Today, MLB’s winter meetings kick off in Las Vegas, and with that, the off-season kicks into high gear. We’ll see a bunch of trades, some free agent signings, and a lot of rumors floating around in the next few days. Here at FanGraphs, we’re going to team up to bring you nearly instant analysis of the transactions, breaking them down as they happen. If there’s a lull, we’ll still be pushing out our regular content, but expect heavy coverage of all the moves over the next few days.

Trade number one, according to Ken Rosenthal, is Gerald Laird going from Texas to Detroit to become the Tigers new starting catcher. Texas clearly needed to move Laird due to their logjam behind the plate, and the Tigers needed a backstop, so this was a good fit between the two teams. But what should Detroit expect from Laird?

Inconsistency is probably the best expectation. Laird’s had an up and down career, where he went from very good in 2006 to miserable in 2007 before bouncing back to be okay in 2008. His skills haven’t changed much, but he’s gotten drastically different results from his balls in play over the last three years: a .345 BABIP in 2006, a .278 mark in 2007, and a .315 mark in 2008. His career BABIP is .310, just a bit above average, so both ’06 and ’07 stand out as random variance. He’s not a .296 or .224 hitter.

For 2009, Marcel has him at .259/.313/.398 for a .310 wOBA, but remember, Marcel doesn’t do park adjustments, so it doesn’t know that Texas is a fun place to hit. We need to knock that projection down to account for the lack of 81 home games in Arlington, so let’s call Laird a .300 wOBA guy for next year.

A .300 wOBA would make Laird worth about 15 runs less than an average hitter over 500 PA, but of course, catchers don’t hit like average hitters, so the +12.5 run positional adjustment covers almost all of that, and leave’s Laird as a -2.5 run offensive player. A bit below average for his position, basically.

Catcher defense is extremely hard to measure as a whole, but we can measure parts, such as blocking balls in the dirt and controlling the running game. Laird is above average at those by about five runs, so we’ll call his defensive value +5, admitting that there’s a huge part of his job that we just can’t measure yet.

-2.5 offense + 5 defense = +2.5 runs compared to a league average catcher. That makes Laird something like a +2 to +2.5 win player, or a guy who should command something like $10 to $12 million per season on the open market. He’s arbitration eligible, however, and unlikely to get more than $3 or $4 million in salary for 2009. That makes Laird a pretty huge bargain.

Detroit did a nice job of identifying Laird as a guy who could help them. Marc will be around to tell you about the prospects they’re sending to Texas, but I’d say this move gets a thumbs up for the Tigers.

With the economy in a rut, people everywhere are cutting back on their discretionary spending. Even with MLB seemingly in pretty good financial shape for now, one clear trend has emerged from the signings we’ve seen occur so far this off-seasom, and that’s that teams have decided that setup men just aren’t worth big bucks anymore.

Last year, 21 non-closer relief pitchers signed major league contracts, ranging from the $500,000 Chad Paronto got from the Braves to the $4.75 million that Scott Linebrink got from the White Sox. Those 21 players signed for an average of $3.1 million per year, and the top of the class got multiyear deals – four for Linebrink, three for David Riske, and two for Octavio Dotel, Ron Mahay, Luis Vizcaino, and Masa Kobayashi.

So far this year, we’ve seen two relievers get a multi-year deal (Jeremy Affeldt, two years at $4 million each and Mike Lincoln, two years at $2 million each) while Doug Brocail and Bob Howry both took $2.75 million for one year, Jorge Julio got $950,000 for one year, and Trever Miller got $500,000 for one year.

Affeldt had a breakthrough year and was probably the top left-handed relief pitcher available. He signed for half of what the top setup men were getting last year. Riske had a 4.45 FIP in 2007 and got $13 million for three years – Howry had a 4.49 FIP in 2008 and settled for $10 million less than that.

If Linebrink were a free agent in this market, it’s not even clear he’d get more than a one year deal after getting four years last winter.

Perhaps Juan Cruz will surprise me and get a monster deal, but right now, it looks like the reliever market is going through a pretty significant correction. If your team needs a bullpen upgrade, this looks like the year to be a strong buyer, because there are deals to be had.

Yesterday, we spent a couple of posts talking about how defensive statistics have to be handled a bit differently than offensive statistics. Today, we’ll use a current event transaction to delve into this a little bit further.

Yesterday, the Cardinals acquired Khalil Greene from the Padres to fill their void at shortstop. The Padres are in cost cutting mode, and didn’t want to pay Greene the $6.5 million he was due in the last year of his contract. The Cardinals wanted to upgrade from Cesar Izturis, and are hopeful that Greene can provide significantly more offensive value now that they’re freeing him from the clutches of Petco Park.

However, Tony LaRussa has always been a defense first guy at shortstop. From Mike Bordick to Royce Clayton, LaRussa wants his shortstops to defend well, and if they hit, that’s a nice bonus. Clearly, the Cardinals wouldn’t have acquired Greene had they not been convinced that he could handle playing shortstop at a level satisfactory to LaRussa’s desires. So what do we make of Greene’s defense?

Well, his +/- ratings from 2006 to 2008 go from +13 to +7 to -4. If each year was considered an isoloated result, we’d conclude that two years ago Greene was one of the game’s best defenders at the toughest position to field but had fallen off substantially since then, to the point of being below average now.

Was Greene a terrific fielder who has since deteriorated to the point of being a minor liability? Maybe, but because of the variance in single year metrics, we certainly can’t state that with any kind of confidence. However, if we view each year as a data point, we’d find that the mean of his defensive value over the last three years is between +0 and +5, depending on how much weight you give to the most recent data points. Because of the larger sample, we can state with significantly more confidence that Greene was something like a +0 to +5 defender over the past three years, which would allow us to make a pretty decent projection for what he’ll do going forward.

As with all projections, a multi-year weighted collection of data will be more accurate than if you simply use the past year’s results and take them as the gospel truth. Every projection system worth it’s salt incorporates regression over several years to determine future output, and with Greene, that’s exactly what we have to do with the defensive data. The -4 rating from 2008 is a data point, but whether it represented an actual decline in his ability or not, we just don’t know. There’s too much noise in the data to support that kind of claim.

So, Cardinals fans should expect Greene to be something like a league average defensive shortstop or maybe a tick above. Toss in enough gap power to overcome his low OBP, and St. Louis just picked up a +2 win player for 2009. Considering his salary only values him at just over 1.25 wins, the Cardinals are getting a bargain for their money. Unless the PTBNL in the day is a significant prospect, we’d have to call this a good move for St. Louis – even with Greene’s -4 +/- score from 2008.

A Definition of Inferential Statistics:

With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone.

This afternoon, I talked about why defensive statistics are not like offensive statistics, and closed with a statement about why I believe that defensive metrics should be viewed as inferential statistics, rather than the results of something that actually occurred. The definition above states it as well as anything I could write – what we want to do with metrics like the advanced defensive statistics we currently have is to make conclusions based on probability that go beyond the data that we have.

Let’s use a baseball example. The +/- system spit out a +47 rating for Chase Utley for 2008, calling him 47 plays better than an average defensive second baseman last year. It’s such an amazingly high number that, on it’s own, it’s basically unbelievable. Did Utley really display such amazing defense that he got to 47 more balls than an average fielder? And if so, how did such a remarkable performance go basically unnoticed by baseball observers?

Perhaps your initial reaction to such an unbelievable number would be to throw it out and discredit the system. After all, if I invented a metric that said that Chase Utley hit .434 last season, you’d just point to the facts and tell me I was wrong. But with defensive metrics, one of the basic tenets we have to accept is that we just can’t know for certain whether an average fielder would have actually fielded a particular ball, because this mythical average fielder didn’t have a chance to field that ball – only the fielder that we’re watching got a chance to field that ball. Whether anyone else could have fielded that ball has to be inferred, since it cannot be known.

This is the fundamental point to accepting defensive statistics – they know very little and infer an awful lot.

This doesn’t make them wrong or invalid. There are all kinds of statistics in life that are inferential and, when constructed correctly, give us meaningful information to make our life better. Political polling data is one of the best examples, and the match between polling data and baseball statistics got quite a bit of play with Nate Silver’s rise to fame this summer. When the data is handled correctly, inferential statistics help us answer questions we can’t figure out through descriptive statistics, and right now, defensive value is one of those things that must be inferred.

So, how do we view these numbers differently than if they were descriptive in nature? The key is to see them as data points as part of a larger sample and not take any one single data point too seriously. +/- thinks Utley was +47 last year. Okay. That’s nice. We’ll toss it into the stew, along with as many other valid data points as we can gather and determine how confident we can be within certain boundaries based on the sample that we have.

If you’ve taken a college course on statistics, you’ve probably learned about t tests and how to calculate necessary sample sizes based on given data. We won’t go through the math here, but research from guys like Chris Dial, TangoTiger, and MGL suggest that we need at least two years worth of data before we can start drawing reasonable conclusions from the defensive data we have now. Two years is a minimum. Three is a lot better, and gets us close to the point where we can be comfortable with the results.

With several years worth of data, we can be confidant that the sample is large enough that the noise in the data can be reduced to the point where our inferences can be at least generally accurate. Viewed by itself, Utley’s +47 is highly questionable. When viewed in concert with his +20 ratings in both 2006 and 2007, we can infer that Utley is probably something like a +25 defender compared to an average second baseman.

The human factor is still there, and we can’t pretend like a larger sample eliminates noise entirely, but we can begin to be confident that we can describe a player’s defensive value within a given range and be fairly accurate. Maybe we can’t prove that Utley’s a +25 fielder, but we could say that the probability of his real defensive value being between +20 and +30 is very high.

When someone tells you that defensive statistics simply aren’t as reliable as their offensive brethren, they’re right – there’s no doubt that the tools we have to measure offense are more precise than the ones to measure defense. But as statistics like UZR and +/- have come along, our ability to infer reasonably accurate conclusions about defensive value has grown immensely. They aren’t perfect, but when viewed as a data point, and analyzed as an inferential statistic, we can gather all kinds of information that we’ve never had before. And that’s exciting.

If there’s been an underlying theme to my writing here over the last year, it’s been that defense is significantly undervalued in MLB. Well, it seems like people are catching on. From the latest Peter Gammons’ article:

“The other thing is that teams are moving away from the base offensive statistics,” says another GM. “They are pouring through defensive studies and seeing that below-average defenders like Ramirez and Burrell in the field depreciate their offensive numbers because of what they give up.”

This isn’t just talk – last week, the Yankees, D’Backs, and Phillies decided that they had no interest in risking a one year arbitration offer to Bobby Abreu, Adam Dunn, or Pat Burrell, all good hitters who are miserable defensive outfielders. These guys made between $13 and $16 million in 2008, but their employers figured out that their overall production (with defense included in the calculations) just weren’t worth those kinds of paydays again.

So, with this shift in thinking apparently creeping into MLB, I figure it’s time that we spend a bit of time talking about defensive statistics and how they should be viewed. Over the next couple of days, we’ll talk about the theory behind them, and how they should be interpreted.

There are, essentially, two kinds of two defensive statistics available right now. The first would best be described as estimators – these include things like Zone Rating (and it’s THT derivative, Revised Zone Rating) and BP’s FRAA/FRAR. These measures have been around for a while, and because they don’t require a huge amount of precise data to calculate, offer very rough ideas of a player’s defensive value. More recently, several advanced defensive metrics have been created based on more precise play-by-play data – these include Ultimate Zone Rating, Plus/Minus, and Probablistic Model of Range.

The latter are the types that have pushed the new wave of defensive valuation forward, and these are the kinds of “defensive studies” that Gammons refers to. The extra data required helps make them more precise, giving us a better view of how much defense actually matters and how good each player is relative to his peers. For understanding defense, they’re a huge step forward from where we were 5 to 10 years ago.

However, there’s a pretty significant difference between the modern defensive statistics and the numbers that we use to value offense, and that lies in the variability of human error. When we talk about something like on base percentage, it is a statistic based on indisputable factual results – Player X reached base Y times in Z plate appearances. There’s no gray area – it happened, it was recorded, and no one disagrees. You could call OBP, and other things like that, a descriptive statistic – it describes a series of irrefutable events that definitely occurred.

Things like UZR, +/-, and PMR, however, are not simply recording incontrovertible facts. In order to get increased data precision, a human judgment is required to determine where on the field the ball landed and how hard the ball was hit, which both go into the calculation of how likely it was that an average fielder would have caught that ball. Humans, of course, aren’t perfect, so their required inclusion makes the data less reliable. We have to factor in human error when we’re looking at the results of these statistics, because we cannot assume 100% accuracy when there’s a subjective call in the equation.

Because of the variability of human error, these defensive metrics are simply not descriptive statistics. Instead, they are what I would consider an inferential statistic, which is still every bit as valid, but requires a different viewpoint for understanding the data. In the 5 pm post, we’ll look at what I mean by an inferential statistic and how they should be analyzed.

Continuing their quest to upgrade their bullpen, the Giants signed Bob Howry to a one year, $2.75 million contract today. That Howry had to settle for such a modest deal shows how much power ERA still has in determining pitcher value in the marketplace.

From 2004 to 2007, Howry was one of the game’s better setup men, posting a FIP between 3.07 and 3.73 in each year, and doing even better than that in ERA – 2.47 to 3.32. He doesn’t walk anyone and strikes out a fair number of hitters, which is a good combination for an 8th inning reliever. Nothing about that changed last year, and his 4.54 K/BB rate in 2008 was actually the highest of his career.

However, Howry posted a 5.35 ERA last year for two reasons – his home run skyrocketed to 1.66 HR/9 and his BABIP was .354. Both of these are more about bad luck than any drop in Howry’s skills – his FB% was the same as always, but more of those flyballs just went over the wall. In 70 innings, that kind of variance is going to happen. That doesn’t mean we should expect it to continue, though.

Marcel projects Howry to post a 4.17 FIP in 2009, and that’s factoring in a pretty significant age related regression – it has him adding nearly a walk per nine innings to his total and retaining a pretty decent chunk of the HR issues that plagued him in 2008. It’d be fair to call Marcel’s projection conservative, and I’d suggest that he’s more likely to post a FIP in the 4.00 range.

If a replacement level NL reliever would post a 4.75 FIP, that makes Howry about 6 runs better than replacement, assuming he pitches 65 innings next year. Adding in a leverage factor to account for the extra value of the runs he’ll be saving as a late inning reliever, and Howry’s basically a +1 win reliever.

The Giants just bought a win for around $3 million. Even in this economy, that’s a bargain. Affeldt and Howry are significant upgrades to the Giants bullpen, and for a total 2009 cost of $7 million, the Giants have gotten quite a bit better already this winter.

Last night, the Braves decided to end all the Jake Peavy speculation and go in another direction – that direction was Javier Vazquez, as they sent a group of prospects to the White Sox for the talented but enigmatic Puerto Rican. With only 2 years and $23 million left on his deal, he’s significantly cheaper than Peavy, and they didn’t have to touch their major league roster or their top prospects in order to add him to their rotation. However, lower cost doesn’t always mean better value – so, let’s look at what the Braves should expect to get from Vazquez.

If there’s one word to describe Vazquez, it’s durable. He’s thrown 200 innings every single year this decade except for 2004, when he threw 198 innings. He’s made 32-34 starts in every one of those years. The man takes the ball every five days without fail. If it’s innings the Braves were looking for, they found the right guy.

The quality of those innings, though, that’s another story. As Eric noted a month ago, there’s not a pitcher in baseball who has underachieved as much in his career as Vazquez. His career FIP is an outstanding 3.93, but his ERA is 4.32 – four tenths of a run higher over 2,270 innings. Based on his BB/K/HR rates, Vazquez should have performed significantly better than he has in his career.

However, for whatever reason, he just can’t seem to strand runners to save his life. His career LOB% is 70.4%, which is pretty much dead on league average – but Vazquez isn’t a league average pitcher, and with his ability to miss bats while not walking batters, he should be better than average at runner stranding as well. Peavy, for instance, has a career 77.6% LOB% on a 3.51 FIP. The difference in strand rate is significantly larger than the difference in their core skills, and it’s why Peavy has won a Cy Young award and Vazquez has been traded numerous times.

Even with his inconsistency, Vazquez should be good for 200 innings with a 4.00 ERA or so in the NL, making him worth about +3 wins compared to a replacement level pitcher. That’s a solid addition to any rotation, but is he worth $11.5 million a year plus four young prospects?

Maybe. If he lives up to his talent, the Braves will be happy with the trade. If he continues to underachieve, though, we may be analyzing next year’s Vazquez trade at this time.

Yesterday, the Houston Astros announced they have signed Mike Hampton to a contract. This time around, Hampton isn’t setting a record for the largest contract ever signed by a pitcher, but instead settling for a guaranteed $2 million for 2009 with the ability to make another $2 million if he hits certain incentive markers.

Considering Hampton didn’t pitch at all in 2006 or 2007 and only managed 78 innings last year, there’s some understandable skepticism about just how long he’ll last before succumbing to another injury. Reaction in some corners to giving Hampton guaranteed money likens it to setting money ablaze.

Honestly, I had the exact opposite reaction – I think this is a classic no risk move that could pay solid dividends for the Astros.

Hampton has one obvious major league skill – the ability to get ground balls. His sinker has good movement, and he’s always been a groundball pitcher. That didn’t go away while he was rehabbing, either – his 2008 GB% of 52.7% was higher than any mark he posted while healthy from 2002 to 2005. Because Hampton keeps the ball on the ground, his primary way of keeping runs off the board has always been to limit the long ball – his career 0.78 HR/9 rate is one of the best in the league for active pitchers.

Because he’s able to prevent home runs, he’s been able to succeed with below average walk and strikeout rates – his career 1.53 K/BB rate is nothing to write home about, but because he hasn’t given up many home runs, he’s posted a career 4.25 FIP.

Hampton showed the same skillset last year – mediocre command, doesn’t miss many bats, but still gets a bunch of groundballs. However, 15.2% of his flyballs went over the wall, leading to a 4.94 FIP. As we’ve talked about, HR/FB rate fluctuates quite a bit from year to year due to factors outside of a pitcher’s control, so we can’t just assume that this was some flaw in Hampton’s pitching that caused the surge in longballs. If Hampton had allowed HRs on just 10% of his fly balls, he’d have allowed four fewer home home runs, and his FIP would have been 4.34, pretty much right in line with his career norms.

Even factoring in some regression due to age, it’s hard to get much lower than a true talent level 4.60 FIP for Hampton. If we assume that a replacement level starter in the NL would post a 5.50 FIP, and that Hampton’s injury problems limit him to 100 innings, then we still get a 10 run difference between Hampton and a replacement level starter.

Signing a +1 win pitcher for $2-$4 million (it’s impossible to know which incentives he’d hit in our scenario) is a pretty big bargain, even in this depressed free agent economy. Good move for the Astros.

If you haven’t yet, read Eric’s post below about the need to add context to contract analysis, depending on a team’s ability to contend. I don’t know that I agree with the idea that bad teams should never improve themselves through free agency if it doesn’t result in a playoff berth, but I did want to build off of Eric’s point about teams having different marginal win/dollar rates at which a contract makes sense.

Intuitively, everyone knows this is true – a contract that makes sense for the Yankees doesn’t necessarily make sense for the Rays, simply due to the massive differences in payroll. Alex Rodriguez can be an asset for New York while he’d simultaneously be a liability for other teams simply due to the context of the specific organizations.

However, I don’t know that I’ve ever seen anyone break down the marginal win/dollar rates for each franchise, based on projected payroll, so that’s what we’ll do this afternoon. For those unfamiliar with the marginal win/dollar metrics, they were first made famous by the late Doug Pappas, and his work has been continued on by countless others. The concept is built around the belief that a team of replacement level, freely available players would finish with something like 50 wins, and due to their freely available nature, they shouldn’t cost any more than the league minimum.

Basically, we’re saying that a team that isn’t trying to contend could win 50 games on a payroll of about $12 million, which assumes a $400,000 contract for 30 players. That team would be paying about $240,000 per absolute win, and since they’re the baseline we’re building off of, obviously they wouldn’t be spending any marginal money or accumulating any marginal wins.

To apply this to a team from last year, the Seattle Mariners spent $117 million and won just 61 games. In other words, they spent $105 million to win an extra 11 games over what they could have won without even trying to contend, so the Mariners spent $9.55 million per marginal win. That’s bad. Really bad. A team with that marginal win/dollar rate would have needed a payroll of about $382 million in order to be a 90 win team, which would put them in contention while not guaranteeing a playoff spot.

Hopefully, you have a pretty good grasp of marginal win/dollar rates now. Now, using 2008 team payrolls, adding a 5% markup to adjust for modest salary inflation, and assuming that every team’s target should be 40 marginal wins added, here are the team specific dollar per win rates for 2009 – obviously, for some teams like Seattle, these won’t reflect a change in direction from potential contender to rebuilding.

The Yankees can spend $5 million per win across their entire roster. Remember, we’ve been saying that wins on the free agent market cost just over $5 million per win – if there were enough free agents available, the Yankees have a large enough payroll to build a contender from scratch just by signing free agents. Once you add in the presence of cost-controlled players like Joba Chamberlain, Chien-Ming Wang, and Robinson Cano, where they’re paying quite a bit less than $5 million per win for players developed from within, and the Yankees can actually justify free agent contracts up to $7 or $8 million per win. They can spend $15 or $16 million on a league average player in order to fill out the roster and have it not be a real problem. That’s a remarkable financial advantage.

On the other end of the spectrum, you see teams like Tampa, Florida, and Oakland, who have to try to contend while spending less than $1 million per win. The only way to do that is to build from within, which is why these organizations covet major league players in their 1st-3rd years of service time – you can get huge production for no money, which is absolutely vital to trying to build a 90 win team for less than $1 million per marginal win.

These payroll specific dollar per win rates are a good step in the right direction to evaluating whether a contract makes sense for a specific team. However, there’s still a pretty huge missing piece of information here, and that’s the amount of wins currently on a roster. A team needs to understand how many wins they really have to add and how much money they have available in order to evaluate a specific move, and we’ve only calculated the latter half in this post. In a future post, we’ll take the next step and factor in the wins already in place.

Every free agent class has positions of strength and weakness. For reasons that are nothing more than cyclical, there’s always a group of similar players who hit free agency at the same time. This year is no different – there is one player type that is found in abundance, and that is the Overrated Run Producing Out Fielder Who Sucks At Defense. The ORPOFWSAD is the new black.

Obviously, the main attraction is Manny Ramirez. His monster finish to the year in Los Angeles has Scott Boras calling him a franchise player and talking about a contract that runs into his 40s. Manny can obviously still hit – Marcel has him projected at a .389 wOBA, which translates into 30 runs above an average hitter over 600 plate appearances, but his defense is miserable. Even if you charitably call it -15 runs over a full season, he’s giving back a huge chunk of his value with his lack of range. While Manny may get paid like a superstar, in reality, he’s more like a +3 win player.

For those who aren’t into the Manny Being Manny show, you can move right along to Adam Dunn. Like Manny, he can hit (Marcel projects a .372 wOBA), but his defense is miserable and he really should sign with an AL team where he can DH. Since he’s not as good a hitter as Manny but has similar struggles in the field, he ranks as a league average player (+2 wins compared to replacement level). You can bet he’ll get paid more than the $10 million or so he’s worth, though.

For owners who don’t want Manny’s antics or Dunn’s strikeouts, have no fear, there’s always Pat Burrell. Like Dunn, he makes up for a low average with a lot of walks, and he’s a pretty solid offensive player whose defense makes him a league average player overall. Not a bad guy to have on your roster, but if teams continue to overpay for RBIs like they have in the past, he’s not going to earn his money for his next employer.

If that’s enough options, or you just don’t like any of those three, Bobby Abreu might appeal to a team that wants a guy who can get on base and has some power. He isn’t the longball threat that the first three are, but he’s every bit as bad with the glove. He’s a decent enough hitter (Marcel projects him at .357 wOBA), but not good enough to be more than a slightly below average player.

Last, and probably least, Raul Ibanez has been lumped in with this group despite being a massively inferior hitter. He hits for a higher average, but he doesn’t have the same kind of power or patience as the others, and Marcel projects him for just a .344 wOBA in 2009. Combined with atrocious defense, Ibanez is a below average player in the +1 to +1.5 win range, but his reputation as a good clubhouse guy, hard worker, and run producer will get him a contract that he just doesn’t have the ability to live up to.

Historically, this player type gets paid very well in free agency. Guys like Carlos Lee and Jose Guillen have cashed in the last two winters, and their teams have simply not benefited from their presence as much as they expected, because this is probably the single most overrated player type in all of baseball. The good hit/bad glove corner outfielder is simply not an impact player, and almost always commands more money than they are worth.

If you find your favorite team bidding for one of these guys, you have my sympathy.