In the course of a discussion at The Book Blog about the Angels’ (of late) recent outperformance of (some) projections, I was reminded of a related yet quite different issue I’d thought about looking into a while back (and then promptly forgot about). The Angels are one of the teams in baseball that are praised for “playing the right way” and “doing the little things.” Whatever people mean by that, one thing we can say is that recently, the Angels have consistently outperformed their Pythagorean Win Expectation. Looking (somewhat arbitrarily) at the last three seasons in which the Angels have won the American League West and comparing their actual record with what we’d expect given their run differential based on PythagenPat.

2007: Actual 94-68, Expected 90-72, difference +4
2008: Actual 100-62, Expected 88-74, difference +12
2009: Actual 97-65, Expected 93-69, difference +4

I should say right now that this post is not saying that I am not claiming either a) that the Angels “just got lucky” and weren’t as good as their record, or b) that they have some “intangible” ability (perhaps from their manager) that has enabled them to outperform their run differential the last three seasons. Both of those are copouts, at least at this point. For now, I’m only going to look at this issue with reference to their offense.

One might say that they’ve been “good in the clutch.” And that is, in fact, true. FanGraphs’ clutch score, which measures whether players outperform not only their peers, but themselves in high leverage situations, has the following win values for the Angels’ hitter from 2007-2009:

2007: 5.19
2008: 7.34
2009: 3.22

These numbers are impressive, but they sort of beg the question. Unlike relievers, hitters don’t “earn” their high leverage playing time — unless you think most of those scores were put up by Angels pinch-hitters picked for their “clutchness.” This seems to say what we already knew — the Angels won more game than their runs scored indicate that they “should have”. Undoubtedly, there are “clutch hits,” but this doesn’t tell us how they did it — just that they did.

One thing that “right way” teams are praised for is situational hitting. FanGraphs has a stat for that: RE24. While FanGraphs’ primary “runs created above average” stat, wRAA, uses the average change in run expectancy given an event irrespective of the base/out situation, RE24 does incorporate base/out state. For wRAA, a home run is a home run whether the bases are empty with none out or loaded with 2 out, while RE24 takes into account the different base/out run expectation. As I discuss here, if we subtract the average linear weight runs (wRAA) from the RE24, we can see how much better the Angels performed in terms of “situational hitting.”

2007: wRAA +7, RE24 30.5, situational +23.5
2008: wRAA -18, RE24 18.7, situational +36.7
2009: wRAA 88, RE24 92.8, situational +4.8

Impressive. However, it actually doesn’t tell us what we want to know. This tells us that we would expect the Angels to have scored more runs than traditional linear weights (wRAA) would suggest, but the Pythagorean expectation is already using their actual runs scored. We want to know why they outperformed their run differential (for now, from the offensive perspective) — not why they scored more than their linear weights suggest, but why they won more than their actual runs suggest.

Enter WPA/LI. While RE24 takes base/out context into account, WPA/LI goes one step further, by taking base/out/inning into account. You can follow the link to read up, but basically, it’s “unleveraged” Win Probability. It sounds like Clutch, but it’s actually WPA without the Clutch/Leverage element. To use an example to differentiate WPA/LI: with two outs in the bottom of the ninth with the bases loaded, for WPA/LI a walk and a home run have the same linear weight, whereas those events would be different for both wRAA and RE24, since they don’t take game state into account. So, if any stat could take into account a player or team adjusting their play to a situtation, this would be it. As I did in my earlier Little Things post for individuals, we can do for teams: convert wRAA to wins (I crudely divide by 10), then subtract that from WPA/LI to get the situational wins above average linear weights.

2007: wWAA +0.7, WPA/LI -1.32, -2.02 Little Things
2008: wWAA -1.8, WPA/LI -1.21, +0.59 Little Things
2009: wWAA +8.8, WPA/LI +6.37, -2.43 Little Things

Now that is just bizarre. With RE24, we saw that the Angels the last three seasons have been very good at maximizing their situational hitting in certain base/out states. But “Little Things” shows the exact opposite in 2007 and 2009. They’re about “even” in 2008, although far short of what RE24 shows, and they’re 2 wins below their traditional linear weights in 2007 and 2009. It’s not just that the Angels’ hittesr aren’t living up to their reputation (according to this measure) of “doing the little things,” it’s the contrast between RE24 and WPA/LI based “little things” that is striking. It’s as if the Angels do a great job of hitting with runners in scoring position when they’re playing in blowouts, but make terrible situational plays (relative to the average run expectancy) in close games. And then if you look at their hitter’s “Clutch” scores from those years… It’s really hard to know what the big picture is.

This post has no conclusion other than to note that the title is ironic. It would be foolhardy to dismiss this all as luck. The Angels have been a very good team no matter how you slice it. And just because we don’t understand “how they do it” at the moment doesn’t mean we can never know. But at the moment, I’m simply struck by the oddity.

While writing on the “three true outcomes” (walk, strikeout, and home run) leaders and trailers from 2007-2009, I was reminded of a toy idea that I’d had earlier to create something like FIP (Fielding Independent Pitching), using the same basic components, except for hitters. I finally got around to doing it recently, and the results were interesting. I’m not saying this is any more than a junk stat. But it might be interesting, who knows?

* You want real sabermetric research? Read Matthew Carruth, Dave Allen, or one of the many other intelligent researches writers here and elsewhere. Trying to waste time at work? You came to the right place. Tom Tango may have created wOBA and FIP, but this a stat that gives me joy.

The basic formula for FIP is ((HR*13+(BB+HBP-IBB)*3-K*2)/IP) + 3.2, where “3.2” is a season/league specific factor to put the league FIP on the same scale as the league ERA. To make it suitable for hitters, I made a couple of minor modifications: 1) I scaled it to RA rather than ERA. The RA scale for the 2009 MLB was 3.52. 2) For IP I used outs made by the hitter (divided by 3 to get on the IP scale): AB-H+SF+SH+GIDP (I left out CS because I want to deal with the pitcher/hitter matchup). Ladies and gentleman, I present the formula for Defense Independent Offense, or DIO:

((HR*13+(BB+HBP-IBB)*3-K*2)/(Outs/3)) + 3.52.

Who (among qualifying hitters) led the league in DIO for 2009? Remember that for hitters, a higher number will be better.

1. Albert Pujols, 9.18
2. Prince Fielder, 8.66
3. Adrian Gonzalez, 8.55
4. Alex Rodriguez 8.32
5. Carlos Pena 8.31
6. Adam Dunn, 8.11

So far, so good, those are some great hitters. Here are the trailers:

150. Yuniesky Betancourt, 4.26
151. Michael Bourn, 4.12
152. Randy Winn, 4.03
153. Cristian Guzman, 3.92
154. Emilio Bonifacio, 3.73

Some of these names — Betancorut, Winn, Bonifacio — aren’t surprising. But what about Michael Bourn, for example? Didn’t he have a decent season at the plate in 2009? Hold on to that thought.

Just as a player’s wOBA can be compared with league wOBA to give up the player’s runs created above average (wRAA), we can compare a players DIO with the league’s runs per game (4.61 in 2009) to produce a DRAA: =(DIO-lgR/G)*(Outs/27).* Here are the 2009 leaders in DRAA and with their wRAA figures for sake of comparison.

* One can also calculate absolute runs created (wRC) with DIO * (Outs/27).

1. Albert Pujols 69.9 DRAA, 69.7 wRAA
2. Prince Fielder 65.6 DRAA, 54.9 wRAA
3. Adrian Gonzalez 62.2 DRAA, 41.5 wRAA
4. Mark Teixeira 53.5 DRAA, 42.9 wRAA
5. Adam Dunn 53.2 DRAA, 35.9 wRAA

The Pujols figures are almost dead-on, and given the crudeness of DIO, Fielder and Teixeira aren’t that far off, but Gonzalez and Dunn seem to be quite overrated by DIO-Runs. The general “in the neighborhood-ness” isn’t that surprising, given that FIP (and thus DIO) are based on linear weights of the relevant events, and wOBA is just linear weights expressed as a rate stat. But what about the discrepancies? Does the perhaps mean we should be rethinking wOBA/wRAA in favor of my awesome new offensive metric, or at least use it more prominently, just as FIP is generally favored (around here) over ERA?

In a word: no. Going back to the origins of DIPS-theory, pitchers generally have little control over balls in play, and thus DIPS, FIP, tRA, etc. are attempts to remove the defense-dependent elements from pitcher evaluation. However, while BABIP generally has less year-to-year correlation for hitters than, say, walk rate, it does correlate far better than for pitchers. That is why traditional linear weights (like wRAA) are preferable for hitters. DIO systematically underrates hitters like Michael Bourn not only because it ignores steals, but because it assumes that the players contributions on balls in play are league-average, whereas Bourn’s contributions in those areas are well-above average. DIO’s also badly underrates hitters like Joe Mauer (40.5 DRAA vs. 54.9 wRAA in 2009) and Ichiro Suzuki (-2.2 DRAA vs. 22.6 wRAA), as well as overrating (still very good) hitters like Adrian Gonzalez and Adam Dunn.

DIO has interesting aspects. It highlights how many good hitters get most of their value from hitting home runs and walking, for example. There is also much to be said for using a rate stat baselined against outs rather than PA (I wouldn’t go so far as to make the mistake of generating a DIO-based Offensive Winning Percentage, although it was tempting). For me, it was worth it just to walk through and see how well the stat did in ranking hitters. Most of all, it was a good reminder of the difference in BABIP as a skill relative to pitchers and hitters. Without reminders like these, I’d be left on my own, like a rainbow in the dark.

(…or maybe Michael Bourn, but let’s not complicate things.)

I remember the first time I seriously paid attention to Nyjer Morgan. I was doing a batting order post on the Pirates. Given the subject matter, I didn’t really deal with defense, but I did notice his (2009) Oliver projection for .308 wOBA — for a left fielder? Ugh. Morgan was soon traded to the Nationals, installed in center field, and not only continued to be a monster defender, but was above average offensively, as well, at +5.2 batting runs, .340 wOBA (.307/.369/.388). Altogether, he was worth almost five wins above replacement… in only 120 games.

That’s a very impressive season from a guy who looked like another “Juan Pierre.” Of course, back in the day, Pierre was a pretty good player, too. 2009 Nyjer Morgan and 2003-04 Juan Pierre both come from the larger class of players that were undervalued prior to the widespread public availability of fielding metrics like Ultimate Zone Rating. No one would have pegged Morgan for a near-five win season going into 2009. But, without saying it is my prediction or projection, I do think that one player who has it within his (non-totally delusional) grasp to have a Nyjer-esque 2010: Yankees outfielder Brett Gardner.

Gardner is currently slated to play left rather than center due to the presence of Curtis Granderson in New York, but, like Dave, I don’t think it makes that big of a difference. In any case, my crude fielding projections for outfielders are expressed in “position neutral” form — so when I say that a player is a +5 outfielder, we apply the positional adjustments (+2.5 for CF, -7.5 for the corners) so say the player would be about +2 CF, +12 on the corners over a full season.

Prior to 2009, I would have projected Morgan as a +7 outfielder, although that would have had a low reliability score because of his relatively low amount of defensive games pre-2009. Gardner so far has more playing time in the outfield than Morgan did pre-2009, and I have him at about +10. Once we take their relative ages into account, Gardner 2010 has more defensive “upside” than Morgan 2009 (the Fans Scouting Report also currently ranks Gardner higher).

The offensive comparison is more interesting. Despite Morgan’s good 2009, CHONE still sees him as a below average hitter (.321 wOBA). Gardner’s CHONE projection is surprisingly good — .335 wOBA. Part of this is Gardner’s relative youth, of course, but their peripherals reveal generally superior skills on Gardner’s part. For example, Gardner has the higher walk rate. This is likely a reflection of Gardner’s superior plate approach. While Morgan swings at bad pitches slightly more often than average, Gardner has been better than average, while still having a slightly higher overall contact rate than Morgan.

While Morgan’s good 2009 relies, as you’d expect, on a high BABIP (.355), Gardner has never really been a high BABIP guy (only .311 during his .337 wOBA 2009). Looking at their batted ball profiles, Morgan again looks like your typical speed merchant, hitting balls on the ground more than 50% of the time, whereas Gardner hits more flyballs. Although, unlike for pitchers, BABIP does reflect a skill for hitters, it varies quite a bit year-to-year, so is regressed fairly heavily. Having said that, given his speed, it might behoove Gardner to hit more balls on the ground (although he is probably best off ignoring me and doing what works for him). The point is that Gardner hasn’t been getting “lucky” with balls in play.

Originally, I wanted to post on just how badly Gardner need to hit to deserve benching in favor of a Randy Winn/Marcus Thames platoon. Given Winn’s offensive decline, Thames non-awesome bat and terrible fielding, and Gardner’s great defense (not to mention his acceptable bat and, contrary to what some might think, non-horrible platoon splits), that seemed pointless. Assuming average offense from Gardner, over ~150 games, he looks like a 2.5-3.0 WAR player in 2009. You can see why the Yanks felt comfortable not going nuts for Johnny Damon, who probably isn’t any better than that.

But Morgan (whom, incidentally, I also see as about 2.5-3 WAR in 2010) is still the more interesting comparison. I wouldn’t have had him as even a 2.5 WAR player before last season, and I doubt many would have. Yet he put up a 4.9 WAR once he got to show what he could do in the field. Should we expect ~5 WAR from Brett Gardner in 2010? No — that would be insane. But if Nyjer Morgan could do it in 2009, Gardner can in 2010. I suppose the Yankees would even settle for Michael Bourn’s “mere” four win 2009.

Earlier this week, the Arizona Diamondbacks settled with former Dodgers/Rays/Tigers pitcher Edwin Jackson, buying out his last two years of arbitration for $13.35 million. According to Cot’s Contracts, Jackson will receive an $800,000 signing bonus, $4.2 million in 2010, and $8.35 million in 2011. The general rule of thumb is to assume the team would be paying him 60 and 80 percent of the player’s “open market” value for the second and third arbitration years. Lumping the signing bonus in with the 2010 salary, the open market value of the contract is about $18.8 million, which in the current market (about $3.5 million per marginal win) would be an average contract for about a 2.7 win player, assuming a 0.5 win-a-season decline. Is this a good value for the Diamondbacks?

Pitcher win value calculations are more complicated than for position players, so I won’t lay out every step — read here for how FanGraphs does it. CHONE projects Jackson’s 2010 FIP at 4.33. Scaled to runs allowed and assuming last season’s MLB run environment, I get 4.76. That is worse than 2009’s average runs per game (4.61), but we also need to account Arizona being a serious hitter’s park (I use a 1.06 park adjustment), which brings his FIP-RA down to 4.49, making Jackson a .513 pitcher (using PythagenPat). .380 is the general replacement level for starters, but following Tom Tango I use .370 (AL) and .390 (NL) to account for relative league difficulty. Over CHONE’s projected 30 starts and 178 innings for Jackson (and accounting for how he impacts the run environment), he projects as a 2.2 WAR pitcher.

It looks like Arizona overpaid a bit. But we should acknowledge that it is very difficult to project playing time. If Jackson repeats 2009’s 214 innings and 33 starts at the FIP projected above, his projected WAR goes up to the 2.7 WAR for which the Diamondbacks are paying. The Fans project 196 innings and 32 starts, which puts Jackson at 2.4 WAR.

Another thing to keep in mind is that we’re assuming an average 0.5-win-a-season decline due to aging-based attrition. While we should be cautious before making any player an “exception,” Jackson won’t turn 27 until September, and hasn’t had major arm trouble recently, so that is another factor to consider.

Jackson is interesting. He was born in (then-West) Germany, was a top pitching prospect with the Dodgers, got traded to Tampa Bay for a couple middle relievers, then the Rays moved him for Matt Joyce after Jackson’s seemingly good 2009. Jackson’s ERA has consistently been better than his FIP (which has, in turn, been slightly better than his xFIP), yet the “collapse” some expected from him after the trade to Detroit has not occurred. It’s interesting just how poorly (according to pitch-type linear weights) Jackson’s fastball has fared against opposing hitters, while his slider has been excellent the last two years. Perhaps he figured that out in 2009, as he threw the slider more often than before, which in turn might be why he was able to get hitters to swing at pitches out of the zone (and strike out) at a much higher rate in 2009. Jackson still has trouble getting ahead in the count, however (54.4% career F-Strike). While Jackson may not be have dominating stuff, his durability is an asset.

Returning to the original topic — while there is reason to believe this deal is fair to both sides, I would give slight “edge” to Jackson. How that factors into the evaluation of The Big Trade is another question.

Everyone knows about Johnny Damon’s longtime love for the Detroit Red Wings and octopus by now, but have you heard that he’s also a big Matt Ryan fan? He also loved WCW. WolfPac 4 ever!

Jokes aside, in addition to the Tigers, the Atlanta Braves have also allegedly shown interest in signing Damon (at least according to the rumor mill). For reasons I mentioned in this week’s podcast, I don’t think Damon is a great fit for Detroit — even if you think he adds a win or two to the Tigers, that isn’t likely to put the Tigers anywhere near the playoffs. The Braves, on the other hand, are in a position to spend a bit for a marginal win. They look like a team that could give Philadelphia a run for its money in the NL East, and failing that, would be a favorite for the wildcard.

What does Damon offer? Offensively, CHONE projects .352 wOBA, or about 13 runs above average per 700 PA. That projection doesn’t adjust for league, so let’s give him a couple extra runs for moving into the NL for +15. Damon’s defense has come under a lot of fire recently, but it’s probably not quite as bad as it seems. After taking positional adjustments, speed scores, and age into account, I have Damon at about minus 10/162 position-neutral outfielder (so about -13 CF, -3 LF). Despite his age, Damon has been pretty durable lately, so 85% playing time is still fair. +15 offense -10 fielding + 22.5 replacement level times 85% = a 2.3 WAR player. That’s a decent player even at $7M for one year.

How does this compare to Atlanta’s current major league outfielders? Nate McLouth is slated to start in center. CHONE projects .355 wOBA, or +15/700. I have him as a -5 position-neutral outfielder. +15 offense -5 fielding + 22.5 replacement times 85% = 2.8 WAR.

Bobby Cox is apparently discussing having Matt Diaz and recently-acquired Melky Cabrera share playing time. CHONE projects Diaz at .349 wOBA, about +12/700, and although he’s been platooned heavily due to massive splits, as a righty I estimate his split to be about even. I have Diaz as a -5 defender, but he also never plays full-time due to injuries and platooning — 75% seems about right. +12 -5 +22.5 times 75% = 2.2 WAR, although that might be high due to injury concerns and platoon uncertainty.

What about Cabrera? I’m with Dave on Melky — people focus too much on his alleged “tweener” status and miss his age relative to performance. CHONE agrees about offense, and projects Cabrera for a .358 wOBA, +17/700. His “tweener” status on defense might mean you don’t want him in center too much, but it also means he’s be a plus defender on the corners, I have him at -3/162 position neutral. +17 -3 + 22.5 times 85% playing time = 3.1 WAR. Far from being a 4th OF part-timer, Melky shouldn’t be taking a backseat to anyone in Atlanta.

McLouth and Cabrera are probably better players than Damon at this point, but Damon is better than Diaz, especially if you think Diaz’s platoon issues and injuries make him worse than the projection above. In any case, as Dave has argued in the podcasts, having four good outfielders is a good idea, especially given Diaz and McClouth’s recent playing time woes and the Braves’ hopes for contention. For the right money, Damon could make a lot of sense for the Braves if they distribute playing time properly. But…

…you’re probably screaming “What about Jason Heyward, the best prospect in baseball that the Braves are going to give the shot in right field?” And you’d be exactly right. I’m not going to bother with projections for Heyward. In short, if Atlanta thinks he’s ready and has him in the majors, they have play him every day, otherwise it’s a waste. At that point, you’ve got McLouth, Cabrera, and Diaz fighting over two spots that should go to Cabrera and McLouth, with Diaz as the 4th OF. If they bring Damon in, there would be a lot less playing time to go around, and the marginal value to the Braves is much lower.

So if Johnny really wants $7 million per from the Braves, it looks like he’d better bust out some new attire.

Frank Thomas, a.k.a. “The Big Hurt,” officially retired today. However his career ended, his up-and-down (but hardly bad) 2000s makes it hard to recall his utter dominance in 1990s. I’m not going to get into the Hall-of-Fame debate about Thomas or designated hitters. Yes, we have to adjust for his defensive “contribution,” but fortunately, Wins Above Replacement does just that. The “FanGraphs Era” currently only extends back to 2002, so for some historical WAR perspective, let’s compare some career WAR numbers from Sean “Rally” Smith’s historical WAR database.

Frank Thomas 75.9
Pete Rose 75.4
Johnny Bench 71.4
Brooks Robinson 69.2
Edgar Martinez 67.2
Duke Snider 67.2
Eddie Murray 66.7

To repeat: these numbers adjust for Thomas’s non-contributions on defense. If you think the players below him on that list are Hall-quality, then Thomas, who was “only” a monster hitter, should get in, too.

Enough of that, let’s discuss Thomas’s greatness as a hitter. For this, I calculated linear weights using data from the Baseball Databank. I use the same basic version of custom linear weights/wOBA that FanGraphs does, but having it on my own database just allows me to manipulate the data for stuff like this.* The linear weights (aka “Batting Runs” or wRAA) are customized so that each event is weighted properly for each season. The runs above average are park-adjusted (thanks, terpsfan). I then convert them to wins, which further reflects the relative value of a run in that season.

* There are probably some slight differences due to discrepancies in source data, different park adjustments, etc. but it’s very close. The batting runs also differ from Rally’s, since his weights are adjusted to reconcile on the team- rather than league-level. Neither is “right” or “wrong,” they are simply two different perspectives.

The top six career leaders in Batting Wins Above Average since 1955 (the first season Baseball Databank records intentional walks):

1. Barry Bonds 126.3
2. Hank Aaron 108.5
3. Willie Mays 91.0
4. Frank Robinson 89.7
5. Mickey Mantle 83.0
6. Frank Thomas 71.5

Granted that good chunks of Mantle and Mays’ value came before 1955… that’s still impressive company. Among those with career numbers inferior Thomas are: Jeff Bagwell (64.0), Willie McCovey (62.8), Harmon Killebrew (60.0), Mark McGwire (56.9), Jim Thome (55.4), and Sammy Sosa (34.8).

Another way of judging impact is to compare overall career numbers with peak value in order to separate guys who just hung on. So let’s look at Thomas and two other great hitters of somewhat recent vintage and compare their career Batting Wins, their top three seasons, and the five-year continuous peaks:

Edgar Martinez
Career Batting Wins Above Average: 54.4
Career wRC+: 151
Top Three: 18.0 (6.8 in 1995, 5.6 in 1996, 5.5 in 1997)
Five-Year Peak: 27.5 from 1995-1999

Mark McGwire
Career Batting Wins Above Average: 56.9
Career wRC+: 161
Top Three: 22.1 (9.3 in 1998, 6.7 in 1996, 6.1 in 1999)
Five year Peak: 30.1 from 1995-1999

Frank Thomas
Career Batting Wins Above Average: 71.5
Career wRC+: 158
Top Three: 20.6 (7.1 in 1991, 6.8 in 1994 [!], 6.7 in 1992)
Five-Year Peak: 31.4 from 1992-1996 (includes 1994 strike)

I included Edgar because of the recent discussions about him, and also because, while he was obviously a great hitter, I wouldn’t have thought his numbers would stand up so well against say, McGwire’s. They aren’t quite as good, but they are in the same territory. McGwire was obviously great, but I think not only Thomas’s career numbers, but arguably his peak was better, too. His five-year peak is slightly better, and though his top three seasons (or best one) aren’t quite as good as McGwire’s, his second and third best seasons are better than McGwire’s.

Moreover, both Thomas’s top three and five-year peak both included the strike-shortened 1994 season. Regression to the mean tells us that Thomas likely wouldn’t have continued at that rate, but do you think he would have hit at a league-average rate or below the rest of the season? There are a lot of “what ifs” in baseball, of course, and in 1994 in particular, as Expos fans know. But 6.8 Batting Wins in 113 games is simply astounding. And keep in mind that the AL was the more difficult league starting in the 1990s.

I’m not sure what better compliment to end on other than to say that when all three were at the top of their game(s), Frank Thomas was a more dominant hitter than Mark McGwire and Edgar Martinez.

In line with our Fabulous Split Week here at FanGraphs, this post will utilize the framework for estimating hitter platoon skill outlined on Monday. If you crave more details, read that post, or, even better, take a look at the sections from The Book on which it is based. Today, I’ll apply this analysis to four of the remaining DH-ish players left on the free agent market. This will allow us to set aside issues of defense and get a simple overview at how platoon skill effects the value of some hitters. Recalling Monday’s post, platoon skills are regressed to the mean (here based on league-wide splits 2007-2009), moreso for righties (regressed against 2200 PA) than for lefties (1000 PA).* For the projected overall wOBA, I use CHONE’s projections as listed on the FanGraphs player pages.

* David Appelman informs me that the “career splits” pages only include stats starting in 2002. That’s helpful in this case because we’re getting the more recent data for older players, although platoon skills usually don’t change much over most players’ careers. But keep in mind that the “career” numbers listed below are post-2002.

Let’s begin with some lefties:

Russell Branyan
Career Split: 15.2% (437 PA v LHP)
Regressed: 10.6%
CHONE projected wOBA: .359
Estimated wOBA vs. RHP: .367
Estimated wOBA vs. LHP: .329

Like most saber-nerds, I love talking about Russell Branyan. Although he has a platoon-guy rep, in 2009, when he got more PAs against LHP than ever before in his major-league career, he hit well against them (.345 wOBA). It’s still a small sample, but it does show that sometimes regression to the mean happens right before our eyes. Branyan actually projects as about league average vs. LHP. His back is problematic and he probably went into the off-season with unrealistic expectations about what he could get in free agency, but it’s hard to believe he won’t find a starting job before Opening Day.

Carlos Delgado
Career Split: 18.2% (1400 PA v LHP)
Regressed: 14.2%
CHONE projected wOBA: .337
Estimated wOBA vs. RHP: .352
Estimated wOBA vs. LHP: .304

In an earlier post on free agent 1B/DHes, I hinted that the once-great Delgado might want to consider hanging it up. But this is a case where a larger split makes a guy more valuable with a decent platoon partner.

Hank Blalock
Career Split: 21.8% (1060 PA v LHP)
Regressed: 15.4%
CHONE projected wOBA: .328
Estimated wOBA vs. RHP: .342
Estimated wOBA vs. LHP: .291

It may seem like Hank Blalock was good just a couple years ago, but it’s really been six. He has even a bigger splits than Delgado, but he’s also not as good of a hitter in general. A .342 wOBA part-time DH can be useful, but not often.

And now some righties…

Jermaine Dye
Career Split: 9.1% (1196 PA v LHP)
Regressed: 7.1%
CHONE projected wOBA: .345
Estimated wOBA vs. RHP: .338
Estimated wOBA vs. LHP: .363

Dye seems to have realized he can’t play the field anymore, which is good. Given how long he’s been in the league relative to Ryan Garko (discussed in Monday’s post), that their estimated split is almost exactly the same points to how much observed RHH platoon splits need to be taken with a grain of salt.

Johny Gomes
Career Split: 15.4% (600 PA v LHP)
Regressed: 8.1%
CHONE projected wOBA: .336
Estimated wOBA vs. RHP: .327
Estimated wOBA vs. LHP: .354

Now this is a righty with a big split, although not as big as people think. Despite Gomes’ reputation, his estimated platoon split isn’t any bigger than the average lefty split. Of course, he’s only about average against RHP.

This small selection reflects what we’d generally expect — lefties have larger splits that vary more widely. This implies that when setting up a platoon, given similar defensive skills (or lack thereof), the key is finding a lefty with a big split, and to find a RHH who is a decent overall hitter. And, of course, there’s the issue of whether bringing in a platoon partner is worth the roster spot. For example, given that the expected performance of Gomes and Dye (the lesser half of the platon) vs. LHP isn’t that much better than Branyan’s overall projection. On the other hand, on the right team, Gomes or Dye + Delgado might make sense.

I don’t think I’m all that different from most fans who glance at stats — when I see them, I automatically tend to view them as a player’s real talent. But one thing I’ve taken away from my reading of baseball analysts far more intelligent than I (granted, that’s not a very high standard), is that there’s an important distinction to be made between observed performance and true talent. Past performance should certainly inform how we estimate future performance. But it isn’t enough on its own. One of the most important tools for estimating true talent relative to observed performance and its sample size is regression to the mean. A good place to start reading with reference to the current discussion is The Book.

One bad habit many of us might get into it looking at the platoon splits of two players at the same position, one with a career wOBA of .390 vs. RHP, the other with a career wOBA of .400 vs. LHP, and thinking, “Wow, that platoon would be almost as good as Ryan Braun!.” It isn’t that simple. As in most other things, regression shows us that the distance from average is closer than it appears. Technical explanations aside, I’ll simply summarize what is relevant for estimating platoon skills.

How much we regress depends on the variation of skill in the relevant population. The less variation there is, the more likely deviations from the mean are random occurrences. Practically speaking, left-handed hitters display more variation in platoon skill than right-handed hitters, so in estimating the platoon skills of left-handed hitter, we use less regression.* According to The Book, we regress lefties’ platoon skills against 1000 PA against LHP of league average splits for left-handed hitters, and righties against 2200 PA against LHP. This means that when hitters have less than 1000/2200 PAs vs LHP, we estimate their platoon skill to be closer to league average than to their observed platoon performance. In practical terms, it also means that for righties, we’re usually safe in assuming they have near-average platoon skills.

* Switch-hitters display the most platoon skill variation as a population, but that is a can of worms for another day. The Book says that after 600 career PA against LHP, one has a pretty good idea of a switch-hitter’s platoon skill.

Some concrete examples might help. For my league average, I’ve taken MLB-wide splits from 2007 to 2009 from Baseball Reference and converted them to wOBA. This is just going to be a very basic demonstration, as, e.g. I wasn’t able to exclude pitchers from the splits, or remove switch-hitters, or leave out steals, weighted, and so on, but I think it will give the general idea. From 2007 to 2009, the average wOBA split for left-handed hitters was about 8.6%, and for right-handed hitter, about 6.1% (following The Book [I think], I use a percentage split to avoid potential logical absurdities and to reflect the reality that better hitters usually have larger splits.

We’ll begin with everyone’s favorite example of a “big splits” guy: Curtis Granderson. For his career, Granderson is a .358 wOBA hitter. However, while he has hit a robust .380 vs. RHP, in 685 versus LHP, he’s been 2009 Yuniesky Betancourt with a .270 wOBA. That’s a whopping 110 points of wOBA difference, about 30.7% in observed performance.

But remember — skill is closer to average than it appears. Regressing Granderson’s 685 PA of 30.7% against 1000 PA of league average (8.6%) — (.307*685+.086*1000)/(685+1000) — we get an estimated platoon skill of 17.6%. “Centering” the split is a bit of a challenge, but I weighted it by the number of PAs the player has against LHP in his career (for Granderson, about 23.7%). For Granderson’s split, then, I have +4.2% vs. RHP, and -13.4% vs. LHP. Applying this to his 2010 CHONE projection of .359 wOBA, we’d forecast his 2010 wOBA against RHP as .374, and against LHP as .311. .311 is below average, but it’s far better than .270, and given Granderson’s skill in the field, you’d be hard-pressed to find a right-handed platoon partner that would offer an overall advantage to just playing Granderson. You’d also need a pretty good right-handed bench bat in order to overcome the “pinch-hitting penalty” when hitting for Granderson.

For a right-handed example, let’s use Ryan Garko, recently acquired by the Mariners as a platoon 1B/DH. Garko’s career wOBA is .347, .332 vs. RHP in 1229 PA, and .382 vs. LHP in 485 PA — a 14.4% difference. But he’s a righty, so we regress toward 2200 PA of the average (6.1%): (.144*485+.0611*2200)/(485+2200) for an estimated platoon skill of 7.6%. Using the CHONE projection of .345 wOBA, we’d estimate Garko to be a .338 hitter versus RHP, and .364 versus LHP. That’s a good hitter versus lefties, and while the .338 isn’t great for a 1B/DH, it isn’t as if he’s helpless against RHP.

Before I call it a post, I thought it would be interesting to quickly estimate the platoon skills of two players who have “reverse” splits for their careers.

Right-handed hitting Matt Holliday has a career wOBA of .400, but has hit .402 vs. RHP (2793 PA) and and .377 vs. LHP (845 PA), a -6.3% split (negative indicating “reverse”). After regression, we get a 2.7% estimated platoon skill. Given CHONE’s .389 wOBA forecast for Holliday, we’d estimate his skill as .387 wOBA vs RHP, and .397 vs. LHP. Not quite a “reverse,” but you don’t really want to “burn” a ROOGY against Holliday, either.

Colorado’s Ian Stewart has a career .337 wOBA, .334 vs RHP (655 PA) and .346 vs LHP, a -3.6% split. After regression, it comes to a 6.7% split. Given CHONE’s .358 wOBA forecast, we’d expect Stewart to his around .363 vs. RHP and .339 vs. LHP, a nice split for a lefty, but not a reverse one.

Like all forecasts, these are estimations (and crude ones, at that). To be more thorough, we’d have to assign confidence intervals/reliability scores. We’ simply trying to minimize our error. But keep in mind that splits in the retrospective mirror are almost always smaller than they appear.

[Note: After completing this post, I realized that Tom Tango had already posted about this on his blog, using Granderson as an example. D’oh. Fortunately, my results are almost exactly the same]

According to Wiktionary, the definition of “enigmatic” is

1. Pertaining to an enigma.
2. Mysterious.
3. Defying description.
4. (variant) Enigmatical.

Is “Enigmatical” really a word? Who cares? I think it fits as a nickname: The Enigmatical Kenny Williams.

I am not mocking Chicago White Sox General Manager Kenny Williams. I’m mocking myself. I have a terrible track record when it comes to thinking about Williams’ moves. When he makes a move I think is silly, it ends up working out. When he makes a move I like, it blows up. So it is with some trepidation that I am posting on Chicago’s designated hitter hole.

The White Sox are built to win now: trading for Jake Peavy, acquiring Alex Rios, and adding questionable stopgaps like Juan Pierre in left and Mark Teahen at third. It’s not an unrealistic hope. The Twins were the class of the AL Central even before adding Orlando Hudson. But with Peavy, Mark Buerhle, John Danks, and Gavin Floyd, the Sox probably have the best starting rotation in the division, and the bullpen is strong. The position players don’t stand out as much, but they aren’t dreadful — Carlos Quentin is a good hitter when healthy, Gordon Beckham is a budding star, and Rios, Alexei Ramirez, and Paul Konerko are solid performers. They’re probably the only other team in the Central with a shot, but it is a legitimate shot.

That makes the DH situation puzzling. The White Sox decided to pass on Jim Thome, who then signed a cheap contract with the Twins. While I’m not sure how much a bench DH really helps the Twins (unless Delmon Young is terrible yet again despite CHONE’s favorable projection), not having him around really is going to hurt Chicago. It’s not clear who Chicago plans on playing at DH, but (Omar Vizquel jokes aside) from what I gather it will be a mix of Andruw Jones, Mark Kotsay, and occasionally guys like Paul Konerko and Carlos Quentin. Perhaps power-hitting catching prospect Tyler Flowers will get some DH starts later in the season.

Seriously, a Jones/Kotsay platoon is the first option? Keep in mind that a league-average hitter is a replacement level DH. While Jones might still have his uses as a bench/platoon player, CHONE projects him as a below average hitter (.324 wOBA, -7/150 in context-neutral linear weights). If you think that’s bad, Kotsay projects at .297 wOBA, -16/150 context-neutral. That’s not useful at any of the positions Kotsay backs up at this point, much less DH. Even if efficiently platooned, that’s ugly. Heck, Mike Jacobs (-6) would be an upgrade, and would also keep the Chicago/Kansas City pipeline active.

They really didn’t have room for Thome? He probably adds just a few runs for the Twins, but he would be at least a one, maybe two win improvement over Jones/Kotsay. Of the remaining free agents, Russell Branyan seems like a great fit. He’s a +15/150 hitter. Even with doubts about his back, as a half-time player he probably adds a win. Carlos Delgado would be an improvement as a DH in a platoon situation, too. There are plenty of league-average hitters still out there who could meet the defensive “requirements” at DH.

The White Sox are in a situation where spending a bit on a DH who can hit better than Zombie Andruw Jones and Mark Kotsay is logical, yet so far they’ve passed. But one thing I’ve learned over the last few years is to never count The Enigmatical Kenny Williams out.

You know, if teams would stop making transactions involving Scott Hairston, maybe we wouldn’t do so many posts about him…

Hairston, who knows the way from San Diego-to-Oakland-and-back quite well, recently settled with the Padres for $2.45 million dollars, avoiding arbitration. Hairston was traded by the Padres to Oakland during the 2009 season, then was traded back to the Padres a few weeks ago along with outfielder Aaron Cunningham. A player in his second year of arbitration is generally expected to get 60 percent of his open market value. Assuming a $3.5 million dollar current market value of a marginal win, Hairston, who will turn 30 in May, is getting paid as if he’s a bit more than a 1 WAR player.

Offensively, Hairston makes up for his below average walk rate and contact skills with good power. CHONE projects Hairston for .254/.315/.448 in San Diego, or two runs above average per 150 games in context-neutral linear weights. Defensively, Hairston has been above average in both center and left according to UZR. I have Hairston as a +2/150 position-neutral outfielder — that is, average as a center fielder, +10/150 on the corners.

Per 150 games Hairston projects as a 2.4 WAR player (+2 offense +2 fielding +20 replacement). However, Hairston has had problems staying healthy, never having played more than the 116 games he appeared in during 2009. The Fans notice this, and project him for 115 games in 2009. At that rate, he’s about a 1.8 WAR player. So this is a good deal for the Padres, depending on how they use him.

Hairston is a useful player at a price the rebuilding Padres can afford. Yet one wonders how long he’ll be in San Diego. The Padres are clearly at the beginning of a rebuilding process. At 30, Hairston is likely declining. Moreover, the Padres have a group of younger outfielders with more upside and years of team control: Tony Gwynn (27), Kyle Blanks (23), and Cunningham (24). (Will Venable (27) and Chad Huffman (25) might be in the conversation, but I’ll leave that to the prospect mavens.) Having Hairston around as a 4th OF or insurance in case, e.g., Cunningham isn’t ready, isn’t a terrible idea, but it’s not as if that is going to be the difference between the Padres and the playoffs this year. With his team-friendly contract, Hairston has more value to San Diego is a trade chip who wouldn’t be missed by the Padres (other than maybe his brother Jerry) as the Padres look ahead.

Hairston’s handedness also makes a difference. It’s easy to see a number of teams who could use a right-handed-hitting outfielder. I don’t want to exaggerate platoon issues, but teams with designs on contention such as the Yankees, Mariners, and A’s have been (or should be) looking for a right-handed bat for the outfield. Of course, the Yankees and Mariners have sort of addressed those needs with Randy Winn (although he’s a switch-hitter) and Ryan Garko (although he’s a 1B/DH). The A’s, of course, traded Hairston in the first place to address their hole at third base (in many ways, Kevin Kouzmanoff is a third base version of Hairston). Those are just a few examples. Given the distribution of handedness among outfielder/infielders, along with Hairston’s abilities and contact, it’s not hard to see him being part of a trade that helps both a trade partner’s present and the Padres’ future.