Archive for Analysis
The $189M Payroll: Part 2 of 2
Posted by: | CommentsThis post was written by Moshe Mandel and Stephen Rhoads
In part 1 of this series we went through six different payroll scenarios for the Yankees over the next decade. We were careful to distinguish between total savings and CBA savings, noting that how you treat the difference in payroll can make a big difference. Where you come down on the question of how much the Yankees can save is very much determined by which figures you’re examining. Let’s use Scenario 1 as an example. In this Scenario, payroll goes from $210M in 2013 to $189M in 2014, and then goes back to $210M in 2015. We summarized the savings accordingly:
2014: Payroll at $189M
Payroll savings: $21M
Revenue sharing refund: $10M
Luxury tax savings ($21M*50%): $10.5M
Total saved: $41.5M
2015: Payroll back at $210M
No payroll savings
No refund
Luxury tax savings ($21M*50%) – ($21M* 17.5%): $6.825M
Total saved: 6.825M
2016: Payroll stays at $210M
No payroll savings
No refund
Luxury tax savings: ($21M*50%) – ($21M*30%): $4.2M
Total saved: $4.2M
2017: Payroll stays at $210M
No payroll savings
No refund
Luxury tax savings: (21*50%) – (21*40%): 2.1M
Total saved: $2.1M
TOTAL SAVINGS: $54.625M
CBA Savings: $23.125M
Now, how you account for 2014 really determines whether the savings are significant or not. We peg the initial savings figure for 2014 at $41.5M saved. This number is comprised of a $21M reduction in payroll, a $10M refund from revenue sharing, and a $10.5M savings in luxury tax. However, the $21M reduction in payroll and the $10.5M reduction in luxury tax don’t really have anything to do with the new CBA per se. This $30.5M savings is a savings they could have gotten at any point in the last decade simply by reducing payroll. Thus, the $30.5M is comprised of savings prompted by the CBA, but it’s not comprised of savings emanating from the new CBA. It’s a $30.5M they could have gotten at any point in the last few years and chose not to. It’s still a cash item – it’s not depreciation in a cash flow statement – and it still means more money in the coffers, but it’s not a CBA savings per se, at least in our estimation. This is an important distinction.
In 2015, the payroll goes back to $210M, which means there are no payroll savings and no revenue sharing refund. There is a luxury tax savings though, as the new CBA allows teams to “reset” the luxury tax by going under the threshhold in just one season, an option that was unavailable under the old agreement. This means that any savings reaped due to the reduced tax rate can be attributed to the new CBA and can therefore be included as “CBA” savings. In this particular scenario, these savings are comprised of a $6.825M difference in what their bill would have been had they not gone under $189M in 2014 compared to what it is since they did go below the threshold. In other words, had they not gone under $189M in 2014, their luxury tax rate in 2015 would have been 50%. Since they did, it’s $17.5%. The difference is $6.825M. This is a real CBA savings and it plays out over the 2016 and 2017 as well (rate goes up to 30% and 40%, respective, per the CBA). Thus, the total amount saved in Scenario 1 is about $55M, but only $23M of it is prompted by the new CBA. Here’s the summary, then, of all six scenarios and how much the team could save by going with each option.
Scenario 1 ($210M to $189M in 2014, returns to $210M in 2015 and beyond): total savings of $55M, CBA savings of $23M.
Scenario 2 ($210M to $189M in 2014, stays at $189 for 3 seasons): total savings of $147M, CBA savings of $53M.
Scenario 3 ($210 to $189M in 2014, stays at $189 for 2 of 3 seasons): total savings of $116M, CBA savings of $54M
Scenario 4 ($220M to $189M in 2014, returns to $220M in 2015 and beyond): total savings of $76M, CBA savings of 29M.
Scenario 5 ($220M to $189M in 2014, stays at $189M for 3 seasons): total savings of $199M, CBA savings of $59M.
Scenario 6 ($220M to $189M in 2014, stays at $189M for 2 of 3 seasons): total savings of $152M, CBA savings of $59M.
Clearly the Yankees would save the most total money in Scenarios 2, 3, 5 and 6. In these scenarios, they’re dropping their payroll down to $189M and keeping it there for a substantial amount of time. The most they could save would be in Scenario 5, in which they shave nearly $40M off their payroll and maintain the reduction. In this case they’d net nearly $200M more, $59M of which would be a derivative of the new CBA.
These gains would be real, but they’re not entirely relevant for our purposes. Saying the team could save nearly $200M in Scenario 5 is true, but it’s also true they could save $75M right this moment if they dropped their payroll down by $75M. Of course, they haven’t done that at any point in recent memory. Our concern is the CBA savings.
The team would obviously save the most by dropping the payroll and keeping it low. Their tax bill would be lower, and they’d receive money back from the revenue sharing refund. However, these CBA-related savings don’t seem to amount to more than $60M. If they don’t maintain the new low payroll, the savings are even less. In Scenarios 1 and 4, in which they drop the payroll for one year and return it to prior levels immediately after, they’d only save $23M-$29M over four years. At most, this amounts to a little over $7M per year. In the latter scenarios, this annual savings figure rises to a little less than $12 million per year.
It’s our opinion that if the Yankees were interested in saving fifteen to thirty-five million dollars a year in payroll and tax, they should have done it already. They could have done it at any point in the last decade. We’re told that the new CBA incentivizes them to get below $189M to incur specific savings, but we see that the only time those savings are truly noteworthy is in the unlikely scenario in which the Yankees stay under $189M for a significant amount of time. Furthermore, we see that the CBA-related savings, at their most extreme, are about $12M a year. Are the Yankees really concerned about $12M a year in “new savings”? Are they suddenly concerned about the fifteen to thirty-five million dollars a year that they could have been saving all along? Perhaps most importantly, are they willing to forgo top free agents and risk missing the postseason to garner those savings?
Without further guidance as to what the true long-term goal is, we can’t get more specific than this. But it seems to be the case that the team will only realize serious, significant gains if they make a permanent move towards a payroll level more reminiscent of the early part of the last decade. Perhaps we’re stuck in the denial stage of the 5 stages of grief. It’s hard for us to understand the prospect of a “new normal” in which the payroll drops 10-20% while the team simultaneously reaps greater and greater revenues from a lucrative television network and new stadium. It’s even harder for us to understand risking contention in an increasingly competitive American League with an already-expensive roster to simply eke out a pittance in savings relative to the team’s balance sheet. But this may be the new Yankees reality, in which the Steinbrenners reach for a modicum of fiscal responsibility at the expense of some performance certainty. If it is, we all need to adjust our expectations accordingly.
The $189M Payroll: Part 1 of 2
Posted by: | CommentsThis post was written by Moshe Mandel and Stephen Rhoads
Yesterday Joe walked through the different stages of grief Yankee fans have been going through since learning that a $189M payroll was a realistic option in the near future. Part of my frustration when reading this (still in stage 2, I suppose) was that I didn’t have a firm handle on how much money the Yankees would actually be saving. If the amount they could potentially save ranges into the nine figures territory, then it’s hard to quibble with the team tightening the belt. If it was significantly less, then a whole host of options come into play, including the possibility that the team is not serious about getting below $189M in 2014 and was using Sherman to broadcast their bluff in advance of the Yu Darvish bid.
Accordingly, Moshe and I have run the numbers for six different payroll scenarios. We used the basic parameters set forth by Sherman in this quote to try and estimate the proper figures for each scenario:
For if they are at $189 million or less for the three seasons from 2014-16, they not only avoid paying one cent in luxury tax, which would rise to 50 percent for them as repeat offenders, but they also would get roughly $40 million in savings via the to-be-implemented market disqualification revenue sharing program. However, only teams under the luxury-tax threshold get reimbursed in this program, which is designed to prevent big markets such as Toronto and Washington from receiving revenue sharing dollars, which in turn will lower how much teams such as the Yanks pay (as long as they are under the threshold).
And even if they just went under $189 million for 2014 before going over again in 2015, the Yankees would receive serious benefits. They would get about $10 million in the revenue sharing disqualification program. Also, by simply going under the threshold once, the Yankees would go back to having a 17.5 percent tax rather than the 50 percent that begins in 2014 for them if they never go under. Keep in mind that since the luxury tax went to 40 percent for them in 2005, the Yankees have averaged paying $25.75 million in tax annually.
In the first three scenarios, we use a $210M payroll in 2013, and then assume that they go back to $210M in later years. In the second three scenarios, we use a $220M payroll. In each scenario, we provide savings figures per year. At the bottom of each scenario we provide a total amount saved, and also provide what we’re calling “CBA Savings”. This figure emanates directly from the new CBA, and would include revenue sharing refunds, and luxury tax savings resulting from a new, lowered rate. It would not include the $21M they’d save from going from a $210M payroll to a $189M payroll, for instance. We get down to business after the jump.
Read More→
Behind Derek Jeter’s unexpected second-half resurgence
Posted by: | Comments
(Bill Kostroun/AP)
I’ve given Derek Jeter a considerably hard time since his offensive game started to fall off a cliff back in May 2010, and so it seemed appropriate to reverse course and give Derek proper due for the remarkable turnaround that saw him hit .327/.383/.428 in the second half of 2011 after a .270/.330/.353 first half and a .270/.340/.370 2010 season. Additionally, while many have acknowledged Derek’s resurgence, few (if any) have taken a look into the why, and so here’s a deeper dive into how Derek Got His Groove Back (and no, it has nothing to do with gift baskets).
The below chart (as always, click to enlarge) shows Derek’s plate discipline numbers graphed against his wOBA on a month-by-month basis, beginning in April 2010.
There’s obviously quite a bit going on here, and I was actually surprised to find that a lot of this data didn’t correlate the way I was expecting it to. I figured Jeter’s best months would feature low O-Swing% and O-Contact% rates; and yet his best month (August 2011′s .398 wOBA) featured his third-highest O-Contact% (77.6%) out of the 12 months shown here. For a player with a career 62.0% O-Contact%, I really have no idea what to make of that.
Fortunately his four best months of the last 12 — August 2011, April 2010′s .380 wOBA, July 2011′s .352 wOBA and September 2011′s .344 — were each among his top four Z-Swing% rates (though not in that exact order), lending some sense of order to the proceedings. Although only two of those four months — again, April 2010 and August 2011 — were among the top four Z-Contact% rates.
The other data type that correlated with Derek’s top monthly wOBAs was Swing%, as his three highest Swing% months were also his three best wOBA months. So based on this data it seems like Derek is at his best the more frequently he swings, which is also driven home by the below table:
| O-Swing% | Z-Swing% | Swing% | O-Contact% | Z-Contact% | Contact% | Zone% | Sw-Strk% | |
| 2010 | 28.2% | 67.2% | 47.1% | 69.2% | 92.9% | 85.5% | 48.3% | 6.7% |
| 1H 2011 | 26.9% | 66.8% | 45.8% | 73.1% | 91.6% | 85.9% | 47.3% | 6.4% |
| 2H 2011 | 29.9% | 71.7% | 48.9% | 71.5% | 90.6% | 84.3% | 45.5% | 7.5% |
Although that probably isn’t terribly surprising news to anyone who’s watched Derek with any frequency of late. While Derek’s never been a notorious hacker (his career 8.9% BB% is certainly respectable) he has seemed less inclined to take ball four as he’s gotten older, and indeed, he’s only exceeded the league average BB% once in the last five seasons (though he did match it last year). This past season his walk rate was 7.6% against a league average of 8.1% — a five-year league-average low.
Of course, plate discipline only tells part of the story; we also need to see what Derek did with the balls he put into play.
Now this chart makes a little more sense. Derek’s worst month — April 2011 — also featured his highest GB% of the 12 months surveyed here, a ridiculous 72.3%. On the flip side, Derek’s best month, August 2011, saw the fewest ground balls (55.8%). His best LD% months were, unsurprisingly, August ’11 (31.6%) and September 2011 (26.2%). He’s only exceeded 20% line drives in a full season once in the last five seasons, so Derek really turned back the clock this past summer.
I also thought it’d be interesting to see how pitchers attacked Derek over the last two seasons. Instead of drilling down on each individual pitch type, I decided to borrow Mike’s binning of Fastballs (FB%=FF, FT, SI, FC, FA and FS), Breaking Balls (BrB%=SL, CU, KN) and Changeups (CH%).
First-half Derek saw a slight decrease in fastballs from 2010, an uptick in breaking balls and a very small decrease in changeups. However, pitchers on the whole seemed to start challenging Derek with more heat in the second-half, which is probably at least partially responsible for his offensive resurgence, as Derek’s been an above-average fastball hitter for all of the years in which we have data for.
Pitchers did continue to exploit his difficulty with the offspeed pitch, and in fact, 2011 was the worst season of Derek’s career in terms of pitch type linear weights for the changeup. Opposing teams undoubtedly know that you can beat Derek with the change, and I wouldn’t be surprised to see that CH% rise even higher next season.
Lastly, I wanted to take a look at where Derek was hitting the ball. Here’s Derek’s first-half 2011 spray chart:
We all know Derek’s made his living going the other way, but Derek rarely pulled anything with power in the first half, hitting 11 balls to left field (though seven went for hits).
Here’s the second-half spray chart:
That’s a nice-looking spray chart. By my count Derek hit 22 balls to left field in the second half, and 21(!) of them went for hits. I’m not saying Derek needs to become a pull hitter or anything crazy like that, but it’s rather remarkable how much different the results were after he started using the entire field.
The one angle I was curious about but didn’t have the tools to dig too deeply into was whether the Yankees faced a disproportionate amount of lefthanded pitching in the second half, though unfortunately none of the usual suspects have the capability of showing platoon splits by half. However, the Yankees only faced (by my count) 21 lefthanded starters out of their 74 second-half games, so even if Derek did presumably continue to feast on southpaws, his numbers were likely also very good against righthanders in the second half as well, a subset whom he has really struggled against (81 wRC+ on the whole in 2011, and 71 wRC+ in 2010).
To summarize, it would appear that the keys to Derek’s second-half resurgence were, in part, as follows: swinging a lot more frequently than he had been doing (and more frequently than league average, but slightly less than league average on pitches out of the zone), hitting the ball in the air, getting a lot of fastballs and pulling the ball to left field. Of course, this begs the question whether any of this is sustainable for the 2012 season (and beyond, if we’re extremely lucky), or if Derek will regress back to being the groundout-to-the-shortstop-on-the-first-pitch machine that frustrated the heck out of Yankee fans for roughly a year-and-a-half’s worth of plate appearances.
Can A-Rod return to the .500 SLG plateau?
Posted by: | Comments
(Christopher Pasatieri/Getty Images)
On the heels of my A-Rod OBP post from several weeks ago, commenter Andy asked whether we can expect Alex to get back over the .500 SLG threshold. While the safe answer is “probably not,” what with Alex turning 37 and all next year, I was curious to see what a breakdown of Alex’s 2011 round-trippers might portend for the future.
As you know, Alex Rodriguez hit a career-low 16 home runs across 428 plate appearances in his injury-riddled 2011 campaign, or a pace of 26.75 PA/HR. However, this pace wasn’t impacted by his second half — up until he hit the DL in early July he’d hit 13 home runs in 344 PAs, which is a 26.46 PA/HR pace. As a point of comparison, for his career he’s a 16.91 AB/HR hitter.
Aside from injury speculation, part of A-Rod’s power outage is likely due somewhat to his recent struggles with left-handed pitching, as he only hit two home runs off LHP all season. However, a more interesting picture begins to emerge when looking at B-Ref’s Play Index breakouts of Alex’s home runs. In 2009, eight of his 30 home runs came while behind in the count, nine while the count was even and the remaining 13 while ahead. In 2010, seven of his 30 home runs came while behind, six while even and 17 when ahead. And in 2011, he hit zero home runs when behind in the count, five when even and 11 when ahead.
Now, clearly hitters fare better when ahead in the count and are subsequently more likely to hit home runs, but based on this data Alex was obviously not a threat to go yard in 2011 once the pitcher got ahead. This is further underscored by the following graph detailing Alex’s last three years of tOPS+ and sOPS+ when ahead, even and behind in the count (click to enlarge):
Not only was Alex not a threat to go yard when behind in the count in 2011, he wasn’t a threat to do much of anything, performing 83% worse than usual in those situations, and 12% worse than league average.
So what were pitchers giving Alex after they got ahead of him?
Versus right-handers, Alex could expect to see a fastball the majority of the time when behind in the count; however, once lefties got two strikes there was a strong chance Alex was going to see a curveball or a slider, two pitches he was largely ineffective (0.22 wCB/C and 0.06 wSL/C) against.
Moving on to pitch location, if we look at his home runs versus swinging strikes, it appears that Alex chased an increased number of pitches low and away in 2011 compared with 2010, which would seem to make sense given that Alex does most of his home run damage middle-in.
As far as pitch type goes, Alex’s home run breakout was as follows:
The two home runs off lefties came on a changeup (hooray!) and cutter (double hooray!), two pitches he’s had some difficulties with. The other changeup homer came off James Shields, which is just awesome considering how much Shields — not to mention changeups in general — kills the Yankees.
So what does all this mean for Alex’s chances of increasing his home run tally in 2012, and hopefully getting that SLG back above .500? For one, it’s pretty clear he’s going to need to be more aggressive when falling behind in the count. However, he’ll also have to improve his ability to stay away from breaking pitches with two strikes in the count, as they’re likely to finish out of the zone.
Now, the same could be said for every single player in Major League Baseball, but as illustrated above this was a pretty big weakness for Alex in 2011, and enhanced pitch recognition should help him battle back more frequently when he gets behind and ideally get a better pitch to drive. This also ties in to getting his plate discipline numbers back in line with his career averages. If Alex can regain the superb selectivity he featured for much of April 2011 combined with a revamped approach after falling behind in the count as well as against left-handers, he should return to being the middle-of-the-order force we know and love, and the SLG will follow suit.
Contracts For Relievers: Paying For Consistency
Posted by: | CommentsVery few things in baseball receive quite as much derision as large contracts given to relievers. Relievers have come to be seen as fungible, volatile assets who are poor investments. Many view the contracts given to established closers as being entirely based on saves, a stat that is rightfully maligned and makes a poor basis for a multi-year multi-million dollar contract. However, the logic underlying these complaints has holes large enough to push Phil Hughes through, and a closer look suggests that the truly large reliever contracts may actually make a modicum of sense.
My theory is that general managers who hand relievers big money have not been looking for saves per se. Rather, they have been looking for pitchers who have provided consistent performance on a regular basis. To test this hypothesis, I decided to take a look at the largest contracts given to relievers since 2000, as well as the most consistent performers over the same time period. For the contracts, I limited my search to 3+ year contracts worth at least $7 million per season. 3+ year deals tend to reflect a level of trust by the club in the player, and $7 million struck me as a reasonable cutoff between the deals handed to top players and to those a level down on the talent chain. For measuring performance, I used a simple ERA+ and IP combination to try and isolate the most consistent performers (a search for relievers who have racked up 35+ saves on a yearly basis unearthed a similar list. Players who provide that many saves regularly tend to have strong underlying numbers, so saves can serve as a proxy for performance when addressing a multi-year sample).
Here’s the list of pitchers who had at least 3 seasons with an ERA+ of 150 or better and at least 65 innings pitched:
1 Joe Nathan
2 Billy Wagner
3 Mariano Rivera
4 Francisco Rodriguez
5 Keith Foulke
6 Mike Adams
7 Joakim Soria
8 Carlos Marmol
9 Jonathan Papelbon
10 Jonathan Broxton
11 B.J. Ryan
12 Juan Rincon
13 Brad Lidge
14 Francisco Cordero
15 LaTroy Hawkins
16 Luis Ayala
17 Eric Gagne
18 Jason Isringhausen
19 Octavio Dotel
20 Armando Benitez
It is important to note that when the search was expanded to players with at least 2 seasons of this sort of performance, an obvious drop in quality could be perceived. To my eye, 3 seasons turned out to be a very good parameter by which to evaluate consistent success. Looking at the list, Adams, Soria, and Marmol have not yet reached free agency, while Broxton, Dotel, Rincon, Gagne and Ayala all suffered injuries that hurt their performance and value before they could cash in. That leaves us with 12 pitchers relevant to our purposes.
Here is the list of relievers who have received large contracts, meaning deals for 3 or more seasons at an AAV of at least 7 million dollars (this is the list I was able to construct. It may not be complete. Please correct me if possible):
Jonathan Papelbon
Mariano Rivera
Rafael Soriano
Francisco Rodriguez
Francsico Cordero
Joe Nathan
Heath Bell
Brad Lidge
Billy Wagner
BJ Ryan
Armando Benitez
Jason Isringhausen
Soriano and Bell are the only players on the “got paid” list not on the “consistently performed” list, and Bell has two seasons of requisite performance and a third that falls just short (146 ERA+). Soriano is the only real outlier here, as he has never had a season meeting the performance criteria yet was paid like the more consistent elite performers. Conversely, Foulke and Hawkins are the only two of the 12 relevant players from the “consistently performed” list who failed to make the “got paid” list, and Foulke missed it by .25 million (3 years, 20.75 million).
Basically, when looking at the two lists, we find that the pitchers who have performed at a high level on a regular basis are the ones who are getting the big money. Now, correlation is not causation, but it does seem reasonable to say that large contracts for relievers have been largely reserved for pitchers with established levels of consistency and performance. Now, the next question to ask is whether it makes sense to be giving those pitchers large contracts. The obvious retort to this is that:
1) relievers are a volatile commodity, and
2) past performance does not guarantee future results, and
3) relievers are fungible and good relief can be acquired cheaply.
As for #1, Stephen Rhoads addressed this very issue in this space a few weeks ago:
In any walk of life, one quick way to open yourself up to embarrassment is to assume that those around you are either unable or unwilling to comprehend the complexities of your worldview, to borrow a turn of phrase from Confederacy of Dunces. I’d wager that most General Managers have a pretty good idea that relievers are volatile creatures, and that they are also aware of the failure of these relievers to live up to the contracts given to them. So, avoiding the arrogance that would suggest that they’re just irrational actors, what would drive a GM to pay a premium for a reliever? It boils down to predictability.
Paradoxically, the volatile nature of relief pitchers drives GMs to pay big money for relievers whom they don’t believe will be volatile. Thus, relievers with a long track record of health and consistently superb performance are the most likely candidates to get big money.
Essentially, reliever volatility actually makes handing big contracts to those relievers who have proven to be more of a sure thing a logical decision. As for #2 and #3, they can both be answered by the same point. While it is easy to look back at the end of a season and find relievers who provided great results for few dollars, it is much more difficult to identify those pitchers ahead of time. For every Joaquin Benoit there are 10 Buddy Carlyles and Lance Pendeltons, pitchers who are blanks in the game of reliever roulette. Additionally, while some of these large contracts have flopped, that is a risk that comes with any free agent contract. In the right context, it makes sense for clubs to take that risk rather than cross their fingers and hope to stumble upon the right reliever. Although past performance does not guarantee future results, it does make good results significantly more likely and predictable.
Relievers being fungible and volatile does not mean that their talent changes yearly. It means that in a small sample, you can often get statistical anomalies in both directions. Since relieving is by nature a small sample, there is more volatility and more risk. But if you have identified relievers who you think are more talented and more consistent, you lower that risk of volatility. There is value in that certainty, such that it makes sense to pay those relievers more than a pure talent to dollars evaluation might suggest. This added level of predictability is why general managers have been paying a premium for top relievers on the free agent market.
Breaking down the payroll, part two
Posted by: | CommentsIt’s been a little over a month since we last broke down the Yankees’ payroll, but a lot has changed since then. Robinson Cano and Nick Swisher had their clubs options officially picked up, Andrew Brackman was cut loose, Rafael Soriano did not opt-out of his deal, and CC Sabathia signed a new contract extension. Let’s take stock of who the team currently has under contract for next season…
- Guaranteed Contracts (eleven players, $172.875M): Alex Rodriguez ($30M), Mark Teixeira ($23.125M), Sabathia ($23M), A.J. Burnett ($16.5M), Derek Jeter ($16M), Mariano Rivera ($15M), Cano ($14M), Soriano ($11M), Swisher ($10.25M), Curtis Granderson ($10M), Pedro Feliciano ($4M)
- Arbitration-Eligible (six players): Joba Chamberlain, Brett Gardner, Phil Hughes, Boone Logan, Russell Martin, David Robertson
- Option Buy-Outs (one player, $0.250M): Damaso Marte ($0.250M)
Freddy Garcia‘s new one-year contract is not yet official, but all reports indicate that it will have a $4M base salary plus incentives. That brings us up to a dozen players and a total payout of $177.125M. Using MLBTR’s projections, the Yankees will have another $17.9M tied up in their six arbitration-eligible players. Chris Dickerson just missed the Super Two cutoff, so he’s not yet eligible for arbitration. That’s $195.025M for 18 players.
There are currently 22 pre-arbitration players on the 40-man roster, and the new CBA raised the minimum salary to $480k. If we estimate those 22 guys at half-a-mil each, it’s another $11M on the payroll, bringing us to $206.025M for 40 players. It doesn’t work like that though, not all 22 of those guys will be in the big leagues this year. Cory Wade, Ivan Nova, Jesus Montero, and Eduardo Nunez seem to be the only guys with a realistic chance of sticking all year. The other 18 pre-arbitration guys will spend the majority of the year in the minors and earn minor league salaries.
Adding Wade, Nova, Montero, and Nunez to the 18 players above gives us a payroll of $197.025M with three spots on the 25-man active roster left open. Preferably, one of those spots will go to Andruw Jones, another to a starting pitcher, and the last to someone filling the Eric Chavez role (backup corner infielder, lefty bat off the bench). The Yankees are all but guaranteed to go over the $200M mark next season, even if they just re-sign Andruw and fill the last two spots with Hector Noesi and Brandon Laird.
If the Yankees are planning to stick to that $200M limit they’ve talked about in recent years, then they won’t be making any major signings this winter without shipping some salary out. They could save a few bucks if the arbitration salaries are lower than projected, but it’s unlikely to be enough to land a big name pitcher. The Yankees are either going to have to start next season with a higher payroll than what they’ve indicated they’d like it to be, or they’re going to have to get creative to make major upgrades this winter.
Inside the best-pitched game of the Yankees’ 2011 season
Posted by: | Comments
(Nick Laham/Getty Images)
For my money, there are few things more thrilling in modern-day baseball than a complete-game shutout. A large part of my thirst for the complete game is that unless you’re Roy Halladay or Cliff Lee, it’s a feat that’s grown rarer as baseball marches on. Last season there were 75 complete-game shutouts, or 2.5 per team, although four teams didn’t record a single one — Cleveland, Houston, Kansas City, and somewhat surprisingly, San Diego.
That 2011 tally of 75 may have been up from 2010′s 59 and 2009′s 63, but even though CGSHOs seem to be coming somewhat back into vogue, it hasn’t necessarily been that way for the Yankees.
The Yankees technically authored three complete-game shutouts in 2011, although only two were of the nine-inning variety. Phil Hughes was credited for a complete-game shutout for his rain-shortened six-inning win against the White Sox on August 2nd, but that really doesn’t count.
Truly, keeping an opposing team off the board for nine full innings is a pretty herculean task. When Bartolo Colon did it on Memorial Day back at the end of May, I was exceptionally pumped, as it was the first Yankee complete-game shutout since Sabathia authored one against the Orioles on May 8, 2009, not to mention the fact that if you’d told me Colon would pitch a CGSHO at any point in the 2011 season I would’ve thought you were crazier than the National League for making pitchers hit. It was also only the third recorded by a Yankee since 2006, and if you go back over the last 10 seasons, Yankee pitchers have only recorded 17 complete-game shutouts. Admittedly the Yankees’ potential shutout tally is inherently limited by the presence of the Greatest Closer of All Time, but that only adds to the scarcity and makes the accomplishment that much more impressive in my eyes.
As great as Bartolo’s game was, if you sort by Game Score, CC Sabathia threw an even more dominating start a month-and-a-half later, which, at 87, was the top Game Score by a Yankee pitcher of the 2011 season. At the time, it represented the second-highest WPA for a starting pitcher in all of MLB after Francisco Liriano’s no-hitter. Sabathia’s CGSHO wound up finishing third overall come season’s end.
| Rk | Player | Date | Tm | Opp | Rslt | App,Dec | IP | H | R | ER | BB | SO | HR | Pit | Str | GSc ? | WPA |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | CC Sabathia | 2011-07-10 | NYY | TBR | W 1-0 | SHO9 ,W | 9.0 | 4 | 0 | 0 | 1 | 9 | 0 | 113 | 79 | 87 | 0.761 |
| 2 | Bartolo Colon | 2011-05-30 | NYY | OAK | W 5-0 | SHO9 ,W | 9.0 | 4 | 0 | 0 | 0 | 6 | 0 | 103 | 71 | 85 | 0.365 |
| 3 | CC Sabathia | 2011-07-26 | NYY | SEA | W 4-1 | GS-7 ,W | 7.0 | 1 | 1 | 1 | 3 | 14 | 0 | 102 | 71 | 82 | 0.167 |
| 4 | Bartolo Colon | 2011-05-18 | NYY | BAL | W 4-1 | GS-8 | 8.0 | 3 | 0 | 0 | 1 | 7 | 0 | 87 | 61 | 82 | 0.629 |
| 5 | CC Sabathia | 2011-06-30 | NYY | MIL | W 5-0 | GS-8 ,W | 7.2 | 6 | 0 | 0 | 2 | 13 | 0 | 118 | 77 | 78 | 0.317 |
| 6 | CC Sabathia | 2011-04-05 | NYY | MIN | L 4-5 | GS-7 | 7.0 | 2 | 0 | 0 | 1 | 6 | 0 | 104 | 67 | 78 | 0.278 |
| 7 | CC Sabathia | 2011-05-19 | NYY | BAL | W 13-2 | GS-8 ,W | 8.0 | 7 | 0 | 0 | 0 | 9 | 0 | 109 | 84 | 77 | 0.097 |
| 8 | CC Sabathia | 2011-07-16 | NYY | TOR | W 4-1 | GS-8 ,W | 8.0 | 3 | 1 | 1 | 3 | 8 | 0 | 110 | 74 | 77 | 0.339 |
| 9 | Ivan Nova | 2011-06-20 | NYY | CIN | W 5-3 | GS-8 ,W | 8.0 | 4 | 1 | 1 | 0 | 7 | 0 | 105 | 70 | 77 | 0.289 |
| 10 | CC Sabathia | 2011-07-05 | NYY | CLE | W 9-2 | GS-7 ,W | 7.0 | 5 | 0 | 0 | 2 | 11 | 0 | 100 | 69 | 76 | 0.162 |
Given Sabathia’s dominance of the Rays on July 10th, I wanted to see how exactly he attacked them that afternoon. The following is a breakdown of Sabathia’s complete-game shutout compared with his insane eight-start run from June 25nd through August 1st (62.2 innings, 78(!) strikeouts, 16 walks, .503 OPSa, 1.01 ERA), his entire season, and the league average numbers for left-handed pitchers:
Sabathia’s four-seamer was something else on July 10th, averaging 95mph, going for a strike over three-fourths of the time, coaxing a swing well over 50% of the time, and generating a well-above average percentage of whiffs. Interestingly, he increased his deployment of the slider both during the July 10th game and throughout his eight-start run, compared with how frequently he used it on the season.
I say interesting because CC appeared to be getting into some trouble later in the season due to increased slider usage, although looking at the data in this chart compared to the August data in that link we see that the slider was breaking slightly less during his rough August stint (-0.43 inches of V-break compared to -0.73 during the dominant run) and was also roughly one mph slower. Those are both such minimal changes that I don’t feel comfortable drawing any conclusions about the slider one way or another, although given how important it is to CC’s repertoire it’s possible something even as minor as 0.30 less inches of average vertical break at one mile per hour slower would have a deleterious effect.
But I digress. The other interesting thing that sticks out to me on the above chart is that CC got zero swings-and-misses on on his sinker during the eight-start beast run, despite throwing it 12% of the time. Like any good sinkerballer, it’s obviously more of a pitch-to-contact pitch for him, but I hadn’t really realized that about his sinker until I looked at the numbers.
In any event, I’ll eagerly await the next CC Sabathia shutout complete-game shutout, not to mention a few more insane 1.00-ERA runs he’d like to string together.
Looking At The Yankees’ Sac Bunts
Posted by: | Comments
(AP Photo/LM Otero)
Baseball is a game without an official clock. In its stead, the 27 outs each team receives serve as the timekeeper, pushing each game to an inevitable conclusion. Avoiding those outs has become the name of the game over the last ten years, and one of the strategic moves that has come under fire due to this philosophy is the bunt. The sacrifice bunt draws a team one out closer to the end of the game without greatly increasing the chances of a run scoring. A look at run expectancy tables, which tell us how many runs are expected given a particular situation, confirms that bunting usually decreases the number of runs expected to score. While there are a few situations where a bunt is actually the statistically prudent move, on balance it is seen as the misused weapon of weaker, backwards-thinking managers, and is the hobgoblin of sabermetricians everywhere.
All that said, there is at least one study that suggests that managers tend to outperform run expectancy tables when it comes to bunting. This means that on average, managers have a reasonably good sense of the moment and of context, and they bunt in situations where it will produce more runs than one might expect given the post-bunt base/out status. While the numbers still suggest that these bunts decrease run expectancy, it is illuminating and encouraging to see that managers are utilizing the bunt reasonably efficiently.
All of this brings us to the manager of the local nine. One of the most common complaints about Joe Girardi’s managing is that he bunts too frequently, playing for one run with an offense that can put up a crooked number in a hurry. I thought it would be instructive to look at every Yankee sacrifice bunt in 2011 to see how many runs Girardi actually cost his club with his small ball sensibilities. I broke the bunts down by player and then calculated three numbers:
- Expected runs before the bunt. This number tells us how many runs were expected to score given the base/out situation prior to Girardi working his managerial magic.
- Expected runs after the bunt. This tells us how many theoretical runs the bunt “cost” the club.
- Actual runs. This should tell us how Girardi’s move actually worked out.
Now, a few caveats.
- Run expectancy is not perfect. It does not account for the score or the quality of offense or opponent, nor does it account for the skills of the hitter at the plate. However, it is a reasonable estimate of how the game has been impacted by a move, and I’ve broken things down by hitter so you can mentally adjust your evaluation based on the quality of the batter.
- This study does not include the attempted bunts that failed and caused batters to fall behind in the count. However, it also does not include bunt singles or bunts in which the batter reached on a fielder’s choice or error, which help to greatly increase run expectancy (I also excluded Nick Swisher‘s bunt against Boston where he lost track of the number of outs and bunted on his own). The analysis is limited to successful sacrifice bunts. I’ve also removed all bunts by pitchers, as I think most of us can agree that bunting with an American League pitcher is almost always the correct move.
- We cannot calculate what would have happened if Girardi had chosen not to bunt. To provide an example of why this is an issue, imagine an inning where Brett Gardner bunts a runner over and then Curtis Granderson homers. While we can figure out the run expectancy before and after the bunt and can observe actual runs scored, we can’t know what would have happened if Gardner had not bunted. So if one run was expected and two actual runs were scored, there is still the possibility that without the bunt, three runs would have scored (because Gardner could have reached prior to the home run). If we assume that everything would have been different and Granderson may not have homered had Gardner reached, the expected runs v. actual runs analysis is relevant. As such, this study is making the assumption that the bunt changes the entire inning, such that whatever happened afterward is connected to (but not necessarily caused by) the base/out state created by the bunt. Discarding that assumption does not make the conclusions irrelevant, but it does sap them of some of their power.
Keeping all that in mind, let’s take a look at the sac bunts Girardi called for in 2011.
Brett Gardner
# of sac bunts: 8
Expected runs, before the bunts: 7.0173
Expected runs, after the bunts: 5.4602
Actual runs: 11
Loss of run expectancy: 1.5571
Actual impact: Gain of 3.9827 runs over expected runs
(To be fair to Girardi and his predilection for bunting with Gardner, it is important to note that all of Gardner’s bunts but one came in the late innings of a tight game, when playing for one run is acceptable. The lone exception came against Justin Verlander, which represents another understandable, if not entirely defensible, use of the bunt.)
# of sac bunts: 6
Expected runs, before the bunts: 5.5296
Expected runs, after the bunts: 4.4064
Actual runs: 3
Loss of run expectancy: 1.1232
Actual impact: Loss of 2.5296 runs under expected runs
# of sac bunts: 4
Expected runs, before the bunts: 4.1974
Expected runs, after the bunts: 3.4954
Actual runs: 2
Loss of run expectancy: 0.702
Actual impact: Loss of 2.1974 runs under expected runs
Curtis Granderson
# of sac bunts: 3
Expected runs, before the bunts: 3.7157
Expected runs, after the bunts: 3.2358
Actual runs: 6
Loss of run expectancy: .4799
Actual impact: Gain of 2.2843 runs over expected runs
# of sac bunts: 2
Expected runs, before the bunts: 1.701
Expected runs, after the bunts: 1.3028
Actual runs: 1
Loss of run expectancy: 0.3982
Actual impact: Loss of 0.701 runs under expected runs
One each for Russell Martin, Frankie Cervelli, Chris Dickerson, and Brandon Laird
# of sac bunts: 4
Expected runs, before the bunts: 3.402
Expected runs, after the bunts: 2.6056
Actual runs: 1
Loss of run expectancy: 0.7964
Actual impact: Loss of 2.402 runs under expected runs
Conclusion
# of sac bunts: 27
Expected runs, before the bunts: 25.563
Expected runs, after the bunts: 20.5062
Actual runs: 24
Loss of run expectancy: 5.0568
Actual impact: Loss of 1.563 runs under expected runs
Regarding that actual impact number, I am uncomfortable concluding that the bunts were always directly responsible for what happened after them. For example, I do not think Granderson’s lone “successful” bunt actually caused all 6 runs that subsequently scored in the inning. That said, I think it is fair to conclude that Girardi’s proclivity for bunting did not hurt the Yankees much in 2011. In terms of run expectancy, all of the bunts over the course of the season only cost the Yankees five runs, and that ignores the fact that many of them came in situations where playing for one run at the expense of a big inning is actually the right thing to do. Furthermore, the team outperformed the “runs expected after the bunts,” suggesting that Girardi may have utilized the strategy in optimal situations. Taking into account the fact that the actual runs scored was about the same as the number of runs expected, it seems clear that Joe Girardi’s bunting problem was not much of an detriment to the Yankees in 2011.
Update (12:28 p.m.): I am new to play index, but I just figured out how to get bunt singles and bunt outs listed properly(still no foul bunts, however). Here are the results for the 18 sac bunt attempts that ended without a sac bunt:
10 runners reached base
8 made force outs or popouts
On the outs:
RE before the bunts: 7.5994
RE after the bunts: 4.431
Actual: 7
Loss of RE: 3.1684
Actual impact: 0.5994
On the hits:
RE before the bunts: 9.9545
RE after the bunts: 16.1122
Actual: 17
Loss of RE: Gain of 6.1577
Actual impact: Gain of 7.0455
New total:
RE before the bunts: 43.1169
RE after the bunts: 41.0494
Actual: 48
Loss of RE: Loss of 2.0675 runs
Actual impact: Gain of 4.8831
Why we can expect a better OBP from Alex Rodriguez, the sequel
Posted by: | Comments
(Photo by Gregory Shamus/Getty Images)
Last offseason, on the heels of Alex Rodriguez posting a career-low .341 OBP over 595 PAs (an OBP only .016 points above league average), I posited that Alex was a strong bet for an improvement on that mark for the 2011 season, and indeed, Alex turned in a .362 OBP over significantly fewer PAs (428). While that mark still falls well short of his career .386 OBP, it wound up being the third-best OBP on the 2011 Yankees, and was well above the league average of .321.
For the second straight winter, I think Yankee fans can realistically expect an improved on-base percentage from Alex next season. Now the obvious reason for optimism is the fact that Alex basically only played half a season in 2011. During his healthy first half, he was hitting .299/.377/.507 through the end of June. The seven games he played in in July before hitting the shelf for knee surgery didn’t do anything to help his cause, and his OBP fell to .366 as he went on the DL (with 33 walks and four HBPs to his name through 80 team games). Alex didn’t really do much of anything in the 19 games he played over the remainder of the season — though he still managed to get on base — putting up a .191/.345/.353 line (15 walks, 1 HBP) over his final 84 PAs.
I don’t think it’s a stretch to think that a healthy Alex would have managed to come close to doubling his first half walk total, finishing the year at around 65 walks, which is what he did in 2008, a season he OBP’d .392 in 594 PAs. Now, this hypothetical healthy 2011 Alex still might not have finished with an OBP quite that high, but he was also hitting .295 at the time of his injury with 90 hits. For comparison’s sake, he hit .302 in 2008 and had 154 hits. Without going too crazy with extrapolations, it doesn’t seem terribly unrealistic to expect a .290-ish-hitting A-Rod to post an OBP somewhere in the high .370s.
Of course, that’s all a bit too intangible, so I’ll expand on the idea some by looking at Alex’s plate discipline data. I gathered PD data for Alex going back to 2009 from both Baseball Prospectus and FanGraphs, as it seems the general consensus has been that BP’s newly introduced data is superior to that of FanGraphs’ BIS-provided percentages and I was curious to see just how much the two data sets differentiated. For the most part, in the small sample that I culled, it appeared that the differences in the data sets were mostly on the order of 100 to 500 basis points — which sounds like a lot, except 100 basis points = 1% — with BP’s numbers generally coming in slightly lower. The major difference between the two sets is in the Swinging Strike%, as it appears that BP’s percentage also includes foul balls.
Anyway, I point all this out to show that yes, there are tangible differences, and eventually BP’s will probably be the more reliable go-to, but I’m going to go with FanGraphs for this analysis because the BP data isn’t backfilled/built-out enough yet, as it doesn’t yet allow you to slice and dice by month or compare against career numbers.
Anyway, here are A-Rod’s plate discipline numbers (per FanGraphs) from the last three seasons:
| O-Sw% | Z-Sw% | Sw% | O-Con% | Z-Con% | Con% | Zone% | Sw-Strk% | |
| 2009 | 21.1% | 67.4% | 42.6% | 58.8% | 84.9% | 78.0% | 46.6% | 9.1% |
| 2010 | 25.3% | 68.5% | 45.0% | 65.6% | 86.1% | 79.8% | 45.6% | 8.9% |
| 2011 | 27.0% | 66.1% | 44.0% | 61.7% | 83.7% | 76.0% | 43.4% | 10.3% |
| Car. | 21.4% | 67.9% | 44.0% | 53.0% | 83.2% | 75.7% | 48.7% | 10.5% |
It won’t surprise anyone to see that Alex’s lowest O-Swing% and O-Contact% of the last three years was in 2009, his last .400-plus wOBA campaign. Somewhat foreboding is Alex’s 27% O-Swing% in 2011 — up from 2010′s 25.3% and considerably higher than his 21.4% career mark — though his O-Contact% was down from 2010′s 65.6%, which was his highest percentage since the data started being collected in 2002. Still, the 61.7% O-Contact% was also a good deal higher than his career mark, and Alex swinging at more bad pitches and making more contact with them is probably not a recipe for OBP success.
However, the 2011 data set is a bit skewed by the fact that Alex only had 31 PAs in July, 19 in August and 65 in September.
Here’s his 2011 monthly breakdown:
| O-Sw% | Z-Sw% | Sw% | O-Con% | Z-Con% | Con% | Zone% | Sw-Strk% | |
| April | 20.1% | 64.0% | 38.7% | 60.0% | 86.2% | 78.4% | 42.5% | 8.4% |
| May | 32.9% | 63.9% | 47.2% | 66.3% | 87.7% | 79.6% | 46.2% | 9.3% |
| June | 26.0% | 65.0% | 43.7% | 58.8% | 83.0% | 75.2% | 45.4% | 10.5% |
| July | 40.4% | 72.2% | 56.6% | 61.9% | 74.4% | 70.0% | 50.9% | 16.8% |
| August | 23.9% | 80.7% | 46.8% | 54.6% | 92.0% | 80.6% | 40.3% | 9.1% |
| Sept. | 24.4% | 67.7% | 39.6% | 59.5% | 74.6% | 68.6% | 35.1% | 12.2% |
| Car. | 21.4% | 67.9% | 44.0% | 53.0% | 83.2% | 75.7% | 48.7% | 10.5% |
Alex’s two best months of the season were April (.422 wOBA; 16.3% BB%) and June (.423 wOBA; 11.9% BB%). May was his only fully healthy month of really poor (.328 wOBA; 4.8% BB%) play, although his May line was dragged down by one of the worst four-week stretches of his career, which I spent quite a bit of time documenting earlier this season. April was Alex’s most selective month of the season (a mere 20.1% O-Swing%), which makes it no surprise it was also his best month. His May O-Swing% of 32.9% along with a 66.3% O-Contact% underscore just how out-of-whack he was that month.
In June, his PD numbers were pretty much where you’d expect them to be given his outstanding month, as he basically matched his career averages in every category except — somewhat unexpectedly — O-Swing% and O-Contact%, though the latter was his lowest percentage of the full months he played in 2011.
I would expect a healthy Alex to be swinging more in line with his April and June 2011 rates, and in turn, better the .362 OBP he turned in on the season. The ever-optimistic Bill James agrees, and has Alex hitting .277/.373/.497 next season. That’s probably a bit aggressive, as much of that OBP is fueled by a projected 70 walks and 12.1% BB% — numbers he’s only eclipsed once in the last four seasons (in 2009) — although I’m also not sure I’d bet against a highly motivated Alex Rodriguez. He may be turning 37 next year, but a healthy year should go a long way in silencing some of the critics that wanted to blame the team’s playoff downfall on a far-from-100% A-Rod.
Locking Up Russell Martin
Posted by: | Comments
(AP Photo/Charlie Neibergall)
Over the last few months, the sabermetric community has made a number of advances in the area of catcher defense. Studies by Max Marchi and Mike Fast on pitch framing and a study from Bojan Koprivica on pitch blocking have begun the process of quantifying the more difficult to measure elements of a backstop’s defense. While these studies are still in their infancy and are likely to be tweaked and altered in the coming months and years, they do provide us with one reasonable concrete lesson: Good defense from a catcher is likely more important than we had previously thought when trying to measure catcher value.
In the past, catchers tended to be put into one of two groups: good defender or weak defender. Sure, you had one or two Gold Glovers at the top and a handful of guys who were execrable enough to be known as terrible at the bottom, but the vast majority of catchers were placed into those two groups. Without any way to truly quantify defense, these broad categories had to suffice, and this resulted in most people evaluating catchers based on their offense. Catcher defense was thrown in at the end of conversations as an aside, possibly with caught stealing numbers and some passed ball data, but little tangible data that would shift an evaluation in either direction. Only those known as excellent catchers would get any sort of boost from their perceived defensive value.
Now, with these new studies, we can begin to quantify catcher defense, and use that to reevaluate the worth of a catcher who performs well behind the dish. As I noted above, one lesson that can be taken from these studies is that defense behind the dish is quite important. Let’s use Russell Martin as an illustration.
While I am far from the biggest proponent of WAR, these new metrics are expressed in terms of runs saved, making WAR a convenient way to weigh the impact of Martin’s defense. Before considering defense, Russell Martin was worth 3.1 wins last season (FanGraphs). However, once you add 1.5 runs saved by controlling the running game, 0.1 runs saved blocking pitches, and 15 runs saved by being among the best at framing pitches (Fast’s research consistently places Martin near the top of the league in this area), you suddenly have an incredibly valuable 4.6 win player. While the first instinct of many is to flinch at the idea that the “unmeasurable” aspects of catcher defense can add that much value, it is important to note that the very best defenders gained at most two wins due to their gloves. That is not much different than the value added defensively by the best at other positions, and catchers are involved on almost every pitch.
The suggestion here is not that Russell Martin is a 4-5 win player, but that he is a very good defender and that has definite value exceeding what some of the value metrics would suggest. Accepting that hypothesis leads me to my point: If the Yankees do not believe that Jesus Montero is their catcher of the future, it would make sense for them to offer Russell Martin a 2-3 year contract extension, either now or at the end of the 2012 season.
While he certainly showed improvement relative to 2009-2010, Martin had a decent but unspectacular season offensively, such that his value is probably not incredibly high at this point. Although he has a reputation as a solid defender, he is not known as one of the best in the sport, which makes it unlikely that he would get a major salary bump on the open market due to his glove. Essentially, if he was a free agent at this moment, he could market himself as a adequate offensive catcher with a solid glove, which is relatively unsexy and would not bring him a major financial windfall.
Being that the market almost certainly will not value his defense quite as much as it should, the Yankees could have the opportunity to lock Russell up at a reasonable rate relative to his value. They could wait until after the 2012 season to sign him, although they might want to avoid the possibility that his price goes up either because 1) he bounces back to 2006-2008 levels offensively, or 2) teams begin to see him as a great defensive catcher. While the latter seems like a long shot, another season of the Yankees getting good performances out of retread pitchers could shine a light on the work that Martin does behind the plate.
Of course, there are downsides to signing Martin to an extension a year early, such as a major injury or a significant decline with the bat that would turn the contract into an albatross. Couple those risks with the fact that the team rarely hands out extensions, and I would bet on the Yankees waiting until after this season to address Martin’s contract. That said, once he does sign on for a few more years, he should provide enough defensive value to help any contract avoid disaster status. Russell’s glove is undervalued, and unless the Yankees believe they already have their catcher of the future knocking on the door, he would serve as an good option to fill the position for the next few seasons.
(Thanks to @jaydestro for inspiring this post)
Just 6 days 'til pitchers and catchers report.
