hit (during which we would typically say “nothing happened”).  Obviously, with all of these data, there’s a lot happening.  Also, by knowing the position of the player at each moment in time, we can track both his speed and acceleration as rounds the bases; very valuable information for measuring “baseball speed.”

Fieldf/x of Probabilities: Converting Time and Distance into Outs (Jeremy Greenhouse): The coolest of the presentations.  As soon as he said the words “probability model,” I was sold.  Jeremy first examined stolen base attempts (in the thirteen games of data released, he only found four) and tried to determine the different component times of the stolen base attempt.  Some things he brought up that were interesting: “Pop” times, or the time it takes a catcher to catch the ball and get it to second base, was between 2.0 and 2.2 seconds for all attempts, which suggests that a lot of stolen bases are taken off pitchers, not catchers.  The ability to get a good lead is now measurable, as well as the jump a runner gets on the pitcher.

Jeremy also developed a model to determine the probability that a player makes a play on a ball hit near him.  The model was based on where the player is, where the ball would come down, and how long it would take the ball to get there.  From there, the player’s probability can change based on his jump, route, speed, and what I called “catching ability,” or the ability to actually make a play on the ball when in the vicinity.  It was shocking to see some of the plays made where players started out with low (less than 10 percent) chances of catching the ball, but by getting a good jump and running (quickly) in a straight line toward the ball, their probability would increase each 1/15 of a second.  He then showed the video of these plays and we were able to see the spectacular catches made by really good outfielders.  This also applies to outfielders who start with a low probability to make the catch, but increase it as they, for example, chase a ball into the gap, close quickly on it, but don’t catch it.  The ability to increase the probability of a catch is very valuable and that knowledge would be immensely valuable to teams.  Lastly, he also showed how bad outfielders can turn outs into hits by reading the ball poorly, getting bad jumps, and being indecisive.  Super cool, and as soon as the presentations are made available online (which hopefully will be soon), I will link to some of them, but especially some of these graphs.

Unfortunately, I missed the following presentations, so I will just show the abstracts presented in the program.

Where Fielders Field: Spatial and Time Considerations (Matt Thomas): Continued application of close-range photogrammetry through high-resolution digital photography to baseball is revealing hitherto unseen patterns of fielding in the game. Matt examines these patterns and where data permit, factors time into this examination. After reviewing general trends he notes specific achievements and then speculates on whether any of this freshly quantified insight tells us what makes for good (and not so good) fielding.

Scoutf/x (Max Marchi): This presentation evaluates players’ tools with Pitchf/x, Hitf/x, and Fieldf/x.

True Defensive Range (TDR): Getting out of the Zone (Greg Rybarczyk): Greg intends to display detailed tracking of the 25 batted balls in the released data that were hit in the air to the outfield. Presented data will include the relative positions of the outfielders and the ball from the time the ball leaves the bat until the time it is retrieved by the fielder. Using the essential elements of this data (fielder starting position, ball hang time and landing point), he outlines the fundamentals of a new outfield defensive metric, called ‘True Defensive Range’ or TDR, which should provide more accurate player defensive ratings with a smaller required sample size than current metrics. Full realization of this metric will require establishment of baseline values using the full data set. TDR for infielders will employ a similar method, but it will not be covered during this presentation.

The Future of Sportvision’s Data Collection (Greg Moore): Greg will talk about several bits of baseball data that Sportvision might collect in the future, and he will discuss how the data can be used in conjunction with Pitchf/x, Hitf/x, and Fieldf/x. Greg will also conclude the 2010 Pitchf/x Summit with closing remarks.

Obviously, there was a lot of cool stuff presented.  As mentioned, only 13 games worth of data were released to the analysts and most of the presentations were about determining what could be done with the data.  But with enough work and research, it will not only change the way teams and analysts evaluate players, but also will give teams another tool with which to teach their players and improve the guys they already have on the roster.  We’ll also know exactly what skills are important in each aspect of the game (base running, fielding, etc.), and as we learn these things we’ll discover other things we want to know.  I’d love to know what you guys think of all this and I’ll try to answer any questions you have about what can and can’t be measured and how we’ll use it in the future.

UPDATE: After I wrote this mess, I discovered this, much cleaner, detailed, mess, written by Baseball Prospectus writer Ben Lindbergh.  I’ll link to it down here because I want you to read what I wrote instead of Ben’s running diary.  Sorry, Ben.

This article was originally published at Knuckleballs, written by Dan Hennessey.

Matt Kemp has been struggling this season, and even if you account for the low BABIP (.303) compared to his xBABIP (.335), he is still striking out at a higher rate this season (28.1%) compared to last (22.9%). What has also confounded me is thatm even though he has a higher strikeout rate, he is also setting a career high in walk rate as well (8.1%). Usually, drawing walks and getting struck out are thought of as tradeoffs, opposite ends of the “patience scale.” Last season, Kemp hit .362/.429/.616 against LHP and .278/.329/.453 against RHP, but this season, he is down to .303/.341/.443 against LHP and .240/.306/.445 against RHP. What happened?

There are two questions I’d like to investigate: 1) Is Kemp swinging at more strikes in 2010 compared to 2009 and how? and 2) Is Kemp making less contact in 2010 compared to 2009 and how?

To answer these two questions, I’d like to look at Kemp’s swinging strike percentages (swinging strikes per pitch) and contact percentages (contact made per pitch) against all fastballs (four-seamers, two-seamers, cutters, and splitters). Checking to see any differences between 2009 and 2010 should lend some insight into Kemp’s offensive struggles this season.

I ran several regressions to model surfaces of Kemp’s swinging strike percentages and contact percentages as well as his swing zones. First up, let’s take a look at Matt Kemp’s SwStr% against RHP fastballs:

The red contour lines tell us that Kemp chooses to swing 50% of the time when a ball is thrown within the contour line. This is what I call Kemp’s swing zone, so the red circles refer to this. Further examples and explanations of these swing zones can be seen here. What the swing zones tell us here is that Kemp is swinging less at RHP fastballs in 2010, but is whiffing at a much higher rate as well. He is also missing more RHP fastballs down the middle as compared to before.

Let’s check Kemp’s Contact% against RHP fastballs to see if his swinging strikes are affecting his ability to make contact:

The red contour lines are the same as in the previous two graphs. Clearly, Kemp is making a lot less contact off RHP fastballs, and this tells me that he is putting the ball into play less. The previous two show Kemp swinging and missing more, while these two show Kemp making less contact, particularly on high inside fastballs. Let’s take a look at how Kemp has been doing against LHP fastballs, first at his SwStr%:

Here are his swinging strike plots, Kemp has actually started to swing more on LHP fastballs down and out of the zone (the red contour lines dip in 2010), so his swinging strike rate there is up. But he is also missing a lot more LHP fastballs this year that come down the middle over the plate, ideal pitches for the right-hander to hit out of the park. This is particularly concerning when you consider that Kemp’s wFB/C (runs above average per 100 fastballs) was at 1.64 last season, while that number is down to 0.38 this season. A major part of that drop must have to do with Kemp whiffing on fastballs down the middle that he used to hit.

Finally, let’s look at Kemp’s Contact% against LHP fastballs:

Looking at his contact plots, we see similar colors in where he makes the most contact (making contact 80% of pitches in those areas). But we notice a huge shift in where the epicenter of that hotspot is. Last year, Kemp made contact off a lot of LHP fastballs down the middle of the plate, but this year, the epicenter of that contact hotspot has shifted a full foot up from the direct middle of the zone to the top of the zone. We can infer that Kemp is making less contact off the sweet spot of his bat, and making more high fastball contact that usually result in pop outs. This is problematic and adds further evidence that Kemp is simply missing fastballs down the middle as well as chasing high fastballs.

In general, what I present here is what Dodgers’ fans already know: Kemp is swinging and missing a lot. But I hope that I was able to demonstrate clearly how Kemp has been struggling against fastballs, showing where he is whiffing on them and where he is making less contact.

An article over at Memories Of Kevin Malone convinced me that perhaps Kemp’s whiffing behavior this season (along with swinging less and drawing more walks) could have been caused by Kemp’s change in swinging mechanics. Finally, if you visit my blog at Think Blue Crew, you can read a longer post about Kemp’s offensive struggles against breaking balls as well.

A variation of this article was originally posted at Think Blue Crew, a blog dedicated to data visualization of baseball, basketball, and football statistics. Check it out for more f/x visualizations like this.

Mark Reynolds is perhaps one of the more interesting power hitters heading into his prime this season. He has led the entire league in strikeouts since 2008, holding the all-time record for most strikeouts in a season with 223 K’s last season.

This year, he leads the league once again in strikeouts, as well as perennial leader in swinging strike percentage. He has whiffed on 17.3% of all pitches this season, second place being Ryan Howard at 14.4%. Interestingly, Reynolds does not actually swing at everything a la Jeff Francoeur (60.7% swing percentage) and is barely in the top 50 in percentage of pitches he swings at with 46.8%. This makes it even more amazing that Reynolds leads the league in strikeouts and swinging strike percentage regularly without even taking that many swings. That’s a lot of whiffing going on, and I do suppose that the rare times he does connect the bat to the ball, he hits it hard.

I wanted to know more about Mark Reynolds’ swinging strike percentages to see how he fares against certain pitch types by handedness. Of the five main pitch types, fastballs, sliders, cutters, curveballs, and changeups, Mark Reynolds has seen cutters less than 200 times since his debut, 139 cutters from right-handed pitchers and 41 cutters from left-handed pitchers. He has seen at least 200 pitches for the other pitch types for right-handed pitchers or left-handed pitchers. Ignoring cutters due to small sample size, I will take a look at Reynolds’ swinging strike percentages against four-seam fastballs, sliders, curveballs, and changeups.

Let’s take a look at Mark Reynolds’ swinging strike percentages against four-seam fastballs split by RHP and LHP (1435 pitches from RHP, 468 pitches from LHP):

Here, it looks like Reynolds falls victim to high fastballs from both right-handers and left-handers. For Reynolds, he whiffs on the outside fastball from LHP stick out as well as the low and inside fastball from RHP.

Here’s a look at Reynolds against sliders (1542 from RHP, 224 from LHP):

This is interesting. Reynolds strikes out far more against right-handed pitchers than against left-handed pitchers, but he tends to swing at (and miss) sliders coming from LHP more than he does from RHP. LHP sliders come low and inside while RHP sliders go low and outside, but even LHP sliders coming in from low and outside are swung at and missed by Reynolds.

Curveballs against Reynolds are a whole different story (567 from RHP, 228 from LHP):

Here, Reynolds clearly struggles at connecting on curveballs from right-handed pitchers, some in the strikezone and most low and outside the strikezone. Curveballs from LHP also get Reynolds to whiff sometimes on the inside part of the plate as well as the lower part.

Finally, here’s a look at Reynolds against changeups, which look like his greatest weakness when it comes to missing pitches (430 from RHP, 338 from LHP):

This is very telling. The splits against changeups are very different, as Reynolds whiffs on over 50% of changeups from right-handers that are located on the edge of the strikezone at the bottom. This is much different from LHP changeups, where any spot doesn’t look to cross over 30% whiff rate, except the lower righthand corner of the zone. What’s also crazy about this is that when you look at Reynolds against changeups in general, he misses at around 20% of nearly all changeups low outside and nearly all areas within the strikezone as well.

From these plots, there are characteristics of Reynolds’ swinging strikes that are similar to conventional thought and common knowledge, such as chasing high fastballs or low breaking balls. But the key to exploiting Reynolds’ weakness at missing the ball when swinging is definitely throwing timely changeups, especially from right-handed pitchers, while it seems that Reynolds is less prone to whiff against LHP curveballs the most.

This article was originally posted at Think Blue Crew, a blog dedicated to data visualization of baseball, basketball, and football statistics. Check it out for more f/x visualizations like this.

As far as value goes, being a +5.8 WAR player is generally considered quite an accomplishment. After all, only three players have accrued that much value this season (Josh Hamilton, Cliff Lee, and Roy Halladay) and the list of those who topped the mark last year is a roll call of stars.

Why, then, is it with a contemptuous sneer that I note that Cesar Izturis is a +5.8 WAR player? Because that’s his career mark. As shocking (or not, if you happen to be an Orioles fan) as this may be, four months of 2010 Josh Hamilton has been more valuable than an entire decade of Cesar Izturis.

And because direct numerical comparisons are always an exhilarating exercise, why don’t we look at the career WAR marks of some other shortstops? Luminaries who have surpassed the “Izturis Line” include Bobby Crosby (in 1,342 fewer plate appearances), Alexei Ramirez (in a whopping 2,637 fewer PA), the significantly-less-than-immortal Damian Jackson (in 1,676 fewer PA), and Pokey Reese (in 1,047 fewer PA). These are but a few members of the list of unspectacular shortstops who were more valuable in fewer plate appearances than Cesar.

In light of this staggering performance, I think the two main points to investigate are why he has been so awful (given his rep as a serviceable option) and what has compelled MLB teams to run him out there on a daily basis since just after George W. Bush took office the first time.

Considering that during every one of my visits to Camden Yards this year I’ve played a mental game of “Is Cesar’s ISO above or below 0.040?” (it currently sits at a tied-for-MLB-worst 0.038), it seems like his prodigious lack of power is a good place to start. Despite posting a very reasonable strikeout rate (9.7%), an OK walk rate (4.9%), and a mediocre but not crippling BABIP (.281) in his career, Izturis boasts a meager .276 lifetime wOBA, a direct result of an equally abysmal .068 career ISO. Never in a full season has he produced an ISO above .100, although he did give the Blue Jays a (comparatively) robust .119 ISO in 46 games in his rookie year in 2001. All of this ineptitude translates to an insane -169.9 batting runs over 4,185 PA spanning the past decade.

But, you argue, he’s not known for his bat. It isn’t fair, hypothetical you argues, to judge him without looking at the superior defense he provides. This segues nicely into my second question, which is why MLB teams continue to pay him to play every day.

As nearly as I can tell, Izturis’ rep among baseball media and traditional fans is that he’s a superb fielder and a decent overall player. After all, you can certainly do worse than a Gold Glove winner at short, right? Unfortunately for anyone who espouses this belief (I’m looking at you, Gary Thorne and Jim Palmer), it just isn’t true.

No matter how much you want to complain about UZR or DRS as defensive metrics, I’m willing to believe that over the course of 8,300+ career innings they paint a fairly accurate picture of Izturis’ true talent level defensively. UZR has him at +6.7 per 150 games at SS (+45.6 runs total among all positions) while DRS has him at 48 career runs saved, so the metrics agree. At that +6-8 runs per season level, Cesar has been above-average but certainly not elite with the glove.

Is this a case of MLB teams overvaluing defensive contributions at the expense of offense? After all, even the two-year, $5 million deal the Orioles gave him prior to the ’09 season has been more than the Venezuelan has been worth (+0.8 WAR during his time with the O’s). Perhaps teams let their scouting departments and Izturis’ hardware (’04 Gold Glove winner) cloud their judgment, or perhaps there just aren’t any other near-replacement-level shortstops out there. Although I’m skeptical about that last possibility.

And yet, for a guy who has hit 15 career home runs (or as many as Jose Bautista has hit since Independence Day), topped the +0.2 WAR mark just thrice in his career, and averaged a paltry +0.4 WAR since 2004, Izturis is certainly doing pretty well for himself. After all, he gets to start every day for a major league team.

Nathan Biemiller is a junior at Franklin and Marshall College who writes regularly for nothing but his college newspaper. If you would like to offer him a place to write consistently (gratis!) or if you just have questions or comments, you can e-mail him at nbiemill@fandm.edu.

I am tired of Mark Kotsay. I am tired of his automatic 4-3 ground outs. I am tired of his lazy fly balls to left center. I am tired of his .190 batting average with runners in scoring position. I am tired of his .688 OPS. But most of all, I am tired of people in the White Sox organization defending Mark Kotsay. From Ozzie Guillen to Hawk Harrelson and Chris Rongey, the excuses are coming from every corner of the organization. And as an objective White Sox fan, the constant excuses are getting tiring. Luck or no luck, Mark Kotsay is a bad baseball player, that much is for certain.

Kotsay does nothing well and he contributes nothing on the field to this White Sox team, as shown by Mark Kotsay’s -0.6 WAR, good for the fourth worst in all of Major League Baseball amongst players who have at least 280 plate appearances. This shouldn’t come as a surprise, as of this moment, Mark Kotsay is hitting .228. Yet you have Ozzie Guillen saying things like, “Personally, the numbers out there for Kotsay [are not what] he deserves.” Followed by…“You can ask his teammates, you can ask [hitting coach] Greg Walker. He should have better numbers than what he has.”

You can ask any average White Sox fan or anybody in the White Sox organization and they will say that Kotsay has been unlucky or he “deserves” better. However, just how much better? Fortunately for us, the great people in the sabermetric community have come up with something that tries to battle this thing called luck. I think everybody knows of BABIP by now, but there is something better, something more contextual: xBABIP (Expected Batting Average on Balls In Play) . The concept is simple, take the mean batting average of line drives, ground balls, and fly balls that are not home runs, then create a BABIP based on those averages.

So what if Kotsay wasn’t lucky or unlucky? What if this was a perfect world where the average always happens? For the record, Mark Kotsay is a good player for this little experiment since his career offensive numbers are about as average as you can get. Currently, Mark Kotsay’s xBABIP is .269. This is more or less based on his line drive rate of 15.9%. His actual BABIP is .239. So as you see, he has been pretty unfortunate as that’s a 30 point disparity. Now let’s take this a step further, let’s say that .269 xBABIP is his actual BABIP. Mark Kotsay has hit 222 balls into the field of play (this does not include home runs), if he gets hits 26.9% of the time on those 222 balls, he would have 60 hits. Add his 7 home runs to those 60 hits and you have 67 hits in 258 at bats, which comes out to a .259 Batting Average. What about his On Base Percentage? Taking those 67 hits while adding his 30 walks divided by his 286 plate appearances, we would get an OBP of .336. So far so good right? Looks like Mark Kotsay would be a decent ballplayer if it wasn’t for those “hang wiffums” right?

Hold on just a second here, we can also apply this to his Slugging Percentage. We can play the rate game, which is a dangerous game to play, but we’ll do it anyways. Of the 53  hits Mark Kotsay has put in play, 38 have been singles, 13 have been doubles, and 2 have been triples. So from this, we can see that 71% of Kotsay’s non-home run hits have been singles, 25% have been doubles, and 4% have been triples. As I said before, this is a dangerous game to play, almost a fallacy, but since Kotsay does have an appropriate sample size here, it might be safer than usual. So taking these new numbers to his 60 expected hits, his new hit figures are 43 singles, 14 doubles, and 2 triples. This would result in a Slugging Percentage of .414. By adding Kotsay’s expected OBP and SLG together we come up with a .750 Expected On Base Plus Slugging or OPS, which is just about average.

Alright, so how do these new expected rates affect Mark Kotsay’s value? If we calculated an expected wOBA from these newly calculated values, Mark Kotsay would have a .329 Expected wOBA,  just about average. We can then calculate this into a run value to produce a new expected WAR. In this case, Kotsay would have produced -0.99 batting runs (without ballpark adjustment) in comparison to the average replacement player, much better than his previous rate of -6.2. So in this case, Kotsay’s WAR goes from -0.6 to -.08. A half win difference can go a long way at times.

So what does this tell us? Well first off, it says that Kotsay is a very average hitter in a luck-isolated world and average hitters should not be DHing 1/3 of the games for a team that already has issues scoring runs.  It also tells us is that Mark Kotsay has no place on this current White Sox team. He is a replacement level player who is only capable of DHing and playing 1B and that’s even if he hit like the “deserved” to hit. With Mark Teahen coming back and Brent Lillibridge already on the team, this team could be incredibly versatile. Isn’t that what Ozzie Guillen wanted? Isn’t that why Ozzie said no to Jim Thome, who is clubbing the ball for the rival Twins and is also a great clubhouse guy? This love affair with Mark Kotsay has gone too far. He is in fact costing this team on the offensive side of the ball. I would have no problem if Kotsay stays on this team as a pinch hitter and starts maybe once a week; he’s apparently a good guy in the clubhouse (as is his wife, I imagine). But the fact that this replacement level player has played 3/4 of this team’s games is disturbing. With the way that this situation has been tended to, you’d think the White Sox’ new slogan would be something along the lines of “White Sox Baseball: Here to Make Friends, Not to Win”.

After getting shelled by the Texas Rangers on June 2^nd, 2010 (2.2 IP, 6 earned) Gavin Floyd’s ERA weighed in at a rotund 6.64, to the uninitiated it would appear that he was having a terrible season, but what is the truth?  During Floyd’s early season struggles it was pretty clear that luck was not on his side as his BABIP in April was 369 and in May slightly better but still high at 343, while his strand rates during those months were 55.8% and 66.8 un-respectively.  Floyd is throwing harder (FB velocity 2009-91.8, 2010-92.4), missing just as many bats (2009 contact%-77.8, 2010-77.0) and even getting hitters to chase his pitches more often (2009 o-swing%-27.6, 2010-28.3).

Someone in my fantasy league made a comment regarding Floyd’s terrible season and I commented “outside of a brutal BABIP and low strand rate, Gavin Floyd has essentially been the same pitcher” to which I was basically ridiculed.  But as we can see from this chart, Gavin Floyd was and is the same pitcher and therein lays the beauty of advanced pitching metrics like FIP or xFIP and the use of the peripherals that help gather these stats (BABIP, strand rate, HR/FB etc).

	K/9	BB/9	BABIP	LOB%	AVG	ERA	xFIP
Apr	7.8	4.1	369	55.8	301	6.49	4.09
May	7.0	2.2	343	66.8	296	5.63	4.12
Jun	8.0	2.3	281	72.5	217	2.58	3.30
Jul	6.4	2.3	290	80.0	234	1.01	3.35
2010	7.4	2.7	320	67.9	261	3.87	3.69
2009	7.6	2.7	292	69.7	246	4.06	3.69

 

When we look at K/9, BB/9 and xFIP from April all the way through this year and even from last season’s totals we see what xFIP is attempting to do for us, take out all of the noise and some of the factors pitchers cannot control (such as what happens after the ball is put in play) and give us a real idea of how said pitcher is performing, relatively speaking.

Look at how steady the xFIP column is in particular, even when Floyd had a month in which his ERA was 6.49, his xFIP remained calm and cool at 4.09.  But just as important look at his unbelievable Bob Gibson-like 1.01 ERA from July, again his xFIP tempers this and brings us all back to planet earth as it checks in at 3.35.

This is simply breaking down who Gavin Floyd is as a pitcher and this is also a simple way to explain and show the value and usage of xFIP.  I think it also helps show how useless ERA really is when evaluating a pitchers overall value and performance. 

For those curious, since that June 2^nd shellacking Floyd has gone on a hellacious run – 62.1 IPs, 45 hits, 14 BB – 52 K’s, good for a 1.74 ERA and 0.95 WHIP.  All things considered Floyd is having a career year, who would’ve guessed?

If Kevin Youkilis is the “Greek God of Walks,” does that make Cliff Lee his mortal enemy? It’s an interesting query to ponder, considering Cliff Lee’s complete refusal to walk batters this season. At this pace, Lee is set to shatter the single season record for K/BB ratio. Given that the record for highest K/BB isn’t as universally celebrated as the single season home run or RBI leaders, let’s take a closer look at Cliff Lee’s historic season.

The current record holder in K/BB rate is Bret Saberhagen, who posted a K/BB rate of 11.00 over 24 starts in 1994. By comparison, in 13 starts this season Cliff Lee’s current K/BB rate sits at 14.83. While the list of K/BB leaders is littered with players from the 1800s, recent players on the list include Curt Schilling, Pedro Martinez, Greg Maddux, Ben Sheets, and Carlos Silva. Since it’s difficult to compare players from the 1800s with players today, let’s take a look at how Lee stacks up against the recent control freaks.

Player               GS     K/BB     K/9     BB/9     FIP    WAR  WAR/GS

Cliff Lee            14    15.17    7.27     0.48    2.58    3.8    0.27

Bret Saberhagen      24    11.00    7.26     0.66    2.76    5.2    0.21

Curt Schilling       35    9.58    10.97     1.15    2.40    9.7    0.27

Pedro Martinez '99   29    8.46    13.20     1.56    1.39   12.1    0.42

Pedro Martinez '00   29    8.88    11.78     1.33    2.17   10.1    0.35

Ben Sheets           34    8.25    10.03     1.22    2.65   8.0     0.24

Carlos Silva         27    7.89    3.39      0.43    4.18   3.0     0.11

Greg Maddux          33    8.85    6.85      0.77    2.43   8.2     0.25

A quick look at the table reveals the true dominance of Cliff Lee this season. On a per start basis, Lee is set to post a better WAR than every pitcher on the list except Pedro Martinez. While WAR/GS is a crude way to predict Lee’s WAR going forward, it does tell us how incredible his performance has been in the first half for the Mariners/Rangers. It’s also worth noting that even though he struggled in his Rangers debut, Lee did not give up a walk, increasing his K/BB rate while decreasing his overall BB/9 on the season. Despite a K/9 rate in line with Saberhagen, Lee is on pace to best Saberhagen in every single category in the table. Outside of the big strikeout guys (Schilling and Martinez), Lee may actually outproduce every other player in the table.

Even though K/BB leader isn’t a highly distinguished title, it’s certainly a sign of a player’s dominance in a particular season. No pitcher in the history of baseball has shown the amount of control Lee has exhibited this season. Since Lee’s strikeout rates are only above-average, you might expect batters to make a lot of contact against Lee, leading to more hits and a higher WHIP. This hasn’t been a normal season for Lee, however. The lefty has posted a WHIP of 0.95 this season, the top mark in the league. Some of that can be attributed to luck, but his current BABIP of .291 is actually fairly close to his career average of .305. With the recent trade, however, it’s going to be tough for Lee to match or improve on his numbers going forward. Leaving Safeco (and the Mariners defense) and moving to Texas will affect Lee’s numbers slightly. Despite that move, Lee still has a chance to complete one of the finest seasons by a pitcher. Even if Kevin Youkilis is Cliff Lee’s mortal enemy, I think it’s safe to say that every hitter despises Lee, especially this season.

*This article was originally written for FoulPole2FoulPole.com.

With the Major League season at its halfway point, and the Jays quickly running away from the playoff hunt, it’s time to look at the top man in their minor league system and see if there’s room to work him into the everyday line-up in The Bigs. This would of course be Brett Wallace, acquired in the Roy Halladay trade this off-season in a three-way deal involving Philadelphia and Oakland. Brett started his pro career at third base, but was moved to first base to start the season in order to mitigate his defensive shortcomings. This also works out nicely for the Jays because the only one in Wallace’s way is the incumbent Lyle Overbay.

Overbay has been a Blue Jay since 2006, and hopefully Toronto GM Alex Anthopoulos makes this year his last. Praised for his defensive abilities (all too often by the Jays’ commentators) he has actually been a below average defender so far this year with a UZR of -1.5, which doesn’t make him a laughing stock, but also doesn’t make up for his below-average .320 wOBA, or (almost exactly average) .334 park-adjusted wOBA. But when you consider that he’s playing the most “hitter-friendly” position, average just doesn’t cut it. So if Overbay is an average to below-average player, is Wallace an upgrade?

Brett Wallace has produced a solid .300/.362/.503 triple-slash in AAA Las Vegas, but with the Pacific Coast League being hitter-friendly we need to take these stats with a grain of salt. Thanks to StatCorner, though, we have a park-adjusted wOBA for Wallace, and it is an above-average .361 wOBA. Expecting it to drop when he goes to the majors, Wallace still projects to be an average hitter and at least as good a hitter as Overbay has been so far this year. Throw in the hitter — and especially HR — friendly nature of the Rogers Center, and Wallace could be a fairly productive player in the Jays’ power-hitting line-up. While it will be hard for Wallace to keep his batting average at the .300 mark, having a 23.2% line-drive rate in the minors just reeks of above-average BABIP (if he keeps it up) which should help him to a sustainable .270-280 avg at the Major League level.

The only problem now is getting rid of Lyle Overbay. The Jays are looking to be big sellers at the deadline, with John Buck, Alex Gonzalez and Lyle Overbay himself being UFAs. All three of these guys should be moveable to teams with a weakness at thin positions (Catcher and Shortstop). Overbay has also been hitting better as the season progresses and could look like an attractive and cheap option for a team like Tampa Bay who is lacking at the DH spot, or a NL contender such as the Reds (as a world series DH) or the Giants. He could conceivably bring in a B- or C+ prospect, depending on how much of his salary the Jays are willing to pick up. With the Giants also lacking at shortstop, the Jays could package the two together and try to pry away a high-level prospect from them, although a Bumgarner is probably out of the question.

All in all, the Jays should actively be looking to shop Overbay ASAP and give Wallace a good long look this season to see if he can cut it at the major league level, and thus get a better idea of where the organization is at moving forward

What is the All-Star Game really about? Joe Posnanski ponders that question in his most recent article.While Posnanski doesn’t answer the question directly, he presents the different opinions of the All-Star Game. When I think about the All-Star Game, I tend to use a combination of two opinions Posnanski presents.

• It’s all about watching the best players in baseball.
• It’s all about watching the best players IN THE FIRST HALF (which is a different thing).

Following that line of reasoning, there is no doubt in my mind that Stephen Strasburg belongs on the NL All-Star Team this season.

Let’s examine the potential arguments against putting Stephen Strasburg on the All-Star team.

1. He hasn’t pitched enough to justify an All-Star selection.

Strasburg has pitched about as much as any reliever on either All-Star team this season. As a matter of fact, the only reliever on the team with more innings pitched than Strasburg is Pittsburgh’s Evan Meek. The skeptics are so quick to point out Strasburg’s lack of playing time, but very few actually seem to realize that most of the relievers selected have actually pitched fewer innings.

2. He’s pitched well, but it’s a small sample. How do we know he won’t regress?

This argument goes hand in hand with our first point (somewhat). Strasburg has accumulated a larger sample (albeit barely) than most of the other relievers selected. A look at his advanced stats reveals a pitcher that is as good as advertised.

Stat	Strasburg	Rank (Among All Pitchers/Among Starters)
K/9	13.01		4th/1st
K/BB	5.3		13th/4th
FIP	1.77		2nd/1st
xFIP	1.88		1st/1st

*Minimum of 30 innings pitched this season

Among starting pitchers, Strasburg ranks 1st in K/9, FIP, and xFIP. Those are truly terrifying numbers for any pitcher, especially a rookie. That level of dominance certainly suggests that Strasburg is already one of the best pitchers in baseball, and is worthy of pitching in the All-Star Game.

3. The league will adjust to Strasburg the second time around.

I suppose this part of the argument cannot be proved until Strasburg gains more experience in the major leagues. At the same time, this is Stephen Strasburg we are talking about! The most hyped pitching prospect in baseball since… well, maybe ever. As the stats in the table above show, it’s not as if Strasburg is using “smoke and mirrors” to confuse opponents. Anyone who has watched Strasburg pitch this season can tell you that he already has three plus pitches, and inferior hitters look useless against him. Much like Mark Prior, it appears only injuries can derail Strasburg’s dominance.

4. He’s young, he will have more opportunities to pitch in the All-Star Game.

Personally, I hope Strasburg goes on to pitch in a number of All-Star Games. The fact is, pitchers are so unpredictable these days, that we can’t be sure Strasburg will remain healthy throughout his career. Perhaps I am overreacting, but because pitching is so uncertain, we can never be 100% sure one guy will remain healthy. If I had to bet, I would guess Strasburg makes a number of All-Star Games throughout his career, but we just never know.

Chances are, Strasburg’s inclusion to the NL Roster would be one of the best things to happen to the MLB. Ratings would probably be higher if Strasburg was on the team. This was the same guy who broke NERD, Carson Cistulli’s method for picking the most exciting baseball games on any given day. Also, and no disrespect to Arthur Rhodes here, most baseball fans would likely rather see Strasburg come out to face Crawford-Hamilton-Morneau in a tie game than Arthur Rhodes. Strasburg’s rise to the majors has been one of the biggest stories of the 2010 season, and this was an opportunity for the “Legend of Strasburg” to grow larger.

Again, Strasburg has already proven that he is a fantastic young pitcher. He will very likely make a number of All-Star Games throughout his career, so I shouldn’t overreact to one snub. While I have dedicated this article to Strasburg, there are many players who were snubbed that were even more deserving than Strasburg this season. I still stand by my premise that Strasburg should have been included on the NL Roster, but (barring health) I’m so glad I will be able to watch his magnificence for many years to come.

*This article was originally written for FoulPole2FoulPole.com

The World Baseball Classic is certainly a noble idea. I mean, what’s not to like about it on paper? You take the best players from each baseball-playing nation and have them battle it out to see which country reigns over the rest of the globe. Can anyone trot out a more thunderous lineup than the USA? Who has the more dynamic pitchers: the Dominican Republic or Venezuela? Does Japan really produce the most fundamentally sound players? Fans all over the world have shown their support for this, as have many players.

All of this would be fine if baseball were like basketball, hockey or soccer; sports where you could wake up, trip over your dog, tumble down the stairs into a pair of cleats, skates or sneakers and play. Those sports employ bio-mechanics the body was designed to handle like running, jumping, kicking and swinging. Baseball, specifically pitching, is not like that. The human arm was not designed to handle the stress and torque put on it by pitching. If you don’t believe me, then I have a few thousand shoulder and elbow scars to show you, including my own.

The lucky few who are able to withstand such actions and be successful are kept on a yearly routine: start throwing in mid-February, build strength and stamina through March before turning up the intensity at the beginning of April. But just like it isn’t wise to turn the ignition on a new Mustang and instantly floor it, it doesn’t seem right to take a pitcher conditioned to ease into a season during Spring Training and tell him to pitch with October-like intensity in March. Unfortunately, this is the case with the WBC.

After looking through the statistics of those who appeared in both WBC tournaments, it is my belief that pitchers who participate in the WBC, especially starters, are far more likely to see a regression in their performance, get hurt or both than pitchers who do not play in the WBC. I reason that the most likely cause is the tournament’s timing disrupts the normal routine of pitchers and their arms are not yet ready to handle the stress and intensity then. With data collected from various sources, I will demonstrate the stark differences between WBC pitchers and their counterparts who did not participate in the tournament, using spreadsheet data and graphs included in this analysis.

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The pitchers who were included in this study had to satisfy a few conditions. First, pitchers in the WBC group had to have pitched primarily in Major League Baseball in 2005, 2006, 2008 and 2009[1]. Players who played in one year but not another (spent one year in the minors or injured; or retired after a WBC) were not included. For the baseline of starters and relievers, a pitcher who made 10 or more starts for the year was counted as a starter while a pitcher who made 25 or more appearances with nine or fewer starts was counted as a reliever. The “all pitchers” category includes every pitcher who made an appearance during the 2005, 2006, 2008 and/or 2009 seasons.

***

At the heart of it, the key to successful pitching is how good you are in preventing runs from scoring, with ERA and component ERA (ERC)[2] being the primary statistics used to measure this aspect. The MLB’s ERA usually falls between 4.25 and 4.45 in most years, with only small differences from season to season. The last four groups saw small-to-moderate increases in their ERA between 2005 and 2006, but WBC starting pitchers saw a dramatic jump, from 3.75 to 4.48 while the ERC inflated from 4.09 to 4.79. WBC relievers also saw a significant jump in their collective ERAs (3.15 to 3.51), but not only is that only roughly half of what starters experienced, WBC relievers saw their ERC drop from 3.86 to 3.41. Compared against the league-wide ERA/ERC jumps of 0.24 (4.29 to 4.53) and 0.25 (4.18 to 4.43), respectively, the WBC starters’ jumps look even more like one of Superman’s single bounds. A major factor for this spike may be the above-average rise in HR/9 ratios. The average MLB starter showed no increase in his HR/9 rates and all other groups had increases of 0.1, but the HR/9 rates of WBC starters rose by 0.2 (0.9 – 1.1).

Home runs aren’t so bad, just as long as there isn’t anyone on base, but WBC starters were putting more and more runners on in 2006. Starting pitchers saw the highest rise in WHIP out of the five groups. The major league-average increase in WHIP between 2005 and 2006 was 0.04 (1.37 to 1.41), but the average WBC-participating starter saw his WHIP rise double that amount (0.08) from 1.29 to 1.37. Part of that increase was fueled by an up-tick in their BB/9 rates, which climbed from 2.9 to 3.1 (0.2). The most startling changes, though, were with the starters’ rising H/9 rates and falling K/9 rates . While all other groups saw a 0.2 increase in their H/9 ratios, WBC-participating starters’ ratios shot up by 0.5, going from 8.7 in 2005 to 9.2 in 2006. This may be attributed to a pitcher’s prematurely tired arm or improper mechanics from being rushed along during what normally is Spring Training. Either way, the pitches became more hittable, which also showed a decrease in these pitchers’ ability to strike batters out.

Every group I collected data on showed an improvement in their K/9 ratios by 0.2…except for WBC-participating starters. Their K/9 ratios actually fell, going from 7.0 in 2005 to 6.7 in 2006—a drop of 0.3 whiffs per nine innings. A good K/9 ratio shows both how good a pitcher is at retiring a batter without the help of his fielders and how dominant his repertoire is. The higher, the better. When I see that one group’s ratio is regressing while all others are improving, that would make me a little curious as to what may be causing such a downturn, especially with a group as valuable as starting pitchers. If I were in a team’s front office, it would make me wonder if this little event that is supposedly good for baseball is actually harming my pitcher and my team’s playoff chances.

***

Now, this wouldn’t so much of a concern if the pitchers who saw this decline in performance were just hurlers on the wrong side of 30 and/or at the tail-end of their contract, but that’s not a case. Pitchers like Jake Peavy and Dontrelle Willis saw their performances take a dive after participating in the 2006 WBC, while promising up-and-comers like Francisco Liriano and Gustavo Chacin suffered major injuries that year. Two of the more alarming examples are Peavy and Willis, two National League hurlers from pitcher-friendly ballparks who use complicated or violent deliveries.

Peavy seemed out of sorts during the first half of the 2006 season, posting ERAs of 5.17 or worse in three of the first four months. It was during this time that Peavy was also prone to the long ball, serving up 14 of his 23 home runs in April, May and June. The “gopher-itis” lessened once July hit, but then Peavy had a little more trouble finding the strike zone. After issuing no more than eight free passes in each of the first three months, Peavy walked 12 or more batters in every month during latter half of the season. Peavy eventually straightened himself out in 2007, but the same cannot be said for Willis. After nearly winning the Cy Young in 2005, Willis never could establish any consistency in 2006. His WHIP climbed an astonishing 0.29 points from 1.13 (sixth in the NL) to 1.42 (outside the top 30). At the same time, his HR/9 rate doubled from 0.4 to 0.8 while his opponents’ OPS climbed from .644 to .745. Since then, Willis’ regression went from bad to worse and is now viewed as little more than a reclamation project for the Arizona Diamondbacks.

***

Whereas 2006 saw a decline in WBC pitchers’ performance, the 2009 tournament participants saw an even more disturbing trend: a steep drop in their time on the mound. There were only negligible decreases in innings pitched following the 2006 WBC—10.1 percent for starters, 2.6 percent for relievers—but those figures worsened dramatically following this past tournament. WBC starters pitched, on average, 21.1 percent fewer innings in 2009 than they did in 2008 while relievers saw their innings totals drop by 27.2 percent. Houston ace Roy Oswalt saw his streak of five consecutive 200-inning seasons come to an end due to chronic back problems. Cincinnati’s Edinson Volquez appeared in one WBC game, then made only nine starts during the regular season before undergoing “Tommy John” surgery[3].

A second trend I noticed involved those pitchers who were in the playoffs the previous season. Out of the 11 pitchers who appeared in both the ’08 playoffs and the ’09 WBC, eight of them missed time due to injury (or, in the case of Javier Lopez, demotion) or saw an overall regression in their performance. The pitchers from this group who spent time on the disabled list pitched anywhere from 13.5 percent to 80.3 percent fewer innings than they had in ’08. Some of the more notable examples include Red Sox right-hander Daisuke Matsuzaka, whose 59.1 innings in ’09 were the fewest he’s pitched in either Japan or America, and Angels set-up man Scot Shields, who had never been on the disabled list for his entire nine-year big league career.

***

There are more examples of pitchers seeing their fortunes change for the worse after either of the two WBCs, like Bartolo Colon’s shoulder falling apart after rushing through rehab and Esteban Loaiza’s collapse in Oakland in 2006 or how Volquez’s elbow went kaput in the middle of 2009. I won’t list every pitcher who suffered, but my point is clear: the WBC increases the chances for pitchers to suffer injuries, see an across-the-board decline in performance or both. As I stated earlier, I feel the biggest reason for these unfortunate trends is the timing of the tournament. Holding this tournament in the early spring can only damage the health and careers of the players who wish to represent their countries and, in turn, hurt the player’s team both on the field and their long-term organizational plan. I feel the best possible resolution would be to hold the tournament at two different times: have the preliminary rounds during the week of the All-Star Game—while giving MLB, the Japanese leagues and all other leagues a mid-season break—and the final two rounds shortly after the World Series. This way, not only would the careers and health of the pitchers be better preserved, but it would also be highly beneficial to MLB as a whole.

Under the current scheduling, the WBC and MLB has to battle against the NCAA men’s basketball championship tournament for ratings and coverage. Since all other major professional and collegiate leagues are inactive in July, it would allow MLB a better opportunity to drum up interest in the tournament and give less well-known baseball-playing nations a bigger platform to perform. The week off would also benefit the players who are not in the WBC, as they would have had time to recover from injuries and spend invaluable time with family and friends. Lastly, the buzz over a recently completed World Series could carry over to the final stages of the WBC, with story lines from the first phase being built up prior to the resumption of the tournament. Playoff-participating players could have the option of continuing in the tournament or allow other players, who spent most of October resting and re-energizing, to go in their places. Those fresh bodies would also improve the quality of play seen by the fans.

The bottom line is this: the World Baseball Classic is an excellent idea, but is poorly executed in its current form, with pitchers suffering the most damage. Pitchers are the most valuable and volatile commodity in baseball and MLB should do its very best in order to protect that commodity. Even though there have been only two tournaments to study, the numbers are very clear and the logical decision to change should be made.

Michael Echan is a freelance sports writer from New Jersey. Please contact him if you would like to see the compiled spreadsheet data and graphs. He may be reached at mcechan@hotmail.com

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[1] Francisco Liriano spent most of 2005 in the minors, but was included because he spent most of 2006 with Minnesota before a season-ending elbow injury in August. Luis Ayala was on Washington’s roster in 2006, but injured his elbow during the WBC.

[2] ERC is a statistic created by Bill James. It takes the number of hits, walks, home runs, hit batters and total batters faced by a pitcher to give an “alternate” ERA that better reflects his performance.

[3] Volquez did pitch a career-high 196 innings in 2008, his first full season in the big leagues, but has had his workload gradually increased during his career. His combined innings progression: 140 in ’05, 154 in ’06, 178.2 in ’07, 196 in ’08.