A lot of great stuff this week online from a baseball stat and sabermetric perspective. I don’t want to take too much time building it up, so I will just get right into the details.

The Great Clutch Project

I mentioned this a few weeks ago, concerning Tom Tango asking fans to join him in a battle in the clutch debate where he will pit his “good” hitters against the fans’ choice for “clutch hitters” to see if there really is a way people can see and perceive clutch. The stats he is using are his Leverage Index scores and wOBA (weighted on base average), so if you are not familiar with those, read up on them. You can find the summary of the project here, and Fangraphs will be running the season tally here for 2008.

Never thought I would see a Ginger/Mary-Anne and clutch/non-clutch analogy used, but I guess nothing should surprise me anymore.

Hardball Times OF Arms

John Walsh of THT reveals his new defensive metric to measure OF arms, something that has always been missing and that is sorely needed in the defense discussion that has escalated in the past few years. You can search by year on their stats page here, and there is a lengthy description of the methodology at this link. The stats for the OF arms goes back to 2004.

Richard Justice’s war with the stat guys

Local sports writer for the Houston Chronicle has the stats world up in arms from his reaction to a post by Mitchel Lichtman on Justice’s blog piece this week about bunting and how it is always a bad idea. Apparently, Justice has long been a target of some bloggers for his inability to look past emotion and personal feelings and look at the numbers. And the blogosphere just can’t get enough of all of this. And all of this from a couple of sentences about how bunting is always bad in the situation with which the Astros were faced.

For the record, I fall somewhere in the middle of what Lichtman and what Keith Law propose (you need to read all the threads to understand where that is). A manager has to make a yes/no decision in that moment, but his job should be to be as prepared as he possibly can with all the available data that will help him make an educated decision. The ones that are too close to call? Well, that’s why a managers are paid the way they are.

Lineup Analysis

This is not a new tool by any means, but something I have been messing around with this week that I recommend. Baseball Musings hosts a page that has a Lineup Analyzer put together by Morong, Arneson, and Armburst that allows you to put in any nine players with their OBP and SLG and it will construct the ideal lineup based on those numbers, and their calculated comparison and analysis of the two.

Here is the page, use it on your favorite team for this year or any year.

I work at a place that often gets involved in the political arena, specifically policy recommendation and research, so times like these are often quite entertaining and quite busy. My past couple of weeks have been spent working on plans for various presidential candidates we have invited to come and also hosting an event for Senator John McCain ahead of the Texas Primary. While I am not using all of that as an excuse for the delay in writing, I am using it as segue into what I will discuss today.

You see, when you host an event for a presidential candidate, there are always questions from the guests or from the audience that they would like answered. Inevitably, the two questions always raised are “would you have done X differently if you were president at that time,” and “if you are president, what will happen when you are faced with X problem?”

The candidates are a little more comfortable with the first question because hindsight is always 20/20 and they can come up with a solution that most people would approve and state how much better their solution is than the one that was made. Conversely, they get a little bit more uneasy when it comes to the second question, there are no decisions that have already been made about the hypothetical problem, thus nothing to base their answer on. And who knows, maybe they will be faced with this same problem in office, do they stick with their answer even though it may not be the best one, or decide differently, and risk looking like a liar or a flip-flopper.

I think the same holds true for baseball. It is a bit easier to look back and plug in a different solution/player/strategy than to predict the course of action for a hypothetical game situation or how a season will play out.

And thus is the nature of projections - a lot is usually right and a lot is usually wrong. However, it’s much easier to look back, take numbers we know are facts, and plug in a few new variables to make educated guesses than it is to base future numbers on unknowns.

And with that we turn our attention to the two new aces of the National League: Johan Santana and Dan Haren of the Mets and Diamondbacks, respectively. Fortunately, for the purposes of this research, both of their new teams were involved in tight races towards the end of the season, with the D’backs turning out a lot better than the now-famous collapse by the Mets the last 17 games of 2007.

While we can’t know for sure how these pitchers will perform in 2008, can we at least try to plug them onto their teams last year and see what kind of difference they would have made? Would it have caused Arizona to miss the playoffs? Caused the Mets to make the playoffs? And what is the best way to find out?

Well, also fortunate for us, we know exactly who these two new pitchers will be replacing on their new teams. Haren will replace Livan Hernandez in the rotation (who left for the Twins), while Santana will replace Tom Glavine (now with the Braves). Otherwise, the rotations seem to be the same.

With a little tweaking, and some playing with the numbers, adjusting them from league to league, I think we can tell how Haren and Santana might have affected their new teams had they been pitching instead of Hernandez and Glavine. Comparatively, Haren had 34 starts to Hernandez’s 33, and Santana also started 33 to Glavine’s 34 - so we almost come out event there already.

Here is what I think we should do:

First, we will remove one perfectly average game from Haren’s line and add one perfectly average game to Santana’s so that they will also each reflect 33 or 34 starts (I want to leave in the best and worst games because those are what make a pitcher’s season and define his consistency. See Ron Shandler’s PQS scores for more on that topic).

Second, we will subtract all of the runs allowed by Hernandez and Glavine for their teams last year from the team’s runs allowed total. We will work with both earned and unearned runs here so that the defensive aspect stays constant - it is something pitchers can not control.

Third, we have to add back in to the teams’ runs allowed totals the number of runs allowed by Santana and Haren last year. This is where it gets a bit tricky and where we have to adjust for context. In 2007, the average ERA in the AL was 4.50. In the NL, it was 4.43. So, the AL was about 2% tougher for pitchers than the NL. Keeping unearned numbers the same, we can adjust Santana’s and Haren’s earned run totals by that 2% to get a sensible estimate of what each pitcher would have done in the NL.

We will then check each team’s actual 2007 won-loss record compared to their expected won-loss record using runs scored vs. runs allowed and the Pythagenpat formula: X = ((rs+ra)/g)^.285 for the exponent and then rs^X/rs^X + ra^X for winning percentage. It has been documented that Clay Davenport, who modified the original Pythagorean Theory for win/loss by Bill James believes this Pythagenpat method is an even further improvement, so we will use that one. We will see how many wins better or worse the two teams were in 2007.

Then using the new runs allowed totals and adding them back into their new teams’ 2007 numbers, we can plug these in for runs allowed, adjust for the number of games better or worse they were above the expected outcome, and see where each team would have ended their 2007 regular season. Would the Mets have held off the Phillies? Would the D’Backs have won the division outright? Won the Wild Card? Missed the playoffs?

Here’s the math, starting with Santana:

2007 Mets - 88-74 record - 804 runs scored, 750 runs allowed for Pythagenpat record of 86-76 - two games better than projected
Glavine accounts for 102 runs - subtract from 750 to get 648
Santana accounts for 88 real runs in 2007, 81 earned
Add three earned runs to Santana’s total (an average start for Johan) to make him equal to 34 starts
Santana now has 91 total runs, 84 of them earned
Take 2% away from 84, leaving him with 82 earned runs, 89 total runs
Add 89 back into the 648 left for runs allowed for 737
Pythagenpat forumla:
X = ((804+737)/162)^.285, X = 1.90
W% = 804^1.90/804^1.90 + 737^1.90, W% = .541
New Pythagenpat record: 88-74
New actual record, 2 games better: 90-72

So now the Mets hang on and beat the Phillies (89-73) by one game to represent the NL East in the playoffs. And the 17-game collapse is all but forgotten. Until they get swept by the D’Backs.

And now for Haren:

2007 Diamondbacks - 90-72 record - 712 runs scored, 732 runs allowed for Pythagenpat record of 79-83 - 11 games better than projected
Hernandez accounts for 116 runs - subtract from 732 to get 616
Haren accounts for 91 real runs in 2007, 76 earned
Subtract three earned runs to Haren’s total (an average start for Haren) to make him equal to 34 starts
Haren now has 88 total runs, 73 of them earned
Take 2% away from 73, leaving him with 72 earned runs, 87 total runs
Add 87 back into the 616 left for runs allowed for 703
Pythagenpat formula:
X = ((712+703)/162)^.285, X = 1.855
W% = 712^1.855/712^1.855 + 703^1.855, W% = .506
New Pythagenpat record: 82-80
New actual record, 11 games better: 93-69

The D’Backs had the best record in the NL to begin with, edging out the Rockies for the WC and beating Philadelphia by one game, so it might not look like it would have affected Arizona’s season too much, much less their sweep of the Cubs and then being swept by the Rockies in the NLCS. But, Hernandez did start game 3 of the NLCS, losing it 4-1. Who knows if Haren had started that game what would have happened (especially since Arizona only scored once). But a 2-1 deficit at that stage would have been much less daunting than down 3-0 with another to play in Coors.

So while this is not ground-breaking stuff by any means, don’t be surprised when these guys make a significant difference on their clubs this year, especially if races end up being close like in 2007. It’s impossible to know for sure what will happen this time, but it just goes to show that one guy could make a difference between the playoffs and going home.

If you catch any errors in my math, please let me know.

While I sort through all of the National League transactions since October and also wait for a few pending AL things to be completed, I thought I would quickly throw out a new round of websites that I recommend whether you just have some time to kill, research to do, or random things you want to look up.

Most of these I have found linked on other sites or through perusing other pages, so I will reference those when applicable.

Baseboogle - Google for baseball? Exactly. Baseboogle is a search engine run by Google that uses a special set of selection criteria and filters searches through a vast list of baseball related websites and documents. My search for simply “clutch” yields more than five pages of links to articles ranging from who is the most clutch, to dismissing clutch hitting, to clutch projects, to “is David Ortiz a clutch hitter?” Also helpful is a list on the right side of the page that lists all of the sites that Baseboogle pulls from when it searches. You can recommend sites to be added so that the directory of pages it searches from becomes more extensive. You can find this site at www.baseboogle.com. I found this site from a post on The Book blog by Tom Tango.
Cot’s Baseball Contracts - I can’t tell you how many times I have wanted exactly this type of information at my fingertips. This site has a page for each team and lists all players, the manager and what contracts they have, what their yearly salary is, what their bonuses are, no-trade clause or not, etc. This is also a great resource for arbitration eligible players, 2008 and 2009 free agents, transaction lists, and other interesting baseball links. Just a great site to look up your favorite team, player, or whatever. You can find it at www.mlbcontracts.blogspot.com/. This site is always used in Pizza Cutter’s sabermetric year in review series for each MLB team currently running on MVN’s Statistically Speaking.

Sabermetric Studies - A useful and well-organized archive of all things having to do with sabermetric analysis. There are pages for each area of discussion and research such as fielding, run estimation, DIPS, clutch hitting, and many more. Each page has multiple links to any study or analysis that has been done on the subject, and the most helpful part is that he mentions which of the articles are available only to those who have subscriptions to respective sites. Also available on this site is a very comprehensive list of sabermetric and other baseball reference sites. This can be found at www.sabermetricstudies.com. This link also came from a Tom Tango post on The Book blog.

Beyond the Box Score - A site much like this one, Beyond the Box Score is a comprehensive look at stats, teams, players, and trends going a little bit deeper than the average analysis. The articles and analysis are always concise, easy to understand, and extremely helpful for understanding teams and their trends. RJ Anderson, the author, also blogs for the Devil Rays and was formerly at Deadspin.com. BTBS also has one of the more extensive blogrolls and link lists that you can find anywhere. You can find this site at www.beyondtheboxscore.com.

There you go, just some more filler for you in your free time desperately counting down the days until baseball season starts again.

If I hear one more person talk or write about how clutch-hitting ability or the perception of clutch hitting is the most debated and written about statistical anomaly in baseball, I might start going a little crazy. So much has been written and discussed on this topic over the past 20 years that it is getting somewhat ridiculous these days. There is the side that believes certain hitters, whether they are typically good or not-so-good hitters, can somehow routinely deliver in the most crucial of circumstances, and then there is the side that believes it is just about perception, and the people who are commonly referred to as “clutch hitters” are only defined as such because they have performed well a few times maybe on the biggest of stages and they stick out in our minds as having this unique ability.

But finally, two different sites are asking people to put their money where their mouths are when it comes to clutchiness (only one involves actual money, but still…).

The famous sabermetric blogger Tom Tango, aka Tangotiger, is challenging readers, clutch advocates, and the like to a simple contest to see if clutch hitting really can be predicted or continued after it is recognized. In this post on his website, Tango challenges the following:

He wants fans/readers/critics or whomever to pick one guy from “their team” who they feel is the most clutch, assuming they believe that sort of thing. The one guy they would want to have come up in the most crucial, pressure-filled situations and then nominate them for their side. Tango will then pick who he thinks is the best hitter from that same team to compare at the end of the season.

Recognizing that some teams will have one player represent both sides (i.e. the Cardinals with Albert Pujols), the thought is that if that happens he will use the next most requested clutch hitter and who he perceives to be the next best hitter on the team. And this will go on down the line until there is a difference in who the readers pick and who Tango picks. Once this is done, you will be comparing 30 “clutch” hitters to 30 good hitters and Tango is predicting that his hitters will come out on top in the clutch situations.

How will he measure it? A while back Tango invented a measurement tool for in-game situations called Leverage Index. Essentially, it takes each moment in a game, a batter’s at-bat or a pitcher’s confrontation with that hitter, and places it in the context of the game. As the game gets closer to the end, the LI goes up for the hitter or pitcher because you have less time to put your team ahead, tie the score, hold the lead, etc. At the beginning of a game when there is no score, there may be an LI of 1.0 or lower per situation. A batter coming up in the 9th with a tie score could have an LI of 10.0 or so. So games that are blowouts by the 8th or 9th inning would have low LI scores for each situation, but tied games that progress into the 6th, 7th, 8th, and 9th innings would have increasingly higher LI scores.

Tango proposes that we compare the plate appearances with the 50 highest LI scores for each player that is chosen for the project. So you would end up comparing 1500 plate appearances for the “clutch” side to 1500 plate appearances for the “Tango” side. At the end of the season, he will look at the aggregated lines of the two groups to see which performed better in their most crucial situations.

He believes if the players actually chosen as clutch hitters do perform better in those situations, there will be some sort of statistical significance separating the two groups. Not to spoil the surprise, but he is not expecting that separation to be there.

In a related story inspired by Tango’s challenge, blogger Phil Birnbaum has laid out his own challenge to clutch advocates in this post on his website. His project is a little more risky on his part and actually involves money changing hands. The details look like this:

Birnbaum is not proposing to compare clutch hitters to other good hitters, but rather perceived clutch hitters to proposed choke hitters - those who absolutely fail in pressure situations. He challenges readers to pick any number of clutch and choke hitters; one vs. one, 30 vs. 30, 100 vs. 100, whatever. And he has finally settled on odds for the bet - 2:3. So he is proposing to every bettor whose clutch players out-perform the choke hitters, he will pay them $10, but if your choke hitters out-perform the clutch hitters, you have to pay Birnbaum $15. The reason he does not offer 1:1 odds is because he states that if you accept those odds, you are basically saying whether or not a player is clutch or not is essentially “a coin flip”, and this bet is supposed to attract those who believe clutch hitting exists.

If you accept this bet, you can define the players, you can define the amount of money, you can define the metric used to measure the players (as long as it revolves around batting average, such as BA in close and late, or LIPS), and you can control your sample size. Hitters suggested with obviously different skill levels (someone wanted to use Ortiz vs. Kevin Millar) will be judged on clutch differentials from seasonal numbers and not overall performance in the defined situations.

What he wants to see is if there is anyone out there who believes with a there is at least a 60% probability (hence the 2:3 odds) that you can predict hitters who will perform extraordinarily in the clutch. Some have accepted and some have declined, but there has definitely been some action on this post. You can also make the bet for charity, with the loser paying the amount to the charity of the winner’s choice. Email Phil Birnbaum from his website, http://sabermetricresearch.blogspot.com/, if you want to take part.

As you know, clutch is a very tricky thing. It has been shown to have patterns across seasons but not necessarily across careers, and every new study that comes out seems to contradict or challenge the rest. And you have so many competing ways to measure it, that it all gets lost in the shuffle anyway. Perhaps, at least for 2008, these two projects will use some real-life examples to put some of the issue to rest. Until it all comes up again next year, that is….

With another regular season come and gone, you will hear a lot of debate amongst the experts over the next couple of months as to who should win the particular offseason awards. But, let’s face it, besides AL Cy Young and NL MVP, the names are already engraved on the trophies for these prizes.

So I thought it appropriate to recognize those that never get their due, those that won’t even come close to being mentioned, those who don’t even deserve to be mentioned. And what I came up with was the 2007 Worsts Team. This team defines the absolute worst there is in offensive baseball. The most god-awful at each position on the diamond, save the pitcher. But there is one caveat; for some unknown reason, their respective teams stuck with them.

To qualify for this list, you had to qualify for MLB’s offensive categories, which means 3.1 PA per team game, or 503 PA on the season. That means that no matter how bad the player was (and we will see some bad ones), their team sent them to the plate more than 500 times over the course of the season.

The stats I will be using are Value Over Replacement Player and Runs Created for the season (remember, if you need a definition of each, check out the Stats Glossary tab). I looked at the list of the worst qualified performers in each and took the composite score of the two rankings for each player on this list, ranked by position. With only one exception, you will see the eight players with the worst overall rankings in VORP and RC for 2007. One for each position.

First, the boring stuff. Here are the top ten for both VORP and RC in 2007:

VORP
Alex Rodriguez - 95.1
Hanley Ramirez - 90.2
David Ortiz - 86.6
Magglio Ordonez - 85.7
David Wright - 81.3
Chipper Jones - 76.6
Matt Holliday - 74.2
Albert Pujols - 73.8
Jorge Posada - 73.8
Miguel Cabrera - 71.4

RC
Alex Rodriguez - 164
David Ortiz - 156
Magglio Ordonez - 149
Matt Holliday - 148
David Wright - 146
Prince Fielder - 143
Hanley Ramirez - 142
Albert Pujols - 133
Carlos Pena - 132
Jimmy Rollins - 132

These end up being petty consistent lists, with seven of the top ten being the same in both lists (and all of the top ten in VORP are in the top 20 of RC). But that’s not why you are here.

So without further ado, the worst of 2007:

Catcher

A.J. Pierzynski
9.8 VORP, 53 RC

Only nine catchers qualified with at least 503 PAs, and five of those only beat 503 by 15 or less PA, but Pierzynski was the worst of the lot. With an OPS that barely reached .700, Pierzynski was one of several White Sox that succumbed to their inevitable decline after great years in 2006. His average dropped by more than 30 points from the previous year and he ended up being only 10 runs better than the catchers Chicago had on the bench.

First Baseman

Kevin Millar
12.1 VORP, 73 RC

A major league first baseman with 16 HR and 62 RBI over 558 PA might be OK if he was playing for the 2007 Yankees or Red Sox, and they didn’t need his bat. But that is not what the 2007 Orioles were. Baltimore ranked 10th in the AL in OPS, and could have used a first baseman with some legitimate power. His 75 walks are commendable, however.

Second Baseman

Jose Lopez
-10.9 VORP, 48 RC

Yes, that is a negative VORP you see there, meaning any old scrub playing second would have been 11 runs better than Lopez given the same PAs over the season. 538 PAs for this kind of production is unexcusable, as his VORP and RC numbers were both in the bottom five for all of MLB. The fact that Seattle was once so close to a playoff spot makes this even more of a head-scratcher. Why would they leave Lopez in the lineup for so long?

Shortstop

Tony Pena, Jr.
-7.8 VORP, 47 RC

Another player with bottom five numbers in both VORP and RC for all of MLB - and that is over 533 PAs. Actually Pena and Omar Vizquel both had the same composite ranking score of 4.5 on the lists, but since Vizquel is once again tops on everyone’s list of the best defensive shortstops, the award goes to Pena.

Third Baseman

Nick Punto
-26.9 VORP, 41 RC

Here we are, the worst of the worst for 2007. Punto, in 533 PA, was the worst in both of these categories while batting .211 and posting an OPS below .600. This means the Twins must have had absolutely NO ONE on the bench whom they thought could replace Punto, because his numbers are by far the worst amongst MLB regulars in 2oo7. Forget talking about extra runs here, the Twins could have had almost three extra wins if Punto had never been in the lineup and some other replacement-level player was. A horrible year.

Left Fielder

Jason Bay
4.2 VORP, 78 RC

The rotisserie darling of so many for two years, Bay struggled mightily this year, batting only .248 in 2007 with an OBP of .328 and SLG of only .419. His power numbers of 25 doubles and 21 HR dropped significantly compared to the past two years. And forget the 21 SB from two years ago. He had but four this year. And he stayed in the Pirates lineup all year, totaling 613 PA over the season.

Center Fielder

Andruw Jones
5.2 VORP, 74 RC

Here is the one exception to my rule of the composite rankings, because Bill Hall actually was worse than Jones according to those rankings, but Hall totaled 503 PA (for his 6.6 VORP and 60 RC) while Jones did it over 659 PA, the most for any player on this list. So much for players having that extra little something in contract years. Jones has not had numbers this poor since his 20-year-old rookie season of 1997. His strikeout rate increased this year as well; he finished with the third highest total of his career. See my other thoughts on Jones here.

Right Fielder

Brian Giles
10.8 VORP, 72 RC

Another former All Star makes the list in 2007. I wonder if moving Giles to the leadoff spot midway through the year had anything to do with his decline in numbers this year - I guess we will have to see what the Padres do in ‘08. Missing a significant amount of time due to injury surely hurt Giles here, and could move Delmon Young up to this spot, but Giles did post the lowest OBP and SLG numbers of his career when he was in the lineup. Even Giles’ BB rate, something he has been famous for, dropped to a career-low 11.6% this year.

So there you have it: My Worst of the Worst team for 2007. Not surprisingly, only one of these players is on a (potential) playoff team - Giles. Teams with a hole as big as these players in their lineups generally will have a tough time making up for the missed production elsewhere, particularly in the NL, where these batters are always asked to bat higher than ninth.

Any disagreements? Let me know in the comments.

Yesterday, my brother and I were talking about our fantasy baseball league we are in together. As usual, he was singing the praises of Jake Peavy after another masterful performance (this time 7 IP, 1 ER, 11 K), and commenting how Peavy has contributed to the commanding lead he holds over the rest of us. He made the comment that he didn’t know how Peavy could not be the NL Cy Young winner this year. That led to a discussion about how voters will probably (and unfortunately) give a lot of weight to teams that make the playoffs, and if the Padres miss out, that could hurt Peavy’s chances somewhat. Who knows? It could go to Webb if the Diamondbacks stay in the lead.

I then brought up the fact that, while Peavy’s ridiculous K-rate could be accomplished anywhere, he is undoubtedly helped somewhat by the park he calls home, spacious PETCO Park in San Diego. This is a well known argument that is often used to explain why Padres pitchers have had so much success the past few years. Most things I read say Chris Young, for example, would just be a pretty good pitcher if not for PETCO Park (citing his high FB% of 53%, but a HR/FB ratio of only 2.7% in 2007), and his ERA ad WHIP would certainly not be 2.12 and 1.01 and leading the majors. In fact his park-adjusted lgERA this season is 4.12 according to Baseball Reference.

This season, according to BR, PETCO’s park factor is 94 for pitchers. If that doesn’t mean anything to you, think of it like this. A park with a rating of 100 is exactly average and even for both hitters and pitchers. Anything under 100 is considered a pitcher’s park, and PETCO rates as the best pitcher’s park in the majors this year. According to ESPN’s park factors, PETCO is in the bottom five in the majors for runs scored, HRs and hits in 2007.
And just when I was thinking about how smart I was for remembering all of this, I had to stop myself.

“Wait a minute. Didn’t I see some stats that showed just the opposite when I was watching the Padres the other night?”

So I had to go check, and sure enough I was right. Here are Peavy’s home/road splits for ‘07 so far:

That really got my mind spinning. Why is that? Not that you can be better than undefeated, but shouldn’t his other stats improve when he pitches at home, not the other way around. Well, like most things, I figured I am not smart enough to figure this one out, so I thought I should ask an expert. So I brought it up in an ESPN chat with author and contributor Rob Neyer. He should have an idea! Here is our exchange:

Ryan (Houston): Explain to me why Peavy (save last night) has been statistically better on the road than at home. Isn’t that park supposed to help him?

Rob Neyer: (12:27 PM ET ) Well, it’s “supposed” to help all pitchers, generally. But statistics don’t always happen the way they’re supposed to, particularly over just five months.

Thanks, Rob, you’re a big help. Now his point is well taken in that even five months of statistics can be misleading, and not TOO much stock should be put into them. So I went back and looked at Peavy’s 2005-2006 numbers, and, sure enough, they were statistically better (with a statistically significant difference) at home for those two years.

Granted, it’s not like he is Sandy Koufax on the road and Jose Contreras at home; he is still one of the most dominating pitchers around when he is at home. But looking past the win-loss record (which he can’t control anyway), why are the other stats significantly worse? His home stats didn’t regress back to where his road stats have always been. It’s like they swapped places or something.

But what if I’m looking at this the wrong way?

I think if you read Peavy’s home stats from left to right, our minds are immediately trained to recognize that 7-5 record, and think, “Wow, that’s not near as good as 8-0, something must be off with him at home.” Even reading the rest of the line, 2.86 is not near as good as 1.22, .236 is not near as good as .163, and .317 is not near as good as .231. But our opinion of Peavy becomes jaded when we start to compare the two lines against each other. If I just gave you stats for pitcher X who in 104 innings had 112 Ks, only 33 ER, a 2.86 ERA, 1.16 WHIP, and .236 BAA, without any other peripheral information, you would think those are phenomenal numbers.

The 7-5 record is partially due to the run support Peavy has received in his home starts versus his road starts. The Padres have scored 4.37 runs per game in Peavy’s 16 home starts compared to 5.81 runs per game when on the road.

So it might not be that there is something “off” or “wrong” with his home stats, but that his road stats are absolutely legendary. Here’s the proof:

Peavy’s K/9 at home is 9.69. That number itself would be good enough for fourth in the majors at this point. His K/9 on the road is 10.48, a number that blows away all NL qualified pitchers by two K/9 and would trail only Erik Bedard for the MLB lead.

Peavy’s HR/9 at home is 0.43 this season. That number by itself is good enough for seventh in MLB and fourth in the NL. His road HR/9 is 0.24, which by itself would automatically be the best number in the majors.

Peavy’s WHIP at home is 1.16 - a number that would be 15th in MLB by itself. His road WHIP is an astonishing 0.90, a number that would beat all other qualified pitchers in the majors by more than a tenth of a point.

Peavy’s batting average against at home is a very low .236. That number alone would be good enough for 16th amongst all starting pitchers in the majors. His road BAA is an unbelievable .163, a number that is 20 points lower than anyone else in the majors at this point (Chris Young at .184 currently leads).

Peavy’s home Batting Average on Balls in Play against him is a respectable .317 (average is about .300 - the median number for all 85 qualified pitchers is .298 this season). So he has been a little bit unlucky in this category at PETCO (perhaps because it is so spacious). But his road BABIP is an almost unheard of .231. This would be second in the majors to only Orlando Hernandez’s .220 (you want to talk about lucky, that number is 56 points lower than Hernandez’s career BABIP average). To put the number of .231 in perspective, here are the MLB leading figures in BABIP for the past five seasons:

2006: .237 (next closest was .265)
2005: .252
2004: .247
2003: .248
2002: .234

Peavy’s road BABIP in 2007 beats all of these numbers.

These numbers tell me a few things, but the most important is this: When asking the question, “Why isn’t PETCO helping Peavy’s numbers? His road stats are so much better!” The answer is - PETCO IS helping! Peavy is one of best pitchers in the majors when studying just his home numbers. The only problem is, anytime you see home numbers, you are also going to see road numbers. And when you look at his road numbers this year, you are talking about late-90’s Pedro Martinez or mid-60’s Sandy Koufax.

So don’t be like me and get fooled that just because you see a 7-5 record at home, and an ERA, BAA, WHIP, and BABIP that are all distinctly worse at home, we are talking about a pitcher who needs to make some adjustments or corrections.

The simple facts say that on the road this year, Peavy has just been flat out lucky. No pitcher is as good as Peavy’s road stats say he is this year. But, one truth in baseball is that these anomalies and things that are contributed to luck tend to correct themselves over time. So just like Neyer mentioned to me, don’t expect to see the same discrepancy in Peavy’s splits next year or any other year. Five months of stats can be tossed out and attributed to luck if you have to. But if this trend does continue; now that would be something to write about.

If you know some of my thoughts on baseball, you know I am an RBI skeptic. I admit it, I am not taken aback by massive RBI numbers like so many of our media and fantasy players these days. RBIs, as I hope to show you, are not a very telling statistic, but they so often contribute to a player getting more All Star votes or MVP votes at the end of the year, or recognition on fantasy teams, etc.

Any good sabermetrician can tell you that RBI are purely circumstantial. They depend on so many different factors: where you bat in the lineup, are you in the NL or AL, who bats in front of you, who bats behind you, how many times you come up with players on, are they in scoring position, how many times do you drive yourself in, how many times do you lead off an inning, etc.

Even in his Sabermetric Manifesto, David Grabiner points out one of the problems with RBI that make them incomplete. And that is simply that “they measure a lot of things which are not the players’ own contribution.” You can’t drive in players who don’t get on base (except by home run. He correctly points out that players who bat behind teammates with high OBPs (”better players” as he puts it) tend to get more RBI.

But it was something he wrote next that made me want to run some numbers to test some things. He states:

In fact, the league leaders in RBI are much more likely to be the players who batted with the most teammates on base or in scoring position (not the batter’s contribution) than those who hit the best with runners on base or in scoring position. Thus RBI are a better measure of who had the most chances to drive in runners than of who was the best at driving in runners.

This is probably quite contradictory to what many people think. It seems logical to think that those who are the best at driving in runs, or who have the highest average with runners on or runners in scoring position (RISP), would accumulate the most RBI. But maybe that’s not the case. Maybe it’s all about opportunity.

So that’s where I want to go from here. I am going to take some of the top RBI men from each of the past three seasons to see which side they fall on. Fortunately, with some helpful tools, we can do this quite simply. Of course, you can find anywhere the league leaders in RBI for the past three years, so that is easy. Our biggest help here will be the RBI Opportunities page on Baseball Prospectus’ website. It lists, amongst other things, plate appearances every player had with men on, the percentage of men driven in, who came up to bat with the most men during a season, and more.

So we will start with the top five RBI leaders in each league from 2004 to 2006:

Geez, I sure didn’t remember that Castilla led the league in RBI just three years ago. Anyway, these are their numbers. So now I will, year by year, break these down by each batter’s average with RISP and then the numbers on base they had during that season and the percentage of those numbers they drove in. I also was able to dig up averages w/ RISP ranks from The Hardball Times for those three years. Now, we can see how each hitter fell into the separate categories. I have included notes on this chart to explain all of the different stats.

What becomes obvious after looking at this chart for a few minutes is that, without even running the numbers, there is a much stronger correlation between high RBI numbers and runners seen on base than there is between high RBI and a high average with RISP.

For the league leaders for each league during these three years, the ranks of number of men on base for the player’s plate appearances are fourth, first, third, second, first, and first. Conversely, the ranks of average with RISP for the RBI leaders in those three years are 42nd, 55th, 5th, 75th, 12th, and 54th. Only one of the six is even in the top ten in avg. with RISP, that being Ortiz in 2005.

Further study of the number shows some pretty telling signs as well. Only three times in the 30 players above was there a batter who was not in the top 17 for ROB during their plate appearances (Dye in 2006, and Rolen and Beltre in 2004). Adrian Beltre, at 27th, has the lowest ranking for any player when determining ROB. For the ranking on average with RISP, there are nine players lower than 27th for their respective year - almost a third of our subjects.

So, apparently, David Grabiner and all other sabermetricians are on the right track. Batting averages can be deceiving and misleading with looked at without any context, even averages as specialized as BARISP. You could have someone who batted .500 with runners in scoring position over 100 plate appearances - but what does that tell you? If that player only had 14 appearances with RISP, you would rather be in the situation with a hitter that bats .315 with RISP but has 44 appearances over 100 PAs.

When batting, circumstances create opportunity, and it is what hitters do with that opportunity that makes them great versus just mediocre. But, like we mentioned earlier, a batter can not control what the players ahead of him do, he can not put runners on base ahead of him who are not there, he can not control how fast they are, and he can not control whether he leads off an inning three times in a game. As with so many things in baseball, how many runners a batter sees on base when he is up has a whole lot to do with luck. A manager knows this, but tries to generally manage that risk by setting his batting order by placing the highest OBP guys in front of the guys who have the most power or the highest SLG% (at least that is how it would work in an ideal world).

Hitters with the most opportunity will have the greatest success rate in driving runs. Whether you are Albert Pujols of 2006 (.397 with RISP) or Andruw Jones of 2005 (.207 with RISP) - you need the hitters ahead of you to do their job before you can start worrying about how good you are at driving them in.

At the end of this baseball season, two of the games best center fielders, Torii Hunter and Andruw Jones, will hit the free agent market. Both are looking to sign long-term deals even though they will both be over 30, and both will be looking for mega-bucks (presumably from their current teams, but perhaps not).

The big difference right now is that one of these guys is having a career year and watching those dollar signs go up and up, while the other one is Andruw Jones.

Partially out of sheer morbid curiosity and partially because I feel like I wasted a third round pick in my most competitive fantasy league on him, I wanted to examine this anomaly that is the 2007 Andruw Jones and see what is really wrong with him and if there are any signs of turning things around. I have a theory here, but I will share it as we get closer to the end. Here’s a clue, though….he really wants to see the money.

I hear people say all the time, “His batting average is so low, and his power is down - that’s why he is having a bad year.” Fine. But WHY is his batting average down and WHY is his power down? He doesn’t appear to be injured. He isn’t very old. It must be something else. Is he unlucky? Is he getting fooled more than he used to? Are pitchers pitching around him more? These are the questions I am looking to answer.

First let’s look at Jones’ production per year since 2000, and then his average year from 2000-2006, and his current 2007 pace for those same numbers:

So the numbers are right here in front of us and they are pretty glaring. Excluding walks, Jones is at least 15% worse than his average season in every one of the these categories. Now we need to start looking at why that is. What is causing a low average, low power, but more walks. For example, you can’t just say, “he has stopped hitting for power,” unless he is injured or old. WHY has he stopped hitting for power?

In order to not to get too confusing here, I am going to list a group of predictive stats where Jones has the lowest numbers of his career this year, and then pick a few to analyze:

Strikeout % - 27.6% (career 21.8%)
Isolated Power - .183 (career .236)
BABIP - .229 (career .284)
RC/27 - 3.77 (career 5.91)
GB/FB Ratio - .82 (career 1.03)
HR/FB - 13% (career 20.5%)
EqA - .245 (career .283)

Looking at these, combined with the fact that, despite his other struggles, he is on pace to draw the third most walks in his career, I think I have noticed a pattern. I truly feel like Jones has fallen in love with the idea that more homeruns will give him more dollars this winter and he is doing everything he can to get every last HR he can. Here are the reasons why I believe this:

Pitch Selection - The relatively high walk rate shows that he has chosen not to swing at bad pitches or ones he can’t handle well. Balls just inside or just outside that he may not be able to drive can’t help him, so he will lay off of them. Balls high or low might lead to groundballs or pop-ups, so he lays off of those too. This leads to more walks.

But, it also leads to the need to swing at almost every pitch in the strike zone, or every pitch that appears to be in the strike zone (sliders, sinkers, etc.). Jones is on pace for 1680 pitches that are strikes this season, the most in his career. Tinkering with your swing, trying to mash homeruns every time up, and favoring the back foot causes severe upper-cut swings (here is the most famous example from this season, a walk-off homerun against the Phillies on April 30) and an increased number of fly balls on balls put in play. Which brings us to the next step.

Ground Ball/Fly Ball ratios - Not mentioned above in the stats is the fact that Jones’ FB ratio is the highest of his career at 45.5%, and that his GB ratio is the lowest of his career at 37.3%. This translates into the number you do see above, which is his GB/FB ratio of 0.82, the lowest in his 10 years in the Bigs. So, almost half of Jones’ balls in play this year are fly balls instead of line drives and ground balls, where the majority of hits come from.

Compound this with the fact that his HR/FB ratio is also the lowest of his career at 13%, and you have a recipe for disaster. More fly balls in play equals more outs being made per time you put the bat on the ball, which brings up the next point; BABIP.

Batting Average on Balls in Play - This stat is becoming more and more popular to try and determine why a hitter has a certain average when it is so far above or below his career norm. A batter with abnormally high or low BABIP can usually attribute it to bad luck, and can expect some regression. But, the same light, high or low BABIP can almost always be directly correlated to success in batting average. We can apply this here to Jones. His BABIP is the lowest of his career at .229. This number is abysmally low, as an average number is somewhere around .290, and Jones is at .284 for his career.

In the second half, one would expect that number to rise just because the law of averages says it almost has to - it really is that bad. But more fly balls do lead to easier outs on balls in play, which leads to less hits and, in turn, a lower batting average. And on the at-bats where he does get a hit, the power is still not following, for reasons we talked about above, and further evidenced in my last point.

Isolated Power - Subtracting AVG from SLG can give an interested party a quick and dirty look at what kind of power a hitter displays beyond just what their batting average looks like. Before this year, Jones’ last time with an ISO number below .208 was his rookie season in 1997 when it was .185, still higher that he .183 he is displaying this year. A combination of many fly balls plus a Line Drive Percentage of 17.3% (below his career average of 18.2%) is leading to much less power for the famed slugger who hit 41 and 51 homeruns the past two seasons. So much so that Jones’ SLG% this year is more than 100 points below what his career average is (.382 to .499).

Now I am not a hitting coach by any means, but my remedy for Jones would be to expand the strike zone a little bit so he can hit to all fields (line drives to all fields, that is), while still keeping the selective eye that allows him to take the walks and get on base. Also, go back to the line drive swing that helped Jones so much in the seasons where he was hitting between 35 and 50 homeruns a year.

Homeruns might look like the key to the offseason treasure right now, but everyone is sure going to be second-guessing a 31-year-old center fielder with a batting average of .200 with no power if he ends the season that way. An all-around hitting approach would do Jones a world of good right now and, more importantly, would also allow him to help save my freaking fantasy team!

I have trouble remembering a year when there was SO much hype about rookies coming up to the majors. Just some common names from this year: Philip Hughes, Hunter Pence, Tim Lincecum, Ryan Braun, Homer Bailey, Elijah Dukes, BJ Upton, Yovani Gallardo, Chris Young, Delmon Young, Alex Gordon, Billy Butler, and Andy LaRoche are just a few names that have dominated club rosters and fantasy teams from the beginning of the year - and some of these weren’t even guaranteed a spot with the big club!

In thinking about rookies, it is always difficult to predict major league success no matter what school you’re from; whether it’s makeup and body type or stats and rates, the guessing game is far from accurate. Keith Law, a former Special Assistant to the GM for the Blue Jays and Baseball Prospectus writer currently working for Scouts Inc. and ESPN, is famous for saying that the player he was the most wrong about was Carlos Pena. Pena had all the tools and all the stats - scouts and managers and GMs drooled over him. Well, ever since he made his major league debut in 2001, he has been traded twice, released twice, made two trips to the minors, and has a career .247 average - and is now 29 and moving past his prime.

This is just one example of how hard it is to predict, even on the sure things. So who knows if great years by Pence, Lincecum, Upton, et al will turn into something more? But I thought it might be fun to look at some of the best rookies from the past and see if very strong rookie seasons translates into very strong major league careers.

My hypothesis is that a even the strongest rookie season does not correlate to major league stardom.

First, we have to determine a sample size. I have chosen to look at more of a modern era for comparative reasons, and therefore look at rookies from 1975 to 2001 (rookies in 2001 have at least 5 full major league seasons and thus a good sample to work with). I want to look at only the best of the best rookie seasons - so I will start with all Rookies of the Year from these 26 seasons. But even that does not help too much because you get your Mark McGwires who batted .283/.370/.618 with 49 HR and you get your Eric Karros’ with .257/.304/.426 - 20 lines - and they were only five years apart. Those two rookie seasons don’t even compare to each other.

So I need a way to establish the best rookie seasons without using stats, because we need a way to look at both hitters and pitchers who were ROY, and compare across a span of 26 years. What I decided to use as my filter is any ROY who finished in the top 25 in MVP voting or top 10 in Cy Young voting during their respective rookie year. This will give us only the best rookie years compared to their peers that season.

For example, Alvin Davis’ ROY campaign in 1984 saw him accumulate a .284 average, 27 homeruns, and an .888 OPS. This would be above average for an offensive player in 2006, but he probably would not have received any MVP votes for that line last year. But in 1984, that line is outstanding. This way, we can compare rookies to their peers and not other rookies that played 20 years later or earlier.

Doing this, we are left with a sample of 17 rookies from 1975-2001:

This list includes 11 who finished in the top 25 for MVP and 6 who finished in the top 10 for Cy Young. Ichiro and Fred Lynn are the only ROY/MVP winners, while Fernando Valenzuela is the only ROY/CY winner.

To look at these players’ careers, we need to think about what stats to use. We could just look at Hall of Fame voting, but half of these players are not eligible, so that does not work. I am going to rely on a heavy dose of rate stats since we are dealing with four players who are still playing today, and we will have to use statistics that range across hitters and pitchers. But I will also look at a few other sorted items that help tell the story such as MVPs, All Star games, etc.

I have included an explanation of all stats in the comments section. All numbers are as of May 28, 2007:

The results I found here surprise me somewhat. All of these players except Pujols and Ichiro have finished their careers or are in their last couple of years. We can save the Ichiro debate for another time, you decide for yourself if you think he should have been labeled a”rookie” in 2001.

The stats here show that the strongest rookie seasons seem to correlate a lot more towards future success for hitters rather than pitchers.

Point in case: Each hitter on this list, assuming no tragic injuries to Pujols and Ichiro, will play at least nine seasons in the big leagues (except for Listach; more on him later). Here are some other examples…

1. Except for Davis and Listach, all other hitters on this list appeared in at least two all-star games that were NOT in their rookie season. So if you take away their great first year, you still see continued success later in their careers
2. All hitters on this list have a career OPS+ of at least 120 (average is 100), except for Listach and Coleman. Coleman is low only because he never slugged over .400, and had 28 career homeruns.
3. Except for Listach, all hitters created at least 200 more runs over their careers than a replacement player would have created (BRAR stat).

“Except for Listach,” is mentioned here three times. You must be thinking, “what about Listach? He proves your theory wrong!”

I agree, he is an outlier in this exercise and skews things a little bit. But one interesting thing about that year’s ROY voting may get us back on track. In Listach’s rookie year of 1992, he beat out Kenny Lofton in a close race. Looking back…..Lofton really should have won. He bested Listach in HR, SB, BB, OBP, SLG, OPS+, Total Bases, and RC. But Milwakuee finished with 92 wins while Lofton’s Indians finished with 76.

Lofton meets all of the qualifications above that Listach does not - 6 All-Star games, 107 OPS+, and 436 BRAR for his career - and he has played 17 seasons so far.

Pitchers on this list are a different story.

The All-Star games by these six pitchers adds up to 15 in 76 total seasons. And 10 of those are from two players (Gooden and Valenzuela).

On this list, you have at least six hitters you can make a legitimate Hall of Fame case for, while there is not a pitcher on the list who comes close to qualifying.

The pitching runs above average range from -7 to 148 - nowhere near the BRAA of 613 for McGwire or 494 for Piazza.

I guess I should not be surprised that the hitters consistently perform better than the pitchers overall on this list. Studies have shown that the peak for hitters and pitchers is from ages 26-28 (use this article as some reference for pitchers’ primes, this one for hitters). Prime years for pitchers tend to last longer than hitters, which is why you routinely see pitchers at age 35 and above still performing at high levels. For the hitters on this list, the mean age is 22.7 during their rookies years, while it is only 21.5 for the pitchers in their rookie years. The hitters compared here came to The Show more seasoned and experienced than their pitching counterparts.

So my hypothesis is only half right. I predicted that an unusually strong rookie season would in no way correlate to becoming a star later in a career. While two of the pitchers on this list (Gooden and Valenzuela) arguably had at least five years of dominant performances, the others essentially amounted to nothing. On the other hand, the hitters on this list average 5.6 all-star games each - and yes, that includes Listach and his zero appearances.

From this small sample, we can make the assumption that a strong showing by a hitter in their rookie year will more likely lead to future success than a strong first season by a pitcher.

Two days ago, I began a thrilling expose of number eight hitters in the majors by looking at the American League teams and examing their numero ocho guys. We looked at John Buck (outstanding), Jason Kendall (abysmal), and everyone in between.

Well now it is the Senior Circuit’s turn. Before I list the number eight hitters for each team, I will offer the disclaimer again that I used the hitter with the most at bats in the number eight spot for that team for this year only:

What a great group of players we have here. The breakdown of position for the NL goes like this: 6 C, 3 OF, 3 3B, 2 SS, 1 2B, 1 1B. For those keeping score, that is 10 catchers out of 30 teams batting eighth. Where have you gone Berra and Bench?

And special congratulations to Craig Wilson for being the only first baseman on either list. You can’t play defense or hit - good for you! Being fair, though, he does play outfield as well. And he is a bench player.

Some other items of note on this list: Quintanilla doesn’t start, but rather spells Carroll and Tulowitzki, Betemit recently lost his job to Andy LaRoche, Counsell just lost his job today to uber-prospect Ryan Braun, JoseValentin is on the DL, Randy Winn has been batting leadoff for a couple of weeks now, and Langerhans has played for three teams this season.

So do we have any Bucks or Kendalls in this group? Here are the numbers as of May 23:

At first glance, Winn and Valentin jump out as huge outliers in this group. Winn actually has more than 90 at bats now in the one or two spot, so his RC and, in turn, RC/27 will be considerably higher. Valentin just drew the short straw, I guess, in New York and bats eighth in a lineup that includes Jose Reyes, David Wright, Carlos Beltran, Carlos Delgado, Shawn Green, Paul Lo Duca, and Moises Alou. All of those, including Valentin, are valuable hitters - but someone has to bat eighth.

Over the whole group, the most interesting thing I see is in the VORP column. If we follow the definition of the statistic, and a VORP of zero is exactly replacement level, look at how many of the 16 are within +/- 2.1 of zero. Ten of them! And another three are worse than -2.1 VORP. Truly, the eight hitters in the NL define mediocrity and replacement level. So apparently teams are sticking their worst hitters as far down as they can in the order. What a novel idea!

Our candidates here for best and worst seem pretty clear.

If you allow Winn to be labeled as an eight hitter, he is the candidate for best. If not, we must look at either Valentin or Yadier Molina, whose power numbers are atrocious, but otherwise has very respectable stats. An eight hitter with a .295 average and .360 OBP? Yes, please.

If we are comparing the NL best to Buck, let’s choose Molina: they are both catchers, spent all year at the eight spot, they are within 15 at bats of each other, and neither are hurt (like Valentin).

In looking at these two catchers, you can analyze a lot of pieces of data, scrutinize a lot of stats, and spend too much time trying to compare the two across leagues. But, I will make it very simple for you. Both of these guys have been catching in the majors since 2004. And this year, they both have career highs in AVG, OBP, BABIP, RC/27 and BB%. So clearly, they are both playing above their heads (normal stats) right now. The advantage goes to the hotter guy, with the better overall stats, and you just ride him until he drops. Here, the winner is John Buck.

The worst spot in the NL goes to (no, not Brad Ausmus!) Ryan Langerhans; he of the Braves, A’s, and Nationals so far in 2007. Using RC/27 like we did with Jason Kendall, a team of all Langerhans would score 1.2 runs per game; worse than Kendall. The only vindication for Langerhans is that the A’s continue to give Kendall at bats (more than 150 now), while Langerhans is a spot starter, and relegated to pinch-hitting most of the time. Langerhans has had four plate appearances in a game only once in the last month, where Kendall has done it 18 times.

That simple fact alone gives the dubious honor to Kendall, and perhaps A’s management. Kendall has been batting 8th or 9th recently, but he also has 36 at bats in the leadoff spot, where his numbers are significantly worse (if you can imagine that) than they are right now. They both have slugging percentages under .200, but if you can keep that feat up for 160 at bats, you deserve the title of the worst.

So now the question become, who constitutes our all-eight-hitters lineup? Some simple rules - nine spots, one DH, all positions have to be filled out, and an outfielder can play any OF position. Here is my team with a note about each:

So there is my lineup - all number eight hitters. You know what? Give me the Nationals, the Royals, and the Pirates. I think we can take ‘em. How many games do you think this team could win with league average pitching?

Hope you had fun reading about this stuff. It was sure fun to write about something unusual for a change.