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Some Brandon Belt Love

January 13, 2011

I’ve been pretty darn busy recently, so I’ve been unable to write as often as I’d like.  I have something pretty cool/fun in the works that should generate some discussion about the upcoming year, but until that’s finished, I thought I’d link to this lovely article on Giants’ first base prospect Brandon Belt.  Some of the highlights:

Seeing Brandon Belt take his cuts last year prompted San Jose Giants hitting coach Gary Davenport to recall another graceful and gifted left-handed batter: Will Clark.

Davenport mentioned this to Clark one day when the six-time All-Star, now a special assistant for the big league Giants, visited the organization’s high Class A affiliate.

Clark quickly disagreed.

“He has a better swing than I did,” Clark said.

Uh…wow.  That’s some pretty high praise for a kid that just retooled his swing last year, and if Belt can approach anything near what Clark produced early in his career with the Giants, they’ve got a pretty special player on their hands.  Then there’s this:

Belt will be under considerable scrutiny as he enters his first Major League Spring Training next month. The reigning World Series champions believe that he has a legitimate chance to earn a spot in the lineup at either first base or left field (Aubrey Huff almost surely will occupy the spot Belt doesn’t). General manager Brian Sabean has emphasized that Belt will begin the season in the Minors to continue his development if he doesn’t win a starting job with San Francisco.

All that really matters to me is that Belt is playing every day no matter the position or level.  He has very little experience in the outfield and he’s supposedly a stellar defender at first, so I imagine shifting Huff to left makes more sense than sticking Belt there.  There’s really no need to put a rookie at a position he has little to no experience at while he’s making the adjustments to big league pitching- he has enough on his mind.  There’s no doubt in my mind that he has the ability to be a good defender in left- he has good instincts, solid speed and a great arm- but I’d rather start him at the position he’s most comfortable with.  And I imagine that’s what the Giants plan on doing.  Anyways, I like the way the Giants are handling this.  There is a part of me that would prefer he start in Fresno, if only to avoid starting his Super 2 clock early, but if the Giants feel he can make an immediate impact, then it makes sense for him to be in the Bigs.  This kid could be a real asset in the lineup- with solid power and fantastic plate discipline, he and Buster Posey could anchor the middle of the lineup for years to come.  And if Pablo Sandoval can regain his form, the Giants will have a young, formidable middle of the order.

The projection systems love Belt.  CAIRO projects Belt to hit .267/.357/.452 with 15 homers 35 doubles and 10 triples per 550 AB, ZiPS has Belt at .266/.357/.440 with 15/34/9 per 550, and Oliver likes him the best- it projects him to hit .284/.365/.481 with 20/34/7 per 550 AB.  That’s a phenomenal projection for a kid that’s yet to hit in the Big Leagues.  The scouts love him and the forecasting systems love him too- all that’s left is for Belt to live up to those expectations.  And it sounds like he has the mental fortitude to do so.

Catcher Defense 2010

January 7, 2011

I present some numbers over at THT Live.  Buster was a +4 in 662 innings…that’s somethin’ special.

2011 ZiPS Giants Projections

January 4, 2011

Courtesy of Dan Szymborski.  I have to say, it’s nice to see Brandon Belt get so much love from the projection systems- CAIRO likes him, Oliver likes him, and ZiPS is a fan as well.  I just hope that his Minor League performance can translate as well as the systems think it will, and it should be interesting to see what PECOTA thinks of him.  Also encouraging is Pablo Sandoval’s projection.  I think he may be one of the most difficult players in baseball to project, as a lot of non-baseball variables will determine his performance in 2010.  At some point I imagine I’ll try and average all of the projections and calculate estimated win values.  I think that would make for an interesting post.

On the Giants and Infield Defense

December 28, 2010

Every once in a while I see people discussing the merits of having a good infield defense to back up the Giants’ pitching staff.  I am admittedly one of those supporters- I’m a pretty firm believer in 1) keeping the ball on the ground, no matter the dimensions of the park you’re playing in, and 2) having a solid defense in the infield to convert those ground balls into outs.  That’s why I’m a sucker for defensive-minded shortstops- I’ve always really liked Adam Everett, I loved Omar Vizquel, and I currently have a slightly irrational man-crush on Seattle’s Brendan Ryan and Texas’ Elvis Andrus.  Sometimes I hear someone mention that the strikeout-flyball tendencies of the Giants’ pitching staff renders their infield support…well, not unimportant, but not something that necessarily has to be prioritized.  So I thought I’d delve into the matter and see what I could find.

Using Baseball Info Solutions’ batted ball data (courtesy of FanGraphs), I split each pitching staff’s batted balls in play (defined as TBF – HR – BB – HBP – K) by their ground ball to fly ball ratio and compared this to the league average (both batted balls allowed per batter faced and the league GB/FB tendencies).  This is based on three years of data- consider that to be something of an arbitrary cutoff point.  I wanted to get a feel for the tendencies of the pitching staffs; obviously, pitchers switch teams- but I think enough pitchers have stayed put to where it will minimize the effect of team-switching.  Anyways, this is what I found:

Pretty cool, no?  Over the last three years, the Giants’ pitching staff has on average allowed 242 less ground balls than the average Major League team.  That’s 62 less grounders induced than the runner-up Cubs.  The Cardinals, as expected (pitching coach Dave Duncan has a reputation for turning pitchers into ground ball machines) have induced a great deal more grounders than the second-best team, the Braves (by 105).  Overall, the Giants have induced less balls in play than any other Major League team.

That said, I’m not too concerned about the state of the Giants’ infield defense- if any team could afford to have mediocre to below average defense in the infield, it would most certainly be the Giants.  This doesn’t mean that the Giants should go out and acquire all-bat no-glove players for their infield positions, but it does mean that the pitching staff effectively minimizes the impact of a infielder’s lack of range.  That said, I don’t think the Giants will be hurt too much with Miguel Tejada at shortstop and Pablo Sandoval at third base.

Marcel and Forecasting Systems

December 22, 2010

I’ve been spending some time recently thinking about forecasting systems.  I was thinking about coming up with one of my own, only to find out that the CAIRO forecasts did essentially everything that I had in mind.  There’s a plethora of systems out there—off the top of my head, I can think of about nine—CHONE (which looks like it’s done for, what with Sean Smith now working for a Major League organization), PECOTA, ZiPS, CAIRO, Oliver, and Bill James.  There’s a few others out there that don’t seem to get as much play—one of them is Ron Shandler’s forecasts, and I think they’re subscriber only.  Mitchel Lichtman, one of the brighter saberists out there (and the creator of UZR) has his own, and a man by the name of John Eric Hanson has done exceptionally well in forecasting challenges in recent years.  There’s one forecasting system that typically stands in the middle of the pack, though, and it surprisingly uses very little information to come to its predictions.

If you’re familiar with sabermetrics, you’ve undoubtedly heard of the Marcel forecasts, which were developed by Tom Tango.  It is explained as “(…) The minimum level of competence that you should expect from any forecaster.”  It is meant to work as a reference point for other forecasting systems—if you’re doing worse than Marcel, you need to tweak your methodology; and if you’re doing just as well as Marcel, you’ve still got some work ahead of you.  It works surprisingly well for a system that does so little; and, since I’ve been thinking about the topic so much, thought I’d talk a little bit about how Marcel works.

I should note that I’ve only focused on hitters; I haven’t taken a look at how Marcel handles pitchers—and, I should note that when I recreate the forecasts, I come out with slightly different figures than Tango’s published ones.  Why that is, I’m not certain—but the differences are tiny enough to the point where I’m not concerned.

Weighting the Data

All forecasting systems weight multiple years of data whenever possible.  Logically, this makes sense—by placing more of an emphasis on the player’s most recent seasons, you’ll be getting a better look at his most recent performance whilst still accounting for possible random variation in the most recent year.

Let’s think of it this way—if we have a player that had a home run per plate appearance of .066 in his most recent season, .025 the previous year and .054 before that, his weighted rate (using Marcel’s standard 5/4/3) would be .049.  His year three rate was well above average (35 per 650 PA), his year two well below average (16) and year one outstanding (43).  He had two seasons of outstanding power sandwiched around one poor one.  In 636 plate appearances, his weighted rate translates into 31 home runs.  The player—Carlos Beltrán, in 2007—actually hit 33 the following season.  Of course, weighted rates don’t always work well like this—but it certainly lends more accurate insight than a straight average would.

If you’re not sure how a weighted average works, you simply multiply the component by its corresponding weight to find the weighted sum—and then divide by the sum of the weights.  That might sound a bit confusing, so I’ll walk you through the Beltrán example:

The home run rates as listed above were .066, .025 and .054.  The weighted sum would then be (.066*5) + (.025*4) + (.054*3) = .592.  The sum of the weights is simply 5+4+3 = 12.  So, our weighted average is .592 / 12 = .049.

Different forecasting systems use different weights—this is just how Marcel handles it.  Ideally, you use best-fit weights, which Oliver does.

Regression to the Mean

In the table above, you’ll see the top ten hitters in the Major Leagues in 2009 ranked in descending order by wRC+.  We’re talking about some insanely good seasons—all players were 50% better than the league average or better, and the best player, Albert Pujols, was nearly twice as good as the league average.  And the following year?  Only two of the ten players saw an increase in performance—everyone else regressed, by at least 11%.  This is why forecasters regress to the mean (“mean,” of course, meaning to their specified population).  We cannot assume that a player will perform just as well as he did in his most recent season, and since we want to strip out things like “luck” and random variation, we must regress to the mean in order to estimate the player’s true talent level.

But how much should we regress, and to what population do we regress towards?

Forecasting systems such as CAIRO regress to the positional mean, and I believe PECOTA regresses to the player’s most similar historical comparison.  Ideally, you want to regress to the most specific mean you can—so the “perfect” population mean would be players that play the same position, are physically similar, match in age, and have a similar toolset, amongst other variables I forgot to mention.  Marcel uses all non-pitchers.

How much we regress is based on two things—the first being how much playing time the player we’re forecasting has—the more time he’s spent in the Majors, the more we know about him and the less we’ll need to regress.  Conversely, the less time he’s played, the less we know about him and the more we need to regress in order to generate an estimate of his true talent.  Additionally, not every component needs to be regressed the same—certain components stabilize (by “stabilize,” I mean a component reaches an “r” of .5-.7 be it through year-to-year [YTY] correlations or through intraclass [ICC] correlation) faster than others and do not need to be regressed as heavily.  Russell Carleton’s work with ICC has been cited often—his findings suggest that a player’s home run rate stabilizes around 300 PA, strikeouts around 150, walks around 200, and so on.  So not all components need to be regressed the same.  Marcel, being the simple creature that it is, regresses all components the same.  Marcel defines reliability as the sum of the weighted plate appearances divided by itself, plus 1200.

David Wright, for example, had 670 PA in 2010, 618 in 2009 and 735 in 2008.  Multiply these by the appropriate weights (2010 = 670*5, 2009 = 618*4 and 2008 = 735*3) and you get 8,027.  The player’s individual reliability score is calculated as:

r = PA / (PA + 1200)

So, the reliability for Wright would be 8027/(8027 + 1200) = .87.  This means that we regress his weighted rates (1 – .87) = 13%.  What about a player like Jason Heyward, who has only amassed 623 PA in the Majors?  His reliability score would be 3115/(3115 + 1200) =  .72, meaning we regress his rates by 28%.  I’m not sure exactly where Tango derived the “1200” figure from—but this is the regression to the mean component used in Marcel.

We can figure the regressed rate two separate ways—one assumes we don’t know the player’s reliability (which is most often the case); the other incorporates it into the equation.  If we know the reliability of a component and the average plate appearances of the population we’re drawing the reliability from, we can generate a constant that will allow us to regress any player, based on his plate appearances.  Let’s say we have an r of .7 for walk rates in a population in which the average player has 300 plate appearances.  Our constant is figured as:

Constant = (1 – r) * Average Opps / r

In this case, our constant would be .3*300 / .7  = 129.  If a player has 600 PA and a walk rate of 12% where the average is 9%, we regress his rate through the following formula:

Regressed rate = (Rate*PA + League Rate*Constant) / (PA + Constant)

For our example, the player’s regressed rate would be: Regressed rate = (.12*600 + .09*129) / (600 + 129) = .115.  For a player with 150 PA and the same walk rate, he would be regressed to a walk rate of .106.  For Marcel, “PA” are the weighted sum of the plate appearances and “Constant” is the 1200.

Back to our example of Wright, let’s regress his home run rate using this method.  He has a weighted home run rate of .035 and weighted plate appearances of 8,027 in a league that has a home run rate of .027.  If we plug in those numbers to the aforementioned equation, we get (.035*8027 + .027*1200) / (8027 + 1200), which comes out to .034.  In 670 plate appearances (his weighted average of PA), this translates into 23 home runs.

If we know the player’s reliability, it’s a tad bit easier to find the regressed rate.  It is simply:

Regressed rate = (Player r * Player Rate) + ((1 – Player r) * League Rate)

In the case of Wright, this would also give us (.87*.035) + (.13*.027) = .034.  Think of regression to the mean as being one part player and one part the population.  The more we know about a player, the more we weight the player’s performance—the less we know, the more we weight the population’s performance.

Aging

Another thing that forecasting systems take in to account are age adjustments.  As a player gets older, he either sees improvement or he declines.  Generally speaking, players see an improvement in their performance until about 27-28 years of age and see a steady decline afterwards—and accounting for this helps aid forecasters in their estimate of the player’s true talent.  Marcel incorporates a simple blanket aging adjustment, which you’ll see below:

According to Marcel, players peak at the age of 29.

Edit: Tango clarifies:

One point about the “age 29” thing:  note that the player is 29 years old, and we are looking at his stats at age 26, 27, 28.  So, when I say I don’t apply an aging factor at age 29, it’s because his talent level at age 29 is right in the middle of his talent level at age 26-28 (more or less).

And just like regression, each component has a different aging factor.  According to Mitchel Lichtman’s studies, a player’s overall peak performance (based on linear weights) is 27, but home runs plateau from 28-29, walks improve until the age of 34, and strikeouts increase after 29.  So, unsurprisingly, other forecasting systems implement component aging patterns into their design.  With Wright, his age-adjusted home run rate is left virtually untouched (1.006); his Marcel home run rate is .034*1.006 = .035.

See how simply Marcel really is?  All he does is weight three years of data, uses a blanket regression (which is easy to derive) and a blanket age adjustment.  And he still manages to hold his own with the big boys.

One thing to note is that Marcel pays absolutely no attention whatsoever to Minor League performance—if the player has no Major League service or anything, Marcel treats him as a league average hitter—nothing more, nothing less.  This is where the other forecasting systems differ from Marcel.

I’m sure there are other factors in projection systems that I’ve overlooked, but hopefully my understanding and explanation of them is a reasonable one.  There are really three main steps that I’m aware of—weighting the data, regressing it, and adjusting for age—the difference between the systems is how it’s done and how they treat minor league performance.

Revisiting AT&T Park’s Affect on Hitters

December 15, 2010

A few days back, Scott Ostler of the San Francisco Chronicle asked a question that’s been plaguing the minds of many Bay Area sports fans for quite some time:

“Why is it that when San Francisco and Oakland teams phone great athletes around the country to gauge their interest in coming here to play, the players fake a foreign accent and say it’s the wrong number?”

I don’t know about foreign accents, but I do know that the Giants have had plenty of issues with bringing free agents to don the orange and black.  This is most likely due to AT&T Park because, as Ostler puts it, it “(…) scares them.”  The question is, of course, “should it?”

I took a look at AT&T’s component park factors based on batter handedness back in July and found that the overall effect was essentially the same between left-handed and right-handed hitters—the park suppressed their production, but not by a whole lot.  Not enough, in my opinion, for so many free agents to spurn the Giants so quickly.  I looked at 2005-2009 rather than the entire decade in part due to the Barry Bonds effect—I thought Barry would have certainly skewed the results for lefties, and I didn’t think it necessary to include every single year of the park’s existence.

I really don’t know what I was thinking.  The Bonds Factor is an easy thing to adjust for—simply subtract Bonds’ home batting line from all left-handed hitters at AT&T Park.  And, of course, when it comes to estimating the effect of a park on hitters, the more data you use the more accurate an estimate you’ll have of how the park truly plays.  Three years is a good starting point, five years is better, and ten years and beyond is ideal.  Chances are you won’t find a large discrepancy between five-year park factors and ten-plus year park factors, but it should help give a more refined estimate.  So I thought I’d update my park factors for AT&T and see how it looks if we include all years rather than just the most recent ones.  The methodology hasn’t changed—all park factors are based on the rate of the event at AT&T Park compared to all other National League parks, regressed 10%.  I toyed with the idea of using different rates per component (i.e. 3B/2B rather than 3B/PA) but found little difference in the results, so I’m sticking with rates per plate appearance for now.  First, the right-handers:

The biggest change we see by expanding the dataset is the effect of triples—rather than being moderately favorable towards hitters, it’s a bit bigger than what I previously thought.  Everything else is essentially the same.  Now the left-handers:

No substantial changes, but we do see triples having a slightly larger effect, home runs being suppressed a bit more, and ROE moving closer towards the mean. Now here’s the interesting part: what if we include Bonds in the calculations?

Good stuff.  Barry changes things up a bit, mostly in the home run department, although we see a decrease in on-base events such as NIBB and HBP by excluding him.  It’s crazy to think that one player who accounts for about 7.6% of all plate appearances at the park could make an impact like that.

Based on the 2000-2010 run environment, right-handed hitters created 75.2 runs in 650 plate appearances (approximately a full season).  If we apply the park factors to their line, it drops down to 73.7 runs; meaning that, on average, a right-handed hitter will be expected to perform at 73.7/75.2 = 98% of their performance in a neutral environment.  For a left-hander, the effect is actually more dramatic—rather than creating 79.5 runs, he would be expected to create 75.2, retaining 95% of his park-neutral performance.  It appears that AT&T certainly does favor right-handed hitters over lefties.  Both hitters will see their numbers suppressed, but the effect on righties is relatively small and with lefties a bit more pronounced—roughly five runs of value over the course of a full season; about half a win.  Generally speaking, it looks like left-handed hitters should be a bit wary of hitting at AT&T.

Lest anyone take this little “study” as definitive proof that left-handed hitters should avoid playing at AT&T Park, I’d like to point something out.  We’re estimating the overall effect of the park; certain hitters will be affected differently than others.  This does not mean that a player like Adam Dunn, who hits moonshots with regularity, would see his home run production drop 18%.  The big-time power hitters would likely see a loss of a few home runs per season but nothing substantial.  The player’s batted ball distribution will also lend some insight as to how much they may be affected at the park- if you’re a dead pull hitter, you won’t be as affected as a guy that tends to hit home runs out to right-center.  If you’re a player that likes to line doubles into the gaps, you’re likely to wind up with a few more triples.

So, should free agent hitters be scared to sign with the Giants?  I’d say it really depends.  For a right-handed hitter, there really shouldn’t be a problem.  For a lefty, I’d say it depends on your hitting style.  If you’re a pull power hitter or a pure doubles hitter, you’ll be just fine.  If you hit home runs to right or left-center, you might want to avoid the park.

Musings on the Giants’ Offseason

December 8, 2010

I love the offseason.  That might be somewhat counterintuitive to a lot of people, but I absolutely love the hot stove season.  I remember a few years back I would immediately turn to the Sporting Green of the San Francisco Chronicle in the hopes that I’d see some interesting rumors about players the Giants were looking to acquire, or players they had signed or traded for the night before (this was well before the days MLB Trade Rumors became a big site).  I remember waking up one morning in 2003 to see the Giants had just traded for a young left-handed hitting catcher by the name of A.J. Pierzynski from the Minnesota Twins, who had been on the American League All-Star team the previous year- they dealt Joe Nathan, whom I liked, along with two names I didn’t recognize- Francisco Liriano and Boof Bonser.  Nathan had been a pretty good reliever for us, but I wasn’t sad to let him go- we had gotten ourselves an All-Star Catcher.  Christmas came early!  Of course, we all know how that trade turned out- it’s often referred to as one of the worst trades in Brian Sabean’s tenure as Giants’ general manager.  Regardless, I remember being so excited that the Giants had acquired a rising star.

They haven’t done anything of that magnitude since.  There have been some big signings- I remember thinking of the Omar Vizquel, Armando Benitez and Matt Morris signings as being pretty big…Aaron Rowand, too, although I didn’t care for the signing one bit when it happened.  The Barry Zito signing scared me from the get-go, but I loved that the Giants were spending big.  Unfortunately, it didn’t turn out well for them.  Since the Rowand signing, the Giants haven’t made a big dollar commitment to a free agent.  And more often than not, our offseasons recently seem to have been full of disappointment; at least, from my point of view.  We’ve had teams that needed a massive makeover or needed that one big bat to make us a respectable team- but it never happened.  A lot of tires were kicked, and, at the end of the day, the Giants’ due diligence came to the same conclusion year in and year out: overpay the players that used to be good in the hopes that they’ll somehow find the fountain of youth; stay away from big trades, and dear Jesus, never trade pitching, ever, for a good hitter.*

So it comes to no surprise that the Giants haven’t made huge moves in 2010.  Then again, it’s not as if the market is really all that great this season- the two headliners are Jayson Werth and Carl Crawford; Werth signed a ridiculous 7-year, $126MM contract with the Nationals, and Crawford is undoubtedly looking to match that offer, if not eclipse it.  Neither player is worth that much, and the Giants shouldn’t bother bidding if the prices are that high.  Not only that, we’re dealing with limited payroll space- the Giants’ projected payroll, with the recent transactions, is right around $120MM.  There really aren’t any sexy names out there, either- Lance Berkman would have been interesting if he actually liked playing in San Francisco.  His three-year weighted wRC+ has him around a +136 hitter; he’s probably closer to 125-130 with aging effects.  Derrek Lee is a guy I’ve never really cared for in the past- I remember thinking of his defense at first base being horrendously overrated (this was years ago, though, and I haven’t seen him with regularity since), but I’ve thought of him recently as a player that would be great in a Giants uniform.  He might not be a left-handed bat, but he’ll probably post a wRC+ around 125.  Carlos Pena?  About 120.  That’s right around what I expect from Aubrey Huff this season.  None of those players listed aside from Berkman provide any flexibility, and this is a must for the Giants organization- Brandon Belt is looking like he’ll hit his way into the Majors at some point next season, and the incumbent first baseman would ideally move to left field.

That said, the Huff signing makes sense.  They likely overpaid a bit for his services, but considering he fits exactly what they were looking for- a left-handed first baseman/corner outfielder with some pop- I really can’t blame them for bringing him back.  And they love his intangibles.  It’s likely the best match for the Giants.

The other major position aside from the 1B/LF hole was shortstop.  The Giants filled this rather quickly, signing Miguel Tejada to a one-year, $6.5MM contract.  Again, there were really no attractive options on the open market- Orlando Cabrera is overwhelmingly “meh,” Derek Jeter wouldn’t fit with our payroll, and Juan Uribe signed with the Dodgers for 3 years and $21MM.  How ridiculous is that?  I was so happy to hear the Giants didn’t offer that amount to him- honestly, I thought “Maybe Sabean is learning…” only to find out shortly thereafter that he matched Los Angeles’ offer.  Yeah.  That’s too much money in my mind for a utility infielder.  Tsuyoshi Nishioka sure was an intriguing idea, but the kid is obviously unproven and would have required a relatively large monetary commitment- combine that with some health issues and questionable defense at shortstop, and you’re looking at a pretty big risk unless you have second base wide open.  Even then it’s still a risk, because we simply don’t know how his bat will translate to the Major Leagues.  So, I’m perfectly fine that the Giants didn’t acquire him.  The trade market was also a bit questionable; it makes more sense to me to sign Tejada than to trade for a more expensive player (since we’d be dealing with both money and trading off players of some value) that would represent a marginal upgrade.

Bringing back Pat Burrell on a $1MM contract will hopefully prove itself to be one of the largest steals of the offseason. The Giants’ starting left fielder’s weighted wRC+ puts him around a 110 hitter, although it wouldn’t surprise me one bit to see him back up to the 120’s- excluding his 2009 would put him around 125, which is a huge bargain.  His glove leaves a lot to be desired and he’ll undoubtedly have to leave in the late innings for a replacement, but I’m fine with this.  This might be my favorite move of the Giants’ offseason, and I’m very thankful that Burrell was willing to come back for such a small price.

I guess this is my long-winded way of saying that I like what Brian Sabean has done this offseason.  Really, the market this year isn’t all that great- and Sabean’s acquired players for a reasonable cost that should be able to live up to their contracts (Huff is perhaps a bit questionable, but it’ll be relatively close).  I just can’t complain about the moves he’s made, even if it’s been a predictably boring and uneventful offseason (and looks to end that way, barring a major surprise).  If we were to begin the season today, I imagine the opening day defensive alignment would look something like this:

C- Buster Posey

1B Aubrey Huff

2B Freddy Sanchez

SS Miguel Tejada

3B Pablo Sandoval

LF Pat Burrell

CF Andres Torres

RF Cody Ross

That’s not a horrible team by any means, and the offense should be right around average (at least, I hope).  I wouldn’t consider it a World Series-caliber offense, but…hey, I’ve been wrong before.

 

*Somehow, this resulted in a World Championship Title in 2010.  I still don’t think I can fully comprehend how this happened- for once, Sabean’s moves panned out- Aubrey Huff played better than he ever has, Pat Burrell was an offensive threat, and the homegrown pitching staff led us to win it all.  Of course, this kid named Buster Posey helped quite a bit- and so did a former minor league lifer in Andres Torres.  Really, the Giants’ lineup was full of castaways- Juan Uribe was originally signed on a minor league deal, Freddy Sanchez was a former batting champ that couldn’t stay healthy, Edgar Renteria a shell of his former self, and so on and so forth.  See, what bothers me about the World Series win- as ludicrous as this might sound- is that it’s positive reinforcement.  For once in his tenure, Sabean’s method of building offenses centered around mediocre veterans with a “proven track record” and young pitching staff worked.  And it’s going to convince him that his method is correct, and he’ll just keep going about things the way he always did.  You see, Sabean isn’t incompetent, and he’s not an “old dog that can’t learn new tricks.”  He’s a very bright man and he is absolutely, 100% capable of making adjustments.  He turned the Giants’ barren farm system into a powerhouse in short notice, bringing the brilliant John Barr on staff to help lead his scouting department.  That’s proof enough to me that the man learns from his mistakes, even if you have to give him a bit of a kick in the ass to get started.  But he won a World Championship with this incredibly flawed formula, and chances are he’ll continue to do so until he eventually retires.

Giants Sign Miguel Tejada

December 2, 2010

Wow, that sure happened quick.  Not long after the Dodgers signed Juan Uribe to a 3-year, $21MM contract the Giants pounced on the 36-year-old shortstop, signing him to a one-year contract worth $6.5MM with a $500K bonus.  The Giants were apparently in on the Rays’ Jason Bartlett but ultimately decided against dealing for the shortstop- and honestly, that doesn’t surprise me much.  General Manager Brian Sabean seems rather tentative to make trades in recent years, with signings being his mode of operation (seriously, I don’t recall Sabean making a “big” trade since the Nathan/Liriano/Bonser for Pierzynski fiasco).  Tejada hit .269/.312/.381 last year with an ISO of .112, 8% below the league average for both the Orioles and Padres.  Tejada is only one season removed from a .313/.340/.455, 113 wRC+ performance, and like fellow former Oriole Aubrey Huff, has been back and forth the past couple of years.  Tejada fits the Brian Sabean mold- an aging player that used to be great, that refuses to take walks (dude hasn’t walked in more than 7% of his PA since 2007).  The difference between most of these aging players and Tejada, I hope, is that Miguel might have a little something left in the tank.

CAIRO (v0.1) puts Tejada at a .288/.328/.421 line in 2010 with 33 doubles and 16 homers.  I’m assuming this is either park-neutral or translated to Camden Yards, in which case the translation will most likely put him around 34 doubles and 14 homers.  The initial translated line puts him at around -1 run per 671 plate appearances; translating to AT&T would likely make him around a -3 hitter.  Projections are nothing more than educated guesses, of course, but this looks about right to me (and I’m assuming the Giants expect somewhere around this level of performance as well).  Tejada is downright horrendous at avoiding double plays however, regularly hitting into them around 20% of the time (league average is 10%, mind you).  Dock him about -4 runs for this (based on estimated opportunities) and we’ve got a -7 hitter.  Tejada is good at productive outs, adding about +1 run- so overall, we’re estimating Tejada to produce about -6 runs with the bat.  That’s not bad for a shortstop, and given his durability (+20.6 replacement runs), this is pretty darn good.  All in all, I have Tejada estimated to be a +15 RAR hitter with the bat (and about neutral on the basepaths).  Add in a positional adjustment- with the assumption that he’ll be playing all of his innings at shortstop- there’s an additional +7.1 runs, bringing him up to 22 RAR.  CAIRO has Tejada estimated at -5 runs at shortstop, and, given some scouting reports, this seems like a reasonable estimate.  This brings Tejada back down to +17 RAR, which translates into 1.8 WAR.  Assuming the $WAR figure is somewhere in the ballpark of $4.5MM per Win, this puts Tejada’s estimated monetary value at about $8MM.

Tejada’s set to make $7MM total, so I’m in the ballpark.  As it currently stands, it looks like the Giants are paying Tejada right around his estimated market value.  No complaints here.

As mentioned before, Sabean is pretty hesitant to pull the trigger on a trade- and in the case of Tejada, I think it makes perfect sense.  Marco Scutaro, Jason Bartlett nor JJ Hardy (unless healthy) represent a substantial upgrade over Tejada; certainly not enough to warrant spending $6MM for their salary in addition to shipping off assets for one year of their services.  In terms of free agents, Orlando Cabrera just isn’t very good any more, and the Giants weren’t going to sign Derek Jeter to a lucrative multi-year deal.  Miggy just makes too much sense for the Giants.

I get the feeling that the signing of Tejada and the re-signing of Burrell is likely all we’ll see from the Giants this offseason.

Guest Post: Giants’ Top Prospects

December 2, 2010

It’s hell week (finals) for me right now, so it’s perfect time for a guest post.  This is Ben Yarrington listing his top 10 prospects for the Giants:

10. Luke Anders, 1B – Anders was selected in the 32nd round (957th overall) of the 2009 amateur draft. Anders attended Texas A&M University and enjoyed a great deal of success (.344/.480/.660 with 15 homeruns). Anders was known in college for his ability to pound right handed pitchers. In Anders’ first year in the minors, he fared somewhat well, posting a line of .284/.370/.413 at Short-Season A, Salem-Keizer. In 2010, Anders saw a bit of improvement in his numbers at the class A Augusta Greenjackets. He is viewed as above average defensively, and has excellent range due in part to his (left) handedness.

2010 stats: 501 AB, .285/.340/.443/.783, 14 HR, 43 BB, 147 SO

Perfect world comparison: James Loney

9. Jorge Bucardo, P – Bucardo was an international prospect the Giants picked up from Nicaragua. He was not drafted by any team and has no college on record. The Giants signed Bucardo in 2007 when he was only 17 years old. From 2007 to 2009 across different levels of the minors, Bucardo compiled a 2.52 ERA in just under 200 innings pitched. In that time, Bucardo has also posted an Impressive WHIP of just 1.06 to go with 154 strikeouts and only 43 walks. He lost a bit of his control recently, surrendering more walks than he’s used too. Unfortunately, Bucardo’s strikeout total has also taken a hit. However, he’s still living up to the expectations that were given when he was signed by the Giants. This year, the 20 year old Bucardo has pitched for the Class A Augusta Greenjackets and the Class Advanced A San Jose Giants. He was just recently called up to San Jose, so a majority of his stats come from Augusta

2010 stats: 11-6, 2.77 ERA, 1.17 WHIP, 152.2 IP, 129 Hits, 6 HR, 121 SO, 50 BB (2.42 K/BB ratio)

Perfect world comparison: Fausto Carmona

8. Roger Kieschnick, RF – Kieschnick was drafted by the Giants in the 3rd round of the 2008 draft. He’s a very interesting player as he is a 5-tool guy, although he has quite a few flaws. He’s reviewed as being very good defensively with lots of range and a strong arm as well as very good power ratings, but appears to be a low contact hitter. It’s said that he has a poor approach at the plate even though he walks a fair amount. Kieschnick is one of the Giants strongest prospects as his swing uses mostly arm strength, as well as having the aforementioned outfield arm.

2010 stats: 223 AB, .251/.305/.368/.673, 4 HR, 18 BB, 55 SO

Perfect world comparison: Hunter Pence

7. Brandon Crawford, SS – Crawford is another very interesting prospect. Crawford was viewed as the steal of the 2008 draft. He had 1st round talent but was drafted in the 4th round due to a poor junior season at UCLA. He continually shows flashes of excellence that often stump scouts, but then settles back down to earth. He’s also viewed as another potential 5-tool player, if he gets his contact hitting ability up – and that’s a big if. Crawford has tremendous defense all around; speed, range, arm accuracy and strength. While he does seem to have some raw left handed power, he’s never been able to translate that into games. Overall, Crawford has tremendous defense, and a lot of potential with the bat, but hasn’t put it together offensively, yet.

2010 stats: 309 AB, .236/.332/.366/.698, 7 HR, 41 BB, 82 SO

Perfect world comparison: Stephen Drew

6. Conor Gillaspie, 3B – Gillaspie was a compensation pick in 2008 due to the Phillies’ signing of Pedro Feliz. He was the 37th overall pick and he bats left handed yet throws righty. He’s shown he can handle the hot corner well enough, but his defense is nothing spectacular. He doesn’t quite have the power you’d expect from a corner infielder, but he does hit the gaps pretty well.  All signs, offensively and defensively, point to him becoming a second basemen eventually.

2010 stats: 491 AB, .287/.335/.420/.754, 8 HR, 37 BB, 67 SO

Perfect world comparison: Placido Polanco

5. Charlie Culberson, INF – Culberson is a bit of a surprise to see on this list, but he’s improved his game in virtually every way possible. In 2 years at Single A Augusta, he posted a line of: .241/.299/.311/.610, 5 HR with a strikeout percentage of just under 20 and a K/BB ratio over 3. Everything was going wrong for Culberson. Not only was he being incredibly impatient, but he just wasn’t seeing or hitting the ball well either. Now, I was almost positive Culberson would return to his old ways this season, but at every possible opportunity, he’s proved me wrong. His K rate has dropped to 17.8%, his power numbers have improved, he’s walking more and he’s showing better overall judgment. If there was ever reason to believe that Culberson’s stats would take a hit, the departure of Brandon Belt to double A Richmond would be it. Culberson did hit a one week slump, but has come back hotter than ever since. Culberson has played over 100 games at 2B, 3B and SS each in the minor leagues and while he is improving nearly every season defensively, he is still considered a bit of a below average defender.

2010 stats: 503 AB, .290/.340/.457/.797, 16 HR, 33 BB, 99 SO

Perfect world comparison: Martin Prado

4. Francisco Peguero, OF – Peguero is a particularly interesting prospect because he’s one of the few players where his natural skill is matched by his intensity and passion for the game. Many of the Giants’ minor league coaches say his enthusiasm and high-energy performance remind them of 2009 star Pablo Sandoval. Unlike Sandoval, Peguero is an outstanding athlete with plus range in the outfield as well as a plus arm. Peguero hits for more contact than power due to his inside-out style swing. His ability to run the bases well is another key attribute for Peguero, as he uses that to make up for his over aggressiveness at the plate which might lead to walks becoming strikeouts. He was added to the 40 man roster not long ago, and if he continues progressing as he has been, he should be a September call up in 2011.

2010 stats: 510 AB, .329/.358/.488/.846, 10 HR, 18 BB, 88 SO

Perfect world comparison: Austin Jackson

3. Zach Wheeler, P – Wheeler is a favorite of mine, as he was the only one I wanted in the 2009 draft. Wheeler has 3 main pitches: Fastball, changeup, slider. Wheeler claims to have a two seem and four seam fastball. His fastball is generally in the 91-94 MPH range, but can top out at 96 on his best days. It tails away from right handed batters and towards left handed batters. Wheeler’s slider is viewed as his best pitch. It comes out of his hands at the exact same point as the fastball and for most of the flight looks exactly like the fastball. It breaks a little earlier than scouts would prefer, so he could improve in that sense. It has a very hard break and generates lots of swings and misses. Wheeler’s changeup is his worst pitch at the moment. The velocity of it is too close to that of the fastball to get hitters off balance. Wheeler is working incredibly hard to develop his changeup, however.

2010 stats: 3-3, 3.99 ERA, 1.45 WHIP, 58.2, 47 Hits, 0 HR, 70 SO, 38 BB (1.82 K/BB ratio)

Perfect world comparison: C.J. Wilson

2. Thomas Neal, OF – Thomas Neal is a very attractive prospect as he’s worked to fix the overwhelming problem he had in previous years. In 2006 and 2008, Neal struck out about 20% of the time he came to bat. In 2009 that number dropped to 17% and in 2010, 16%. Other than that, Neal could be considered a miniature version of a five tool prospect. Neal hits for good average, floating around .300 for his entire minor league career. Neal has also hit around 15 homeruns in each of his full minor league seasons. On top of that, he’s been rated as a +4 in total zone rating as far as defense goes throughout his minor league career, but these ratings are subject to speculation, he is also viewed as having an above average – but not excellent – throwing arm. As far as base running goes, he has above average speed, but does not have a particularly good jump and doesn’t attempt very often. In three full seasons of minor league play, he has 17 steals, but this is not very indicative of his natural athleticism and speed.

2010 stats: 525 AB, .291/.359/.440/.799, 12 HR, 46 BB, 94 SO (2.04 K/BB ratio)

Perfect world comparison: Nick Markakis

1. Brandon Belt, 1B – Now for the surprise of the century, Brandon Belt as the Giants’ #1 prospect. Belt, a 5th round pick out of Texas, was barely even on some top 20’s prior to this year. But Belt surprised everyone this year, flying through the minor leagues before ending the season in AAA Fresno. Belt, a 6’5”, 205 pound 1st basemen has incredible raw power as well as surprisingly good fielding fundamentals. Belt put up numbers that beat even that of the incredible Buster Posey, posting a line of .352/.455/.620/1.075 in 3 different levels of the minors. Belt figures to be a factor in determining who plays where next season, potentially pushing current first basemen Aubrey Huff into the outfield.

2010 stats: 492 AB, .352/.455/.620/1.075, 23 HR, 93 BB, 99 SO (1.06 K/BB ratio)

Perfect world comparison: Justin Morneau

2011 CAIRO Projections v.0.1 Now Available

November 30, 2010

A few weeks ago, I began constructing my own forecasting system to help project players’ future performances in order to estimate 1) their true talent level, and 2) make an educated guess as to how they will perform in the next season.  I had a few ideas in mind as to what I wanted to do- use about five years of data (weighted, with the most recent season weighted the heaviest), regressed to the population mean, along with component aging factors and component park factors.

Sure enough, somebody has done this already.  SG of The Replacement Level Yankees Weblog has released his CAIRO forecasts (version 1.0) for the 2011 season.  CAIRO uses four years, however, but it also uses Major League Equivalencies to translate Minor League performance in an attempt to estimate the player’s likely performance at the Major League level.  I guess you could say that this is the type of system I wanted to put together- luckily for me, this means I won’t have to do any of the heavy lifting myself!

By the way, I’d like to mention one huge pet peeve of mine before sending you to the link.  It’s pretty common to see responses to a player’s forecast along the lines of “what the ****?  How can they say (insert player name) is going to have only ___?  I can’t take this seriously- this is pure bull****!”  And boy, it gets annoying.  There are a few things to keep in mind when it comes to projection systems.  The first is that since we’re dealing with human inputs, there’s a good likelihood that there will be a bug here or there in the initial releases of the system.  This happens.  Secondly, projection systems do not take in to account esoteric knowledge about the player’s recent weight loss, improved vision, revamped mechanics or anything along those lines.  It’s nothing more than an educated guess, and more often than not it will be exceptionally pessimistic due to regression and the like (unless you’re the Bill James Forecasts).

I hope you enjoy these as much as I do.

CAIRO 2011, Version 0.1