SB Nation User Blog: danmerqury

AFL PitchFX Pt. 3: Grant Desme

danmerqury — Wed, 23 Dec 2009 02:28:54 -0000

Well, it occurs to me now that PitchFX really isn’t all that useful for batters, especially on this small sample scale. Anyway, I’ll post what little information I was able to throw together.

PitchFX caught only seven games from Grant Desme, for a total of 34 plate appearances. Unfortunately, I have data for only three of his eleven HRs. I’ll get to those in a second, but first, here are all of the pitches that Grant Desme saw (that were in parks with PitchFX installations), separated by swinging or not swinging.

Nothing really jumps out, aside from a weakness to sliders outside the strike zone (everyone has that) and a few wacky verging-on-Zito-esque curveballs that fell from eye level to the thigh area (TW…I know, I know).

Here are the pitches that he swung on, this time separated by contact vs. no contact.

Again, not very many useful observations here. He’s not so good at hitting things that are outside the zone (normal), but fouls off most of the pitches that are inside. Nothing out of the ordinary.

As far as his home runs, I can’t say much with only three points of data, but for what it’s worth, they were three very different pitches. One was a hanging slider right in the middle of the zone, one was a changeup off the plate inside, and one was a high two-seamer with a wicked amount of tailing action. Three home runs mean almost nothing, but it’s a little encouraging that they didn’t all come on easy fastballs.

Odds and Ends

For those that really care, here's the pitch locations of the baseballs sitting outside of the fence.
The little batter shadow guy isn't necessarily to scale (probably isn't, actually), but the strike zone box is, based on the "strike zone top" and "strike zone bottom" numbers that the Gameday operator keys in for every at bat.
Once HitFX rolls out, I'll hopefully be able to generate something a little more substantial for batters, like spray charts and the like.
That about wraps it up for the notable A's representatives in Arizona this year. Now I'm just hoping they'll install the cameras in all of the parks next season, or at the very least, in Phoenix Municipal.

AFL PitchFX Pt. 2: Sam Demel and Mickey Storey

danmerqury — Wed, 02 Dec 2009 05:29:00 -0000

Hope everyone had a great turkey weekend (even if turkey wasn’t your dead animal of choice). Today I’m going to cover two more of the pitchers we sent to the Arizona Fall League.

Sam Demel (RHP) - 2007 3rd Round, Texas Christian University

PitchFX caught only 2.2 innings of work from Demel, on three separate occasions. He has a two-seam fastball that averages around 92-93 mph with good lateral movement, tailing inside to right-handed hitters by nine inches. He also throws a mediocre mid-80’s changeup that doesn’t tail as much, unfortunately. It looks like Demel spent the AFL experimenting with his slider. On 10/17, he featured an 85-mph slider with plenty of lateral movement. On 10/29, he slowed it down to 82-mph, and sacrificed some of the lateral movement for more sink, making it more of a slurvey hybrid pitch. I have PitchFX data for his appearance on 11/19, but he didn’t throw a single slider. Definitely would have been nice to see if he ended up settling on any particular movement. Here’s his movement chart. It’s kind of a mess, but I’ve read scouting reports that say that he’s comfortable adding or subtracting from his pitches. If that’s the case, I’ll give him the benefit of the doubt and say that he knows what he’s doing with this spread out pitch distribution. This is a league to try new pitch movements and styles, but if he's not generating this kind of graph on purpose in the regular season, it's worrying.

Mickey Storey (RHP) - 2008 31st Round, Florida Atlantic University

And now we come to the deception artist that is Mickey Storey. I’m just going to post his movement chart first, allowing you guys to, er, soak in the mediocrity.

He’s definitely not a guy who will wow you with his raw stuff, but when you have his control, you don’t need to. His fastball is an 88-89 mph affair that ranges from an acceptable amount of tailing action to being as straight as an arrow. I do like his changeup quite a bit because it comes in looking like his fastball, except that it’s 15-mph slower and drops 10 inches lower. He didn’t throw the changeup much, though, preferring to dismantle hitters with his 72-mph curveball. Storey’s curve doesn’t have very much drop, but it has a decent amount of lateral motion, coming into left-handed hitters. His stuff really isn’t all that great, but I can’t argue with his obscene 12.4 K/9 and ridiculous 8.88 K/BB in 2009, with time spent everywhere from low-A to AAA. PitchFX doesn't do a good job of showing how good a pitcher’s control is, and in some way or another, smoke and mirrors or not, Mickey Storey’s getting it done.

Odds and Ends

Here's two more graphs for Demel, showing velocity against lateral and vertical movement, respectively. And the same for Storey (lateral, vertical).
It looks like these two guys weren't fiddling with their mechanics. Everything looks nice and tight as far as release points go.
Well, it turns out that spin graph I created earlier was entirely useless. Somewhere along the line, I screwed up a calculation. Now, with the right math, the polar spin angle vs. spin rate graph is entirely identical to the movement graph, giving me precisely no new information for a whole lot more work.
Note to self: Excel works in radians, not degrees.
In the the last part to this AFL PitchFX series, I'll touch on the last of the four pitchers we sent to Arizona, Justin Friend. I'll also figure out something to analyze about everybody's favorite incendiary topic: Grant Desme.

AFL PitchFX Pt. 1: James Simmons

danmerqury — Tue, 24 Nov 2009 03:50:15 -0000

As you all know, Major League Baseball runs a fall league in Arizona for upper-level minor-league players, appropriately named the Arizona Fall League. The big news this year is that, for the first time, MLB installed PitchFX cameras. This is huge—it’s the first PitchFX look we’ve ever gotten from minor-leaguers. I’m going to crunch the numbers and take a look at the results, hopefully churning out some useful scouting reports in the process. I’ll start with our 2007 first-round draft pick out of UC Riverside, James Simmons (RHP).

Okay, one caveat. MLB didn’t install PitchFX cameras in all of the Arizona stadiums. They unfortunately excluded Phoenix, the park that our A’s representatives call home. They did, however, install them in Surprise and Peoria, home of three of the six AFL teams, allowing the cameras to catch 10 of the 33 games our Phoenix Desert Dogs played. In Simmons’s case, I have data for only two of his appearances, for a total of 7.2 innings. It’s not much, but enough to build a scouting report on.

The problem with the PitchFX data I have is that the cameras caught two very different James Simmonses. The James Simmons that showed up on October 17th throws a 90-mph four-seam fastball, an 86-mph changeup with very little tailing action, and a 79-mph curveball that comes into left-handed batters more than it drops. The James Simmons of October 29th throws an 86-mph fastball that acts like a two-seamer which darts inside to right-handed batters, a 79-mph changeup that tails quite a bit (it matches his two-seamer), and a 67-mph curveball, identical to Simmons the First’s curveball in movement, but slower. Here’s a combined movement chart. These are all from the catcher’s point of view, by the way.

And here’s the same chart, broken down by game to emphasize the difference in movement.

Simmons achieved this big change in movement by lowering his release point by an entire 1.8 feet.

It was reported that James Simmons was testing out a cutter, and the graphs show that. Unfortunately, it seems to have extremely little lateral motion, and he only threw it six times in the games that PitchFX caught.

Of course, there’s always the possibility that the change was caused by imprecise cameras, as the two games were recorded at different parks (10/17 at Surprise, 10/29 at Peoria). Thankfully, another pitcher, A's prospect Sam Demel, pitched alongside Simmons on both occasions. If the difference in movement was an error in detection, Demel’s pitches and release point should have changed as well. They didn’t.

The AFL is a league for trying out new pitches and techniques, and it looks like Simmons used Arizona for that precise purpose. The sample size is obviously far too small to judge whether or not the new release point was effective or not, but it looks like Simmons will have 2010 to decide on a release point, and whether trading his already low velocity for extra movement is worth it.

Odds and Ends:

I generated a couple other charts if you guys want them. This one is velocity and lateral movement, and this one is velocity and vertical movement.
I also wanted to try something a little different, so I plotted spin angle vs. spin rate on polar coordinates. After wrestling with Excel for a long time, it turns out that polar graphs are impossible. So I converted the polar coordinates to rectangular coordinates—you're going to have to imagine the rest. The distance from the origin indicates spin rate, and the spin angle is denoted by the placement angle on the graph. Each hash mark on the axes is 1000 revolutions per minute. Oh, and unfortunately, the axes and their scales aren't equal. Thanks, Excel.
The huge change in release point confused PitchFX as well. It labeled all of the changeups that Simmons threw on the 29th as sliders. I'm not sure where the system got that result from, as that would make his supposed slider come into right-handed batters. An odd screwball, basically. I just relabeled them as changeups.
Any ideas for future PitchFX analyses are definitely welcomed. I had a lot to say about James Simmons, but I expect to have less material for the other players we sent to Arizona. Any other ideas of what to do with the data would be great.
Update: Hey, front page. Cool! Thanks, whoever you are.

Making Sense of the Fangraphs Game Graph: A Basic Look at Win Probability

danmerqury — Wed, 28 Oct 2009 06:03:06 -0000

This is a Fanpost aimed at those of us who may not be as stat-literate as others, but want to know what the Fangraphs game graph is all about. I’ll start from the very beginning, hopefully passing along useful knowledge about run expectancy and win probability, among other things. And if you already have a good handle on the principles, hey, it’s good review. Or something.

There’s a fundamental concept in this area of baseball research called base/out states. It’s simple, really. If we’re just talking about one inning, what’s the least amount of information you absolutely have to know to understand the situation? All you really need is the number of men on base and how many outs there are. It turns out that there are eight different ways to have men on base: no one on, a guy on first, a guy on second, a guy on third, guys on first and second, first and third, second and third, and bases loaded. Furthermore, all of these can occur with zero, one, or two outs, which leads to 24 total base/out states.

Most of the data that I'll talk about in this post comes from The Book: Playing the Percentages in Baseball (Tom Tango, Mitchel Lichtman, Andrew Dolphin). In The Book, the authors created a run expectancy table. They threw all of the games played from 1999-2002 into a massive database (over 85,000 innings!) and crunched the numbers. What they primarily looked at was each base/out state, and how many runs the average team scored starting from that state until the end of the inning. For example, they were able to show that, on average, a double that starts the inning makes a team score 0.634 more runs than they would have scored before the hit. And a strikeout with the bases loaded and no outs hurts the team to the tune of -0.767 runs. Over a full season, we can add up a player’s contributions (positive and negative), and come out with a number which expresses the total amount of offense that our example player produced. Fangraphs tracks this stat under the name RE24.

To take run expectancy a step further, Tom Tango decided to expand the context. Sure run expectancy is fine for an inning, but what if we wanted to look at a full ballgame? By crunching the numbers again, Tango created a set of tables that express the win probability for both teams at any point in the game. In other words, if team A was up by four runs in the bottom of the sixth, and team B had a man on first with two outs, there’s a section of the table that gives the probability of either team pulling out a win. As you can imagine, due to including the 24 base/out states in addition to the inning number and score difference between the two teams, it’s a really large set of tables.

It’s this win probability that appears on the game graphs generated by Fangraphs. Here’s an example. Click the graph to see the full page.

via www.fangraphs.com

Yeah, I chose a good one. Ignore the bar graph at the bottom, I’ll get to that in a bit. For now, focus on the crazy line graph. Remember, in win probability, the numbers assume perfectly average teams. So at the beginning of the game, both teams have exactly a 50% chance of winning. In this game, by the top of the second, after Justin Morneau’s grand slam, the Twins had an 87.6% chance of winning the game. The score was 7-2. And by the middle of the third inning, the Twins had a 98.9% chance of winning, assuming perfectly average teams. Of course, we know what happened. Most game graphs aren’t nearly this dramatic, but that’s the beauty of the model. When the graph takes crazy dives and turns, it’s visually obvious that it was a great game to watch.

Also, much like RE24, we can tally up a player’s contributions to win probability. This stat is known as Win Probability Added (WPA). In WPA, 0.500 corresponds to one full win, as the probability of winning changed by 0.500 (50% to 100%). In the aforementioned game, Gio Gonzalez was tagged with -0.587 WPA, more than enough to singlehandedly lose the game. Thankfully, Holliday and Cust combined for a positive 0.638, enough to overcome Gio’s poor pitching. The unique thing about WPA is that, unlike other stats, it’s not context-neutral. Almost every other statistic out there (batting average, OPS, wOBA, whatever) deals with players in a theoretical vacuum. But WPA deals with actual game occurrences. Give up a solo home run when your team is up by ten? Not a big deal, and WPA shows that. Give up a bases-loaded walk in a tie game in the bottom of the ninth? That’s far worse. WPA reflects that.

As an extension to this, Tom Tango created what he called a Leverage Index (LI). Leverage Index basically explains how critical a situation is. To put it in more technical terms, Leverage Index gives a numerical representation of the biggest possible swing in win probability, where 1.0 is the average. To use my previous examples, the LI in a game situation where you’re up by ten is very low. If you give up a home run, the win probability isn’t going to change much. But the LI in the bottom of the ninth of a tie game with the bases loaded is off the charts. The high LI shows that any little mistake could be devastating. Leverage Index is always denoted in a Fangraphs game graph by the bar graph underneath the regular win probability graph. In that crazy Twins game I mentioned earlier, the LI sinks to near-zero levels when the Twins are up 12-2. And yet, when the A’s came roaring back, the LI started to rise again. The last play of the game had a huge LI, as Michael Wuertz had runners on first and second with two outs in the bottom of the ninth of a one-run game.

I admit I’ve gotten into the habit of keeping the game graph open in another tab alongside MLB Gameday (and AN, of course) during a game. It’s pretty addicting to watch the graph unfold in real-time, and wonderfully cathartic to see a crazy game in graphical form.

Moneyball Revisited: How Much Did We Overachieve?

danmerqury — Tue, 06 Oct 2009 00:48:49 -0000

We all know about the size of our payroll. We all know that it makes us trade away, buy low, and otherwise obtain undervalued players. It's been seven years since the 2002 Moneyball draft, and our payroll hasn't gotten any bigger relative to the league. In part 2 of my apparent Jack an Idea From Another Website and Use It to Make a Cool Chart series, I found out how much we truly overachieved.

Click for a bigger version.

First things first: The idea came from Beyond the Box Score. Go there. They rock.

This chart simply shows how much the 2009 A's overachieved. To calculate the numbers, I pulled performance dollars from Fangraphs and subtracted their 2009 salary. Fangraphs converts their WAR numbers (wOBA, FIP, and UZR based) into performance dollars by finding out the average market value of a 1 WAR player and extrapolating. (More information here.) Last year, it came out to around $4.5 MM per win. All of the salary numbers came from Cot's Baseball Contracts. For the players who left or came to Oakland midway through the season, I calculated their prorated salary based on how long their stay with Oakland was. For the players that Cot's didn't have salary info for, I assumed the league minimum ($400,000).

Couple things:

The players that Oakland didn't have on their payroll for a full season are Matt Holliday, Adam Kennedy, Jack Hannahan, Orlando Cabrera, Scott Hairston, Craig Breslow, Russ Springer, Edgar Gonzalez, Brett Tomko, and Clay Mortensen. Some of these were trades and some were waiver pickups.
Included in the calculations are $1.5 MM that we paid St. Louis as part of the Holliday deal and $250,000 that we paid Orlando Cabrera as an "assignment bonus". Also, Jason Giambi got a full $4 MM from Oakland, even though he didn't play the whole year. I didn't include the $1.5 MM buyout for Giambi, as that's on the 2010 payroll.
The small line on both sides of the chart indicates $10 MM worth of production over salary.
I used a 20 innings pitched, 100 plate appearances cutoff, with two exceptions.
If you're getting paid almost 18% of the entire team payroll, you're on the chart, even though you only played in, say, 8 games. Also, Duchscherer's on the chart for the same reason.
Brett Anderson and Ryan Sweeney are really, really good.
Some good news: Out of the players that didn't produce as much as their salary, numbers 2, 3, 4, 5, and 7 are off the books in 2010. Number 6 is Scott Hairston, who has had injuries all year.
Out of these 39 players, we spent $59.3 MM. We got $165.1 MM worth of performance, which is good for 36.5 wins above market value. Not bad, Billy.

A Pitchf/x Primer, and why Trevor Cahill should start 2010 in Sacramento.

danmerqury — Mon, 21 Sep 2009 04:53:52 -0000

I don’t know how familiar the AN population is with the specifics of the wonderful Pitchf/x system, so I’ll start from the beginning. For those that do…I’m sorry. Skip ahead if you’d like.

In 2006, Sportsvision (the company behind the original yellow TV first down line and pretty much every piece of TV sports graphic wizardry out there) launched Pitchf/x. They installed multiple high-speed cameras in every MLB stadium that track every pitch, computing the ball’s trajectory and location to within a half an inch. Over time, the power of the system grew, and today, the cameras allow the Pitchf/x system to calculate a huge collection of variables about the trajectory of every pitch, definitely not limited to what you see on MLB Gameday.

Thankfully, data for every pitch is freely available from MLB.com. It’s easier to access data from a source like FanGraphs or Brooks Baseball, where they have tools and graphs already set up, but if anyone wanted to work with the raw data, you can download the spreadsheets too. I used FanGraphs for this FanPost, but it’s really up to you. In either case, the data generated by the Pitchf/x system gives us the greatest tool we’ve ever had for analyzing a pitcher’s pitches. For example, here’s a movement graph from Justin Verlander’s start on 8/8.

Keep in mind that this isn’t a location graph. That is, the dots don’t correspond to where they crossed the plate. Rather, this is a movement graph. The vertical axis corresponds to vertical movement—from the graph it looks like his fastball (the green dots) averaged about seven or eight inches of backspin. In other words, his fastball crosses the plate roughly eight inches higher than it would have if it was thrown without spin. The horizontal axis is very similar, showing horizontal movement. In these graphs, all movement numbers are seen from the batter’s point of view, so Verlander’s fastball comes in to right-handed batters by approximately six inches. It’s counterintuitive, but fastballs break just as much as curveballs do. The very best curveballs can break downwards by around ten inches (think Zito), but almost everyone throws a fastball that breaks upward anywhere from 6-11 inches. Upward vertical movement has to fight against gravity, however, so a hard fastball will fly far straighter than a spinless ball will, where downward breaking curveballs fall with gravity working for it.

The reason I used Verlander as an example is because his pitches are well-defined. He throws them repeatedly, with consistency. Take a look at this.

This is from Trevor Cahill’s 5/7 start, which, by all accounts, was a good one. He went seven innings, generated loads of groundballs, and only allowed one run. But yet, his movement chart is troubling. Unlike Verlander’s, Cahill’s pitches are not well-defined. Instead, the graph looks more like a smear of pitches, as opposed to tight bunches. And this is one of his good starts. When he’s bad…

Gio Gonzalez has, to put it lightly, control problems. He has trouble locating his pitches. But when he throws his fastball, the same pitch comes out every time. Cahill has trouble throwing the same pitch start after start. Here are all of Cahill's starts this year, from the beginning to 9/2. The date text is in green if he had a quality start, and it appears in red if he didn’t. (I changed the definition of "quality start" from 6.0 IP/3ER to 5.0 IP/3ER because he’s a rookie, and, well, he wouldn’t have too many quality starts if I left it as is.)

I see no consistency. I also don’t see much of a pattern. In an attempt to find one, I sorted his starts again, but instead of using earned runs as the deciding factor, I sorted them by the number of groundballs. He’s a sinkerballer, so I considered any start where he generated 50% groundballs or more as good. Anything under 50%, again, appears in red.

Now there’s some semblance of a pattern. I came to the rather obvious result that Cahill tends to generate more groundballs when his fastball has more sink. To illustrate the difference, I pooled all of his starts together, sorted by groundball rate.

In the starts where he generates less than 50% groundballs, his fastball has a nasty tendency to rise, losing it's characteristic sink. When his fastball is working, it tends to lose some of that positive vertical movement and causes batters to get poor contact. It looks like Cahill tends to overthrow his fastball at times, causing it to get more backspin than it should, which leads to the rise.

Cahill needs to get time in Sacramento to work on his consistency. If his fastball can become that consistent sinking force it needs be, he can succeed on the highest stage.

Poll

So, Cahill, 2010. Sacramento or Oakland?

Prospect Buzzkill '09!

danmerqury — Wed, 16 Sep 2009 00:39:48 -0000

This isn’t a fun post to read. It’s not even a fun post to write. That’s right, folks, it’s the latest installment of doom and gloom.

Okay, it’s not that bad.

I mainly wanted to write this post after seeing this article on Fangraphs. It’s something we should all read. I’m not preaching ultimate pessimism or anything, but, well, I’m just a little tired of seeing something like this.

C1: Donaldson, Josh
1B: Carter, Chris
2B: Weeks, Jemile
SS: Cardenas, Adrian
3B: Wallace, Brett
LF: Desme, Grant
CF: Brown, Corey
RF: Doolittle, Sean

It’s fun. It’s exciting. They’re shiny new toys. But we’ve got to be, at least a little bit, realistic. The Victor Wang SABR article that Fangraphs cites is a great read, but you can glean all of the necessary information from the tables he attaches. Not all prospects are going to succeed. Victor Wang analyzed Baseball America’s top 100 rankings from 1990 to 1999. He then put each prospect into categories according to Wins Above Bench (think WAR with a slightly different baseline). Prospects that averaged over 4 WAB per year he considered "stars". Those averaging between 2-4 WAB/year were "everyday" players. Those between 0-2 WAB/year were merely "contributers", and those below zero he deemed as "busts". I’m going to take a look and revisit Baseball America’s 2009 Top 100 Prospects preseason rankings, and give this an A’s related look.

Brett Anderson (7)
Pitchers in the top 10 busted 31% of the time. Only 8% went on to be solid regulars or better.

Trevor Cahill (11)
Pitchers ranked 11-25 busted 32% of the time. Interestingly, 15% became at least solid regular players.

Brett Wallace (40)
Hitters ranked 26-50 busted 35% of the time. 20% became everyday players or stars.

Michael Ynoa (54)
Aaron Cunningham (55)
Adrian Cardenas (74)
Pitchers ranked 51-75 busted 39% of the time. 8% were regulars or better.
Hitters ranked 51-75 busted 45% of the time, yet, 18% reached everyday status.

Chris Carter (76)
Gio Gonzalez (97)
Pitchers ranked 76-100 busted 43% of the time. Only 7% were regular players.
Hitters ranked 76-100 also busted 43% of the time. 13% were ever really any good.

Remember, no team is ever fully homegrown. Of course, these are preseason rankings, and things chance (hell of a season, Carter), but we can’t count on a lineup or rotation full of prospects. It’s just not going to happen. Remember Javier Herrera? Me neither.

Fangraphs article on Michael Wuertz, and how dominant he's been.

danmerqury — Mon, 17 Aug 2009 17:24:10 -0000

Fangraphs article on Michael Wuertz, and how dominant he's been.

That chart at the bottom of the link is definitely something. And on 25% of his sliders, he gets not just a strike, but a swinging strike? Wow.

In Defense of Jack Cust

danmerqury — Thu, 13 Aug 2009 23:47:48 -0000

Yeah, I know, a lot of the AN faithful already know a majority of what I’m trying to say, especially the justification of 2007-2008 Cust. But for those who don’t, and especially for newcomers to the AN scene, I’m going to lay out my case: Jack Cust is not only an above-average hitter, but a very welcome and valuable addition to the Oakland Athletics.

We all know how it started. In his first week as an Athletic, he almost averaged a homer a day. His OPS didn’t fall out of the 1.000’s for just shy of a month. Jack Cust finished his first regular major-league season with an awesome line of .256/.408/.504. To put that in perspective, the only other .900 OPS or better seasons we’ve had this decade are 2006 Frank Thomas, 2004 Erubiel Durazo, and 2000-2001 Jason Giambi.

A lot of the Cust criticism comes from his strikeout rate, which, let’s face it, is enormous. In 2007, he struckout in 41% of his at bats, easily toppling Ryan Howard, the only other player remotely close. Cust is a textbook example of a "three true outcomes" player, meaning that every time Cust comes up to bat, he either strikes out, walks, or hits a home run. Thankfully, in 2007, he led the league in walk percentage, at 21% of his plate appearances. It’s worth noting that with the exception of moving a runner from first to third, a walk is every bit as valuable as a single. Even with his anemic batting averages, Cust still managed to get on base 40% of the time. That’s a fine baseball player, folks.

Cust manages to walk more than virtually anyone else due to his phenomenal eye and plate discipline. In 2007, Cust was second in the league in outside-swing percentage. He swung on pitches outside the strike zone less than 14% of the time, behind only Luis Castillo. In total, he swung only on a third of all pitches. Of the balls he did hit into the field of play, he came in seventh in line drive percentage. Additionally, he led the league in home run to fly ball ratio, an indicator of power, coming ahead of Ryan Howard, Jim Thome, Carlos Pena, and Alex Rodriguez.

So what does it all mean? In 2007, Cust put together a fantastic offensive season. In 2008, his walk rate fell a little, as did his home run to fly ball ratio. He still managed an .851 OPS. In this decade, we’ve generally only had one player per year with an OPS that high, save for a few years in the early part of the decade (Giambi, Chavez, Tejada).

Of course, 2009 has been a down year, and most of the criticism that doesn’t stem from his strikeouts comes from 2009. I’ve heard "quad-A player". I’ve heard that the league has figured him out. It’s simply not true.

Keep in mind that before the season, Cust was told to cut down on strikeouts and swing more. It’s safe to say that the new contact-oriented Cust experiment was a failure. Thankfully, the experiment is over, as of the beginning of July. Take a look at these.

Since the around the beginning of July, almost all of Cust’s peripheral stats took a sharp turn upward. His average increased, and more importantly in Cust’s case, his on-base percentage took a huge upswing. This was buoyed by his walk percentage shooting through the roof. Along with his changing walk percentage, his strikeout percentage increased dramatically. Normally, this would be a worrying sign, but here it just signals that "three true outcomes" Cust is returning. And of the balls Cust did hit fairly, his groundball percentage decreased while his line drive percentage increased. These both triggered an increase in his batting average on balls in play (BABIP). These are all good signs.

Cust isn’t fully back yet, though. The power simply isn’t there. His slugging percentage actually decreased since July, while the number of fly balls he hit dipped down for about a month. It looks like Cust’s plate discipline and his approach to taking pitches has returned back to normal. He’s just not hitting the Custian homers we all know and love. He’s again being extremely selective about the pitches he swings at, and, thankfully, as evidenced by the large number of loud outs he’s been getting recently, it’s only a matter of time before the power returns.

Expect to see the return of Spartacust, folks. If not for the rest of the year, "three true outcomes" Cust should be back in full force for 2010, provided that the management doesn’t try any new experiments.

Visual Representation of the 2006 Oakland Athletics

danmerqury — Wed, 12 Aug 2009 01:56:20 -0000

It's nice to look back at brighter days. Although it probably wasn't the best team of the 2000's (that would either be 2001 or 2002, they both won 100+), it was the only team that got past the ALDS. After the jump is a visual diagram of the 2006 Oakland A's, showing when they came and left Oakland. Hint: Just three years later, we don't even have a fifth of this roster still with us.

via i20.photobucket.com

Orange marks a season with the A's, where gold highlights the 2006 ALDS-winning season. Brown indicates a season played elsewhere, not in Oakland.

In the case of a player playing for multiple teams in a season, I went with whichever team he played more games for in that year. Batters are sorted by games played, and pitchers are sorted by innings pitched.

I got the idea when I found a similar chart done by Craig Robinson on the 1986 World Series-winning Mets. Check his site out, guys, it's got a lot more cool and interesting visual charts.

http://www.flipflopflyin.com/flipflopflyball/index.html