Spring Training Invites, 2010

The Giants have invited the following players to Spring Training this year, courtesy of Henry Schulman:

Here is the entire list of invitees: Right-handed pitchers Denny Bautista, Santiago Casilla, Rafael Cova, Steven Edlefsen, Eric Hacker, Osiris Matos, Tony Pena Jr., Felix Romero, Dan Turpen and Craig Whitaker; left-handed pitchers Craig Clark and Clayton Tanner; catchers Steve Holm, Johnny Monell, Hector Sanchez and Jackson Williams; infielders Ehire Adrianza, Brandon Crawford, Nick Noonan; outfielders endell Fairley, Roger Kieschnick and Thomas Neal.

A pretty nice crop of players to invite to camp, and I’m looking forward to seeing them in action.  It’s nice to see the Giants giving Hector Sanchez a look- the 21-year old catcher had a .387 wOBA in Rookie League last year and nearly as many walks as strikeouts.  I’m not as educated on Giants prospects as I used to be, but I recall hearing great things about Sanchez.  I’m a tad bit surprised to see Adrianza coming up here; chances are he (and Sanchez, I suppose) won’t be in camp for very long.  I’ve been wanting to see him for a while now, so I’m looking forward to watching this Vizquel-lite youngster in the field.  Wendell Fairley is also a surprise, and I don’t expect him to stick around very long either.  He’s quite the intriguing prospect, and hopefully he can put it together in San Jose this year.

The guys I’m most excited to watch are Thomas Neal and Brandon Crawford.  I got the opportunity to watch Crawford last year in the Spring and was extremely impressed with his glove- there’s little doubt in my mind that he can be an impact fielder in the Major Leagues.  The instincts are good, the footwork is smooth and that arm of his is outstanding.  Neal had a real breakout year in 2009 (.444 wOBA and 49 RAA), and he sounds quite promising.  It’ll be nice to see him with my own two eyes for once.

I’ve been swamped with work lately, and I’ve got a number of projects that I’m working on right now.  Hopefully I’ll find some time to finish them up and post some interesting stuff soon.

Will Aubrey Huff Improve the Giants?

Will Aubrey Huff make the Giants a better team in 2010?

The Giants have signed first baseman Aubrey Huff to a one-year, $3 million contract.  There is no real consensus on the signing, as most people are simply stepping back, shrugging and saying… “meh.”  After being spurned by Adam LaRoche, the Giants quickly turned to a 33-year old coming off of a below replacement level season.  Huff provides the Giants with a known run producer, having been a well-respected middle of the lineup hitter during his career.  And he’s a left-handed hitter, which the Giants wanted.

The Huff signing does a few things—some of which are good, others not so much:

1. It ensures that Travis Ishikawa is relegated to a bench role.
2. It ensures that Fred Lewis is relegated to a bench role.
3. It ensures that the Velez/Torres platoon never sees the light of day.
4. It ensures that Juan Uribe will not be a starter.
5. It moves Mark DeRosa to left field.
6. It moves Pablo Sandoval back to third base.

I think #1 is inconsequential.  The Giants made it explicitly clear that Travis would be a backup player from the very beginning of the off-season, as it was widely known that they were pursuing corner infielders such as Nick Johnson, Adrian Beltre, Mark DeRosa and Adam LaRoche.  As we all know, Pablo Sandoval ain’t goin’ nowheres.  #2 is a bit more bothersome to me, as Lewis is one of the few above-average players on the Giants.  He walks, he gets on base, he has marginal pop, he’s a good baserunner, and he plays a fine left field.*  We need a leadoff hitter?  Fred is our best option at that slot in the lineup.  Unfortunately, he’s “uncomfortable” hitting in that slot and our manager is uncomfortable asserting his authority.  #3 and 4 are a relief more than anything else.  Juan Uribe should not be considered a starter under any circumstance, nor should the Velez/Torres platoon even be considered in the first place.  Numbers five and six are just fine by me, although I was hoping that the player to man the position would be Adam LaRoche.

At first glance, the Huff signing looks like a good one to me.  He’s a good left-handed hitter with a fine track record (career 112 wRC+) and a bit of pop in his bat.  The Giants have solidified their heart of the order—it appears the lineup will read Sandoval, Huff, Derosa—but does bringing Huff into the lineup give us a substantial improvement in the quality of our offense, a marginal one, or does he actually subtract from the team?

I have decided to use four projection systems—James, CHONE, ZiPS and Marcel—to get a consensus as to how a number of players project to perform in 2010.  I’d use PECOTA over James, but Baseball Prospectus has yet to release their figures and the James projections seem surprisingly reasonable this year.  In fact, the systems were all in wide agreement with one another for the players that I’m testing here.  So, here’s Huff’s projection:

System	XBH-HR	HR	BB	AVG	OBP	SLG
James	32	20	49	0.267	0.334	0.445
CHONE	33	19	44	0.263	0.327	0.438
ZiPS	35	18	49	0.260	0.323	0.431
Marcel	34	18	47	0.267	0.329	0.443
AVG	34	19	47	0.264	0.326	0.439

All of the systems see Huff hitting around 34 doubles and triples while coming close to 20 big flies.  The systems also see Huff hitting between .260-.268 with an OBP between .323-.334 and a SLG between .438-.445.  For the OPS lovers, that’s a worst-case scenario of a .761 OPS and a best case of .779.  And that’s not taking specific park factors into account.  Left-handed hitters tend to see an increase of doubles by around 6%, triples by about 20%, and a decrease of home runs by about 9%.  This varies, of course, by the hitter—a left-handed pull hitter won’t be affected as much than by a left-handed gap hitter, for example.  So let’s take a look at Huff’s home run distribution the past three years within the confines of AT&T Park:

2007:

That’s at least three home runs lost, with one more possibly caught at the wall or falling in for extra bases.

2008:

This was Huff’s monster 2008 year in which he blasted 32 home runs.  I can count at least 7 home runs that would have fallen short.

2009:

And another three falling short.  So we’re looking at an average of about 4 home runs lost to AT&T.  So if his true talent home run rate is 19, as the projection systems suggest, then we can assume that AT&T would decrease his totals by at least a few.  Instead of being a 19 home run guy, 15-17 sounds a bit more reasonable given his HR distribution.  Conversely, a decrease in home runs could very well result in an increase of doubles and triples—but there’s really no way to be sure.  And to be perfectly honest, there’s really no telling whether or not Huff would really have lost all of those home runs at AT&T, since these figures assume neutral wind conditions.  Since there’s so much uncertainty in using these “true landing spot” estimates (not to mention some long flies at other parks that didn’t go out and aren’t included), I won’t be altering Huff’s aggregate projection.

Now, on to the projected additions or subtractions Huff’s presence brings to the team.  First, I’m estimating runs created through linear weights in a 4.5 runs per game environment. The formula**:

.468*1B + .753*2B + 1.03*3B + 1.395*HR + .311*NIBB + .186*IBB + .337*HBP – 0.09*(AB – H – K) – 0.113*K – 0.293*GIDP

This is then converted into runs above average.  Baserunning is measured through Dan Fox’s Equivalent Baserunning Runs (the player’s “true talent,” prorated to projected times on base in 2010) and UZR projections based on expected playing time in 2010.

Hit	BsRun	Field	Rep.	Pos.	WAR
1.5	-2.2	-3.8	19.2	-10.3	0.4

Ouch.  Not quite what I was hoping for.  I think there’ll be a bit of confusion as to how Huff’s hitting could be rated so low compared to the average hitter among the less stat-savvy crowd.  So, let me explain: through the runs created formula above, we project Huff to create about 67.3 runs with the bat.  That’s good, but not great.  We then convert Huff’s runs created into a runs scored per game format—imagine an entire lineup of Huff’s.  How many runs would they score per game?  This is done by dividing runs created by outs and then multiplying by 26.25.***  This gives us an estimated 4.6 runs scored per game.  Then we finish it off by using (RpG – LgRpG) / 26.25 * Outs.  Through this process, Huff is exactly +1.5 runs above the average hitter.  The most optimistic of the projections, James, has him as being +3.6 RAA.  That’s good, but again, it’s not great.  When all is said and done, Huff comes in well below an average Major Leaguer when factoring in the difficulty of his position, his poor baserunning and below average defense.  My estimates have him as being a 0.4 WAR player.  Given that a marginal win will cost somewhere around $4 million in 2010, Huff’s projected to be worth somewhere around $1.6 million.  Ouch.  Huff’s most optimistic projection, James, would have him being 0.7 WAR—and this would have him being worth near $2.6 million, closer to what he’s being paid.  Unless he outperforms his projections—which could very well happen (although I have my doubts because of his age), it looks like the Giants overpaid by about $400 K to $1.4 million.

But as sad as it may sound, 0.4 WAR could very well represent an upgrade to the Giants’ lineup.  Ignoring positional runs and replacement runs, Huff comes in at -1.8 runs below average (offense + baserunning + defense).  By using the same process, we can look at the other options available:

First	Last	Hit	BsRun	Field	Total
Aubrey	Huff	1.5	-2.2	-1.06	-1.8
Juan	Uribe	-4.0	-0.9	-0.48	-5.4
Fred	Lewis	1.8	3.3	3	8.1
Travis	Ish	-1.3	1.4	3.3	3.4

There’s no fun in presenting some work you’ve done unless there’s a surprise, right?  It turns out that #1 might not be insignificant after all!  There’s a bit of playing time discrepancy with all players, but if we standardize them to a full season, this is what we get:

First	Last	Hit	BsRun	Field	Total
Aubrey	Huff	1.8	-2.7	-1.3	-2.2
Juan	Uribe	-6.5	-1.5	-0.7	-8.6
Fred	Lewis	3.0	5.4	4.9	13.3
Travis	Ish	-2.2	2.4	5.8	6.0

Naturally, there are a number of issues with standardizing these rates.  But, it raises an interesting point—is it possible that Ishikawa outperforms Huff in 2010?  The discrepancy between their bats is apparent—Huff is projected to hit about +3-4 runs better than Ishikawa, but the gap isn’t as big as one would like to imagine.  There’s also an issue with the baserunning projection, as I am quite skeptical that Travis’ true talent is a plus base runner.  Additionally, there’s a source for error within the projections—the 2009 season is, of course, weighted heavily—but Huff’s batted ball data suggests that his .263 BABIP was too low and that a .265/.332/.416 line makes more sense (once factoring in his tendencies to over or under perform his expected BABIP).  It’s possible that his 2009 was an outlier of sorts and was due to some poor luck, in which case the projections are being a bit more harsh than they ought to be.  Huff is, after all, only one year removed from a 4.2 WAR season (albeit after three years of near replacement-level status).  Then again, it’s always wise to be conservative while projecting.

Despite what the projections say, I cannot entirely condemn the signing.  I’d bet good money that Huff’s 2009 was an outlier, and the switch from the AL to the NL should be favorable to him.  Additionally, there’s also the chance that Ishikawa fails to meet his projections, and the discrepancy between the two favors Huff—not the other way around.  Finally, when all is said and done, this is an upgrade from the current rotation of Uribe, DeRosa and Sandoval.

That being said, it’s becoming painfully obvious that Fred Lewis is being mishandled by management.  He is one of our better hitters, and at this point in time appears to be capable of outperforming Huff as well.  Having Lewis in left, DeRosa at third and Sandoval at first may very well be our best chance of maximizing production—but, of course, the Giants would never do such a thing.  For the time being, they’ll be happy with their “proven run producer” while we’re left wincing and praying that Huff isn’t as bad as he projects to be.

*I can hear a universal moan coming from Giants fans as soon as I begin to think of writing that sentence.  It’s true; he’s got brick hands and his routes are downright atrocious.  But this does not negate the fact that he is a good defender relative to other left fielders.  When the pool of players in your field includes Manny Ramirez, Jason Bay and Adam Dunn, an average or slightly below average defender immediately becomes “good.”  I would never argue that Fred would make an adequate center fielder or that he could handle the dimensions in right field, but he’s a good left fielder.

**I should note that there’s a potential source for error in the run values for outs, strikeouts and ground into double plays.  Generally speaking, 0.09 is the conversion of linear weights runs above average to an absolute runs scale, but that’s not always the case.  Additionally, the value of a strikeout (-0.113) and a ground into double play (-0.293) are borrowed from empirical linear weights from recent years.  The other values are derived through Base Runs.

***There are some variations on this number—the original figure is 27 (because there’s 27 outs to a game), but there have been modifications to it that vary from 25.5-26.5.

Giants Sign Mark DeRosa

The Giants made their first significant transaction of the offseason, signing veteran utility INF/OF Mark DeRosa to a two-year, $12 million deal.  DeRosa hit .250/.319/.433 with 23 doubles and 23 big flies for a .327 wOBA.  Here’s a few projections on DeRosa for 2010:

Bill James: .260/.335/.418, 27 2B, 17 HR, .328 wOBA, -0.2 RAA

Fans: .273/.345/.437, 25 2B, 18 HR, .344 wOBA, +6.9 RAA

CHONE: .255/.333/.414, 24 2B, 17 HR, -1 RAA

Both James and CHONE see DeRosa as being roughly league average with the bat, while the Fans are quite optimistic that he’ll be a good, above average hitter.  Call me an optimist, but I’m more likely to side with the Fans on this one.  Mark’s BABIP in 2009 was .286, which is substantially different from the consistent .330-.340 marks he was posting in his 500+ AB seasons the past couple of years.  His batted ball rates dipped a bit, which explains why his average on balls in play would be lower, but his expected BABIP is around .304, which would raise his line to .267/.334/.453.  So it looks like Mark suffered from a bit of “bad luck” last year.  DeRosa’s versatility will make him an asset to the organization, as his ability to play left field allows the Giants to pursue a third baseman (I’m thinking Adrian Beltre, whom the Giants have been linked to) or a first baseman (Adam LaRoche).  He’s not an impact player by any means, but he’s a welcome addition to the lineup in that he’s an above average hitter.  As of now, it looks like DeRosa will be the starting third baseman, shifting Pablo Sandoval to first.  So we’re essentially replacing the current Giants’ first base situation with DeRosa’s bat.  Parsing through the numbers, it looks like DeRosa’s luck and park-adjusted line (if you believe in that sort of translation) suggests that he’d present a +10.3 run difference in 2009, which would have given the Giants an extra win with the bat (not taking into consideration glovework).

For two years and $12 million, the Giants have made a solid signing.  Let’s just hope that DeRosa’s slip in production was due to poor luck and not actual regression, and that the Giants bring in a complementary bat to solidify the middle of the order as best they can.

My Gift to You

Since it’s the holidays, I thought I’d give you all a bit of a present.  So here is a spreadsheet containing complete linear weights data for the 2009 season.

A breakdown of the spread:

“LWTS_RC” are linear weights on an absolute scale; these figures differ from FanGraphs’ “wRC” in that they include intentional walks, sacrifice hits, reaching base on error, catcher’s interference, strikeouts, ground into double plays, and pickoffs.  These values are derived from Retrosheet’s play-by-play data (via Terps) rather than the “quick-n’-dirty” method that is implemented at FanGraphs.  I’d like to think that means these values are more accurate, and they should be.  The figures with asterisks implement BP’s park factors.  To be perfectly honest, I’m not sure how they develop their PF.  Since the PF varies from player to player on the same team, I imagine it’s based on a per-plate appearance basis or they’re using an “individual” park factor.  I’ve included the non-adjusted page so you can apply the PF of your choice.

“R/G” stands for Runs per Game.  Based on the rate of runs and outs the player creates, we can estimate the amount of runs a lineup of that player’s production would score per game.  It’s simply (LWTS_RC/Outs *26.25).

“RAA” and “RAR” are Runs Above Average and Runs Above Replacement, respectively.  Personally, I prefer to stick with a baseline of average since it’s universally recognized and easy to work with (+0 is exactly average), but RAR seems to be the more current popular hitting valuation method out there.  Replacement level in the spreadsheet is defined as +20 runs per 600 plate appearances for the National League and +25 runs per 600 PA for the American League.

“VORP” is the player’s Value Over a Replacement Player (I’m just using the same name as BP’s statistic).  This is simply RAR plus the positional adjustment.

“rAVG” and “rOBA” stand for “relative average” and “relative on-base average.”  I cleaned up the methodology for “wAVG” and thought re-naming it might be better, since it’s not set up the same way wOBA is.  The same goes for rOBA.  Both rates are set so that the league average rAVG is always .260 and rOBA is .330.  These are just meant to provide some rate statistics for people to play around with.  I’m not claiming these to be new statistics or anything like that.

EDIT (1/1/10): I was using the wrong set of data- the spreadsheet now includes the proper league adjustments in both the linear weights used and the replacement levels.

Here are the leaders for 2009:

1. Albert Pujols, +84.7

2. Joe Mauer, +83.0

3. Hanley Ramirez, +73.9

4. Prince Fielder, +70.6

5. Derek Jeter, +68.5

6. Chase Utley, +66.8

7. Ryan Braun, +65.6

8. Pablo Sandoval, +62.5

9. Ben Zobrist, +58.4

10. Adrian Gonzalez, +57.6

A Review of Chris Jaffe’s “Evaluating Baseball’s Managers: A History and Analysis of Performance in the Major Leagues, 1876-2008″

You know, we have a lot of measurements to quantify different aspects of baseball.  We’ve got a multitude of formulae designed to estimate individual player performance, be it hitting, pitching or fielding—things that we see on the field that we find to be relevant to our understanding of the way the game works.  One aspect of the game that has been left largely untouched, at least on the surface, is the impact and the tendencies of the men writing the lineup cards day in and day out.  Managerial evaluation has been left largely untouched relative to the rest of the game.  I personally have not seen much work done on the subject aside from Mitchel Lichtman’s chapter in The Hardball Times Baseball Annual 2009, in which he measured managerial skills based on the team’s component statistics for the 2008 season.  His article looked at managerial “value,” so to speak—since there is so much variability within a single season, there’s no means with which we can identify trends and actual managerial talent.

Bill James wrote about managers in The Bill James Guide to Evaluating Managers: From 1870 to Today.  I don’t have a copy of the book, so I’m unaware as to how thorough he is in his investigations.  In any case, the book was published twelve years ago—so the information provided in it, while undoubtedly invaluable, is certainly outdated.

A few weeks ago, Chris Jaffe—a writer from The Hardball Times—asked if I would take a look at a part of his new book, Evaluating Baseball’s Managers, 1876-2008, and give a review on it.  Since the book hasn’t been published yet, there are limitations of what I’m allowed to read.  Chris was generous enough to let me read his analysis of Giants managers, past to present.

From what I’ve read, Jaffe’s book reads something like a history textbook.  While that might come across as a negative to some, I like the way that it’s set up.  The layout of each section is extremely accessible to all, it’s very well organized, and Jaffe’s coverage of managers is an ideal blend of statistics and history.  You don’t need to be a numbers cruncher to understand or to enjoy the relevance of the statistics Jaffe provides in the book.  He provides a thorough depiction of each manager—not only giving a description of their character, but the reputations that they gained throughout the course of their career.  And he does a fantastic job at providing statistical evidence to back his claims.

There were a few sections that really jumped out at me—the very first being Jaffe’s section on John McGraw and the others being the more contemporary managers of the Giants—Baker, Alou and the present manager, Bruce Bochy.  That being said, I’ll comment on those sections.

John McGraw: As “lame” as it may sound, I was actually entranced by Jaffe’s depiction of the legendary manager that is John McGraw.  I knew little about him aside from his reputation as a “great” manager, but I really had no idea about his strengths—and reading about him makes me wish that the Giants had a manager like him in this day and age.  I was shocked to learn that this early 1900’s version of Bobby Knight (according to Jaffe) was not only an advocate of breaking in younger players, but that he had the greatest walk, hit by pitch and hit differentials of any Major League manager in history.  He advocated patience and selectiveness far before on-base percentage was recognized as highly relevant to the run scoring process, and apparently he’s still ahead of the current general manager of the Giants one hundred years later.  Some would say that it’s a testament to McGraw’s baseball acumen; others would say that it’s an indication that Brian Sabean is simply incompetent when it comes to evaluating hitters.  I’d say it’s both.

Dusty Baker: It was interesting to read Jaffe’s description on Dusty Baker because I have something of a personal tie to him.  I don’t remember the Craig era because I was simply too young to remember it, and Dusty managed the Giants from ’93-’02.  I attended a few of Baker’s baseball academies when I was younger and had the opportunity to meet him multiple times—and I must say, he’s one of the more pleasant men that I’ve ever met.  Jaffe is absolutely correct when he says that Baker is a manager with exceptional people skills.  There was one quote from Jaffe in the Baker section that I’ve held on to, since I found it to be an extremely important thing to remember when looking at managers:

In reality, managers are better at some parts of the job than at others.  Place a man in a situation that fits his strengths, and he will look like a savant.  Put that same individual on a team that highlights his weaknesses and people will call him a dullard.

And this highlighted quote describes Baker in a nutshell.  He was built for San Francisco, which was a strong offensive team at the time—and being a former hitting coach, Baker thrived.  Chicago, on the other hand, was a different story—their best players were pitchers, and Baker just doesn’t have the same feel for pitchers as he does with hitters.  Jaffe also points out Baker’s distaste for walks, and that his presence in Chicago led to the worst walk differentials in Cubs history—and the year after he left, Cubs hitters drew over 100 more walks than the year before.  That’s some crazy stuff.

Felipe Alou: Jaffe asserts that Alou would be Cooperstown-bound as a manager if it weren’t for racism—his managerial career began at a much later age (57) than average, and this was a trend for minority managers.  Alou ranks fourth overall for managerial wins past the age of 57, and two of the managers ranked higher (Mack and Torre) worked with extremely talented teams—Alou, on the other hand, had no such luxury.  Jaffe also mentions Alou’s tendency to lean on his bullpens, but he doesn’t go into detail about it.  I seem to recall Alou’s managing of his bullpens to be headache inducing, but it’s quite possible that there wasn’t a noticeable difference between Alou and the average manager and I’m suffering from observer bias.

Bruce Bochy: According to Jaffe, the Birnbaum Database lists Bochy as being “the greatest manager in history with a losing record.”  Since the manuscript in my possession is limited to the Giants, I don’t know how this is being measured—so naturally, I’m a tad bit skeptical.  Jaffe notes that Bochy works well with young pitchers but poorly with well-regarded position player prospects, and this is the feeling I’ve gotten from Bochy as well.  I’m particularly worried about his handling of Buster Posey, but I’m hoping that Posey’s superior talents will end Bochy’s “curse.”  When the only position player you’ve developed in a dozen years is Khalil Greene, that means either one of two things: the first being that San Diego’s farm system lacked good position player prospects in Bochy’s tenure.  The second, of course, is that Bochy simply cannot develop position players well.  I’m hoping that it’s the former and not the latter, but it still worries me.  This little tidbit made me feel a bit better about Bochy:

(…) Bochy possessed a corps of solid but generally unimpressive hitters, journeymen starting pitchers, toxic middle relievers, and a superlative closer.  Yet that bunch played .494 ball for him from 1995-2006.  Somehow, 75-win talent transformed into 80-win results (…) Bochy produced those results because he had an impressively effective track record coaxing unexpectedly strong performances from veteran hitters.

Hopefully Bochy can “work his magic” on guys like Edgar Renteria, who the Giants desperately need to have a solid season.

To be perfectly honest, I’ve never been that interested in learning about managers—that is, until I read Jaffe’s manuscript.  I’m not sure when the book will be hitting stores, but I know that I’ll be purchasing a copy (which can be done here).  If you’re interested in reading in-depth analysis of a relatively uncharted territory in baseball, this is the book for you.

Kelly Johnson: Bounceback Candidate

Kelly Johnson is a nice bounceback candidate for 2010 and would be a nice fit for the Giants.

Kelly Johnson was non-tendered by the Braves after posting a line of .224/.303/.389 in 346 plate appearances in 2009.  Johnson, who will be 28 in 2010, had posted a .273/.356/.440 slash line between 2005 and 2008. With a walk rate of 11% and a wOBA of .348, Johnson’s talent level resided around +10 runs above the average Major League hitter, or roughly one win.  That being said, it was a shock to see his numbers dip the amount that they did- but was it due to a wrist injury, regression, or bad luck?

Injury and bad luck seem more logical explanations than regression; players typically don’t see a drop in physical ability at the age of 27- this is usually the beginning of a player’s peak years.  While sifting through Johnson’s numbers, I noticed something that really stuck out: his batting average on balls in play.  Up until 2009, Johnson’s weighted BABIP had him as being a .328 BABIP hitter.  His 2009 BABIP, rather than being anywhere near this rate, dipped all the way down to .249.  That’s a drop of 79 points from his norm and is highly unusual.  Now, it’s very possible that this dramatic drop in BABIP is due to poor hitting- perhaps Johnson wasn’t hitting the ball as hard as he used to as a result of his wrist issues.  But I thought it’d be something worth looking into, and I did.  And the results were quite pleasant.

I’ve used Peter Bendix’s xBABIP calculator in the past to take a look at Jay Bruce’s 2009 season.  And while I love Bendix’s method, it isn’t infallible- while it is very useful, it is a bit limited in the data it uses (since his calculator is a “quick and dirty” method).  That being said, I’ve decided to bring in another xBABIP calculator into the mix simply for a bit of variety.  Since the creator of the second method uses the handle “Slash,” I’ll just refer to his BABIP estimator as sBABIP.  His equation:

sBABIP = 0.391597252 + (LD% * 0.287709436) + ((GB% – (GB% * IFH%) * -0.151969035) + ((FB% – (FB% * HR/FB%) – (FB% * IFFB%)) * -0.187532776) + ((IFFB% * FB%) * -0.834512464) + ((IFH% * GB%) * 0.4997192)

After plugging in Johnson’s batted ball rates, we see that his sBABIP is .296.  Now, this doesn’t mean that he should have had a .305 BABIP, but it certainly suggests that it should be around there, rather than the .249 he posted.  Hitters also have a tendency to either out perform or under perform their expected BABIP, and so I looked for a trend in his full seasons.

BABIP, expected BABIP, (Difference)

2007: .330, .343 (0.013)

2008: .344, .335 (0.09)

So we’re looking at a weighted average of +0.0105 difference between his expected BABIP and BABIP.  Once we apply the “Johnson Effect” to his sBABIP, we have an expected BABIP of .307.  This is still very much a drop from his career rate, but that’s to be expected due to his batted ball distribution.  If we apply this BABIP to his component rates and assume each ball that’s falling in is a single, this gives us a slash line of .273/.346/.438.  Johnson’s wOBA in 2009 was .306; this swing in BABIP increases his wOBA to around .345 and his RAA goes from -6.5 to about +5.0.  Over a full season (600 PA), that’s about +8 or so runs above the average hitter.  That’s not far from the “talent level” that I spoke of earlier.

What can we take from this?  Johnson’s component batted ball rates suggest that his BABIP should have been closer to .310 than the .249 he posted, but of course there’s always uncertainty in an estimate.  We don’t know for a fact that Johnson had 15 singles stolen from him- perhaps it seems unlikely, but it remains a distinct possibility.  Ultimately, I believe that this is an indication that Johnson’s rates will improve back to his norms and that 2009 was nothing more than an outlier.  So we’re looking at a second baseman/outfielder coming off of what appears to be a season mired in bad luck, with above average hitting skills and above average defense in left field (albeit in a limited sample size).  I can think of a number of teams that could be looking for a player like Johnson- and naturally, being a Giants fan, he seems like a sound option for the Giants.  Johnson’s above average bat and defense in left field, along with a good approach that fits at the top of the order, could give the Giants a respectable leadoff option for 2010, at a relatively cheap price.

Triples Alley on Twitter

I’ve finally given in and am on the Twitter bandwagon.  I don’t know how often I’ll use it, but I figure I might as well have one:

https://twitter.com/TriplesAlley

Ramblings on Batting Average

I’ve got a lot on my plate right now, and I’m hoping that this will change soon.  I’ve got some ideas for future articles/research/what have yous, and I wish that I had the time to sit down and take care of everything at once.  So here I am with a few minutes to spare, and I’m going to ramble about batting average.  I know that’s something of an odd thing to want to write about- Lord knows there’s a ton of other topics that are far more interesting to discuss- but the amount of times that I’ve seen it referred to as a measure of player value is somewhat alarming (i.e., “Dan Uggla is a bad hitter because he hits .250 with a lot of strikeouts”).  I can’t blame someone who barely follows baseball to refer to batting average, since they don’t spend enough time thinking about or paying attention to the game to really give a damn.  But for the individuals that post on forums and write articles (MLB.com writers- I’m wagging my finger at you), it’s really time to begin using your noggins a bit and really think hard about the way baseball works.  I should say that it really isn’t my place to tell someone that they’re right or wrong, but it’s obvious that there are more reliable measurements.  And if you’re having a debate about which player is better, you’re going to want the most accurate measurements available.

What does batting average tell you about a hitter?  It tells you the amount of times that the player got a hit in x amount of at-bats.  It’s a very simple rate statistic that implies two things about a hitter that make it so darn attractive- 1) it implies that the hitter is either good or bad at getting on base, and 2) it implies that the hitter is good or bad at moving runners over or driving them in.  Okay.  So perhaps it can give us something of a rough estimate of a player’s provided value with the bat.  But those two implied benefits of batting average are woefully incomplete.  For one, getting a hit is not the only way of reaching base.  Players also reach base via the walk or being hit by a pitch, and so on.  These events cannot be discounted. A walk is worth slightly less than a single, but it still provides ample value.  As for the other benefit- implied run driving production- batting average is empty in that regard.  You can be 100 for 300 with 90 singles and 10 doubles and be considered as good of a run producer as a player that is 100 for 300 with 60 singles, 20 doubles, 5 triples and 10 big flies.  It’s obvious that the second player provided more run driving value, but both players are .300 hitters.  By the way, there are two statistics that measure the things batting average implies:

On-Base Percentage: (H + BB + HBP) / (AB + BB + HBP + SF)

Slugging Percentage: (H + 2B + 2*3B + 3*HR) / AB

Now, I want you to pay close attention to the bolded letters in each equation.  See the “H” in the numerator and the “AB” in the denominator?  That’s right; that’s our old friend batting average.  But he’s not lonely any more- in fact, he’s brought some friends along with him to help paint a more complete picture of a hitter’s performance.  On-Base Percentage (OBP) measures…you’ve guessed it, the percentage of times a player reaches base in his plate appearances, and slugging percentage (SLG) measures the player’s run driving ability*.  If you think about it, the more data you’re including, the more accurate the statistic should be (if constructed correctly, of course!).  And if you combine OBP with SLG, you just might be able to get a pretty good grasp of a hitter’s value at the plate.  This is what we call OPS (On-Base Plus Slugging).  At least, we would hope that it’s more accurate than batting average.  So just how good (or bad) is batting average when it comes to estimating runs scored?  Let’s take a look.

First, the equation we’ll be using to convert rates to runs:

Runs = (2 * Rate / LgRate – 1) * Innings Batted * (LgRuns / Lg Innings Batted)

This is from The Hidden Game of Baseball (which I picked up a few weeks ago- definitely a fun read and I highly recommend it), and I honestly wouldn’t have noticed it if it weren’t for Patriot.  Innings Batted are outs made divided by three, since there are three outs to an inning.  I’m not sure how Palmer defined “outs,” but I do it as (1-OBP)*PA.  And I measure accuracy through Root Mean Square Error (RMSE).  I’m doing it this way because it gives us the average difference between the estimated value and the actual value.  The lower the RMSE, the better.

Rate, RMSE

Batting Average: 46.15

On-Base Percentage: 37.94

Slugging Percentage: 38.14

On-Base Plus Slugging: 25.60

(Data is based off of 2000-2009 data only.  This gives us a good sample size to work with- preferably, I’d go back even further to get maximal accuracy, but I just wanted to illustrate my point.)

Batting average is usually within 46 runs of the actual runs scored, while OBP and SLG are around 37-38 runs.  OPS takes the lead at 25.60.  So let this be a lesson to you batting average lovers- batting average does not relate very well to run scoring at all.  In fact, it’s downright terrible compared to other simple methods such as OBP, SLG and OPS.  It tells us very little about the value of a hitter, aside from whether or not they’re good at making contact.  And really, this is all that batting average should ever truly be used to measure- a player’s contact skills.  I should note, however, that OPS, while much better at estimating the run scoring process that any of the individual “slash” rates, is still extremely flawed.  This is because we’re adding two rates with different denominators: OBP is a per-plate appearance rate, while SLG is a per-at-bat rate.  It is this reason that OPS should be used as an offhand method of player evaluation only.  If you’re looking for a rate statistic that is strong at predicting runs, there are two that stick out:

EqA: 23.64

wOBA**: 24.14

If you want to use a rate statistic to identify player talent, either EqA or wOBA should be the way to go.  EqA, however, uses some awkward weights and isn’t as good at evaluating individual hitters as wOBA is, despite its stronger RMSE in this limited “study.”  If you’re new to EqA, it’s adjusted so that the league average is exactly .260 in every year.  wOBA is on the same scale as On-Base Percentage (hence the name Weighted On-Base Average).

*SLG doesn’t necessarily measure actual power production, but it attempts to model it.  Since AVG is part of the equation, a lower AVG will result in a lower SLG.  That being said, SLG – AVG (which we call ISO) is a better way of measuring a player’s “power.”  Additionally, the intrinsic weighting of SLG- a double equals two singles, a triple equals three singles, etc.- is not proportional to the actual run scoring process.  So while SLG is a useful tool, and it’s a better measurement of a player’s value than average is, it’s still not particularly accurate.

**This version of wOBA is different than the traditional wOBA in that we’re not using specific linear weights tailored to fit the run environment.  Rather, this rendition of wOBA is derived from nothing more than OBP and SLG.  The equation is -(.53*(.56*OBP+.31*SLG)^2 + 1.35*(.56*OBP+.31*SLG) – .045).  Looks like Kincaid’s “quick and dirty” wOBA predicts run scoring pretty well.

“Evaluating Baseball’s Managers, 1876-2008: Dusty Baker”

Chris has an interesting article up on THT about ol’ Dusty’s effectiveness as a manager.  If I have some time later, I’ll post my thoughts on it.

EDIT (12/6): Chris has asked me to read and review his entire section on Giants managers, so I’ll be writing a full review within the next couple of weeks, when I’m less busy.

Awards 2009: MVP and Cy Young

Albert Pujols was by far the Most Valuable Player in the National League in 2009.

Well, I’ve already covered the gold gloves, silver sluggers and rookies of the year for 2009- so now all that’s really left are the MVP and Cy Young Awards.  I briefly thought about covering the “Manager of the Year” award- but to be honest, I’m not particularly interested in looking into it.  I can’t think of a way I can effectively evaluate the managers, aside from looking at the difference between the team’s expected win percentage and their actual win percentage.  And I’m not convinced that is the best way of going about it.  I also believe that a manager’s impact on his team’s effectiveness is somewhat limited.  If you give a manager a team of above-average players, they’re going to perform at their talent level regardless of who their manager is.  Perhaps a few individuals will see a slight increase in production, but for the most part the impact is small.  But I could be wrong, for all I know.  I played long enough to know that it didn’t have an effect on me, but it could be different for others.

But I digress.  The methodology I’m using is the same in my last post, where I combine offense (linear weights), baserunning (equivalent baserunning runs), defense (UZR) and a positional adjustment (based on UZR). Pitchers are evaluated by tRA, a batted ball DIPS statistic.  Also, pitchers will be excluded on the MVP ballot.  Perhaps we should just re-name the award the MVPP (Most Valuable Position Player).  The Cy Young is reserved for pitchers; and I believe the MVP should be reserved for position players.

National League Most Valuable Position Player:

1. Albert Pujols, +61.6

2. Chase Utley, +54.3

3. Hanley Ramirez, +50.0

Pujols comes to no surprise- everyone knew that he was undoubtedly the best position player in the National League and perhaps all of baseball this year.

American League Most Valuable Position Player:

1. Ben Zobrist, +60.4

2. Joe Mauer, +57.6

3. Derek Jeter, +35.4

I’m having something of a crisis here.  Zobrist, by my ratings, provided more value than Mauer did.  How is this possible?  Quite simply, it’s all a matter of defense.  Mauer has an edge of +15.3 runs over Zobrist offensively.  Zobrist has a +26.4 UZR, and Mauer saved about +5.5 runs based on very simple catching statistics.  That being said, the edge most likely goes to Mauer due to aspects of catching that we simply haven’t quantified yet.  And if I were to give a personal vote, I’d go with Mauer.  But I’m giving objective awards here, so the MVPP goes to the player with the highest rating (and I do feel better knowing that the difference between the two is 2.8 runs, though, which is smaller than FanGraphs’ gap of 4.1 runs).  Both Zobrist and Mauer had fantastic years, and it’s a shame to see Zobrist come in eighth in the MVP voting.

National League Cy Young Winner:

1. Tim Lincecum, +53.3

2. Chris Carpenter, +38.9

3. Dan Haren, +35.9

There was a lot of hullabaloo over the Lincecum selection because he only won 15 games, while Carpenter and Wainwright won a few more.  I’m not going to argue the merits of wins and losses in pitcher evaluation because…well,  it’s been talked about to death.  This isn’t to say that Wainwright didn’t have a good year; he most certainly did.  He comes in sixth behind Javier Vazquez and the extremely underrated Ubaldo Jimenez.

American League Cy Young Winner:

1. Zack Greinke, +68.2

2. Justin Verlander, +46.2

3. Roy Halladay, +44.3

This one was a definite no-brainer.  Greinke had one of the most dominant years in recent memory, and he was by far the most deserving Cy Young candidate out there.

Well, that about wraps up this short series.  I realize that this post is a bit shorter than the other two, and I apologize for that- this time of year is especially hectic for me and I’m low on energy.  Hopefully I’ll have some good material coming up soon- I’ve got some things in mind, and I’ll see where it takes me.