Teams are dancing the Depth Chart Shuffle, but the closer landscape has remained relatively steadfast. Per MLB.com, 27 of 30 teams have denoted who will be their respective 9th-inning man on their depth charts (labeled “(CL)”, for reference). For reasons largely pertaining to simplicity, I have completed a preliminary round of projections for closers and have provided it for your viewing pleasure. Keep in mind that things will (likely) change as the offseason progresses into the preseason progresses into the real, authentic season.
The rankings are catered to classic 5-by-5 rotisserie leagues with $260 budgets. Bonus feature: You can manually input a budget amount as well as an expected share of total spending on closers. For example, the teams in my league historically spend about 10 percent of the aggregate wealth on closers. If your league values closers more highly, you can accordingly adjust for such.
The players on teams that have not solidified their closer situations are marked with asterisks. Note that the very elite Dellin Betances is one of these players. This will inevitably be sorted out by March.
Craig Kimbrel will likely fall short of 49 saves — although, if the Braves can compete in the few games they are expected to win, he may have a lot of small-margin-of-victory save chances coming his way. Tough call, but there’s legitimate arguments to be made about him being maybe only a top-3 RP — which is really nothing about which to write home.
The aforementioned Betances is projected for the second-best ERA, second-most strikeouts and third-best WHIP among all closers. Betances threw a ton of innings last year, so it suffices to say I’m eager to see how his usage shakes out. Given how the Yankees have historically used closers, however, I think he’ll be closer to his projected 63 innings than his 90 last year.
Sean Doolittle isn’t an upside play, but I suspect he will be underrated on draft day. Koji Uehara is perhaps an upside play: his projection factors in his health concerns, so if he can stay healthy all year, he should bolster his return on investment.
Will Zach Britton continue to induce an absurd number of ground balls? Yes, although perhaps not as extremely as he did last year.
No offense to Brett Cecil, but I think the Blue Jays will trade for someone in due time.
Dark horse candidates in Mark Melancon and Jake McGee as they round out the top 10. I think they may be a bit overrated, but I would take them over literally everyone below them except maybe Cishek, if we’re pulling hairs.
Bobby Parnell is competing, so to speak, with Jenrry Mejia; Jonathan Broxton is competing with who the heck knows. Santiago Casilla could likely cede the role back to Sergio Romo. Other pitchers in some sort of danger of losing their jobs during the seasons include Fernando Rodney, Joaquin Benoit, Drew Storen, LaTroy Hawkins, Neftali Feliz and Chad Qualls.
Jonathan Papelbon, Joe Nathan and Addison Reed seem to have some semblance of job security, but they also seem to have a semblance of not being very reliable anymore. Papelbon and Nathan will be the most interesting bullpen storylines, especially if Nathan struggles again and the Tigers are competing.
I haven’t contextualized these rankings for points leagues or a top-300 type of thing for roto formats, but hey, that’s why it’s preliminary.
(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
Meanwhile, Albert Pujols consistently out-performs his xHR/FB:
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.
Expected strikeout rate, or what I will henceforth refer to as “xK%,” is exactly what it sounds like. I want to see if a pitcher’s strikeout rate actually reflects how he has pitched in terms of how often he’s in the zone, how often he causes batters to swing and miss, and so on. Ideally, it will help explain random fluctuations in a pitcher’s strikeout rate, because even strikeouts have some luck built into them, too.
An xK% metric is not a revolutionary idea. Mike Podhorzer over at FanGraphs created one last year, but he catered it to hitters. Still, it’s nothing too wild and crazy like WAR or SIERA or any other wacky acronym. (A wackronym, if you will.)
Courtesy of Baseball Reference, I constructed a set of pitching data spanning 2010 through 2014. I focused primarily on what I thought would correlate highly with strikeout rates: looking strikes, swinging strikes and foul-ball strikes, all as a percentage of total strikes thrown. I didn’t want the model specification to be too close to a definition, so it’s beneficial that these rates are on a per-strike, rather than per-pitch, basis.
The graph plots actual strikeout rates versus expected strikeout rates with the line of best fit running through it. I ran my regression using the specification above and produced the following equation:
xK% = -.6284293 + 1.195018*lookstr + 1.517088*swingstr + .9505775*foulstr
R-squared = .9026
The R-squared term can, for easy of understanding, be interpreted as how well the model fits the data, from 0 to 1. An R-squared, then, of .9026 represents approximately a 90-percent fit. In other words, these three variables are able to explain 90 percent of a strikeout rate. (The remaining 10 percent is, for now, a mystery!)
In order for the reader to use this equation to his or her own benefit, one would insert a pitcher’s looking strike, swinging strike and foul-ball strike percentages into the appropriate variables. Fortunately, I already took the initiative. I applied the results to the same data I used: all individual qualified seasons by starting pitchers from 2010 through 2014.
The results have interesting implications. Firstly, one can see how lucky or unlucky a pitcher was in a particular season. Secondly, and perhaps most importantly, one can easily identify which pitchers habitually over- and under-perform relative to their xK%. Lastly, you can see how each pitcher is trending over time. Every pitcher is different; although the formula will fit most ordinary pitchers, it goes without saying that the aces of your fantasy squad are far from ordinary, and they should be treated on an individual basis.
(Keep in mind that a lot of these players only have one or two years’ worth of data (as indicated by “# Years”), so the average difference between their xK% and K% as a representation of a pitcher’s true skill will be largely unreliable.)
It is immediately evident: the game’s best pitchers outperform their xK% by the largest margins. Cliff Lee, Stephen Strasburg, Clayton Kershaw, Felix Hernandez and Adam Wainwright are all top-10 (or at least top-15) fantasy starters. But let’s look at their numbers over the years, along with a few others at the top of the list.
Kershaw and King Felix have not only been consistent but also look like like they’re getting better with age. Wainwright’s difference between 2013 and 2014 is a bit of a concern; he’s getting older, and this could be a concrete indicator that perhaps the decline has officially begun. Darvish’s line is interesting, too: you may or may not remember that he had a massive spike in strikeouts in 2013 compared to his already-elite strikeout rate the prior year. As you can see, it was totally legit, at least according to xK%. But for some reason, even xK% can fluctuate wildly from year to year. I see it in the data, anecdotally: Anibal Sanchez‘s huge 6.7-percent spike in xK% from 2012 to 2013 was followed by a 5.5-percent drop from 2013 to 2014. Conversely, David Price‘s 5-percent decrease in xK% from 2012 to 2013 was followed by an almost perfectly-equal 5-percent increase from 2013 to 2014. So the phenomenon seems to work both ways. Thus, perhaps it shouldn’t have come as a surprise when Darvish couldn’t repeat his 2013 success. To the baseball world’s collective dismay, we simply didn’t have enough data yet to determine which Yu was the true Yu. I plan to do some research to see how often these severe spikes in xK% are mere aberrations versus how often they are sustained over time, indicating a legitimate skills improvement.
I have also done my best to compile a list of players with only one or two years’ worth of data who saw sizable spikes and drops in their K% minus xK% (“diff%”). The idea is to find players for whom we can’t really tell how much better (or worse) their actual K% is compared to their xK% because of conflicting data points. For example, will Corey Kluber be a guy who massively outperforms his xK% as he did in 2014, or does he only slightly outperform as he did in 2013? I present the list not to provide an answer but to posit: Which version of each of these players is more truthful? I guess we will know sometime in October.
Name: [2013 diff%, 2014 diff%]
- Andrew Cashner: -0.45%, +0.71%
- Chris Archer: -1.60%, -0.43%
- Corey Kluber: +0.62%, +3.30%
- Garrett Richards: -0.80%, +0.35%
- Hector Santiago: -3.44%, -1.70%
- Hyun-Jin Ryu: -0.10%, +1.11%
- Tyson Ross: -0.17%, -1.46%
And here some fantasy-relevant guys with only data from 2014:
Let’s be honest: Did anyone see Los Angeles Dodgers shortstop Dee Gordon‘s breakout coming? No. Not one person. It was fair to say he could hold his own, maybe fight off Cuban import Alexander Guerrero for a month or two. But Gordon, who hit .229 across 2012 and 2013, did not give really any indication that he’d be this valuable.
So I want to amend the question. Rather than did anyone see it coming, could anyone see it coming? Perhaps the answer is yes.
His first year in the majors was, by most measures, pretty successful. A 23-year-old Gordon batted .304 with 24 stolen bases in 56 games. It’s no wonder why people have hoped for Gordon to break out and have been wildly disappointed in his failure to do so. Leading up to 2014, his strikeout rate skyrocketed from 11.6 to 19.8 percent, and his low batting average on balls in play (BABIP) relative to other speedsters coupled with an absolute lack of power made for poor batting and on-base rates.
Fast-forward to 2014, and Gordon has shaved his strikeout rate by 4.5 percent, a huge margin. Meanwhile, his BABIP is way up — at .378, I can tell you without looking that it’s one of the highest in Major League Baseball. Thing is, he’s a guy with enough speed and to make it work, especially if he keeps racking up hits on bunts and balls in the infield. When I say “make it work,” though, I simply mean he will maintain an above-average BABIP, maybe in the .325-.335 range, rather than stay lofted in the .370s.
Meanwhile, the steals… Oh, man, the steals. They are legit, people. It’s hard to believe that he’s stealing in almost half of his opportunities, but he is. I thought, maybe the guy is getting lucky with the number of stolen base opportunities relative to all other baserunners. According to Baseball Reference, the average baserunner has the next base available to him about 37 percent of the time. So Gordon must have, like, a rate north of 40 or even 50 percent, right? Nay, squire — Gordon has had an open base before him only 33 percent of the time.
I want to do two things, now: predict Gordon’s end-of-season stats, and predict his rest-of-season stats. Without further ado:
Revised end-of-season 2014 projection: .276/.316/.352, 82 R, 2 HR, 42 RBI, 81 SB (156 games)
That’s right, folks. This is the Billy Hamilton you were looking for. It’s important to note that I project him for 156 games, but there’s a possibility that if he falls into a deep funk, Guerrero could usurp Gordon’s role. Worse, Guerrero could do so before a slump even hits, given the $28 million the Dodgers are now watching waste away in Triple-A.
As much as it is important to see Gordon’s end-of-year stat line, it’s the rest-of-year stats that truly matter most, especially if you’re trying to decide whether to sell high on the guy or simply hang tight.
Rest-of-season projection: .261/.300/.326, 58 R, 1 HR, 31 RBI, 57 SB (118 games)
Bottom line: he’s worth his weight in gold based solely on his steals. But a .261 batting average and .300 on-base percentage don’t bode especially well for his high runs tally as well as the frequency at which he will be able to steal bases. With almost a steal every other game, though, you’re nitpicking if you are complaining about a few percentage points of OBP affected his steals.
Although I just trivialized OBP, it is worth monitoring his decline, because it will happen — trust me. Dee Gordon is not a .322 hitter, let alone a .300 hitter. He may be able to luck his way to a nice batting average, though, with a few more bunt base-hits here and there.
Overall, though, he is still not a great hitter and doesn’t get on base as much as you’d like to make him much more than a one-category player. If you’ve already staked yourself to a massive lead in steals, I’d sell high — although when I say high, I mean really high. Fifty-seven swipes in a rotisserie league is incredibly valuable. My main roto league has 80 percent more home runs than steals — that is, a home run is worth about 55 percent of every steal. Now, that’s not to say Gordon is worth a guy who can hit 103 home runs, because 1) that’s impossible, 2) Gordon simply doesn’t contribute in many other categories other than maybe runs, and 3) he’s no guarantee to finish out the year at second base. But you could probably get a really solid, well-rounded top pick (read: top-50 player) for Gordon in a trade today — maybe better.
When it’s all said and done, I think Gordon could finish as high as top-30 on the ESPN Player Rater if he can last an entire season with Guerrero breathing down his neck. And I would hold on to him until I observe a sizable downward trend in his on-base abilities midseason.
Bold Prediction #3: Corey Kluber is this year’s Hisashi Iwakuma
Bold Prediction #2: Brad Miller will be a top-5 shortstop
Bold Prediction #1: Tyson Ross will be a top-45 starter (until he reaches his innings cap)
I’ve heard it through the grapevine: maybe Washington Nationals starter Dan Haren is due for a bounce-back season. In his defense, he recovered from the second-worst WHIP of his career, and his strikeout rate leaped back up to classic Haren at his peak. The problem lay in his propensity to give up home runs at an alarming rate. A blip on the radar, right? Eh…
First, the too-many-homers thing has been a problem for two years now, and it hasn’t really gotten better. Second, batters’ contact rate against Haren was the highest of his career last year. And in 2012? It was the second-worst. It’s an alarming trend, indicating that batters are squaring up his pitches better than ever before. It reinforces both the notion that home runs may continue to plague him as well as the following: last year’s strikeout rate was a fluke.
I have previously discussed the strong correlation between strikeouts per nine innings (K/9) and contact/swinging-strike rates. In it, I mentioned Haren in particular, whose PITCHf/x data predicted a strikeout rate closer to 7 K/9 than 8 — and it made sense, given his decreasing strikeouts (7.25 K/9 in 2011, 7.23 K/9 in 2012, 7.07 xK/9 in 2013) and increasing contact rates against him by hitters (79.3% in 2011, 80.9% in 2012, 81.0% in 2013).
Maybe Haren will recover and start missing more bats, but projections calling for a K/9 in the high 7’s is disconcerting to me. I’ll be bold and take the proverbial under: Haren is fully in decline, and his strikeout rate will reflect it in 2014.
Rankings based on 10-team standard 5×5 rotisserie format.
Name – R / RBI / HR / SB / BA
- Mike Trout – 119 / 91 / 31 / 39 / .320
- Ryan Braun – 98 / 103 / 30 / 28 / .308
- Andrew McCutchen – 102 / 90 / 23 / 27 / .298
- Adam Jones – 97 / 91 / 32 / 15 / .283
- Jose Bautista – 101 / 96 / 37 / 6 / .276
- Carlos Gonzalez – 92 / 86 / 24 / 20 / .299
- Matt Holliday – 95 / 97 / 24 / 5 / .300
- Carlos Gomez – 95 / 69 / 24 / 39 / .268
- Alex Rios – 91 / 82 / 21 / 28 / .284
- Hunter Pence – 88 / 99 / 23 / 14 / .275
- Jay Bruce – 86 / 101 / 33 / 8 / .253
- Jacoby Ellsbury – 84 / 56 / 13 / 45 / .286
- Justin Upton – 95 / 77 / 24 / 15 / .270
- Josh Hamilton – 79 / 92 / 28 / 8 / .272
- Austin Jackson – 105 / 53 / 16 / 13 / .292
- Alex Gordon – 90 / 76 / 19 / 12 /.281
- Shane Victorino – 91 / 62 / 16 / 26 / .278
- Yoenis Cespedes – 78 / 87 / 26 / 12 / .265
- Michael Cuddyer – 86 / 84 / 21 / 10 / .271
- Giancarlo Stanton – 75 / 85 / 31 / 5 / .259
- Bryce Harper – 88 / 60 / 21 / 15 / .273
- Yasiel Puig – 91 / 73 / 19 / 16 / .256
- Carlos Beltran – 75 / 80 / 22 / 3 / .286
- Torii Hunter – 79 / 83 / 17 / 6 / .283
- Curtis Granderson – 81 / 63 / 32 / 15 / .250
- Jayson Werth – 68 / 62 / 23 / 13 / .298
- Starling Marte – 89 / 51 / 14 / 43 / .249
- Adam Eaton – 98 / 45 / 10 / 29 / .274
- Norichika Aoki – 87 / 47 / 11 / 25 / .289
- Matt Kemp – 70 / 68 / 20 / 13 / .294
- Jason Heyward – 82 / 65 / 25 / 11 / .263
- Melky Cabrera – 77 / 66 / 14 / 11 / .297
- Michael Bourn – 94 / 52 / 7 / 31 / .269
- Alfonso Soriano – 72 / 99 / 27 / 7 / .241
- Carl Crawford – 81 / 62 / 12 / 20 / .284
- Shin-Soo Choo – 77 / 66 / 17 / 19 / .272
- Nelson Cruz – 66 / 81 / 25 / 10 / .267
- Coco Crisp – 84 / 59 / 11 / 29 / .264
- Wil Myers – 82 / 86 / 17 / 8 / .258
- Nick Markakis – 83 / 75 / 13 / 1 / .281
- Khris Davis – 74 / 74 / 23 / 8 / .254
- Desmond Jennings – 87 / 51 / 14 / 26 / .255
- Rajai Davis – 68 / 44 / 8 / 47 / .267
- Billy Hamilton – 77 / 39 / 2 / 68 / .241
- Brett Gardner – 92 / 48 / 7 / 27 / .263
- Justin Ruggiano – 63 / 63 / 22 / 18 / .253
- Angel Pagan – 70 / 51 / 8 / 22 / .285
- Domonic Brown – 68 / 79 / 19 / 6 / .251
- Michael Brantley – 66 / 59 / 8 / 17 / .285
- B.J. Upton – 72 / 60 / 15 / 27 / .224
- Christian Yelich – 80 / 53 / 11 / 21 / .246
- Josh Reddick – 71 / 66 / 19 / 8 / .240
- Will Venable – 61 / 51 / 12 / 24 / .265
- Josh Willingham – 67 / 77 / 21 / 3 / .237
- Andre Ethier – 60 / 64 / 15 / 3 / .281
- Dayan Viciedo – 61 / 68 / 21 / 0 / .264
- Colby Rasmus – 75 / 63 / 19 / 4 / .244
- Corey Hart – 64 / 61 / 16 / 3 / .272
- Kole Calhoun – 61 / 65 / 16 / 5 / .269
- Gerardo Parra – 66 / 51 / 10 / 10 / .281
Thoughts, lots of ’em:
- Full disclosure: I have NO IDEA what to do for Billy Hamilton. I did a brief bit of research to see how a player’s stolen base trend changed throughout the minorsand into the majors, and for the most part, a player still attempts to steal at about the same frequency in the majors as he did in Triple-A. As for Hamilton’s on-base percentage, that’s the million-dollar question. He’s a game-changer, but I don’t know if he’s worth taking in the first five or six rounds, as I’ve clearly shown above.
- Ryan Braun, folks. He’s being drafted 17th on average in ESPN mock drafts right now, but I don’t see how he won’t be a top-10 or possibly top-5 fantasy player by year’s end. On their Fantasy Focus podcast, Eric Karabell and Tristan Cockcroft argued about how many bases Braun will steal. My projection is lofty; Karabell is pretty negative about it, thinking closer to 15 swipes. Still, give him a mere 10 stolen bases and he’s still the game’s second-best outfielder. He’s a rich man’s Andrew McCutchen formerly on PEDs. So… not quite McCutchen, but you know.
- Speaking of PEDs, it’s weird to see Melky Cabrera’s name on that list, yeah? A look at his peripherals last year shows he may have suffered some bad luck beyond any PED regression (if such a thing exists), including a horrid AB/RBI rate that’s all but out of Melky’s hands. I’ll give it another season before writing him off completely; we tend to have too short of memories when it comes to players in fantasy. He was solid for two years, and I’ll take a two-year trend over one. Considering he’s being drafted 52nd overall, I guess this officially makes him a sleeper.
- CarGo is ranked uncharacteristically low, but my projection took the under on his games player. I maintain if he can play a full year, he’s actually a smidge better than Braun. If you’re cool with risk and can build a roster around the possibility that CarGo will be sidelined at any given moment, he’s worth the massive upside of staying healthy just once. Please, CarGo. For us.
- Speaking of guys with built-in injury risks: Ellsbury, Stanton, Harper, Granderson, Werth. If you want to construct a risky, huge-upside team, make these guys your five outfielders. Don’t forget the Grandy Man hit more than 40 home runs in 2012 and 2013, and Stanton can hit 40 home runs with his eyes closed. He’s, what, 24 years old? That’s insane.
- Touching on Harper again, I know he’s pretty low here. If he can play a full 162 or a close to it, he’s a 30/20 guy who will crack the top 10. I think the MVP talk can be put to rest before the season starts, though.
- Wait, guys — WHAT? Jose Bautista? Yeah, dude. He’s a monster and, like Granderson, he still has huge power. It never left, and he was on pace for big things last year before it got derailed. Take a leap of faith. One of these guys has to stay healthy this year, right?
- Puig will naturally be a topic of discussion all year. I paid careful attention to Puig’s projection; let me be very clear that I think this is his absolute floor. This is looking at huge regression in BAbip (batting average on balls in play) and HR/FB (home runs per fly ball). Honestly, he’s probably better than a .300-BAbip batter, and if the power and speed is real, this is a huge undervalue. I’m well aware that every other projection has him snugly in the top 30 or so players, so this is likely falling on deaf ears.
- I wrote about Cruz’s immense power potential that is perpetually muted by his inability to stay on the field. You know what’s super interesting? He’ll likely be used in some weird rotation with Nolan Reimold and Henry Urrutia all at left field and the designated hitter, with him seeing the lion’s share of at-bats at DH — all but removing his injury risk. Give him another 150 at-bats and he’ll gladly reward you with eight to 10 bombs. Now, to remove that PED risk, too.
- Khris “Krush” Davis is interesting because it’s hard to tell if his power is super-for-real or just regular for-real. Like Puig, I think this is more of a floor projection — and that’s saying a lot. The strikeouts might be a problem, but if you’re drafting him for his batting average, you’re not doing it right.
- Yelich at No. 51 was really interesting to me. He’s a sneaky speed guy with something like a 15-homer, 25-steal upside and a solid batting average, making him a must-draft outfielder. If only there were Marlins on base for him to knock in…
- Honorable mentions for cheap power Raul Ibanez and Mike Morse
Honorable mentions for cheap speed: Leonys Martin and Ben Revere. I actually like Martin a lot more than his lack of projection here indicates. He’s got pop, and a full season in the Texas Rangers’ outfield makes him 100-percent draftworthy.
- P.S. I don’t have much faith in Marlon Byrd. But take a chance on him if you want.
Rankings based on standard 5×5 rotisserie format.
Name – R / RBI / HR / SB / BA
- Miguel Cabrera – 105 / 124 / 39 / 4 / .332
- Adrian Beltre – 95 / 106 / 31 / 1 / .297
- Evan Longoria – 93 / 108 / 32 / 2 / .282
- David Wright – 88 / 90 / 22 / 20 / .299
- Ryan Zimmerman – 84 / 85 / 24 / 4 / .283
- Josh Donaldson – 78 / 81 / 22 / 6 / .274
- Manny Machado – 86 / 74 / 19 / 6 / .276
- Kyle Seager – 75 / 78 / 21 / 11 / .259
- Pedro Alvarez – 68 / 94 / 33 / 1 / .238
- Aramis Ramirez – 61 / 76 / 20 / 2 / .291
- Xander Bogaerts
- Pablo Sandoval – 65 / 77 / 15 / 1 / .289
- Will Middlebrooks – 54 / 74 / 23 / 5 / .256
- Chase Headley – 64 / 64 / 14 / 12 / .259
- Nolan Arenado – 59 / 62 / 13 / 2 / .282
- Brett Lawrie – 59 / 50 / 11 / 12 / .268
- I think 19 home runs for Machado is waaaaaaay too optimistic. I would be happy for just 14 bombs again. Still, taking those five homers away doesn’t affect his placement in the rankings, as he’s being buoyed by counting stats and a reliable batting average (compared to everyone on the list who follows him).
- Bogaerts is a sneaky pick for power up the middle once he moves to shortstop. He may be worth a bump in the rankings for that. I don’t want to get too optimistic the numbers he can put up, but somewhere between 15 to 20 home runs and a .290 batting average (hence, why he’s snugly between Ramirez and Sandoval) sounds about right.
- For all of Ramirez’s consistency, he’s a good bet to bounce back. However, he hit a career-high percentage of ground balls, something of which he may not fully control, but he still needs to hit fly balls to hit home runs. If you can squeak 150 games out of him, he’s still good for 20 homers, but that may be asking too much at this point.
- I will not, not, not support Lawrie. I get it: he was a top prospect once with massive potential. Now what? Am I going to put a basically unproven third baseman in my top 10 with the hopes this will be his breakout year? No way. If I miss the Lawrie train as it leaves the station, and he goes off this year, then so be it. But I have Middlebrooks with huge power (31 home runs per 162 games) and the opportunity to have third base to himself. His BAbip 2012 was high and then it tanked in 2013. Watch it find a happy medium in 2014 as Middlebrooks is able to keep the keystone to himself.