As part of “projection week” here at FanGraphs, this post follows Monday’s by discussing two phenomena that are often brought up in relation to projections: “Outliers” and “Breakouts.” Although they contain elements of truth, these notions are often used in problematic fashion to show that projections are “wrong.”

An “outlier” is a season that appears to differ greatly from a player’s usual performance. Some will claim that said season should be ignored when projecting a player, since it “obviously” does not represent his real skill. A “breakout” season is one in which a (usually young) player greatly exceeds expectations and/or past performance. The season is seen as establishing a new level of performance such that prior performance should be weighted much less heavily or ignored.

You may have noticed the potential contradiction. While the “outlier theory” claims that a single season deviating from an apparently established level of performance should be thrown out, the “breakout theory” claims that a single season deviating greatly from earlier seasons means that it should be looked at to the exclusion of the others. This isn’t necessarily a contradiction, as one could hold that there are particular conditions for outliers and breakouts — outliers might only apply to players in their prime, or breakouts to young players. Still, it’s worth noting, as you’ll often see the same person assert both.

The deeper and more important point is that by looking at one-year deviations as establishing a new level of performance that thus takes on a greater weight (breakout!) or as being irrelevant and thus in need of exclusion (outlier), both positions implicitly assume they already know what we’re trying to find out when projecting a player: his “true talent.” Recall the “general formula for player performance” from Monday’s post: performance = true talent + luck. The various methods that projection systems use (regression, weighted averages, age adjustments, etc.) are meant to take the (limited) data we have for a player and filter out luck in order to estimate his current true talent. These methods are predicated on the fact that we can’t pinpoint the player’s true talent given the limited performance samples we have, so we make our best estimate based on probabilities.

Labeling a single season as irrelevant or supremely relevant to estimating a player’s true talent implicitly assumes that one already knows that player’s true talent. One can certainly cite examples of each kind to support the case for a “breakout” or “outlier.” One could just as easily come up with (many more) examples of the opposite — where a perceived “breakout” or “outlier” turned out not to have the (in)significance assigned to it. But to do either obscures the important point. It is true that individual players age differently and deviate from expectations. However, projection systems only obtain the overall accuracy they have by projecting players as a whole based on the data on hand. An apparent “outlier” season from two years ago may weigh less heavily because time passing and/or, say, BABIP being regressed more heavily than other skills. An apparent “breakout” by a young player may have more impact on the projection because of age adjustment, greater playing time, etc. But projection systems do not and should not take these into account beyond their standard adjustments.

A famous German sabermetrician once wrote, “the owl of Minerva begins its flight only with the onset of dusk.” Although in retrospect we can look back on the careers of particular players and identify certain seasons as “outliers” or “breakouts,” this can only be done years later when we have a perspicuous overview of a period of a player’s career as a whole. Projection systems work in the midst of player performance without the benefit of historical perspective, and have to do the best they can based on the information at hand. Doing anything more would revoke the humble presuppositions upon which player projection rests.

It’s “projection week” here at FanGraphs, which is a nice coincidence, since I was going to post about projections, anyway. While I dabble with my own projections (which probably will never see the light of day), no one wants to hear about that. Instead, I’ve just assembled some (very) non-technical reminders that might be helpful when looking at projections.

I’ve often heard the complaint that projections are “arrogant,” “put too much faith in the numbers,” or the classic “they rely on what a player has already done, but they don’t tell you want a player will do.” I want to emphasize that projection systems are not based on esoteric “tricks,” but rather are based on the fact that we don’t know very much about the player from the numbers.

Projection is not divination. I’ve sometimes heard that projection systems aren’t worth looking at because “after all, they projected an .800 OPS for player x and he ended up with an .850 OPS.” That’s a straw man, but it gets at the general point: projections are not prophetic divinations of the future, but attempts to measure what the “true talent” of players at any given point in time. The “general formula” for player performance is: true talent + luck + environment. (I’ll table discussion of parks and aging for now.)

The problem is that we don’t know, at least from the raw stats, what exactly is “luck” and what represents a player’s “true talent.” Moreover, “luck” doesn’t just mean things like BABIP rates. Even a player getting 700 PA in a season will have varying levels of performance around his true talent, what we call “hot streaks” or “cold streaks.” (Cf. Willie Bloomquist, April 2009.) To single these streaks out begs the question: how do we distinguish the “streaks” from the “true talent” parts of the seasons from which the projections draw? Projection systems use different methods; here I’ll mention basic factors that are used by most good projection systems. This may be old hat, but they are worth discussing because of how often they are passed over.

Regression to the mean. This is a very important concept, so important that I’m leery of screwing up the explanation. The best introductory piece I’ve read is one by Dave Studeman. In short: given a lack of any other information about a player, our “best guess” is that he’s an average member of (some particular) population. The more data we have on the player, the more we can separate him from the “average” population. This is one place where sample size issues come into play. [Note that there is a great deal of debate about how to regress, e.g., what the “population” should be. For examples, search at The Book Blog or Baseball Think Factory.]

Weighted average. Say a projection involves the last three years of performance. Do you simply take the three year average? Well, no, true talent can change from year to year. More recent years are thus weighted more heavily (5-4-3 for hitters and 5-3-2 for pitchers are common weights). Alex Gordon had a .321 major-league wOBA in 2009, and a .344 in 2008. Do we automatically assume that .321 is closer to his true talent? No, because the .321 was in only 189 PA, while the .344 was in 571 PA.

This isn’t all there is to projection, but you’d be surprised how much work those basic concepts do. Tom Tango’s Marcel works entirely from a weighted average, regression, and a very basic age adjustment, and it hangs in with the “big boys” pretty well. No projection system will ever be perfect, of course. Part of that is the influence of “luck” and the limited samples we have from all players. Part of it is also that some players don’t have that much information available on them. Players develop differently.

The point is that we simply don’t know ahead of time which players will be exceptions. Projection systems generally do better when looking at how the project groups of players, rather than focusing in on individual successes or failures, as in the case of Matt Wieters (ahem). The point I’ve been trying to make in a roundabout way is that regression, weighted averages, generic aging curves, etc. might miss out on certain players, but are based on studies that show how most players would do. They are humble confessions of ignorance on an individual level, but are still the best overall bet. Expecting anything more leads to folly.

One might express the difference as that between a making a conservative, diversified investment and “just knowing” that Enron stock will continue to rise. Tough choice.

More later this week on “breakouts,” “outliers,” and other traps.

Remember Josh Phelps? In 2002, he came up as a catcher-turned-first-baseman with the Blue Jays and absolutely smashed the ball (.396 wOBA) for 287 plate appearances. His 2003 was decent, if not mind-blowing. Phelps struggled badly in 2004 and got traded to Cleveland mid-season. He bounced around the majors and minors for a while, and though he never blew anyone away for any length of time again, he was mentioned as recently as last off-season as a minor-league deal or possible stopgap/platoon guy at 1B/DH. With glorious half-season in 2002 far off in the rear view mirror, the days of being the semi-darling of a few isolated bloggers are probably over for Phelps; he’ll be 32 next season and only saw action at the minor-league level for the Giants in 2009.

The point with Phelps was not that he was some super-duper mystery pickup that would put a team over the top. The reason he was brought up was because he was F.A.T. (Freely Acquirable Talent) that could make a contribution in the right situation. Unless a player is utterly horrible defensively (and I realize that one could have made such a claim about Phelps), if he’s an above-average hitter, he probably has a place somewhere, especially if he can be had on a minor-league deal.

Which brings us (finally) to the player at-hand: Jeff Bailey. He’ll be 31 next season (almost as old as Phelps). He’s accumulated 159 plate appearances in the majors from 2007-2009. He’s primarily played first base, but has seen a bit of duty in the outfield as well, although that’s a stretch. He’s basically a glorified career minor-leaguer who’s seen the majors when the Red Sox had injuries.

I’m hardly an expert on all things Bailey, but his CHONE projection caught my eye: .249/.348/.417, or 5 runs above average per 150 games. ZiPS concurs, projecting him at .258/.345/.415. The UZR data is in too small a sample to be relevant; Rally’s TotalZone projection for Bailey last season and the Fans Scouting Report this season suggest that Bailey is probably average-to-below-average defensively.

Bailey looks like about a 1 WAR player. He’s going to be 31 and has little (if any) upside. He’s not a player every team should be after for even the right price. It depends on the situation. For example, if there’s an NL team with a hole at first base, maybe a platoon of Bailey and, I dunno… Eric Hinske might be a good idea if the team lacks other options. If a team really has no AAA depth, Bailey’s definitely worth a look.

Like the latter-day Phelps, Bailey doesn’t have much to offer other than a non-horrible right-handed bat. He should be available on a minor-league deal, or, if a bidding war breaks out, at the major-league minimum. Perhaps this is obvious, but remember the New Josh Phelps when you read about a team trading actual talent for or giving millions to the New Mike Jacobs.

Like many wannabe saberdorks, I love Joe Posnanski’s work. It’s not just because he’s so much better than, say, [horrible-and-inexplicably-award-winning columnist for major newspaper] or [rumor-mongering baseball reporter prone to bouts of self-righteousness]. This isn’t a Posnanski tribute, but in short: Posnanski is great because he tells an engaging story and incorporates good baseball analysis without confusing one for the other.

This doesn’t mean that I always agree with Poz.* I disagree with many things written by sportswriters. In Posnanski’s case, I think highly enough of him that it’s worth quibbling over minor points, unlike, say, with [arrogant breaker of stories for your dad’s favorite sports magazine that we would have found out about anyway], who is only worth refuting because of his [alleged] influence. I hold Posnanski to a higher standard (not that he knows I exist).

*Or “JoPo”; has a sports journalist ever had so many different nicknames?

Which brings us to today’s Poz post on likely future Hall-of-Famers currently under 30. It’s an entertaining (if unsurprising) read. One claim in particular caught my eye. Posnanski writes that Ryan Zimmerman is “probably better defensively” than Evan Longoria. Now, Longoria didn’t qualify for the list (hasn’t played 500 major-league games), so while I do think he is the better player, that isn’t the point here. The issue is whether Zimmerman is “probably better defensively” than Longoria, as Posnanski claims.

Although he doesn’t cite specific defensive numbers in this piece, Posnanski has used Dewan’s plus/minus system in the past (although he has increasingly cited UZR). Here are the Dewan numbers for Zimmerman and Longoria in seasons in which they’ve both played (2008 and 2009):

Plus/Minus 2008:
Zimmerman: +10 plays (+11 runs) in 910.2 innings
Longoria: +11 plays (+9 runs) in 1045.2 innings

Plus/Minus 2009:
Zimmerman: +28 (+22 runs) in 1337.2 innings
Longoria: +21 (+17 runs) in 1302.2 innings

Over the last two seasons, Zimmerman has been 7 runs better in about 100 fewer innings according to plus/minus. Seven runs is seven runs, but given everything that is rightly said about the large error bars on defensive metrics, the gap isn’t as significant as it looks.

Given the various issues with defensive metrics, looking at other systems will give us a more perspicuous overview. Here at FanGraphs, UZR is used to measure fielding. I’m not qualified to argue which metric is the best; I’m simply using them as separate data points. UZR has a helpful “rate stat” version, UZR/150 (runs above/below average per 150 games). I’ve included the “non-rate” runs in parentheses.

UZR/150 2008:
Zimmerman: +3.4 (+2.1)
Longoria: +20.1 (18.5)

UZR/150 2009:
Zimmerman: +20.1 (+18.1)
Longoria: +19.2 (+14.9)

Suddenly things are less obvious. While 2009 was practically even, in 2008 UZR has Longoria almost two wins better. Their career UZR/150s: +12 for Zimmerman, +19.6 for Longoria. It’s a smaller sample for Longoria, but if you check Jeff Zimmerman’s regressed and age-adjusted 2010 UZR/150 projections, Zimmerman is at +10, and Longoria +12.

Defensive stats are obviously important, but when estimating fielding skill, in particular, we need to weight visual evidence — scouting — heavily. I’m not a professional scout, and unlike Posnanski, I don’t have access to them. Perhaps legendary scout Art Stewart, who told Poz “You will remember this day for the rest of your life” after Royals great Chris Lubanski’s first batting session at Kauffman Stadium, thinks Zimmerman is way better than Longoria. Jokes aside, scouting is essential for estimating defensive ability.

While most of us don’t have access to professional scouts, we do have access to the
Fans Scouting Report. In both 2008 and 2009 Longoria was rated as (slightly) better than Zimmerman.

Given that plus/minus seems to “prefer” Zimmerman — and UZR, Longoria — does this make the Fans Scouting Report a tiebreaker in Longoria’s favor? No. Given the relative closeness of the rating, neither the numbers nor the testimony of observers has the degree of reliability for us to make that kind of call. However, contra Posnanski, I do not think we can say that either player is “probably better defensively” than the other.

Molehill converted to mountain? Check. Happy American Thanksgiving, everyone!

Miguel Cabrera getting a first place MVP vote is pretty silly. That said, as a player, dude is awesome. He’s not Keith Hernandez with the glove or Willie Wilson on the basepaths, but in case you haven’t noticed, he’s pretty good at the whole “hitting” thing. From 2007 to 2009, Caberara generated 110.5 batting runs above average. During that period, he’s accumulated more Wins Above Replacement than fellow first basemen Lance Berkman, Adrian Gonzalez, Carlos Pena, and Ryan Howard. Cabrera will only be 27 next season. Rumor has it that he may be available in trade with the Tigers trying to clear salary. If so, what is his value?

To reiterate: Cabrera is an excellent (and still young) player. However, as fans, we’ve lately become more aware that a player’s value includes not only his (total) baseball skill, but, as Dave pointed out earlier in a different context, the player’s contract. Think about it this way: if someone gives you a house worth two million dollars, then you’ve gained two million dollars in assets. However, if someone “gives” you the same house conditional on you paying off the same two million dollars, you haven’t really added an asset, have you?

The valuation of baseball players is similar. Without getting into methods for calculating dollars per marginal win (see Colin Wyers’ excellent series at THT), this is perhaps the most important function of WAR. Teams spend money to add wins. WAR tells you how many wins a player adds above “freely available” talent. On its own, WAR tells us how much a player helps his team even if he’s below average. When WAR is connected with relative dollar value of marginal wins, we get a sense of how much a player exceeded or fell short of the value of his salary. Let’s apply this to Cabrera.

CHONE projects Cabrera as 37 runs above average per 150 games a hitter next season. Jeff Zimmerman projects him as a -1 defender at 1B. Looking at Cabrera’s baserunning numbers from the last few seasons, let’s call him -2. Prorated for 150 games, that’s: +37 hitting, -1 fielding, -11.5 position, -2 baserunning, +23 AL replacement level = about a 4.5 WAR player in 2010.

Following Tango, I’ll assume the current market value of a marginal win is $4.4 million. Again following Tango’s generic model, assume post-peak players decline by half-a-win per year. We need to build in annual salary inflation, (which I’ve set at 7%). With those assumptions in place, over the next six seasons (2010-2015) we’d expect a 4.5 WAR player like Cabrera to be worth about $102 million. Cabrera’s only 27, so the decline curve may be a bit harsh. If we add on a half-win a season to the original calculation, his estimated value from 2010 to 2015 is $118 million.

From 2010 to 2015 (six seasons), Cabrera is guaranteed $126 million. Think back to the house example — no matter how nice the house is, if you have to pay full price (or more) for it, you aren’t adding an asset. Cabrera is an excellent player, but he’s going to be being paid as much (or more) than he’s (likely) going to be worth.

Of course, the Tigers could pick up a chunk of Cabrera’s future salary and/or throw in cheap talent to add value from their side. However, straight up, given his estimated talent and large contract, Miguel Cabrera’s intrinsic trade value appears to be… nothing?

This is a bit of an extreme conclusion. Cabrera’s trade value is not “nothing.” He is one of the best hitters in the league and is young enough that he will probably remain so for at least the next few years. Having an efficient payroll is just a means to winning, not an end in itself, and players like Cabrera are rare indeed. Still, since Cabrera is being paid (at least) his likely market value over the life of the contract, he would only really help teams that can afford to pay market value on a regular basis — the Yankees, and perhaps the Red Sox (though probably not the Dodgers at the moment given their ownership situation). And the Yankees already have an expensive first baseman signed long-term in Mark Teixeira. Cabrera isn’t worth “nothing,” but his contract gives the Tigers much less leverage than one would expect given his age and skill.

Other than Zack Greinke’s historic season, the 2009 Royals had little go right. Billy Butler was one non-Greinke bright spot. After a disappointing 2008, Butler raked in 2009, hitting .301/.362/.492 (.369 wOBA). He even became the everyday first baseman despite questions about his defense, beating out celebrated glove-man Mike Jacobs.

The Royals’ other “Savior,” Alex Gordon, has not quite (ahem) lived up to expectations. Hailed as “the next George Brett” upon being drafted in 2005, Gordon started at the hot corner on Opening Day 2007 and received a standing ovation. Things went downhill from there, as Gordon ended 2007 with a .317 wOBA. In 2008, he posted a merely decent .344 wOBA. Gordon got seriously injured to start 2009 , struggled upon returning, got demoted, and finally limped to a .321 wOBA (although 189 PA tells us next to nothing). The current attitude of many is understandable: Butler is The Man, and Gordon is a question mark at best.

Butler is clearly superior to Gordon as a hitter, and his minor league performances always indicated a higher offensive upside. But it is curious that so many smart people following the Royals have so readily hailed Butler’s 2009 as an awesome breakthrough while saying “meh” to Gordon’s decent 2008. Why is this curious? Because despite the glaring offensive disparity, we live in the Age of WAR. Let’s compare each player’s best season so far: Butler’s 2009 vs. Gordon’s 2008.

Butler’s 2009 value was excellent offensively at 21 runs above average. It was less impressive defensively. Despite looking better than expected, Butler posted a -6.7 UZR at first base (with a -12.6 overall positional adjustment). Butler’s overall WAR for 2009: an above-average 2.4.

Gordon’s 2008 value was more evenly distributed. +7.7 runs hitting, but only -3.0 UZR. However, the latter was accumulated while playing the much-more-valuable 3B. Altogether Gordon had a 2.6 WAR in 2008. It is obvious why many were down on Gordon’s 2008 relative to the Butler’s awesome 2009. Wait, what? Gordon was actually slightly more valuable in 2008 than Butler was in 2009?

Not really. After all, FanGraph WAR doesn’t currently include baserunning (other than SB/CS, which are included in wOBA/wRAA). Looking at the non-SB elements of baserunning using Baseball Prospectus’s EqBRR, as Erik did, we find that Butler was one of the worst baserunners in baseball in 2009 at about five runs below average, putting his WAR at about 1.9. In 2008, Gordon was about +3, which puts his WAR at about 2.9. So Gordon’s 2008 wasn’t “slightly” more valuable than Butler’s 2009, it was significantly more valuable. In fact, once baserunning is fully taken into account, Butler’s 1.9 WAR 2009 isn’t even quite as good as Gordon’s 2.1 WAR from his “disastrous” 2007.

My point is not about the relative value of Butler and Gordon going forward. Batting generally improves the most in the early 20s (whereas fielding and baserunning are relatively static), and Butler is two years younger than Gordon. There are legitimate questions about Gordon’s future given his performance and health. My intent is neither to run down Butler nor celebrate Gordon. One might respond that “Gordon’s value was primarily due to defense, position and baserunning!” But that is exactly the point — those things matter. Despite living in the Age of WAR, informed observers sometimes still focus on only one aspect of a player’s performance. And that can lead to a gap between a perception of one value disparity and the reality of the opposite.

Jim Thome hits free agency this offseason, after playing out the last season of his 2003-2008 (with 2009 vesting in 2008) contract. Injuries in Philadelphia and his eventual inability to play the field are among the reasons the contract probably didn’t work out in the end for the teams involved, but Thome has been a very good hitter when he’s been healthy.

How can Thome be expected to hit in 2010? Over the last four seasons, Thome’s wOBAs have been .420, .410, .370, and .367. Regressing to the mean and accounting for age, I estimate his 2010 wOBA at about .365, or about 21 runs above average.

Other (better) projection systems are already coming out with their estimate. I haven’t seen a ZiPs‘ projection for Thome (sorry if I missed it, Dan), but the CHONE projection is much less optimistic about Thome’s 2010 abilities than mine, having him at +9 runs per 150 games, or about +10 per 700 PAs.

There are two other important considerations with Thome: (1) his age and health and (2) his inability to play first base on even a part-time basis. Thome will by 39 at the beginning of the 2010 season. Despite all of this, from 2006-2008 he played in 143, 130, and 149 games. Even in 2009, he played in 107 games for Chicago before getting traded to the Dodgers, where he could not DH. While we should still be cautious in playing time projections for a 39-year-old who can’t play 1B because of back problems, given that, when he’s DHed, he’s been able to play pretty much every day, an 80% playing time projection doesn’t seem unreasonable.

Being a full-time DH obviously hurts Thome’s value. Only teams in the AL (and possibly the Nationals) will be interested. While normally we assume that a full-time DH’s value above replacement is simply his runs created above average, given that Thome has shown he can DH the last few years, we can use the -17.5/700 positional adjustment rather than -22.5.

Splitting the difference between projections (this does not mean I think my projection is in the same league), we get the following: +15 hitting -17.5 position + 22.5 replacement level times 80% playing time = 1.6 WAR player. We’d currently expect a team to give a 1.6 WAR player about six or seven million dollars on a one-year deal.

Is Thome really worth that? After all, a guy like Eric Hinske, whom CHONE projects to be a +6 hitter, and can also play a decent 1B, perhaps an acceptable LF/RF, and even an emergency 3B, would seem to be worth just as much. As a full-time 1B, with average defense, you’d expect him to be worth about almost as much as Thome over a full season, and at a much lower cost.

For most teams, Hinske would be a better investment. However, if there is an AL team that just needs the DH hole filled and is contending, given that Thome has proven he can DH, he would be the better choice. Whether he’s worth the extra money and if suitors recognize that is another question altogether. The recent awards voting has hinted at a change among the writers; will the market for older DH-types like Thome and Hideki Matsui demonstrate one among the front offices? Last season seemed to indicate so; it will be interesting to see if the trend continues.

Most of us are still recovering from this week’s Big Awards Euphoria, especially from Monday’s announcement of the 2009 Carter-Batista Award winner (I recommend reading that post before this one), which found that Ryan Ludwick was the 2009 player whose RBI total most exaggerated his offensive contribution.

Personally, I feel that the RBI/wRC system is the best way for figuring out how much RBI totals reflect true offensive contribution. But I also understand that some prefer a more “contextual” approach. As I did at greater length in an earlier series, let’s revisit the same ground using one of FanGraphs’ more context-sensitive stats — RE24 (Cf. Part Two of my Driveline Series) — to discover an “Alternate Universe” winner.

RE24 might appeal to those who believe situational hitting is a repeatable skill (I’m currently agnostic on this). The basic difference between RE24 and traditional linear weights (e.g. wRAA) is that it takes base/out state into account. For traditional linear weights, a double with two men on and two outs “counts” the same as a double with none on and no outs. RE24 recognizes that in those situations, the run expectancy both before and after the plate appearance are different. To quote myself:

There are 24 base-out states (hence the “24” in “RE24”): eight different combinations of baserunners (e.g., runner on first, bases empty, runners on second and third, etc.) multiplied by the three out states in which hitter might have that situation (no outs, 1 out, 2 outs). RE24 measure the difference in Run Expectancy from the beginning of the play until the next play.

For our purposes, the application is obvious — RE24 might identify players who were particularly good in situations with high run expectancy, and thus “earned” their RBI more than wRAA lets on.

To convert RE24 to an “absolute” measure like wRC, subtract the wRAA from wRC and add RE24. I call this “24RC“. Divide RBI by 24RC to get the comparison of real (situational) production to RBI. [Note that it’s not quite apples-to-apples, RE24 is park-adjusted, and the RBI are not, although it’s not a big problem.] The players are ranked by RBI/24RC. I’ve also included a number that sort of isolates situational contribution by subtracting wRAA from RE24. I dubbed it “Sitch.” Clever, huh?

Here are the 2009 Alternate Universe Carter-Batista Award leaders (among qualified hitters with at least 90 RBI).

5. David Ortiz, 1.134 RBI/24RC. .340 wOBA, 99 RBI, 6.40 Sitch
4. Alex Rodriguez, 1.138 RBI/24RC. .405 wOBA, 100 RBI, -10.03 Sitch
3. Michael Cuddyer, 1.141 RBI/24RC. .370 wOBA, 94 RBI, -17.48 Sitch
2. Cody Ross, 1.188 RBI/24RC. .342 wOBA, 90 RBI, -3.02 Sitch
1. Jose Lopez, 1.202 RBI/24RC. .325 wOBA, 96 RBI, 3.72 Sitch

Congratulations, Mr. Jose Lopez! You may have been just outdone by Mr. Ludwick on Monday, but here in the alternate universe, You’re the Man. Maybe in that alternate universe you’re on Shaq Vs., too. Kate Hudson works wonders, I wonder what B-list actress Big Papi is dating? Michael Cuddyer is showing that it’s not his Sitch (or defense) that got him resigned, but those awesome RBI. And what can I say about Cody Ross? Seriously, what can I say?

2009 “Trailers”

47. Adrian Gonzalez, .772 RBI/24RC. .402 wOBA, 5.29 Sitch
48. Joe Mauer, .751 RBI/24RC. .438 wOBA, 0.32 Sitch
49. Chase Utley, .727 RBI/24RC. .402 wOBA, 4.14 Sitch

Someone recently asked me what it would take for Chase Utley to win the NL MVP. I said to wait a couple years for Pujols to reach free agency and come home to Kansas City. I guess I didn’t realize how terrible Chase is at maximizing his RBI opportunities.

2007-2009 Leaders and Trailers (qualifed, 250 RBI minimum):

1. Jeff Francoeur, 1.30 RBI/24RC. .313 wOBA, 252 RBI, -17.62 Sitch
2. Bengie Molina, 1.28 RBI/24RC. .317 wOBA, 256 RBI, 23.29 Sitch
3. Robinson Cano, 1.28 RBI/24RC. .346 wOBA, 254 RBI, -53.47 Sitch
4. Garrett Atkins, 1.19 RBI/24RC. .339 wOBA, 258 RBI, -6.31 Sitch
5. Mike Lowell, 1.18 RBI/24RC. .359 wOBA, 268 RBI, -4.79 Sitch
6. Ryan Howard, 1.16 RBI/24RC. .385 wOBA, 423 RBI, 22.80 Sitch
…
43. Lance Berkman, 0.80 RBI/24RC. .397 wOBA, 288 RBI, 25.32 Sitch
44. Albert Pujols, 0.80 RBI/24RC. .440 wOBA, 354 RBI, 15.22 Sitch
45. Hanley Ramirez, 0.72 RBI/24RC. .409 wOBA, 254 RBI, -27.34 Sitch

Note how much the Sitch scores fluctuate on both ends of the rankings and draw your own conclusions. Any list with Frenchy and Bengie on one end and Pujols and Han-Ram on the other speaks for itself. Other than noting Cano’s Sitch issues (!), I’ll leave it to you all to fill in the blanks. Perhaps this spreadsheet with complete rankings will help.

As the Official-Baseball-Awards-Are-Awarded-Amid-The-Bitter-Protests-and-Feigned-Indifference-from-the-Internet season winds down, it’s also time for websites and individual bloggers to hand out their own made up awards. I have already crowned the King of the Little Things for 2009, so it’s time to move on to the Carter-Batista Award for 2009. What’s that? If an award is named after Joe Carter and Tony Batista, you might surmise that it has to do with players whose offensive value is exaggerated by their RBI totals.

Readers of this blog don’t need a lecture on why RBI are a bad measure of offensive performance, value, and skill. Like much of my work, this is an excuse to play with a “toy” or “junk stat” to get a point across. Earlier this year, I did a three-part series (1, 2, 3) where I go into much greater detail on the methodology, etc. Here, I’ll just give you the bare-bones.

The idea, inspired by Jonah Keri, is that by dividing a players RBI total by a better counting stat, we can get an idea of how much a players RBI total “overrates” his offense. My earlier version had a more complex construction, but interactions with Tango and terpsfan convinced me that the best way to go about it was to simply use unadjusted “absolute” runs created, like wRC (wOBA Runs Created). The idea stays the same: the higher a player’s RBI/wRC, the more RBI totals “overrate” his contribution, and the more he enters Carter-Batista territory.

[In case you’re wondering I didn’t park-adjust: I did initially, but realized that the RBI are a just as much a product of the environment as wRC, so dividing an unadjusted RBI by an adjusted wRC would be problematic. As usual, simpler turned our to be better.]

Who is this season’s winner? The pool is qualified hitters with at least 90 RBI. Here are the top five candidates:

5. Jorge Cantu, 1.18 RBI/wRC. .343 wOBA (.289/.345/.443), 100 RBI
4. Brandon Phillips, 1.20 RBI/wRC. .337 wOBA (.276/.329/.447), 98 RBI
3. David Ortiz, 1.22 RBI/wRC. .340 wOBA (.238/.332/.462), 99 RBI
2. Jose Lopez, 1.26 RBI/wRC. .325 wOBA (.272/.303/.463), 96 RBI

This stat should not be taken to mean that these guys are bad players or even bad hitters. It just says something about their RBI totals in relation to their true offensive contribution. Brandon Phillips isn’t a great hitter, but he’s a good player because of his 2B defense. Jose Lopez managed to contribute at an above average level this season because of decent defense and durability. We shouldn’t look down on him just because he hit behind Ichiro and his .386 OBP. Sure, Big Papi had a down year with the bat, but his other contributions are incalculable. Literally.

And now, your 2009 Carter-Batista award winner:

1. Ryan Ludwick, 1.26 RBI/wRC. .336 wOBA (.265/.329/.447), 97 RBI

Wow! Ludwick already won the prestigious Average-est Player of 2009 Award. This is entering Michael-Jackson-at-the-1984-Grammys territory. I’m not sure how he did it. Are there any high-OBP guys hitting ahead of Ludwick?

It’s illlustrative to look at the “trailers,” as well. In the last two spots:

47. Joe Mauer, .753 RBI/wRC. .438 wOBA (.365/.444/.587), 96 RBI
48. Chase Utley (naturally), .751 RBI/wRC. .402 wOBA (.282/.397/.508), 93 RBI

Finally, the 2007-2009 leaders and trailers (minimum 250 RBI)

1. Bengie Molina, 1.45 RBI/wRC. .317 wOBA (.278/.302/.440), 256 RBI
2. Ryan Howard, 1.24 RBI/wRC. .385 wOBA (.266/.363/.565), 423 RBI
3. Jeff Francoeur, 1.19 RBI/wRC. 313. wOBA (.271/.314/.409), 252 RBI
…
43. Albert Pujols, .827 RBI/wRC. .440 wOBA (.337/.444/.626), 354 RBI
44. Chase Utley, .821 RBI/wRC. .404 wOBA (.301/.395/.536), 300 RBI
45. Hanley Ramirez, .664 RBI/wRC. .409 wOBA (.325/.398/.549), 254 RBI

Much more could be written, but you all can take it from here draw your own conclusions. Check out the extended list of rankings on this Google spreadsheet.

I’ll be back Tuesday or Wednesday with a follow-up on situational hitting.

I know this is about a week late on the uptake, but I just had to get it off my chest. Sorry.
While I’m going to use numbers here to “prove” my point, I will admit that the terms “overrated” and “underrated” are (a) overused and (b) relative to a number of factors that are difficult-to-impossible to quantify in terms of measuring fan perception, what counts, etc. I’m clearly as guilty of overusing them as anyone.

But why do I care about this? After all, I like Jimmy Rollins, but I’m not a Phillies fan.

During the World Series buildup, different writers whom I enjoy wrote that Rollins isn’t really a “star” despite being treated like one (whatever that means) and that he is overrated. So it wasn’t so much what they said, but who said it. No, I’m not going to name them — this isn’t a “call out.” I’m not anyone people should be afraid of being called out by, and this isn’t a lame attempt to shame anyone. The point is that even smart people usually conversant with the numbers can get carried away without examining the numbers. (Not me, of course. I’m always completely objective.)

What could these people who said Rollins is “overrated” be talking about? Obviously, in 2009, he had a dreadful time at the plate. Of course, a player is probably pretty good if he has a down year that’s so bad he ends up being “only” league average. Another complaint is that Rollins leads off, and he’s never had a particularly great on-base percentage. While OBP is very important, it’s only part of a players’ value. Moreover, it’s his manager who makes him lead off, a role for which Rollins isn’t well-suited.

More importantly, though, saber-friendly writers know that current season stats don’t tell the story about a player, right? That’s MGL 101. Rather than going through the hassle of a projection, let’s see what kind of company this player has been keeping while being “overrated.” Over the last three seasons (including 2009), Rollins has accumulated 14.4 wins, making him “only” the 17th most valuable position player in that period according to FanGraphs’ WAR. Whose company is he in? Ichiro Suzuki is at 15.0, only half-a-win away (practically nothing over three years). Derek Jeter is Mr. Overrated Guys Bloggers Love To Whine About, and he’s the same as Ichiro. Ryan Zimmerman has been exactly as valuable. Grady Sizemore has been ever-so-slightly less valuable; is he a hack? How about Lance Berkman or Adrian Gonzalez?

Look, Rollins shouldn’t have won the 2007 NL MVP, when he wasn’t even the best player on his own team (ahem). Again, I don’t know exactly who’s been doing the “rating.” But if you ask me (and you didn’t), it’s tough to imagine that a guy who’s been about as good as Ichiro!, Jeter, Zimmerman, Grady Sizemore, Big Puma, and A-Gon over the last three years deserves the “overrated” label.

Then again, does Rollins have his own cologne?