Playing around with the new splits yesterday Dave C. noted Joe Mauer’s bizarre spray chart numbers. To right field Mauer hits ten grounders for every fly ball and Mauer’s ISO and wOBA by direction in play resemble a RHB more than a LHB peaking in left (opposite field for Mauer) and smallest in right (pull field for Mauer).

Some commenters to Dave’s article questioned how Mauer handled pitches to different parts of the plate, and whether that was responsible for this pattern. I thought these were very interesting questions. I plotted the average angle of his grounders and balls in the air based on the horizontal location of the pitch. I show the average lefty for comparison. Here -45 corresponds to the left-field line, 0 to second base and dead center, and 45 to the right-field line.

Not surprisingly all of the lines are increasing, the farther inside a pitch is the more it is pulled (greater angle meaning farther to right field). For LHBs grounders are, on average, pulled while balls in the air depend on the pitch location: inside pitches hit in the air go, on average, to right while outside pitches go to left. I have previously shown this with the HITf/x data and Matt Lentzner has a simple, but very cool, bat-ball collision model that shows why this is the case. Anyway Mauer’s ground balls are not all that different than the average lefty’s, but his balls in the air are. No matter where the pitch is Mauer, on average, hits balls in the air to left field. Even on far-inside pitches the average fly ball Mauer hits will be to center-left. This is how he ends up with all his pulled hits as grounders.

This backs up Dave’s suggested defensive alignment, “teams should consider employing two different shifts against Mauer; an outfield shift playing him as if he was a pull-heavy right-handed batter, and an infield shift treating him as a pull-heavy left-handed hitter.”

How does this affect how well he does on those pitches? Here is Mauer’s and the average LHB’s ISO by horizontal pitch location.

The average lefty has the most power on pitches middle-in and on such pitches Mauer has about average power. But Mauer’s power keeps increasing as pitches get father away from him and peaks middle-away. On pitches on the outer half of the plate Mauer has substantially more power than the average lefty. Since Mauer is going the other way with his fly balls anyway it makes sense that he would do best on pitches slightly away.

Taro, a commenter to Dave’s post, noted maybe it would be best to pitch Mauer inside, where he has just average power. Have pitchers adapted against Mauer and thrown more inside pitches to him?

Doesn’t seem so; in fact if anything pitchers pitch even farther away to Mauer than they do to the average lefty. It looks like faced with the already Herculean task of trying to get Mauer out pitchers are not doing themselves any favors with their approach. It will be interesting to see if that changes this coming season.

So, we have splits now. We’ve talked about how to use them and how not to use them. Today, I want to talk about why our splits matter. After all, Baseball-Reference has a great splits section, and has for a while. But, B-R is focused on providing factual data (which they do very well – this is no knock on the site that Sean has built), and as we’ve talked about before, sometimes what actually happened is not the best predictor of what is going to happen.

Let’s use an example of why having split data for metrics like xFIP is important. If you go to Jered Weaver’s page at Baseball Reference, you’ll notice his career L/R split is pretty large. Righties have hit just .232/.283/.365 against him, but lefties have managed a significantly better .267/.327/.449 mark. There’s a 126 point gap between his OPS against RHB and LHB.

Now, look at Weaver’s splits page here.

Vs Left: 7.17 K/9, 3.01 BB/9, 36% GB%, 4.51 xFIP
Vs Right: 7.47 K/9, 2.30 BB/9, 28% GB%, 4.42 xFIP

That’s a pretty different story. In terms of the numbers that measure skills, Weaver’s not actually all that much better against RHBs than LHBs. The things that drive Weaver’s large career platoon split? HR/FB and BABIP, two of the least predictive metrics out there.

For his career, lefties have turned 10.6% of their flyballs off of Weaver into home runs. Right-handers have just managed 5.6%, causing a big gap in home run rate. Also, lefties have tagged him for a .307 BABIP, compared to just .282 for righties.

Maybe more research into these issues will reveal that handedness effects these two metrics more than we currently know, but right now, Weaver’s profile is not that of a guy who will continue to post big splits going forward. He’s no Vicente Padilla. He’s a little bit better against right-handed batters, but not much more than the norm.

You’d have a really hard time knowing that from his results. This is one of the great things that we’ll be able to flush out with split data here on the site, and one of the reasons we’re so excited to have them now.

Eric Gagne’s run from 2002 to 2004 is historic. Over this three-year period, the goggled Canadian saved a staggering 152 games against only 6 blown saves, including a streak of 84 straight. Gagne was truly dominant, as he compiled FIPs of 1.80, 0.86, and 2.05, good for 3.3, 4.5, and 3.1 WAR, respectively, in 2002-2004. His 2003 season of 4.5 wins was over half a win better than the second best RP season, K-Rod’s 2004.

Any pitcher who can sustain 12+ K/9s for three years is likely to have a pretty dominant run, and in order to do that the pitcher has to be able to shut down hitters on both sides of the plate. Indeed, Gagne pitched quite well against LHBs with the platoon advantage against him – he struck out nearly 8 more batters per 9 innings than he walked, and never allowed a FIP greater than 2.36 to lefties.

To truly tyrannize the league like Gagne did, however, it takes more than a slightly human 12:3 K:BB ratio against one side of the plate. Without truly destroying right handed battters, we would have seen more of the 2003-2005 run of Tom Gordon – 6 wins in 3 seasons. Nothing to sneeze at, but certainly not historic.

Right handed batters just could not beat Gagne. Of the 463 righties that faced Gagne, only 81 reached base. That’s a .174 OBP. Gagne struck out 208 of these batters, 44.9% of them, good for a 14.8 K/9, to complement a sub-2 BB/9. Let the utter ridiculousness of those numbers sink in for a bit, as they’re tame compared to what’s next.

Ready? Now, let’s restrict ourselves to 2003. Gagne faced 151 right handed batters that season en route to 55 saves. 84 of them struck out – just over 55%. Over 55% of hitters failed to make non-foul contact against Gagne. He only had to rely on his fielders to make plays against 45% of the batters he faced, whereas the average pitcher needs help on over 80% of hitters.

Only 26 batters reached base. Only one hit a home run, and only four others recorded extra base hits. With a nearly 12 K/BB ratio against righties and a microscopic HR rate, his FIP was -0.04. That’s not a typo: -0.04. Of course, having a -0.04 ERA is mathematically impossible, but that number is truly representative of what little right handed hitters could accomplish against him at his best.

By dominating such a large population of the league, Gagne cemented his status as one of the best closers in the league. It’s disappointing that his career was derailed as it was by injury, as his potential at the time was seemingly limitless. We can only speculate as to what could have been, but we are still left with a historic run, and one we should not forget.

Because you’re a savvy, go-getting sort of reader, you very probably came across Craig Calcaterra’s announcement about a fortnight ago that not only has the very famous Rob Neyer (a) formed a partnership with Diamond Mind Baseball, but that he (i.e. Neyer) (b) was/is arranging a league of elite baseballing writers to promote it.

First off, I want to say: If this move constitutes “selling out” on Neyer’s part, it must be the best, most nerdly case of selling out ever (although, I concede that it depends on how much Wallace Shawn has received in the way of royalties for his My Dinner with Andre action figure). Allow me to announce it here and now: I am willing to lend my name, likeness — whatever — to almost any product, provided the Scrilla Factor (SF, for short) is sufficient.

Follow the money trail, indeed.

The original lineup for what’s being called the Rob Neyer Media League has changed slightly — Messrs Glanville and Posnanski have both recently, and somewhat bizarrely, broken their wrists while washing their pickup trucks — but the idea is the same.

Viola (team name in parentheses):

*Craig Calcaterra, Famous Blogicator (Matewan Massacre)
*Gordon Edes, Boston-Area Newsman (Sons of Ring Lardner)
*Rany Jazayerli, Constantly Aghast Royals Fan (The Process)
*Bob Keisser, Resident, The City They Call Long Beach (The Write Stuff)
*Jonah Keri, Twin-Maker, etc. (Montreal McGaffigans)
*Barry Koren, Owner/Operator, Diamond Mind (A Team of Their Own)
*Richard Lally, Actual, Real-Live Bookwriter (Park Slope Muggles)
*The Man Himself, Sabermetric Evangelist (Wabash Mashers)
*Norm Wamer, Radio Giant (Hall of Wamers)
*Josh Wilker, Dream-Maker, Love-Taker (East Randolph Kerouacs)
*Charles Wolfson, A More Differenter Owner/Operator of Diamond Mind (Pittwater Dolphins)

Moreover, in what appears to have been a terrific accident, Neyer has invited yours truly to the awesome, nerdly dance party. (I won’t dwell on it, but it appears as though Neyer’s invite appeared in my inbox at the very moment his judgment was almost definitely being impaired by narcotics.)

The league has just finished its draft, so there’s only so much to say about it at this point. Still, here are some observations from a week or so of noodling around on the site:

*If the message board comments at the site are correct, the salaries for each player are pretty carefully calculated to represent their (i.e. the players’) true talent levels. Therefore, it’s tough to go all Andrew Friedman and exploit market inefficiencies — especially when one of the other owners in your league is, like, BFF with Friedman himself.

*That said, it’s possible to manufacture inefficiencies with ballpark selection (and maybe some other ways I haven’t realized). To that end, I’ve chosen Fenway Park ca 1914-1918 as my home field. As you can see by means of this long, nerdy list of park effects, Fenway has the lowest home run factor (22) of any available park.

That being the case, I’m constructing a team of pitchers with low HRA+s (that is, below average in home runs allowed relative to the league) and batters with low HR+s (that is, below average in home runs hit relative to the league). Obviously, that won’t make for a great combination when we (The New Enthusiasts, that is) visit U.S. Cellular next Monday, but it’s excellently suited for our home field.

*One player I’ve drafted, and who would undoubtedly command some attention for All-Time All-Joy Team consideration is Oliver “Ghost” Marcelle. Marcelle is one of the elite defensive third basemen in the Diamond Mind system (one of the few who qualifies as Excellent defensively). He was also one of the best — and most interesting — Negro League players ever, it seems.

But don’t take my word for it! From Wikipedia:

In a strange incident in the late 1920s, Marcelle’s teammate Frank Warfield reportedly bit Marcelle’s nose off after the two got into a fight, when both men were playing in the Cuban Winter League. Bill Yancey, another teammate of Marcelle’s, said, “What got [Marcelle] out of baseball, he and [teammate] Frank Warfield had a fight in Cuba [probably in the winter of 1927-28, over a dice game] and Warfield bit his nose off. He was a proud, handsome guy, you know, and then he used to wear a black patch across his nose and he got so he couldn’t play baseball anymore.”

*User jaxxr, whom I contacted through the site because he seemed to be a knowledgeable fellow, was super-thorough and -patient with me in explaining how much certain of the ratings (defensive range, outfield arm, running) translate into run value. Thank you very much, sir!

Anytime a new offensive statistic or function is added to the site, I tend to gravitate to Barry Bonds’ page to see what the outer bounds look like. The splits function is no different.

For instance, did you know that in his 268 high leverage plate appearances Bonds was walked intentionally 58 times. All told, Bonds walked in 42.2% of his total plate appearances. When he did hit, his ISO was a ridiculous .360. That’s good for a 1.354 OPS and a .524 wOBA. I don’t know if people will reference these numbers in 200 years after reading up on baseball history (folklore by then) and how Buck Showalter walked him with the bases loaded, but if they do, such a factoid should help to create understanding, if not acceptance.

Even the immortal saw the typical platoon advantage, which is to say that the left-handed Bonds was superior against righties. A .492 wOBA against them versus only a .480 wOBA against lefties suggests the Giants wasted a golden opportunity for a platoon. Bonds was more discriminatinh when it came to hitting the ball hard in certain directions. He hit the ball well to right (.524), center (.513), but not nearly as well to left (.394). Of course, a .394 wOBA is nearly .020 points higher than Evan Longoria’s career wOBA, but this is Bonds we’re talking about. Unacceptable, Barry.

Somehow he hit more home runs at home (one more, to be exact) than he did on the road. This came in light of nearly 40 fewer plate appearances at home, too, and while playing in one of the more homer-constricting parks in the National League. Oh, and this, well, this I just have to replicate in full These are Bonds’ 2002 month-by-month wOBA figures:

April: .563
May: .509
June: .536
July: .514
August: .607
September/October: .530

A .509 wOBA was a down month for him. Goodness gracious … goodness gracious.

Previously, I looked at the pitchers with the biggest increases in fastball speed this past season. The list of the top 20 was dominated by relief pitchers, which is not a huge surprise given how volatile relief pitchers tend to be and since they tend to throw harder, on average, than starters, those fluctuations can cause bigger shifts in absolute speed.

Looking at the list from the other end, however – that is, from the pitchers that lost the most speed on average on their fastballs – produces more starters. Whether because starters will get more innings even when injured, a usual byproduct of diminished fastball speed, or some other cause is open for speculation, but the results are definitely interesting.

As promised, a list of the biggest drops in fastball speed from 2009 to 2008. A minimum of 50 innings pitched in each season was needed to qualify.

Joba Chamberlain, -2.5
Ervin Santana, -2.1
Ross Ohlendorf, -2
Jared Burton, -1.7
Tim Lincecum, -1.7
Daniel Cabrera, -1.7
Manny Delcarmen, -1.6
Chan Ho Park, -1.6
Brian Fuentes, -1.6
Jeremy Sowers, -1.5
Lance Cormier, -1.4
Chris Young, -1.4
Grant Balfour, -1.3
Mariano Rivera, -1.3
Tim Redding, -1.3
Oliver Perez, -1.2
Aaron Cook, -1.2
Kevin Gregg, -1.2
Kyle McClellan, -1.1
Aaron Heilman, -1

Obviously, the decrease in fastball speed meant little to Tim Lincecum as he went on to repeat his NL Cy Young. Ervin Santana’s recovery from injury in 2009 was far from 100% and how his fastball shows up in 2010 could have a major impact on the close AL West race. The Yankees might be starting to worry about the future ceiling of Joba Chamberlain and the Mets, well, the Mets should have been worried about Oliver Perez long before they inked him to that ridiculous extension.

The introduction of splits here on FanGraphs offers us a wealth of new information as my fellow writers have been expanding upon today. I do not have a post centered around splits today, but instead focused on an area that I continually find incredibly useful here somewhat related to splits: pitch-type breakdowns.

I love pitching and love dissecting its fluctuations from year to year. Tonight, I looked at changes in fastball velocity, both in terms of absolute change and in terms of a percentage over 2008 averages. Changes in average fastball speed do not tell an entire story by themselves, but I think they present several interesting points of fact.

I took a look at all pitchers with at least 50 innings pitched in both 2008 and 2009 and compiled lists of the biggest gains and losses in both fastball speed and fastball frequency. One thing that I found interesting was the seeming lack of relationship between the two. I would have expected pitchers that gained speed in their fastballs to be throwing them more often, but there was little to no correlation between changes in fastball speed and changes in how often pitchers threw fastballs.

Among pitchers with the biggest increases in average speed was Justin Masterson (+2.8 mph), Nick Masset (+2.6), a host of other relievers in the 2.0 range and a few starters mixed in as well, notably Justin Verlander (+2) and, surprisingly, Barry Zito at +1.6. Zito however, had a 5% reduction in percentage of fastballs thrown. Nick Masset was even more dramatic, throwing 15% fewer fastballs.

A top twenty list of increases in fastball speed follows and later tonight, a list of fastball speed drops.

Justin Masterson, 2.8
Nick Masset, 2.6
Matt Capps, 2.1
Ryan Madson, 2.1
Kevin Correia, 2
Justin Verlander, 2
Mark Lowe, 2
Huston Street, 1.7
Barry Zito, 1.6
Brad Penny, 1.6
Josh Johnson, 1.6
Jose Contreras, 1.5
Jon Lester, 1.5
Jonathan Broxton, 1.4
Wandy Rodriguez, 1.3
Luke Hochevar, 1.3
Miguel Batista, 1.3
Chris Volstad, 1.2
Bob Howry, 1.2
Ubaldo Jimenez, 1.2

The fall of the newspaper business is not news anymore. In nearly every city in the country, papers are scaling back coverage of everything, including baseball, in order to save costs. In some cities, such as Washington, the scaling back represents almost a complete removal of day-to-day coverage of the team. This is, simply, not good news for anyone. Basic capitalism demands competition to get the most efficient outcome, and even in a business where there isn’t necessarily a tangible product being sold, quality declines when people leave the industry.

To combat this, Mark Zuckerman, a laid-off writer from the Washington Times, is raising money to go to Florida and cover the team on his own. I asked Mark to sum up why he’s doing this, and this was his answer:

“Between the Times eliminating the entire sports section and leaving all of us unemployed, and the Post still searching for a new beat writer, there’s been a real lack of quality Nats coverage outside of websites owned by MLB and the team. I’m hoping I can at least somewhat fill the void and provide the kind of comprehensive coverage fans have always counted on from newspapers.”

He estimates that it’s going to cost about $5,000 for him to spend six weeks in Viera, covering the Nationals on a daily basis. If you’ve ever planned a trip to Florida, you know that $5,000 doesn’t go very far, so Mark is clearly cutting corners in order to get down there and give Nationals’ fans another option in their coverage of the team. He’s not making a profit on this.

I know there are a lot of worthy places for us to give our money right now, and the economy still sucks, but I highly encourage you to donate to Mark’s cause, even if you are not a Washington Nationals fan. It’s in the best interest of fans everywhere that the information stream about baseball news is not restricted solely to those who work directly for an organization. That Mark is willing to do this beat for such a pittance is an opportunity that we should not pass up.

As of this writing, he’s almost halfway there. If you have some disposable income, consider giving to Mark’s cause, and let’s all make sure that the Nationals fans can enjoy spring training news – no matter how mundane it may be at times – just like the rest of us.

There are a lot of interesting tidbits of information that can be gained through the new splits pages found here on FanGraphs, but there is one that shines above all the rest – Joe Mauer. The Twins star catcher is not just a tremendous hitter; he’s also a tremendously weird hitter.

Take, for example, Mauer’s career spray chart numbers for balls hit to different areas of the field.

There are a few things that should jump off the page immediately. How about that crazy 10.09 GB/FB ratio when he pulls the ball, which is the result of that even crazier 7.6% FB% on balls to right field. Seriously, seven point six percent of his balls to right field are categorized as fly balls. When Mauer turns on a pitch, he’s beating it into the ground. That flips entirely when he hits the ball the other way, though, as nearly 50% of his balls in play to left field are fly balls. That latter number is entirely normal, actually – it’s the batted profile on pulled balls that is so nutty.

Actually, for reference, let’s just give the league averages for all hitters on their spray chart data.

That’s the norm. You’ll see the obvious pull power, with both LH and RH hitters posting .280 ISOs when they hit to their pull field. That gets cut in half when a normal hitter goes up the middle or the other way. For pretty much every hitter in baseball, their lowest wOBA is going to be to the opposite field, where fly balls are high but home runs are low, leading to a lot of weak fly outs. Mauer, though, is no normal hitter. His power is almost entirely to left field.

When he goes the other way, he runs a .410 wOBA, and his ISO is twice as high to LF (.257) as it is to RF (.122). When he pulls the ball, he’s pretty terrible, posting just a .289 wOBA, thanks to the copious amount of ground balls. The difference between his pull/opposite field numbers are stunning, especially in comparison with how pretty much every other hitter on earth functions.

In fact, given this data, there’s actually a case to be made that teams should consider employing two different shifts against Mauer; an outfield shift playing him as if he was a pull-heavy right-handed batter, and an infield shift treating him as a pull-heavy left-handed hitter. Groundballs to the left side and flyballs to the right side comprise such a small percentage of Mauer’s batted ball profile that a straight-up alignment is an inefficient way of defending him, and he’s made a living by taking advantage of it.

If you employ the traditional infield shift, with three defenders on the right side, you should be able to limit his hits through the hole from the crazy amount of grounders. At the same time, shifting your outfielders the other way, shading towards left field, will cover more of the areas where he traditionally racks up his extra base hits. By having the outfielders shifted towards left, you’re also more likely to cut off balls that roll past the third baseman, who is left to defend that side of the infield by himself, before they get to the wall.

It would look really weird, and Mauer’s a good enough hitter that he may just render the whole thing moot by changing his swing and swatting balls to the right field corner, but I’d love to see a team give this a try. The current way of trying to get him out certainly isn’t working, and his batted ball profile is so unique that it almost demands a radical change in how you position your fielders when he’s at the plate.

So, stat guys working for MLB clubs reading this, this is your challenge for 2010 – convince your manager to give the double-shift against Mauer a chance. Make him change his approach in order to get on base. Stop letting him beat you just because he’s so different than a normal hitter.

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]