Tagged: Chris Davis

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.
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How should Chris Davis be valued?

Answer: Not sure.

I’m trying to figure out this BABIP (batting average on balls in play) puzzle. Orioles first baseman Chris Davis, who notched a BABIP south of .324 just once in his career before last year (.275 in 2010), saw said statistic drop by almost 100 points in 2014. It’s easy to point to the defensive shift as a cause — when defenses shift on you 83 percent of the time, you almost have to — but I’m reluctant to buy in on this just yet.

Unfortunately, there is not much, if any that I know of, publicly-available defensive shift data. Prior to the 2014 season, Jeff Zimmerman published 2013 data courtesy of The 2014 Bill James Almanac. A haphazard calculation yields a 2013 “shift BABIP” about 18 points, or about 6 percent, lower than the MLB aggregate BABIP of .297. During the 2014 season, an ESPN feature projected defensive shifts to reach an all-time high, and by quite a margin, too. In light of this, one could hypothesize that more overall shifts would cause a lower aggregate BABIP. However, MLB’s aggregate BABIP in 2014 was .298.

None of this really tells us a whole lot. The shift BABIP would be awesome if it could be broken down by location of the ball in play — accordingly, I would strictly focus on a pull-side shift BABIP — but, alas, it does not. FanGraphs also breaks down a hitter’s spray chart numerically — you can view Davis’ pull-side splits here —  but it does not indicate how many times defenses shifted against him when he pulled the ball. Until this gap in the data can be both a) plugged and b) made publicly available, the answers we seek regarding the true effectiveness of the shift may evade us.

No matter, because I still want to try to figure some things out. Let’s talk a little bit of theory. Like a hitter’s BABIP, I think his shift BABIP is also likely to be volatile. No matter where you place your fielders, you cannot predict where a batter will hit the ball. If you study the spray charts and play the probabilities just right, you’ll surely turn a few more would-be hits into outs. But just like regular BABIP, there will still be an element of luck involved.

Thus, when I look at this table, reproduced from Mike Podhorzer’s FanGraphs post

Season At-Bats Balls in Play Shift Count % Shifted Shift BABIP No Shift BABIP
2012 515 346 110 31.8% 0.364 0.323
2013 584 385 199 51.7% 0.302 0.431
2014 450 277 230 83.0% 0.230 0.353

… I see all sorts of luck. I think the mistake is made when one relates shift percentage with shift BABIP. I expect more shifts to correlate with greater effectiveness — results that would be reflected in the hitter’s depressed batting average. But more shifts does not equate to greater effectiveness on a per-play basis, which is essentially what shift BABIP measures. In short: given that a player’s batted ball profile is identical year to year, his shift BABIP should have some semblance of consistency. We know that BABIP is pretty volatile, but there is a small element of consistency to it (for example, Edwin Encarnacion‘s BABIP is perennially stuck in the mid-.200s while Mike Trout‘s is typically buoyed in the upper-.300s). Thus, I would expect shift BABIP to exhibit at least a little bit of consistency, and for that consistency to produce consistently lower marks than that of the regular BABIP.

Speaking of batted ball profiles, Davis’ pull-side profile was consistent between 2013 and 2014:

Season LD% GB% FB%
2012 21.9% 60.9% 17.2%
2013 30.4% 48.5% 21.0%
2014 29.9% 52.1% 18.1%

Yet Davis’ pull-side BABIP dropped from .338 to .185. The decrease makes sense intuitively, but he saw the fewest shifts in 2012 and actually had a worse pull-side BABIP than he did in 2013. I don’t have to run a regression to show there’s no correlation to be found there (albeit in a minuscule sample size). Now, his increasing tendency to pull the ball (43.3% in 2012, 46.2% in 2013, 50.9% in 2014): that is something that should correlate well with shift BABIP. Because the shift BABIP doesn’t differentiate among ball placement, where the player hits the ball ought to affect his shift BABIP, especially if he predominantly pulls the ball. Thus, an increase in balls in play to the pull side should correlate with a decrease in shift BABIP. Despite all this, Davis recorded his highest shift BABIP during the year he pulled the ball with the least amount of authority.

Now, forgive me, but I have to try to make something of all of this. Let’s take the 6-percent decrease in aggregate BABIP when accounting for shifts (from earlier), and let’s say that teams shift on Davis 100 percent of the time. (It’s not unfathomable, given defenses shifted against him five times out of six, and it appears — it appears — to have succeeded with flying colors.) Given an identical batted ball profile from year to year, maybe I could expect his BABIP, which sat at .335 and .336 the two years prior to 2014, to fall to around .315 permanently. Even if his “true” BABIP benchmark is closer to .300, then maybe his overall shift BABIP is in the .280 ballpark. As he hits more and more balls to his pull side, his shift BABIP will decrease, as will his batting average. That I can fathom.

But I cannot bring myself to accept that a 10-percent increase in pull-side balls-in-play from 2013 to 2014 correlates with a 24-percent decrease in shift BABIP. I don’t think the latter can reasonably be larger than the former without a significant luck element involved. Then again, the 7-percent increase in pull-side balls from 2012 to 2013 resulted in a 17-percent decrease in shift BABIP produces an almost identical ratio (24/10 = 2.4, 17/7 = 2.429), so maybe there’s something I’m missing. But allow me to speak hypothetically: Let’s say Davis puts 100 balls in play, consisting of 50 to his pull side and 50 everywhere else. This silly 2.4-to-1 ratio demonstrates that one more ball hit to the pull side — that is, now he hits 51 balls to the pull side and 49 everywhere else — means not only is that one extra pulled ball an automatic out but also almost one-and-a-half more balls not to the pull side become outs. It’s simply incomprehensible, and I maintain that a percentage increase in balls hit to the pull side would correlate with at most a percentage decrease in shift BABIP.

Wrapping things up: I think it goes without saying that Davis got unlucky in the BABIP department in 2014 — it’s more a matter of determining how unlucky and why. I think his shift BABIPs betray Davis; I think he got especially lucky against the shift in 2012 and especially unlucky in 2014. In general, more shifts should suppress a hitter’s batting average but not his shift BABIP, and it’s Davis’ shift and pull-side BABIPs that absolutely tanked in 2014. Considering he still managed to hit a home run in 5 percent of his plate appearances, I a full 600 from Davis to yield at least 30 bombs, and I think that’s a modest projection. Couple that with a batting average rebound — which I fully expect at this point, strikeout rate disclaimers withstanding — and the down-and-out Davis could be a nice draft day bargain.

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.

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.

Belt, Trumbo, home runs, and knowing when to sell high

San Francisco Giants first baseman Brandon Belt will never be more valuable than he is now. Many expected his breakout, and it seems those who invested in the late bloomer will be rewarded handsomely, depending on how much they paid for him or in which round they drafted him. He leads MLB tied for most home runs (5) with Arizona Diamondbacks outfielder Mark Trumbo, a free-swinging, powerful fella. Those are important words, because that is exactly what Belt has been so far.

The sample size is very small — 35 plate appearances — but the statistics are telling: He has 10 strikeouts and zero walks. Meanwhile, Belt is batting .343, which is buoyed by a .350 batting average on balls in play (BAbip). Savvy readers will be quick to point out that his 2012 and 2013 BAbips were both .351, so perhaps that’s his baseline. And it’s possible. But that would be his saving grace. If his BAbip fell to a league-average level around .300, we’re looking at Trumbo numbers, or maybe even (Pittsburgh Pirates third baseman) Pedro Alvarez numbers.

It’s realistic to think he will walk a little more and strike out a little less. His fly ball rate is conducive for home runs given his power, but it’s unrealistic to think he will hit a third of all fly balls out of the park. That’s territory reserved for, well, no one. Only a dozen batters hit 15 percent of fly balls as home runs (15% HR/FB), all of them fabled power hitters. Even Toronto Blue Jays first baseman Edwin Encarnacion and Boston Red Sox designated hitter David Ortiz notched HR/FB rates of 14.0 percent and 12.6 percent, respectively.

I think projecting a HR/FB rate of 13 percent is fair, and it would afford him 30 to 35 home runs for the season — a tremendous performance, indeed. But the batting average is bound to plummet (not that it took a rocket scientist to know he can’t sustain a .343 batting average), and it’s entirely dependent on his plate discipline and whether or not his BAbip is actually real. Today’s power hitters have pretty polarized BAbips, and it mostly comes down to their plate discipline: Ortiz, Detroit Tigers first baseman Miguel Cabrera, Los Angeles Angels of Anaheim outfielder Mike Trout and Diamondbacks first baseman Paul Goldschmidt all struck out in, at most, 20 percent of plate appearances last year, and all of them posted BAbips above .320. Meanwhile, Alvarez, Oakland Athletics third baseman Brandon Moss, New York Yankees outfielder Alfonso Soriano, and Chicago White Sox designated hitter Adam Dunn all strike out in at least 25 percent of plate appearances, and only Moss posted a BAbip above .300 (fun fact: it was .301).

It’s possible that Belt is a unique breed of hitter that can strike out a lot and hit for a high batting average on balls in play, and it’s certainly possible he sustains it for the rest of the season. But strikeout-prone power hitters tend to be batting average liabilities — one of the reasons why Baltimore Orioles first baseman Chris Davis is, I think, due for some heavy batting average regression.

This has all been a long-winded way of me saying: Belt’s batting average will regress to the mean, but it’s impossible to know whether he’ll end up hitting .295 or .245. Even somewhere in the middle means it’s a long way to fall for Belt.

I would absolutely sell high on Belt, depending on the format. If I’m in a dynasty league, or I can keep him next year at a discount, then I would be inclined to keep him. But if I owned him and had the opportunity to swipe Cincinnati Reds outfielder Jay Bruce from a panicked owner, I would pull the trigger. Bruce will probably hit more home runs the rest of the way, and his batting average will only trend upward while Belt’s trends downward.

When it comes down to it, I think Belt will hit about .275 and end up with 32 home runs. But I also think the possibility of him pulling a Justin Upton or Domonic Brown circa 2013, during which both players hit 12 home runs in one month and slept the rest of the year, is very real.

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Meanwhile, Trumbo has also hit five home runs. This isn’t anything new from him, although the frequency and earliness of the bombs is surely delightful for owners. It’s worth keeping in mind that Trumbo hit no fewer than five home runs and no more than seven in any given month last year. It’s possible he surpasses his monthly high from last year by next week, but it’s also worth noting he hit seven, nine and eight home runs in May through July of 2012, only to go cold in the other three months. Every player has ups and downs, and I would be wary that such a high in April will lead to, say, an equally low August, as he regresses to the mean.

It probably sounds like I’m super down on these guys, but I’m not. I swear! It’s just that smart fantasy owner knows when to sell high and buy low, and even Trumbo can be a sell-high candidate. He will probably also hit 32 home runs, just like Belt, but if you can somehow trade him for a slow-to-start Encarnacion, who has the potential to hit 40 bombs, I would again pull the trigger. That’s at least 10 more home runs you would have otherwise gotten had you kept Trumbo all year, and Encarnacion will hit for a better average in the long run.

Other home run leaders, per ESPN’s MLB home page: Blue Jays outfielders Melky Cabrera and Jose Bautista (both at 4), Tigers outfielder Torii Hunter (3), White Sox outfielder Alejandro De Aza (3), Milwaukee Brewers outfielder Ryan Braun (3), and Colorado Rockies outfielder Carlos Gonzalez (3). Bautista, Braun and Gonzalez are legit. Cabrera is not legit, but that’s not to say he doesn’t have power. I projected him for 14 home runs and 11 stolen bases, but at this point I think he’s well on his way to a 15/15 season supplemented by a .280 batting average at the top of Toronto’s batting order. De Aza and Hunter also have pop, but they are not noteworthy hitters — go ahead and sell high, but they are still valuable commodities otherwise.

Panning for gold using spring stats, hitter edition

You’ve probably heard a hundred times this month alone: spring training statistics don’t mean anything. Too many times a player has had a monster spring only to completely flop during the season (do Aaron Hicks or Jackie Bradley circa 2013 ring a bell?). Still, in disbelief we all watched Julio Teheran‘s monster spring last year, and he humiliated batters and baserunners throughout his rookie campaign.

Ultimately, spring stats do tell a story, albeit a short or biased one. But if you know where to look — that is, if you know the stats on which to focus your attention — you can maybe decipher which spring performances are legit and which are not.

Dee Gordon, LAD 2B
Important stats: 12 for 42 (.286 BA), 9 SB, 8 K
Why they’re important: Well, holy smokes. Look at those steals. We’ve always known he’s fast, but wow. Also, he has struck out in only 19 percent of at-bats, which certainly isn’t the worst thing in the world. What I’m looking for here is if he can hold his own at the plate, even if it’s just for a month or two, and right now he’s hitting .286 — nothing spectacular, but not miserable, either. Oh, and did I mention he has four triples already?  Gordon isn’t a top-10 second baseman, but handcuff him to Alexander Guerrero (or simply jump ship when Guerrero finally gets the call) and this could be a great draft strategy.

Billy Hamilton, CIN CF
Important stats: 10 for 33 (.303 BA), 9 SB, 4 K, 6 BB
Why they’re important: Not only is Hamilton stealing bases at an unfathomable rate, he is also barely striking out (only 12 percent of at-bats have ended in a K) and has actually walked more times than he has struck out. Everyone and their mothers were worried Hamilton would be overpowered at the plate. Don’t get caught in the hype, I hear them saying. Yet I can’t help myself. If he keeps putting the bat on the ball the way he’s doing, he will get on base, he will steal, and he will score runs.

Billy Burns, OAK LF
Important stats: 8 SB, 13 K in 52 AB
Why they’re important: OK, maybe I was little too obvious when I sorted MLB.com’s spring training stats by stolen bases. Burns is getting way more hype than anyone in spring training right now, or at least it seems that way. He’s effectively blocked in the A’s outfield, but his speed, plate discipline and glove-work will fast-track him to the majors. Unfortunately, 25 percent of at-bats are ending in strikeouts, so he may be overmatched. No skin off our backs, though, especially if he doesn’t start this year in the majors.

Other stolen base leaders who are legitimate fantasy options: Jarrod Dyson (6 SB) and Rajai Davis (5 SB). I’ve raved about Davis’ fantasy value before.

Mike Moustakas, KC 3B
Important stats: 17 for 35 (.486 BA), 4 HR, 4 K, 6 BB
Why they’re important: Moustakas has been mostly a letdown during his major league career. He’s crushing home runs right now and has walked more than he’s struck out, and people are starting to be optimistic about the guy. I’m hesitant, and I would still leave him undrafted in standard mixed leagues, but he could be worth an extra couple of dollars in AL-only leagues. I’ll watch his name as the season progresses, though. He’s worth following if you’re picking a risky or injury-prone third base asset such as Ryan Zimmerman or Aramis Ramirez.

Brad Miller, SEA SS
Important stats: 14 for 34 (.412 BA), 2 3B, 4 HR, 1 SB
Why they’re important: Guys… are you serious. I cannot love this guy any more. And he’s still hitting triples!!! It’s not a fluke, people. I think Miller is the second coming of Ian Desmond.

Jason Heyward, ATL RF
Important stats: 14 for 40 (.350 BA), 3 HR, 1 SB
Why they’re important: …Jason Heyward? Is that really you?

Javier Baez, CHC SS
Important stats: .297/.297/.703, 4 HR, 1 SB, 11 K, 0 BB
Why they’re important: Is Baez even a real person? The split between his slugging and on-base percentages is impossibly large. Meanwhile, zero walks and 11 K’s in 37 at-bats. This kid is going to be amazing, if not occasionally frustrating at first.

Other business-as-usual home run hitters: Russell Martin (kind of — he had a huge spring last year, too, if I remember correctly), Hunter Pence (4 HR), Giancarlo Stanton (4 HR), Jose Bautista (3 HR), Miguel Cabrera (3 HR), Chris Davis (3 HR), Andrew McCutchen (3 HR).

Nick Castellanos, DET 3B (formerly LF)
Important stats: 18 for 45 (.400 BA), 7 2B, 2 HR, 2 SB, 16 RBI
Why they’re important: Castellanos is a highly touted prospect with very little major-league exposure with which we can form solid opinions about him. But nine multi-base hits in 45 at-bats, plus a pair of bombs and swipes, makes it look like this kid is the real deal, regardless of his sort of lackluster minor-league stats. Don’t get too enamored with the RBI total, but clearly he’s not afraid of so-called clutch situations, either.

Dustin Ackley, SEA LF (formerly 2B)
Important stats: .432/.462/.703, 1 HR, 6 K in 37 AB
Why they’re important: Maybe the former No. 2 pick can recoup some of his losses. He had a somewhat strong showing in the latter half of 2013. It will be interesting to see if it carries over. As the Magic 8-Ball might say, “All signs point to yes.” Or something like that.

As for players who scare me right now, Corey Hart is batting .129/.250/.161 with 16 strikeouts in 31 at-bats; B.J. Upton is batting .297/.366/.351 but with 14 strikeouts in 37 at-bats, an unsustainable rate for that batting average; and Domonic Brown is batting a miserable .171/.326/.229 with 12 strikeouts in 35 at-bats, albeit with eight walks.

Do your own research, form your own opinions. This is just a sampling of the many names that are shining bright or falling flat. And, of course, it’s simply too risky to make a decision on such a small sample size. But it never hurts to remember a name or two.

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.