The biggest move this offseason saw Johan Santana heading to the Mets in exchange for Carlos Gomez and some more prospects. The former two-time (should be three-time) Cy Young Award winner looked to solidify a pitching rotation that seemed more than capable of making fans forget all about last year’s end of season breakdown. Coming off of a relative down year—a down year for him was still better than the up year of most others—there were some who questioned whether or not Johan would be able to regain whatever made him successful pre-2007.

One of the biggest reasons his performance suffered last year came in the form of home run balls. From 2003-2006 his HR/9 ranged from 0.85-0.97; in 2007 it jumped to 1.36 as he allowed 33 dingers. I recently took a look at his Pitch F/X data over the last year and a half to see if he had done anything differently on hits as compared to fouls or swinging strikes. The results also showed that his home run balls—or other hard hit balls—generally came from pitches not just with lesser velocity and/or movement but also very poor location: Most of his home run balls came on pitches right down the middle.

In Johan’s first 60 innings this season he surrendered 11 HR; over his last 34.2 he has surrendered just one.

First 9: 60.0 IP, 52 H, 11 HR, 15 BB, 57 K
Last 5: 34.2 IP, 36 H, 1 HR, 9 BB, 29 K

Of course it is too small of a sample to generate definitive conclusions but we can still investigate and make observations pertaining to whether or not any discrepancies in relevant Pitch F/X data exist in this split. For starters, here are the velocity and movement data for his first nine starts:

FA: 90.54 mph, 5.63 horiz/9.22 vert
SL: 84.18 mph, -0.98 horiz/4.53 vert
CH: 79.69 mph, 5.51 horiz/8.13 vert

And here is the same data in his last five starts:

FA: 92.39 mph, 6.68 horiz/9.64 vert
SL: 84.89 mph, -0.57 horiz/4.64 vert
CH: 79.94 mph, 6.48 horiz/7.54 vert

He has thrown harder and with more movement lately. One of the problems with his hard hit balls, as mentioned above, dealt with the percentage of pitches he threw down the middle. Here are his splits of pitches thrown down the middle:

First 9: 11.5%
Last 5: 11.9%

Though it appears he has thrown slightly more down the middle recently the small sample detracts from any real discrepancy. How about his accuracy? Here is his Ball/Strike/In Play breakdown for the first nine starts, followed by the last five:

K: 46.8%, 45.1%
B: 35.1%, 34.5%
X: 18.1%, 20.4%

Speaking of balls put in play, have any less fallen in for hits lately?

Outs In Play: 67.5%, 64.2%
Hits In Play: 32.5%, 35.8%

Despite sustaining a similar level of accuracy and balls put in play he has actually allowed a slightly higher percentage of those in play to fall in for hits. Looking at his WHIP in these two different spans (1.12 compared to 1.25) it seems that he was hit less in the early going though those hits were of a higher value than recently, despite the increase in hits given up lately. Lastly, has he gotten ahead of hitters any more or less lately? Here is his first-pitch strike split:

First 9: 51.2%
Last 5: 44.8%

All told, not much can truly be garnered in terms of data discrepancies but Johan has gotten ahead of hitters less as of late, has essentially sustained his patterns of accuracy, is throwing virtually the same percentage of pitches down the middle, and is allowing more hits. All of these signs would intuitively point toward similar or worse performance and yet he has thrown better lately. Perhaps his increase in velocity and movement over his last five starts has prevented hitters from getting the fat part of the bat on the ball quite as often. Definitely something to look out for as the season progresses.

For those who read the title and thought this post had something to do with food, I apologize, it does not. Instead, the spread I speak of refers to the pitch distribution in the Toronto Blue Jays starting rotation. Last month, when writing about Shaun Marcum’s hot start, some loyal readers commented that he was one of very few pitchers that threw five different pitches at least 10% of the time. Trying to verify this assertion I discovered that were only two other pitchers that fit this bill: Adam Eaton and Andy Sonnanstine.

It was recently revealed to me that Jesse Litsch joined the 5/~10% club. Catchy title, eh? I named it myself.

Now, not many starting pitchers throw even four different pitches at least 9-10% of the time and the Blue Jays have three of them: Dustin McGowan (4), Jesse Litsch (5), and Shaun Marcum (5). Group the three of them with the steady three-pitch mix of Roy Halladay and the fastball-curveball combo of A.J. Burnett and you have one extremely solid rotation.

Here are their pitch distributions, with velocity/frequency:

Roy Halladay: FA 92.7/45.9, CT 90.5/25.0, CB 78.2/23.6
A.J. Burnett: FA 94.1/66.2, CB 80.5/26.4
Dustin McGowan: FA 95.1/59.2, SL 87.6/19.3, CB 81.4/11.3, CH 86.7/10.1
Jesse Litsch: FA 88.8/17.7, SL 82.2/22.9, CT 85.0/37.5, CB 76.8/12.9, CH 80.0/9.0
Shaun Marcum: FA 86.8/39.0, SL 81.4/15.5, CT 84.5/14.0, CB 74.8/10.0, CH 80.9/21.5

Not only does this rotation mix their pitches effectively but their speeds as well; McGowan’s changeup is the same speed as Marcum’s fastball. Lastly, take a look at their stats:

Roy Halladay: 3.01 ERA, 1.01 WHIP, 5 CG, 12 BB, 72 K, 1.51 WPA
A.J. Burnett: 4.14 ERA, 1.37 WHIP, 33 BB, 71 K, 0.17 WPA
Dustin McGowan: 3.95 ERA, 1.42 WHIP, 28 BB, 55 K, 0.72 WPA
Jesse Litsch: 3.05 ERA, 1.13 WHIP, 9 BB, 33 K, 0.80 WPA
Shaun Marcum: 2.63 ERA, 0.94 WHIP, 22 BB, 67 K, 1.89 WPA

Their “worst” ERA is 4.14 and just one WHIP is over 1.40. Overall, the rotation has contributed 5.09 wins while being a steady and major factor in the success of the team. Perhaps their pitching coach has preached different spreads in order to, as a rotation, keep teams off kilter; whatever it is, though, it definitely seems to be working.

We’re about two months into the season, and it’s not a bad time to look which pitchers are allowing too many home runs. Fortunately, there’s a useful metric on FanGraphs to do just that. It’s called HR/FB and while I’m sure many of you are familiar with it, here’s a brief summary of how it works.

There’s been a number of studies done on HR/FB and for the most part, they conclude that pitchers do not have control over how many home runs they allow on outfield fly balls. Your typical starting pitcher should be expected to have a HR/FB of around 10% every year. Anything that deviates from 10% could be contributed to the park he pitches in, or to “luck”. So let’s look at who has been allowing an inordinate number of home runs this season:

Roy Oswalt (23.4%) – Oswalt leads baseball with a rather ridiculous HR/FB rate. Basically one in four of his fly balls have become home runs. I don’t care where he’s pitching, this is just Oswalt having some terrible luck. He’s never had a HR/FB above 12.9% to end the season. A couple weeks ago, Eric Seidman asked if you should trade Oswalt in your league; the answer is still no and now is another prime opportunity to go acquire him.

Brett Myers (21.4%) – Sure he plays half his games in Citizen’s Bank Park and he does have a career HR/FB of just over 15%, but 21% even for him seems quite high. He probably isn’t due for such a drastic adjustment as Oswalt, but I’d imagine it should start to trend towards his career average. He hasn’t allowed a home run in his last two starts either, so perhaps he’s well on his way to normalcy.

Carlos Villanueva (16.9%) – Currently, Villanueva leads baseball with a 2.09 home runs per 9 innings. He’s about as much as a fly ball pitcher as he is a groundball pitcher so he really shouldn’t be tied for 5th with most home runs. While Miller Park isn’t all that favorable to fly balls, he should be able to do considerably better in the home run department and decrease is ERA by more than a little come season’s end.

Johnny Cueto (16.4%) – It looks like the phenom has himself a bit of a home run problem. Since he hasn’t been around for very long, it’s a little tough to say if this is just a luck thing, or of it’s a real problem. I’d venture to say it has more to do with luck then anything else, even if he does play in a park that is prone to home runs. Unfortunately, Cueto is an extreme fly ball pitcher and isn’t expected to be particularly stingy with home runs in general.

Mike Mussina (16.4%) – We all know about Mussina’s decline in fastball velocity. John Walsh’s research suggests that mis-located fastballs of the slower variety could certainly cause an increase in home runs and it’s possible that could be happening to Mussina. I still think his HR/FB should drop as the season continues, but it’s hard for me to be enthusiastic about.

Johan Santana (15.9%) – Santana developed a home run problem last year and it seems to have continued into this year. Shea stadium is slightly worse for home runs than the Metrodome, but it really doesn’t explain such a high HR/FB. It’s hard to imagine it won’t decrease as the season goes on, but unless it drops back down to around 10% or lower, it will be difficult for him to return to sub-3 ERA levels.

Last year the Yankees struggled in the first half of the season and fought their way back into playoff contention. This year, it is no secret they are underachieving, prompting many analysts to question whether this will be the season in which the Yanks miss the postseason. In an attempt to determine what is going wrong with the team I turned to their team page and became fascinated with the numbers of Jason Giambi.

Believe it or not, Giambi is one of just three Yankees hitters with a WPA of at least 0.15; his 0.17 comes in behind just Hideki Matsui and Bobby Abreu. Additionally, he has a WPA/LI of 0.73, much higher than his WPA.

What will turn many fans off is his lowly .236 batting average. When put into perspective with the rest of his slash line—.236/.384/.516—it becomes clear that the batting average truly does not do his production justice. He has just 29 hits but 8 are doubles and 9 are home runs. Those 9 HR lead the Yankees and his 24 RBIs ranks second to Abreu.

Giambi has increased his BB% from a year ago and decreased his K% from 26 to 15. His LD/GB/FB rates were virtually identical in both 2006 and 2007, coming in at 16.4/30.2/53.4; this year he has BIP rates of 19.4/28.7/51.9. He is hitting more line drives and yet has just a .208 BABIP. We have talked here a lot about expected BABIP and how it works for hitters, so we would expect Giambi to be closer to the .314 range with this percentage of line drives. Now, this isn’t to say he will sustain 19.4% LD all season but that frequency should roughly correlate to the aforementioned BABIP.

Looking at Giambi’s numbers from 2002-2007, the only year in which his BABIP and xBABIP differed significantly was 2003; generally speaking, his BABIPs have been close to what his percentage of line drives would suggest.

What really interested me about Giambi is his shift in swing and contact percentages. He currently leads the league with the lowest percentage of swings at pitches outside the zone. Giambi has swung at just 9.9% of outside pitches, making contact on 51.7% of those swings. Last year he swung at 18.2% of the pitches outside the zone, likely contributing to his higher K%.

He has swung at 67.9% of pitches in the zone, making contact on 88.5% of them; the 88.5% puts him right in the 50th percentile. Overall, these swing and contact shifts have resulted in Giambi making contact four percent more often than a year ago. Giambi might not be the player he was five years ago, steroids or not, but his numbers seemingly absolve him from blame for the Yankees early struggles.

When I posted my article on Kosuke Fukudome yesterday, loyal reader VegasWatch pointed out that the Cubs outfielder’s opening day home run likely contributed the bulk of his 0.52 clutch score. Therefore, after being given the label of “clutch” the net sum of all of Kosuke’s clutchiness would not add up to much.

The formula for clutch, as defined in the glossary here, is:

Clutch = WPA/pLI – WPA/LI

For further clarification, pLI refers to the average leverage index of all game events for a given player while WPA/LI refers to context neutral wins; in other words, what the player produced regardless of the situation he entered into. This formula calculates the performance level of a player in crucial situations relative to his standard production. If a player has a .330 batting average in high leverage situations but hits .330 everywhere else, he is not considered clutch. This is not to say he lacks talent, but rather he just produces at a high level in all situations and isn’t necessarily stepping his game up in crucial plate appearances.

The Kosuke example made me wonder which other players were greatly benefiting from a big play. Looking at the top eight clutch scores before the stats updated last night, I tracked the biggest individual play for each of the eight and compared the clutch score of that singular play to the net sum of their other plays. This way we can see which player’s clutch labels are truly derived from one big play as opposed to those who have been a bit more consistent in stepping up. Here are the eight, with their overall clutch score and the three required components of their biggest play – note that the pLI refers to the season average, not the game average:

Pat Burrell (1.33): 0.899 WPA, 3.56 LI, 1.09 pLI
Melvin Mora (1.30): 0.418 WPA, 5.14 LI, 1.04 pLI
Freddy Sanchez (1.27): 0.363 WPA, 4.65 LI, 1.03 pLI
Skip Schumaker (0.93): 0.287 WPA, 4.29 LI, 1.04 pLI
Jeremy Hermida (0.86): 0.294 WPA, 2.61 LI, 0.94 pLI
Bobby Abreu (0.84): 0.512 WPA, 5.44 LI, 0.92 pLI
Manny Ramirez (0.81): 0.482 WPA, 2.38 LI, 0.95 pLI
Joe Mauer (0.80): 0.364 WPA, 4.35 LI, 1.07 pLI

With these figures, here is the breakdown of the big play clutch vs. the clutch in all other plate appearances:

Pat Burrell: 0.57 big play, 0.76 other
Melvin Mora: 0.32 big play, 0.98 other
Freddy Sanchez: 0.27 big play, 1.00 other
Skip Schumaker: 0.21 big play, 0.72 other
Jeremy Hermida: 0.20 big play, 0.66 other
Bobby Abreu: 0.46 big play, 0.38 other
Manny Ramirez: 0.30 big play, 0.51 other
Joe Mauer: 0.26 big play, 0.54 other

Pat Burrell had the most clutch “big play” when he hit a walkoff two-run home run against the Giants on May 2nd. However, according to these numbers, Abreu actually benefited the most from his play; he is the only one whose big play exceeded the net sum of all other clutch plays.

On the flipside, Freddy Sanchez and Melvin Mora have been very consistent in raising their performance level in high leverage situations. When talking about a player’s clutchiness, though, it really only takes one or two big plays to cement the label. We could remove the one big play and look at all other performances but since one play can change a fan’s perception of clutchiness that just would not be fair; regardless of whether or not the clutch benefits from a huge play or a group of smaller plays added together, the bottom line is that these players have helped their team win games by stepping up in crucial situations.

Recently on FanGraphs, we’ve been referring to a stat called xBABIP or Expected Batting Average on Balls in Play to help justify a pitcher’s current BABIP. There’s been a few questions about what this stat means, so I thought it’d be as good a time as any to try and explain the ins and outs of this particular metric.

The initial concept of BABIP is that pitchers do not have control over what happens to balls once they are hit into the field of play.

BABIP typically fluctuates from year to year with a baseline of around .300. If a pitcher has a particularly high or low BABIP, we may say he’s been lucky or unlucky. Things are of course not quite this simple, but for the most part the rule holds true.

In enters ball in play data; we know how many line drives, fly balls, and ground balls a pitcher allows in to play. Line drives fall for hits the most often and ground balls fall for hits more often than fly balls. What types of batted balls a pitcher allows into play are going to effect a pitcher’s overall BABIP.

BABIP by Type (2007):
Fly Balls – .15
Ground Balls – .24
Line Drives – .73

Ideally, the formula is going to look something like this to find out a player’s expected BABIP:
expected BABIP = .15 * FB% + .24 * GB% + .73 * LD%

For more accuracy you could remove home runs from the batted ball percentages at a rate of 92% from fly balls and 8% from line drives. You could even account for infield fly balls and remove that from total fly balls, but the formula above will get you pretty far.

Dave Studeman a couple of years ago calculated that adding .12 to LD% was good enough for a ball park estimate of a player’s expected BABIP. This is what you’ll often see writers on FanGraphs refer to as xBABIP.

The best way to use this statistic is to attempt to validate a pitcher’s current BABIP. For instance, a pitcher might have an high line drive percentage and a high BABIP. This would give a pitcher a high xBABIP as well and you could say: “Yes, his high line drive percentage is responsible for his high BABIP.”

While this is useful for looking at past performances, the difference in xBABIP and BABIP should not be used in an attempt to evaluate future performance. This is because LD% and BABIP are somewhat independent of each other. While there is some correlation between LD% and BABIP, it isn’t enough to suggest that they will always track each other.

LD% in itself is highly variable and it would be difficult to say that a pitcher with a BABIP of .300 and a LD% of 22% (xBABIP of .340) should do considerably worse going forward because you really don’t know what his LD% is going to be the rest of the season. His xBABIP of .340 was his expected BABIP and will not be his expected BABIP in the future. Typically a pitcher’s expected BABIP in the future will be around the original baseline of .300.

In the early parts of a season there is no such thing as middle ground… at least in the eyes of the media and most baseball analysts. Players off to scorching starts are publicized all over the place, just like their initial statistical opposites. I’m looking at you, Barry Zito. Though I have no problem with guys like Cliff Lee and Joe Saunders appearing everywhere there are numerous pitchers performing quite well that are overshadowed by these hot and cold starts.

These are not pitchers necessarily under- or over-achieving but rather those whose names do not make headlines, primarily because they are hogged by the likes of Lee and Saunders.

Looking at some statistics this morning I found six pitchers that fit this bill the best. Some (at least one) names might surprise you but here are The Unheralded Bunch:

Javier Vazquez: 4-3, 3.55 ERA, 2.44 FIP, 1.23 WHIP, 58.2 IP, 12 BB, 58 K
Always a personal favorite of mine, it’s good to see Javy kicking some statistical butt. His FIP suggests his ERA should be the second lowest in baseball right now. He has a 72% LOB rate, which is just about the league average; he has not been lucky or unlucky in stranding runners or letting them score. He has the 4th best K/BB, at 4.83, but he has already posted high strikeout to walk ratios. In fact, since 2000, his low came with the Yankees, at 2.50; otherwise, his K/BBs have always been very, very good. Adding fuel to the fire is his .347 BABIP which just about perfectly matches his .345 xBABIP. Vazquez is not doing this with smoke and mirrors and his statistics should become a bit more well-known as the year goes on.

Johan Santana: 5-2, 3.30 ERA, 4.27 FIP, 1.12 WHIP, 60 IP, 15 BB, 57 K
Nobody is saying Santana is bad, underperforming, or overperforming, but the fact of the matter is that he simply has not been in the news all that much this year. After becoming the prized possession this offseason it was safe to say Santana would be scrutinized by the media, forced to live up to the expectations just like all other New York acquisitions. That simply has not been the case. Santana has been good, not tremendous or outstanding, but it says a lot about a pitcher when we have come to expect a 3.30 ERA or less, with K/BB numbers of 57/15. The one chink in his armour is the 11 home runs allowed, which becomes a bit off when we realize he is allowing around seven percent less flyballs from a year ago. He has also left 85.6 percent of his runners on base. A big strikeout pitcher like him could theoretically sustain a high LOB%, but it is not very likely he will set the millennium high at 85.6.

Ryan Dempster: 5-2, 2.70 ERA, 3.46 FIP, 1.06 WHIP, 63.1 IP, 26 BB, 53 K
The starter-turned-closer-turned-starter has exceeded expectations in the early going, going from a sight Cubs fans did not want to see walking towards the mound in the ninth inning to one they welcome in the first inning. Dempster has allowed just 41 hits in 63.1 innings; that, combined with his decreased BB/9, have contributed to the fifth lowest WHIP in the senior circuit. His xBABIP of .286 greatly outdoes his current .228 clip, so it is not likely Dempster will sustain this performance all year long, but he has definitely been a big part of the Cubs early success.

Mark Hendrickson: 6-2, 3.72 ERA, 3.78 FIP, 1.34 WHIP, 58 IP, 20 BB, 31 K
Yes, I had to double-check this a few times, run tests through SPSS, and even cross-reference with NASA to ensure this was correct, but Hendrickson…is…pitching….well. He is allowing six percent less line drives from a year ago, which are split between his grounders and flyballs. Due to this decrease, his BABIP of .293 matches his xBABIP of .294, which is right near the .300 mark. The one red flag in his corner deals with his K/BB dropping from 3.17 to 1.55.

Chad Billingsley: 4-5, 3.76 ERA, 3.09 FIP, 1.41 FIP, 52.2 IP, 29 BB, 60 K
The swingman finally placed in the rotation has not disappointed the Dodger faithful this year. Tied for fourth in the NL with 60 strikeouts, Billingsley has allowed just 2 home runs. His LOB% of 71.8 is right around the league average and he has maintained his line drive rate from a year ago. Despite this, his grounders have increased while his flyballs have decreased. His K/9 and BB/9 are also way up; he is striking out 10.3 batters per nine innings. If he can keep missing bats and keeping the decreased flyballs in the yard he could be a very effective pitcher all season long.

Joe Blanton: 2-6, 3.87 ERA, 3.77 FIP, 1.33 WHIP, 74.1 IP, 83 H, 16 BB, 34 K
Due to Greg Smith and Dana Eveland making headlines for contributing so early into the Dan Haren trade, Kentucky Joe does not get much love. As his numbers above indicate, he probably deserves some. There is not much to say about Blanton, statistically, other than how his LD/GB/FB, LOB%, and BABIP of current all seemingly match his career averages. He might not be worth Carlos Silva or Gil Meche money to a prospective team, but this As team will be in good shape if Blanton sustains his current performance and still manages to be just the third best pitcher on his team.

With the new design of the home page up and running I recently noticed that Orioles closer George Sherrill not only has the oldest-sounding name in baseball but also leads all relievers with a 2.03 WPA. Sherrill, part of the Erik Bedard trade, has 17 saves out of the Orioles 23 wins; his saves:team wins percentage of 73.9 leads all closers as well.

Something interesting about his success—other than the fact that five of his saves have come against his former employer Seattle—is his higher than expected 3.43 ERA. Granted, ERA is not too useful of a barometer when analyzing the efforts of a closer, but his high saves total and high WPA led me to believe he has been shutting down opponents with the greatest of ease.

A closer look at his game logs shows that, of his 8 earned runs allowed, three have come in non-save situations and another two in his blown saves. In all successfully converted saves, Sherrill has allowed just three earned runs. Despite this relative success, there are four other closers who have been performing extremely well while surrendering next to nothing, regardless of whether or not their appearances coincide with blown saves or non-save situations.

Billy Wagner: 16 GP, 17 IP, 9 H, 0 ER, 3 BB, 19 K
Brad Lidge: 19 GP, 19 IP, 9 H, 1 ER, 8 BB, 21 K
BJ Ryan: 14 GP, 14 IP, 11 H, 1 ER, 6 BB, 17 K
Mariano Rivera: 16 GP, 17 IP, 9 H, 1 ER, 0 BB, 14 K

Here are the averages of these four stacked up next to Sherrill:

Sherrill: 21 GP, 21 IP, 13 H, 8 ER, 10 BB, 16 K
Others: 16 GP, 17 IP, 9 H, 1 ER, 4 BB, 17 K

Another interesting area to look at is the situation in which the closer entered. Coming into a bases loaded, no out situation with a one-run lead is much different than entering into a nobody on, one out situation with a two-run lead. The statistic gmLI measures the difficulty level when the pitcher entered the game. Here are the average gmLIs of these five closers:

Wagner: 1.34
Lidge: 1.83
Ryan: 2.10
Rivera: 1.65
Sherrill: 2.21

Generally speaking, the average LI, or neutral event, is 1.00; 10% of all events will be over 2.00.

Sherrill has the highest average gmLI of the five while Wagner has the lowest. While it is definitely remarkable that Wagner is yet to surrender an earned run—he has given up 4 unearned runs—it looks as though Sherrill has been less successful in preventing runs due to pitching in much tougher situations.

These other four may have better peripherals, but do not let Sherrill’s ERA fool you: In just 21 innings pitched he has contributed two wins while pitching in tough situations.

There has been speculation recently that, should the Rockies continue to struggle, they may be looking to part ways with offensive juggernaut Matt Holliday. Holliday, a Scott Boras client, will be a free agent following the 2009 season and will likely enter the market looking for a long-term, big-money contract that either a)the Rockies cannot give or b)the Rockies won’t want to give. Instead of focusing on the fiscal aspects of this situation, though, I wanted to take a look at his home and road splits; last year it became somewhat common knowledge on the East Coast, when discussing Holliday vs. Jimmy Rollins, that much of Matt’s stats came from his home park.

Here are Holliday’s yearly splits, from 2004 until now:

2004 H: .338/.406/.603, 10 HR, 29 K, 229 PA
2004 R: .240/.287/.367, 4 HR, 57 K, 210 PA

2005 H: .357/.409/.593, 12 HR, 45 K, 264 PA
2005 R: .256/.313/.416, 7 HR, 34 K, 262 PA

2006 H: .373/.440/.692, 22 HR, 44 K, 334 PA
2006 R: .280/.333/.485, 12 HR, 66 K, 353 PA

2007 H: .376/.435/.722, 25 HR, 58 K, 363 PA
2007 R: .301/.374/.485, 11 HR, 68 K, 350 PA

2008 H: .356/.440/.671, 4 HR, 13 K, 84 PA
2008 R: .283/.371/.402, 2 HR, 13 K, 105 PA

In case you hadn’t noticed, he has done leagues better at home than on the road. Put together, here are his career splits:

Home: .363/.426/.662, 73 HR, 189 K, 1274 PA
Road: .274/.336/.444, 36 HR, 238 K, 1260 PA

Finding comparisons generally helps to further a message so I probed the BR Play Index for players with career numbers similar to those in each of his splits. I found just one person from 2004-now with overall numbers anywhere near his home production: Albert Pujols.

In looking at his road numbers a plethora of names appeared but the closest match was the .275/.339/.456 line in this 4+ year span of Aubrey Huff.

While there is little doubt Holliday could have won the MVP award last season and little doubt about his talent, prospective teams looking to acquire his services and ink him to a mega-bucks deal might want to take into consideration he has been Albert Pujols at Coors Field and Aubrey Huff everywhere else. Not to say Huff is a bad player, which is the common misconception when looking at drastic statistical differences such as this, but he is not on the same level as Pujols.

Perhaps Holliday likes being at home, in general, regardless of whether said home field is Coors Field, but I would tend to think he is someone that truly benefits from that park.

One of my favorite features here at Fangraphs is the data on minor leage players. With the recently added “items on screen” drop down we can now easily update and maintain our own minor league database. Delving into the statistics I decided to look at the top hitters and pitchers in both the International and Pacific League; both contain AAA affiliates. This is not to suggest these players deserve promotions or that their major league counterparts should be demoted, but rather just a simple scan of who has been producing at a high level in areas many of us tend not to follow.

International League Hitters
Mike Hessman, Det, 1B: .308/.392/.747, 1.138 OPS, 17 HR-30 RBI
Brad Eldred, CHW, 1B: .298/.348/.672, 1.019 OPS, 12 HR-36 RBI
Dewayne Wise, CHW, OF: .351/.396/.613, 1.011 OPS, 7 HR-15 RBI, 12 SB
Jay Bruce, Cin, OF: .352/.391/.613, 1.004 OPS, 7 HR-30 RBI
Darnell McDonald, Min, OF: .336/.392/.600, .992 OPS, 4 HR-26 RBI

International League Pitchers
Dan Giese, NYY: 2.27 FIP, 39.2 IP, 9 BB, 35 K
Charlie Morton, Atl: 2.58 FIP, 48.0 IP, 15 BB, 39 K
David Purcey, Tor: 2.82 FIP, 44.2 IP, 16 BB, 52 K
Matt Maloney, Cin: 2.96 FIP, 41.1 IP, 13 BB, 37 K
Eddie Bonine, Det: 2.97 FIP, 48.1 IP, 6 BB, 29 K, 7-0 W-L

Pacific League Hitters
Nelson Cruz, Tex, OF: .336/.471/.700, 1.171 OPS, 11 HR-32 RBI, 11 SB
Matt Brown, LAA, 3B: .365/.416/.679, 1.095 OPS, 8 HR-28 RBI
Terry Tiffee, LAD, 3B: .430/.474/.620, 1.094 OPS, 3 HR-33 RBI
Russell Branyan, Mil, OF: .54/.434/.638, 1.072 OPS, 8 HR-26 RBI
James D’Antona, Ari, 3B: .421/.430/.627, 1.057 OPS, 4 HR-22 RBI

Pacific League Pitchers
Brian Stokes, NYM: 2.65 FIP, 38.0 IP, 14 BB, 38 K, 5.68 ERA
Mike Burns, CHC: 2.69 FIP, 35.0 IP, 6 BB, 34 K
Carlos Alvarado, LAA: 2.86 FIP, 38.2 IP, 14 BB, 37 K
Ryan Feierabend, Sea: 3.11 FIP, 43.0 IP, 10 BB, 30 K
RA Dickey, Sea: 3.20 FIP, 42.2 IP, 6 BB, 25 K

The most intriguing player here is Jay Bruce, who has been performing at a very high level; you might remember the dismay of many fans in Cincinnati when news broke of Dusty Baker’s decision to send Bruce to the minors in favor of Corey Patterson. Some of the others here, such as Cruz, Hessman, Eldred, and Wise, have seemingly been given the AAAA tag; they may get chances here and there but are likely to maintain tremendous production in the minors and below average production in the majors.

We’ll have to wait and see what happens but I would not be surprised if any of these players is called up to the big club at some point in the near future due to poor production or injuries.