Tagged: Jose Abreu

Fairly early 2015 1B rankings

I posted my very early 2015 closer rankings a couple of weeks ago. In continuing with the trend, I present to you my preliminary, but mostly complete, rankings for first basemen. The prices are based on a standard 5×5 rotisserie league with a budget of $260 per team. In this instance, I assume 60 percent of all teams’ budgets are spent on hitters, as is done in mine.

In a later version of this, I will enable the spreadsheet to be dynamic and allow users to input their own budget amounts and percentages spent. In the meantime, here is the static version.

Let me try to be as clear as possible about how I determine prices: I do not discount or add premiums based on positional scarcity or relativity. I like to know exactly what a home run, a steal, a run, etc. is worth, no matter who it comes from. It gives me a better idea of the depth at each position and how urgently I need to overspend at the so-called shallower positions, such as catcher and third base, as y’all will see in future installments of these rankings.

Some thoughts:

  • The statistics, to my eye, are all scaled down slightly (except for maybe home runs). However, this effect happens to every player, so the changes are relative and, thus, the prices are theoretically unaffected.
  • Jose Abreu is the #2 first baseman, and it’s not even that close of a call. I honestly thought Paul Goldschmidt‘s stock would be a bit higher — remember, my computer calls the shots here, not me — but the projections believe more in Goldy’s 2014 power (which paced out to 27 home runs in a full season) than his 2013 power, when he dropped 36 bombs. He’s also no lock to stay healthy. Which no one is, really. Still, I may take the over on all his stats, but not by a large margin.
  • I will, however, take the over on Edwin Encarnacion‘s statistics, as he has bested all the projected numbers each of the past three seasons, and he does it all while battling injuries. I will take him at the price simply because of what I will call “health upside” — everyone assumes he will get hurt, but if he can play a full 162, he’s a monster — and because if his batting average on balls in play (BABIP) ever reaches a normal level, his batting average boost will send his stock through the roof.
  • No surprise to see Anthony Rizzo at #5 after last season. I’m a believer, and he will be surrounded by a slew of talented youngsters next year.
  • Freddie Freeman, hero of my hometown, is simply not where I expected him to be after his 2012 season. Granted, he’s an excellent player, but until he chooses to hit for power rather than spray line drives (again, not a problem in real, actual MLB baseball), and until the Braves stop sucking (which may not be any time soon), he may not be that great of a first base option.
  • The two Chrises — Chris Davis and Chris Carter — round out the top 10 with almost identical profiles. Lots of power, lots of strikeouts, low batting averages. The shift may have suffocated Davis’ batting average, but it shouldn’t happen again, and I am considering investing in him if his stock has devalued enough after last year’s atrocity.
  • Joey Votto, Prince Fielder and Ryan Zimmerman are shells of their former selves.
  • Lucas Duda is for real, but his batting average is a liability, as is a lot of the Mets’ lineup.
  • The projections have what amounts to almost zero faith in Ryan Howard, Joe Mauer and Brandon Belt. Mauer may be the saddest tale of them all. He’s still good for a cheap batting average boost, but single-digit homers? I just feel bad for the guy. And the owner who banks on the rebound.
  • Looking at Adam LaRoche‘s projection, I’m starting to really like that move by the White Sox. Part of me feels like he’s going to be undervalued or maybe even not considered on draft day, and that’s appealing to me.
  • Steve Pearce at #16 is an upside play, given his 2014 looks all sorts of legit.
  • Jon Singleton: the poor man’s Chris Carter.
  • And just because Matt Adams is beating the shift instead of hitting home runs doesn’t render him without value. He’s not my cup of tea, but 19 home runs could be conservative for him.

Predicting HR/FB rates for hitters using weighted pitch values

(If you care only about results and not about the process, scroll down to the section aptly titled HERE YA GO.)

Victor Martinez indirectly and semi-strangely inspired this post. I was browsing FanGraphs’ weighted pitch values for hitters — something I hadn’t done before, as I’ve really only used the metric for pitchers — for 2014 and my thought process went something like that:

Jose Abreu feasted on fastballs; V-Mart feasted on sliders… Wow, V-Mart actually fared better against sliders and curveballs than fastballs and cutters. I wonder if that has any correlation with his plate discipline.

In short: no. A hitter’s success versus pitches according to weighted pitch values (per 100 of that pitch) determines about 40 percent of his walk rate and barely 4 percent of his strikeout rate. (I’m ballparking it on the K% figure.)

But I got to thinking a little more: these weighted pitch values have to be good for something other than scouting hitters (which, moving forward, maybe we starting throwing Abreu some more offspeed stuff? I don’t know).

Alas, I took a crack at it: I tested the correlation between weighted pitch values and home runs per fly ball (HR/FB) rates. And I was very pleasantly surprised.

Let’s start with context. Each player, very obviously, records his own HR/FB rate each year. Players with more power will record higher HR/FB rates, and players with less power will record lower rates. Therefore, each player, in a sense, creates his own benchmark (which, arguably, is his career HR/FB rate: he hits this many home runs as a percentage of fly balls on average). However, we know that HR/FB fluctuates annually: a player with a 15% career-HR/FB does not hit exactly 15 of every 100 fly balls over outfield walls every season like clockwork. Still, there is an expectation that he will hit a certain number of them out — hence, the benchmark.

Using regression analysis, the idea of the benchmark can be captured by seeing how, say, 2014’s HR/FB rates correlate with 2013’s rate, as well as 2012’s, 2011’s and so on. I downloaded all available ball-in-play data for seasons by “qualified” hitter as separate seasons dating back to 2002, thereby representing an exhaustive list. The line of best fit looks as follows, where L1 represents the year prior, L2 two years prior, L3 three years prior:

x(HR/FB) = .018 + .321*L1.(HR/FB) + .252*L2.(HR/FB) + .228*L3.(HR/FB)
Between R-squared: .74

One might astutely observe that a player who hit exactly zero home runs the three previous years can still be expected to hit about 1.8 percent of his fly balls over the wall, and one might call to arms to force the intercept term to zero. It seems absurd, nay, impossible that a player who never hits home runs could be expected to suddenly hit one, but let us not forget we witnessed the impossible happen just last year. That’s what makes baseball a beautiful sport: anything can happen.

Anyway, the equation above is actually really helpful in predicting expected HR/FB; its R-squared indicates the line explains almost three-quarters of the model’s fit. It also bestows the greatest significance to the most recent year as measured by its coefficient, with declining significance associated as years become further removed, which makes sense. But… BUT.

It’s not helpful in predicting HR/FB for hitters who have only been in the league fewer than three years. Moreover, it seems especially difficult to predict future HR/FB rates for hitters with only one year of data, such as the monstrous Abreu. (Maybe Abreu did inspire this post after all.) Observe:

x(HR/FB) = .032 + .694*L1(HR/FB)

After a little bit of algebra, we can intuit that the equilibrium HR/FB rate is roughly 10.4 percent. I use the term “equilibrium” because it appears that no matter what HR/FB a hitter posted in his first career season, his next-year HR/FB will be expected to converge (aka regress) toward the magical number of 10.4 percent. Again, observe:

.032 + .694*(12%) = 11.5%
.032 + .694*(8%) = 8.7%

You can perform this exercise with any value, and the results will be the same: a 2014 HR/FB rate lower than ~10.4 percent will be expected to increase in 2015, and a rate higher than ~10.4 percent will be expected to decrease in 2015. Now this, this, is actually absurd. Granted, the equation is communicating what would happen on average, but hitters are not homogeneous.

This is all a very long-winded way of saying two things:

1) When the sample is incredibly small — namely, one observation — using history as a guide fails us.
2) I think I may have found an alternative that relies not on a single year’s worth of HR/FB data but on a single year’s worth of weighted pitch value data.

HERE YA GO

Let me be clear, up front: I know there will be a lot of multicollinearity inherent in this analysis — that is, HR/FB and weighted pitch values are dependent on each other in some fashion. I don’t know how weighted pitch values are calculated exactly — it would behoove me to look it up, but I am lazy, a current self-descriptor of which I am not proud — but, intuitively, a hitter who hits home runs more frequently off of particular pitch types will likely record higher weighted values for those pitches. Essentially, the weighted values are calculated using home run frequency, and I am now trying to reverse-engineer it.

But I don’t see that as a bad thing. There is a profound correlative capability in the data, and using that information to glean whether or not a hitter was, perhaps, a bit lucky when it came to his HR/FB frequency is, I hope, less preposterous than pulling a number out of your rear-end.

HERE YA GO, FOR REALSIES

I will use strictly weighted pitch values per 100 pitches (denoted wXX/C, where XX represents the pitch abbreviation). I omit knuckleballs because not all players saw them, and I omit splitfingers because they are statistically insignificant, probably because they aren’t thrown very often, rendering the weighted pitch values more volatile. I also add K% and BABIP presuming the following: strikeout rates are positively correlated with HR/FB rates, and BABIP, which positively correlates with hard-hit balls such as line drives, is likely to also positively correlate with similarly-hard-hit balls such as home runs. (A regression that includes only weighted pitch values and excludes K% and BABIP produces an adjusted R-squared of .45.) The line of best fit equation is as follows:

x(HR/FB) = .2049 + .0352*(wFB/C) + .0081*(wSL/C) + .0014*(wCT/C) + .0041*(wCB/C) + .0063*(wCH/C) + .5244*K% — .6706*BABIP
Adjusted R-squared: .75

Again, the model produces a great line of best fit per its R-squared — almost identical to its lagged-variable counterpart. As it should; if there’s multicollinearity, it should. (And there is.) But reverse-engineering the process should create accurate predictions of what should have been a hitter’s HR/FB rate in a given season because of the multicollinearity; in this instance, it’s not a bad thing.

Some trends emerge instantly, trends similar to those I saw in the xK% and xBB% studies I performed earlier: regardless of a player’s power potential, he will over-perform or under-perform his expected HR/FB rate, and he will do so with consistency. For example, Adam LaRoche, despite his apparent power stroke, consistently under-performs his xHR/FB:

HR/FB: actual minus expected
2010: -1.89%
2012: -1.87%
2013: -1.77%
2014: -0.75%

Meanwhile, Albert Pujols consistently out-performs his xHR/FB:

2010: +2.02%
2011: +5.67%
2012: +1.80%
2014: +2.13%

Each data set has its noise, but you can see based on these limited samples where each hitter experienced a bit of luck: LaRoche, in 2014, saw a minor spike, and Pujols saw a major spike in 2011.

Rather than going through each player individually, I will highlight a few extreme, fantasy-relevant outliers from 2014 and reflect accordingly. Without further adieu (and in alphabetical order by first name):

Adam Eaton, -8.03%
This is the largest negative differential in the 2014 data. Without another full season of data to compare, this huge difference is likely a sign of bad luck, although there is a chance that he is a severe under-performer in the same vein as Matt Carpenter (who has under-performed his xHR/FB by about 7 percent the past two years). I already liked the guy for his speed and control of the strike zone, and the prospect of a pending power spike is enticing.

Coco Crisp, -5.78%
Crisp is a great case study: he notched a career-high 12.4-percent HR/FB in 2013, then promptly slid back down to single digits in 2014. His 2014 xHR/FB, however, indicates his HR/FB should have been closer to 11.5 percent, almost 6 percent higher than his actual mark and only 1.2 percent less than 2013. Meanwhile, his 2012 and 2013 expected and actual HR/FB rates are almost identical. His power-speed combination was pretty valuable two years ago — when he wasn’t on the disabled list, at least.

Curtis Granderson, -5.92%
Granderson bottomed out in woeful aplomb last year, but his xHR/FB offers a glimmer of hope. I’ll be honest, though, I can’t remember the last time this guy was fantasy relevant. But if you’re looking for sneaky power at the expense of everything ever, he could be your guy.

Giancarlo Stanton, +5.33%
The Artist Formerly Known as Mike posted positive differentials in 2011 and 2013, but each was one-half and one-third the magnitude of last year’s differential. His 2013 and 2014 xHR/FBs are practically identical — 20.16% and 20.17% — so it looks like Stanton chose a good year to get a little bit lucky.

Jason Heyward, -5.56%
Speaking of bottoming out, Heyward’s power all but evaporated last year. Fear not, however, as his 2014 xHR/FB is only 4 percentage points less than 2013’s — which still sucks, but at least it’s not as bad as a whopping 10 percentage points. It’s probably too obvious to count on a comeback, but no matter.

Jason Kipnis, -4.39%
His year-by-year differentials: -0.01%, -2.61%, -4.39%. His year-by-year xHR/FB: 9.71%, 15.01%, 9.19%. I don’t know what to believe, really, because it’s hard to tell what’s real here and what’s not. But, again, here ye beholdeth another bounceback candidate.

Jonathan Lucroy, -3.77%
His 2014 xHR/FB was a percentage point better than 2013’s. The dude is too good.

Jose Abreu, +8.52%
Now this man, THIS MAN, is the real reason why we’re all here. What can we make of that? We know that prodigious power hitters such as Pujols and Stanton can exceed expectations. But this expectation is set pretty high. I think we’re all expecting regression, but it’s everyone’s best guess as to how much. I’m thinking a drop from 27-ish percent closer to a Chris Davis-esque 22 percent.

Lucas Duda, -3.38%
I don’t have any other reliable full-season data for Duda to compare, but at least it wasn’t a positive differential. The negative implies that last year’s breakout was probably legit — and maybe there’s still room for improvement.

Matt Adams, -3.49%
Similarly to Duda, Adams’ only full season came last year. But the mammoth power we saw in 2013 didn’t disappear as much as it did suffer some bad luck. His 2014 xHR/FB of 12.19 percent still isn’t where any of us would like it to be, but again, maybe there’s still room for improvement.

Matt Holliday, -3.09%
Holliday, who perennially out-performs his xHR/FB, appears to have gotten pretty unlucky last year. Of the last five years (dating back to 2010), 2014’s xHR/FB was right in the middle. I know he’s getting old, but man, he’s a monster, and I think there’s juice still in the tank.

Nick Castellanos, -5.20%
Might be a little more pop in that bat than we know.

Nori Aoki, -6.08%
His power simply vanished, but the xHR/FB is in line with past years. He could return to his 10-HR, 25-SB ways in short order.

Robinson Cano, +2.33%
This is my absolutely favorite result in the entire 2014 data set. Cano always out-performs his xHR/FB; that part does not concern me. It’s the xHR/FB itself: it dropped off almost 7 percent from 2013 to 2014. Seven percent! Say what you will about Safeco Field sapping power, but methinks a larger share of that 7 percent is a 32-year-old man in decline.

Xander Bogaerts, -3.88%
See Castellanos, Nick.

Yasiel Puig, -4.58%
Remember how Puig hit way fewer home runs last year and all that stuff? Hey, I traded him midseason (he will cost only $13 next year, but I won my league so it all works out) for Carlos Gomez and a closer. In the moment, I think I made the right move: Puig’s home run rate never really improved. But his 2013 differential was +5.24%. Cutting the crap, his 2013 and 2014 xHR/FB rates were 16.56% and 15.68%, respectively — smack-dab in the middle of both years. Thus, taking the average of the two may not be such a bad method for projection after all.

OK, that’s everything. The players listed above were merely a sample and are by no means exhaustive when it comes to the peculiar splits I saw. More importantly, the implications are most interesting where they are hardest to draw: players such as Abreu and Eaton very clearly seem to have benefited (and suffered) at the hands of luck, and we can surely expect regression. But… how much? ‘Tis the question of the day, my friends.

Edit (1/8/15, 11:42 am): FanGraphs’ Mike Podhorzer, who coincidentally posted a xHR/FB metric for pitchers today, developed a similar metric for hitters a while back, to the tune of a .65 adjusted R-squared. I feel pretty good about my work now.

What did and didn’t work this year

A part of me feels like I need to provide some credentials if I’m dishing out fantasy advice. I’ve been waiting all year just to see if following my own advice would pay off. I played in four leagues, and the results are in:

1st place – 10-team roto, auction (League of Women Voters)
1st place – 10-team H2H roto, snake
2nd place – 10-team H2H points, snake
3rd place – 10-team H2H points, snake

The most important victory to me is the first one, in the League of Women Voters, a league in which a bunch of my dad’s friends have been playing for decades. I want to look back and 1) try to remember my exact draft strategy; 2) see how well I adhered to it; and 3) see where I went wrong.

I went into the draft knowing I would target a very specific and short list of players. This did not allow a lot of room for flexibility, although I did leave a couple of outfield spots open that I would fill on the fly. I can tell you right away I wish I was stricter on those last two outfield spots. I also did not target any specific category, although I did punt saves for the most part. Although I simultaneously led every offensive category except for stolen bases for most of the summer, it became obvious to me that I accidentally loaded up on batting average and undervalued steals.

What I did right:

  • $1 for Yan Gomes. I guaranteed Gomes would be a top-10 catcher with the chance to break the top 5; he finished No. 4 on ESPN’s player rater. (I also drafted Victor Martinez, and once he gained catcher eligibility, I dropped Gomes. It happened early in the season — too early for me to know better — but I wish I hadn’t.)
  • $16 for Jose Abreu. There’s no way I knew he’d be this good, but after snatching up Yoenis Cespedes off of free agency in the first week of 2012 and drafting Yasiel Puig to my bench in 2013, I pledged to gamble as much as $20, maybe more, on the MLB’s most recent Cuban import.
  • $13 for Martinez. I think he’s perpetually underrated, but I can tell you that not a single person in the world knew V-Mart would hit 30 home runs, let alone 20. I won’t pat my back on this one. I normally wouldn’t keep him, but I may have to in the off-chance he’s pulling a late-career Marlon Byrd on us (in terms of power, that is).
  • $1 for Corey Kluber. My love for Kluber is well-documented. I tempered my expectations and slotted him as my No. 32 starting pitcher, but I vastly underestimated his innings total (45 more innings than I projected), his wins (17 to 10) and, of course, his strikeouts (10.3 K/9 to 8.4 K/9). But I’m glad I took a conservative approach; the most important takeaway is that Kluber clearly exhibited the talent to be at least a middle-tier fantasy starter with upside. And boy, did everyone underestimate that upside.
  • $11 for Cole Hamels. I liked this play at the time, and I still do: I waited maybe a month to get a potential top-10 starter at about half-price. He’s a possible keeper next year ($14 on a $260 budget), but the Phillies’ inability to help him reach double-digit wins is troubling.
  • $2 for LaTroy Hawkins. He’s terrible, but at least I wasn’t the idiot who overspent on the perpetually inept Jim Johnson. How he lucked into more than 100 wins in two seasons is beyond me.

What I did wrong:

  • $51 for Miguel Cabrera. It was the most a player had ever gone for in the league, at least since the Rickey Henderson days. It was hard to predict such a massive drop-off in power — maybe 30 home runs was understandable, but only 25? — and I didn’t leave myself any room for savings. That is, I paid full price instead of looking for bargains, the latter of which was my game plan from the start.
  • $37 for Ryan Braun. An even worse bid, in hindsight, and another instance of paying full price instead of finding the bargain.
  • $10 for Everth Cabrera. Cabrera was a keeper, and he may have gone for more at auction. But wow, what a bust. Again, tough to see something like that coming, especially such a steep decline in on-base percentage.
  • $10 for Brad Miller. I made a bold prediction about Miller before the season started. I think the only thing more amazing than his plate discipline completely vanishing is how much owners in my league were willing to spend on a largely unknown quantity. I really thought I was being sneaky on this one, especially so late in the draft. This was a case in which I was too sold on a guy to budge and take a different name — especially when Dee Gordon and Brian Dozier were still on the board.
  • $12 for Shane Victorino. Was 2013 a flash in the pan or what? I don’t know if this guy’s legs will ever be the same again.

I’m excited to start preparing my projections for next year. I have made some revisions, tweaked some formulas… I’m looking forward to how the projections turn out.

And now I have a concrete idea in my head of how I should approach my ideal draft.

V-Mart, Abreu could join elite 30/.330 club

This morning, ESPN’s David Schoenfield mentioned that the Detroit Tigers’ Victor Martinez is baseball’s best hitter, leading all of Major League Baseball in wOBA (weighted on-base average) and wRC+ (weighted runs created plus), which measures a player’s offensive contributions after controlling for park effects. It’s a shame he won’t earn many American League MVP votes — I wouldn’t be surprised if teammate and former MVP Miguel Cabrera blindly earned more — simply because he contributes little to no defensive value.

Still, V-Mart is batting .337 with 30 home runs, setting him up to be part of an elite club. It’s not a popular one, mostly because it doesn’t have a flashy name or fancy title, but it is still very much meaningful: The 30-HR, .330-BA Club.

There have been only 62 such seasons in the past 50 years. There are repeat offenders, however, so the club actually consists of only 37  hitters dating back to 1964.

And the names in this club are not nobodies. Cabrera. Albert Pujols. Todd Helton. Frank Thomas. Vladimir Guerrero. Chipper Jones. The list goes on. It’s a group of men that consists of seven Rookies of the Year and three Hall of Famers (and more to come) and has collected 31 MVP awards and 244 All-Star nods. The inclusion of Martinez and perhaps Chicago White Sox first baseman and Cuban rookie sensation Jose Abreu, who currently sits at 33 homers and a .317 average, would add another six All-Star berths and possibly another Rookie of the Year.

This is nothing more than a cool historical footnote. It doesn’t really feel like we are witnessing history because fans have witnessed 39 such seasons of 30/.330 since 2000, and Martinez’s teammate Cabrera has achieved the feat each of the past three years on his own. Still, when we discuss seasons of truly amazing hitting — commending not only a player’s power but also his incredible plate discipline and coverage — Martinez’ (and Abreu’s) names should be included in the conversation. And maybe it’s just me, but given Martinez’ age and career trajectory, his inclusion on the list will certainly be surprising — and impressive.

The comprehensive list (1964-2013), with number of times each player achieved 30/.330, is listed below.

5 times
Albert Pujols

4 times
Barry Bonds
Manny Ramirez
Todd Helton

3 times
Frank Thomas
Larry Walker
Miguel Cabrera
Mike Piazza
Vladimir Guerrero

2 times
Gary Sheffield
Jason Giambi

1 time
Adrian Beltre
Albert Belle
Alex Rodriguez
Billy Williams
Bret Boone
Carlos Delgado
Carlos Gonzalez
Chipper Jones
Dante Bichette
Dave Parker
David Ortiz
Derrek Lee
Don Mattingly
Ellis Burks
Fred Lynn
George Brett
Ivan Rodriguez
Jeff Bagwell
Jeff Kent
Josh Hamilton
Lance Berkman
Matt Holliday
Mo Vaughn
Moises Alou
Ryan Braun
Tim Salmon (Salmon is the only player on the list without an All-Star Game appearance. He achieved the feat in 1995, hitting .330 with 34 HR and a 1.024 OPS. He won Rookie of the Year honors in 1993 alongside Piazza.)

Player Rater Watch: First Base, 2014 season

Is there anything more warped right now than the first baseman player rater? It’s nice to see Pujols back on top, but with all the talk of his decline, who knows if it’s for real. (I’ll take a stab at it in a second.) Cuban wunderkind Jose Abreu and his alleged “slider-speed bat” are punishing the league right now. Is Adrian Gonzalez back? Justin Morneau and Adam LaRoche, too? Who are you, Chris Colabello, and what have you done with Chris Davis and Edwin Encarnacion?

Albert Pujols, LAA | #1 1B
I’m honestly puzzled by Pujols’ stats viewed through a sabermetric lens. He is striking out in only 7.9 percent of plate appearances, down from 12.4 percent last year and second-best of his career (good thing). He is walking in only 7.9 percent of plate appearances as well, good for second-worst of his career (bad thing). He’s sporting a .240 batting average on balls in play (BABIP) (good thing, in terms of his future batting average). However, he’s hitting more ground balls than fly balls for the first time in his career, and by a wide margin (bad thing). More than a quarter of his fly balls are leaving the park, too, when his career rate is around 15 percent (bad thing, but not as bad as it sounds).

So what I’m expecting from here on out is some weird combination of: he’s not going to get as lucky on fly balls, but perhaps he will start hitting more fly balls, which will counteract some of the regression he may experience in the power department. His batting average will rise with his BABIP but fall as he hits fewer home runs, two effects that also may counteract each other.

If I’m a Pujols owner, I would watch his strikeout and fly ball rates religiously. As the season wears on, I think they will be the difference between a .265/30/100 Pujols and a .290/40/120 Pujols.

Jose Abreu, CHW | #2 1B
Abreu is about as WYSIWYG as it will get, save maybe his home run total. His batting average isn’t so high that you’ll expect him to ever hit .280, let alone .300. His ratio of home runs to fly balls (HR/FB) is identical to Pujols’ — 25.7 percent, or nine homers in 35 fly balls — but it would be wise to expect something closer to 15 percent to hedge your bets. Still, that makes him a good bet for somewhere in the 32- to 36-homer range, given that he doesn’t get worse or tire out as the season progresses. Will he still be the 2nd-best first baseman come October? It’s hard to say; Paul Goldschmidt is doing his best impersonation of Paul Goldschmidt, and it’s only a matter of time before Miguel Cabrera, Encarnacion and Davis find their respective grooves (although the latter-most name just hit the DL). For rest-of-season production, I would still take Goldschmidt, Cabrera, Encarnacion, Pujols and Votto over Abreu at this point. However, he has a legitimate chance of being a top-3 first baseman for the season.

Adrian Gonzalez, LAD | #3 1B
Here’s your first sell-high candidate. He’s striking out at a career-worst rate, and his HR/FB rate is a career best — almost double his 2013 rate, in fact. Given his recent history, he can’t feasibly maintain any of this. His strikeout rate could lead to his first batting average below .293 since since 2009, and the home runs will eventually slow to a crawl. It would be nice to see him crack 25 home runs again, but delusions of 30 home runs are exactly that: delusions. I’ll give him 24 home runs, 26 if he’s fortunate. Either way, he’s not hitting 40 and batting .300 like his pace indicates, so if you can swindle another owner, do it!

Justin Morneau, COL | #6 1B
In his defense, his strikeouts are way down, but so are his walks. Frankly, he’s not going to hit .349 (although, after Michael Cuddyer‘s showing last year, maybe Colorado has a lucky charm stored in it somewhere), but with his much more contact-oriented approach, he could hit for a higher batting average than he has in recent years. Moreover, his 12.8-percent HR/FB, his best since 2009, could actually be sustained considering he gets to call hitters’ haven Coors Field his home park. Still, I can’t imagine he will end up in the top 10 by season’s end, as when the batting average starts to tank, so will the RBI and everything else.

Brandon Belt, SF | #7 1B
I wrote about selling high on Belt. Your window of opportunity may have closed — which is not to say that he’s going to be a bad player, but his value will never be as high as it was two weeks ago. He has hit only two home runs with four RBI in the last 14 days which striking out in almost a quarter of plate appearances. Belt’s a line drive hitter, so his above-average BABIP should keep his batting average from ever becoming a liability. But career-worst strikeout, walk and fly-ball rates coupled with an unsustainable 20.6-percent HR/FB rate make Belt’s stock in continual decline. I question now if he can even hit 20 home runs given the peripheral data. He needs to tighten up his zone and hit more balls in the air to realize his true potential, or he will drop to the back-end of the top-15 first basemen or disappear from it completely.

Chris Colabello, MIN | #8 1B
His RBI pace is near impossible. So is his batting average: a non-power hitter who strikes out in more than 25 percent of plate appearances (not to mention a .410 BABIP) is a recipe for a sub-.250 batting average. The home runs, however, could be real, and he could hit 20 home runs with 80 RBI at this point. But the chances of that happening will become more slim as the batting average plummets. Still, he’s worth your extra corner-infield (MI) or utility slot until further notice. Just be aware of the regression when it starts so you can limit the damage he does to your batting average. However, if he’s your main first baseman, I would sell high, and quickly, to get a reliable (if underperforming) first baseman in return. There’s probably no better time to simultaneously sell high and buy low given how wild first base has been this year.

Adam LaRoche, WAS | #9 1B
It’s the batting average, folks. It’s coming down. Again, in his defense, it appears he has made adjustments at the plate for the better this year. But all his value stems from his high batting average relative to his career. Sell high, ride the hot hand, whatever. But I don’t think is a repeat of 2012 by any means.

Chris Davis, BAL | #15 1B
Buy low, buy low, buy low! He’s actually striking out and walking at career-best rates. He’s even hitting a normal number of fly balls relative to recent years. He’s just getting unlucky on said fly balls, to the tune of a 6.1-percent HR/FB (compared to 22.6 percent in 2013 and 16.9 percent in 2012). Even his batting average is right where it should be. It’s only the home runs that are out of sorts. You’re a fool to think he’ll hit 50-plus home runs again, but if you buy low now, you could be the beneficiary of a big burst of them come late-May or June.

Miguel Cabrera, DET | #26 1B
Patience, young Padawan. The Magic 8-Ball says “all signs point to yes.” He’s already starting to get hot, and he’s going to get hot in a big way. One problem: he hasn’t struck out this much since his days with the Florida Marlins, and he’s not walking as much. Boy, was he ever in a slump to begin the season, though. If that has anything to do with the abnormalities in his plate discipline, then you can expect them to be corrected over the next five months. As a Cabrera owner in one of my leagues, I’m still a bit nervous, but I’m also excited for a thrilling five months.

Matt Adams, STL | #27 1B
The opposite of Pujols: lucky batting average, unlucky home runs and RBI. He’s hitting a ton of fly balls and line drives, and he’s striking out a whopping 6-percent less than last year. Me gusta. Again, patience is a virtue.

Edwin Encarnacion, TOR | #28 1B
If Cabrera was mired in a slump, then Encarnacion is in a super slump. Like Adams, given his batted ball profile, the home runs will come in due time. The most alarming statistic is the strikeouts, coming 1-in-4 plate appearances compared to 1-in-10 last year. I drooled over Encarnacion’s plate discipline last year, and how he had, by far, the highest ISO of any player to walk more than he struck out. He was, and still is, a very special hitter because of this. But if he’s devolved into a stereotypical free-swinging power hitter, he may hit close to 30 home runs than 40, and at the expense of his batting average, too. I still hold out hope — it’s hard to believe a player of his caliber can go sour overnight. It would make sense, though, if his wrist problem from the end of last year is still bothering him. That would be especially bad news, news I’d like to hear sooner rather than later.

2014 Rankings: First Base

Rankings are based on a standard 5×5 rotisserie league.

Name – R / RBI / HR / SB / BA

  1. Paul Goldschmidt – 106 / 116 / 34 / 17 / .292
  2. Edwin Encarnacion – 101 / 109 / 41 / 6 / .294
  3. Chris Davis – 102 / 119 / 43 / 3 / .272
  4. Prince Fielder – 92 / 110 / 33 / 1 / .290
  5. Albert Pujols – 97 / 102 / 29 / 4 / .295
  6. Joey Votto – 90 / 90 / 26 / 7 / .310
  7. Freddie Freeman – 94 / 105 / 27 / 2 / .286
  8. Adrian Gonzalez – 89 / 102 / 24 / 1 / .297
  9. Allen Craig – 87 / 112 / 21 / 2 / .293
  10. Brandon Moss – 85 / 96 / 31 / 2 / .250
  11. Jose Abreu
  12. Mark Teixeira – 82 / 96 / 28 / 2 / .259
  13. Mark Trumbo – 76 / 99 / 34 / 4 / .242
  14. Eric Hosmer – 78 / 78 / 20 / 13 / .276
  15. Kendrys Morales – 68 / 83 / 27 / 0 / .283

Thoughts:

  • Just to be clear: these are my projections, so I’m very familiar with the system and most players’ outputs. Still, it doesn’t mean a few don’t surprise me now and then.
  • Hosmer at No. 14 is certainly one of the aforementioned surprises. I’m not as bullish as most other projections, but other projections honestly aren’t too different, either. It’s mainly in the runs and RBI categories where you can find the biggest difference. It’s a toss-up.
  • I can’t project Abreu, but I would project him to be almost an identical clone to Moss: 30-homer potential, a batting average that may drag along and modest counting stats (for a first baseman) while playing for a lackluster White Sox team.
  • I’m big on Freeman — I think he’s due for a breakout of sorts — but I think being as bullish as ESPN is on his batting average is a mistake. Count on Freeman to provide everywhere else, but I’m expecting more modest numbers, and anything better will be gravy.
  • I’ have my doubts about Teixeira, as does everyone else, I’m sure.
  • Matt Adams barely missed the cut at No. 16.
  • Lastly, yes, Encarnacion is better than Davis, even with injury risk. But as I’ve confessed before I have a huge man-crush on Edwin. Regardless, whether you pick one or the other won’t make a huge difference, barring an unpredictable injury to either one.

A look at international players’ value, or “Might as well give Tanaka his Yankee jersey now” (Updated Jan. 14)

Let’s avoid all talk about who’s right or wrong in the Alex Rodriguez debacle, spectacle, three-ring circus, what-have-you. I liked the White Sox as sleepers to win Japanese phenom Masahiro Tanaka‘s services this winter. Now that A-Rod is suspended for 162 games, though, the New York Yankees will have something like $24 million in payroll freed up for 2014.

Although the Yankees were allegedly among two or three frontrunners in the bidding war for Tanaka, it appeared to me their payroll would pose a huge obstacle if they truly wanted to obey the luxury tax threshold. But Rodriguez’s suspension blows everything wide open, upgrading the Bronx Bombers’ status from Possible to Probable.

Updated Jan. 14, 2014: The Angels are a distant third to the Yankees and Dodgers, and with Los Angeles looking to extend pitcher Clayton Kershaw… well, the deal is as good as done. Although, in defense of the L.A. teams, Tanaka has mentioned he wants to play on the west coast.

As for the White Sox… get ’em next time, boys. Keep looking for those good deals. I tell you what, every high-profile international signing in the past three years has been a winner.

It is commonly accepted that each win a player provides in value (a “win above replacement,” for those just piecing two and two together) has a market value of about $5 million, although Lewie Pollis at SB Nation argues it is closer to $7 million. Even using the quick-and-easy (and lower) $5 million as a benchmark, the value (by means of WAR) of the 2013 performance of every notable international player in MLB exceeded the average annual value (AAV) of his contract:

Yu Darvish: 5.0 WAR ~ $25 million (AAV: $18.62 million)
Hisashi Iwakuma: 4.2 WAR ~ $21 million (AAV: $7 million)
Yasiel Puig: 4.0 WAR ~ $20 million (AAV: $6 million)
Hyun-jin Ryu: 3.1 WAR ~ $15.5 million (AAV: $6 million)
Leonys Martin: 2.7 WAR ~ $13.5 million (AAV: $4.1 million)
Yoenis Cespedes: 2.3 WAR ~ $11.5 million (AAV: $9 million)
Norichika Aoki: 1.7 WAR ~ $8.5 million (AAV: $1.65 million)

Let’s note here that the AAV for all the players listed above exceeded their actual 2013 salaries. For example, Martin made $3.25 million last year, and Ryu made $3.33 million. Thus, even Cespedes, with his disappointing production compared to 2012, still managed to be a boon for his team, and he should only improve from last year.

It’s a small sample size, but hey, the results seem pretty substantial so far in the post-Dice-K era. Don’t be surprised when my fantasy team has Jose Abreu, Alexander Guerrero and Miguel Alfredo Gonzalez on it.