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|
… 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:
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.
(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.
Maybe this is absurd, but I’ve never honestly checked the accuracy of my projections. It’s partly because I have placed a lot of trust in a computer that runs regressions with reliable data I have supplied, but it’s mostly because I originally started doing this for my own sake. I used to rely on ESPN’s projections, but the former journalist in me started to realize: it has a customer to please, and the customer may not be pleased, for example, if he sees Corey Kluber ranked in the Top 60 starting pitchers for 2014. (At this point, I am giving ESPN an out, given that everyone at FanGraphs and elsewhere knew the kind of upside he possessed.) Kluber is not the issue, however; the issue is that although ESPN (probably) wants to do its best, it also does not want to alienate its readers who, given its enormous audience, are more likely to be less statistically-inclined than FanGraphs’ faction of die-hards.
In sum: I started doing this because I no longer trusted projections put forth by popular media outlets.
So I didn’t really care how every single projection turned out. I wanted to find the players I thought were undervalued. For three years, it has largely worked in my rotisserie league. (Honestly, I am a complete mess when I enter a snake draft.)
Anyway. All of that is no longer. I quickly sampled 2014’s qualified pitchers — 88 in all — to investigate who panned out and who didn’t. I will ignore wins because they are pretty difficult to project with accuracy; I’m more concerned about ERA, WHIP and K’s.
Here is a nifty table that quickly summarizes what would have been tedious to transcribe. You will see a lot of repeat offenders, which should come as no surprise. At least there is some semblance of a pattern for the misses: I underestimated unknown quantities (and aces, who all decided to set the world ablaze in 2014) and overestimated guys in their decline. There isn’t much of a pattern to the guys I got right. Just thank mathematics and intuition for that.
Here would be a shortlist of my most accurate projections from last year, measured by me using the eye test:
Name: 2014 projected stats (actual stats)
Nathan Eovaldi: 5 W, 3.82 ERA, 1.32 WHIP, 6.4 K/9 (6 W, 4.37 ERA, 1.33 WHIP, 6/4 K/9)
R.A. Dickey: 11 W, 3.84 ERA, 1.24 WHIP, 7.2 K/9 (14 W, 3.71 ERA, 1.23 WHIP, 7.2 K/9)
Alex Cobb: 12 W, 3.49 ERA, 1.17 WHIP, 8.1 K/9 (10 W, 2.87 ERA, 1.14 WHIP, 8.1 K/9)
Hiroki Kuroda: 11 W, 3.60 ERA, 1.18 WHIP, 6.7 K/9 (11 W, 3.71 ERA, 1.14 WHIP, 6.6 K/9)
John Lackey: 10 W, 3.67 ERA, 1.25 WHIP, 7.5 K/9 (14 W, 3.82 ERA, 1.28 WHIP, 7.5 K/9)
Kyle Lohse: 9 W, 3.60 ERA, 1.17 WHIP, 6.1 K/9 (13 W, 3.54 ERA, 1.15 WHIP, 6.4 K/9)
If it brings consolation to the reader, I have since tightened the part of the projection system that predicts win totals. I’m not gonna lie, it was pretty primitive last year because I thought it’s already a crapshoot to begin with. Obviously, it shows, even in the small sample above. It’s still difficult given the volatility inherent in the category, but the formulas are now precise.
I think someone flipped the ESPN Player Rater upside-down for second basemen. It’s, how to say it… bizarre. Two, maybe three of the top 10 now were drafted as such on draft day. The rest are scattered all over. Maybe this is how it looked last year, but honestly, I don’t remember. However, I know enough about statistics and this beautiful sport to know that a lot of crazy stuff can happen in small sample sizes, and when it happens at the beginning of the season, the strangeness magnifies.
I don’t have high hopes for the current top 10, though. I think six of them have a chance to stay there, but as we know all too well, anything can happen.
Dee Gordon, LAD | #1 2B
I wrote a lengthy post about Gordon, but if you are lazy, here is the quickest of recaps: I project .261/.300/.326, 58 R, 1 HR, 31 RBI and 57 SB for his remaining 118 (or so) games — that is, if his caliber of play enables him to stay in the lineup all year with Alexander Guerrero and his $28 million contract breathing down Gordon’s neck from Triple-A.
Brian Dozier, MIN | #2 2B
Man, did I ever underestimate this guy. It’s difficult when I player who never really hit for much power in the minors launches 18 long balls and swipes 14 bags to boot. It’s hard to give him credit for what he did, and I regressed his ratio of home runs to fly balls (HR/FB) given his lack of track record. It does make a bit of sense, though, if you remove the historical component: the dude is 27 years old, essentially entering his prime years. It doesn’t really matter, though; the fringe 20 HR, 20 SB candidate is now on pace for a 40-40 season. That won’t happen, but do I think there’s a legitimate chance he hits 25? Absolutely. And who can even say about the stolen bases. You have to think it’ll slow down, but will it? At one time, I essentially called Dozier a poor man’s Ian Desmond (I also called him Brian “Bull” Dozier, which I since regret) because of his 20-20 potential but at the expense of batting average. At this point, he’s going to be a 25-25 guy, and that plus a .240 batting average perhaps makes him more valuable than Desmond and his potential 21-19-.280. And yet, after all this talk, maybe we haven’t even touched upon the most impressive statistic: his walk rate of 15.7 percent (1oth among all qualified batters), which hedges against his poor batting average. Maybe it regresses, but it doesn’t matter — Dozier is for real.
Daniel Murphy, NYM | #3 2B
I’d rather just write An Ode to Murphy, as he has long been a man-crush of mine. I owned him in 2012, when he was still a fledgling 10-10 candidate with a solid batting average. In 2013, I owned him for a little while but dropped him before he stole all the bags in all the stadiums. Now, he’s doing it all again: hitting for a high average, stealing bases and lobbing a few over the fence. He may be at his all-time best, striking out less than he did in 2012 and 2013 and walking at a career-high rate. He has also been able to sustain the fly ball rate that fueled his homer binge last year, so a 15-20 season is not out of the question at this point. Oh, the leagues we could have conquered together, Daniel…
Emilio Bonifacio, CHC | #7 2B
Bonifacio had one good year, back in 2011. This year is looking a lot like that year. The first thing that pops out about that year, though: a .372 batting average on balls in play (BABIP), a good deal above his career .336 rate. This year? A .398 BABIP. He’s batting .313 now, but that’s not as exorbitant as I would expect it to be. It has a long way to fall, and it’s only a matter of time until Bonifacio is no longer relevant in a 10-team league — that is, unless he maintains his high BABIP, which he has done before, but only on one occasion. I would sell high while the window is still open.
Anthony Rendon, WAS | #10 2B
At this point, he is what he is. I think you can expect this kind of production from here on out, which doesn’t make him an excellent second baseman, but you could certainly do worse.
Robinson Cano, SEA | #11 2B
I projected Bob Cano for 30 home runs, and I defended his plate production in a prior post. I can’t backtrack, and I won’t appear credible for recanting my statements. But I had run a projection on Cano back in March that factored in Safeco Field’s park factors, and let me tell you, it wasn’t pretty: 17 home runs. I kind of wish I stuck with it; it seemed ridiculous, especially for a home run hitter in the prime of his career. Even if he hit fewer at home, he could make up for the deficiency on the road. In his defense, his HR/FB rate is incredibly low, and that will regress; in that sense, he’s a buy-low candidate. But he’s hitting the fewest fly balls of his career, and as a Cano owner, I would feel fortunate to notch, say, 23 bombs. With that said, that means he’s still got 22 more home runs to hit, so if you succeed in buying low on him, it should pay dividends.
Dustin Pedroia, BOS | #12 2B
OK, I’m gonna say it: I’m afraid Pedroia may not reach 10 steals or 10 home runs. He simply does not hit as many fly balls as he used to, and given his HR/FB rate is right where it was last year, maybe this is the new norm, and last year wasn’t an anomaly. I would prospectively buy low on Pedroia, but I’d do it for cheap. Mark my words: Pedroia may no longer be a viable top-10 second baseman after this year.
Matt Carpenter, STL | #17 2B
Strikeouts are way up, and the BABIP is down. He has hit for high BABIPs the past two years, so it’s conceivable that his batting average rises a few ticks, but it’s the aforementioned strikeouts that are most concerning. He’s still on pace to record something like 100 runs, which is pretty awesome, so you can’t really drop him. But the RBI aren’t really there, and with not much power, speed or batting average ability, he’s starting to become a liability.
Jason Kipnis, CLE | #25 2B
Patience, young Padawan. Kipnis will come around. Don’t be surprised when he’s not near the top of the year-end Player Rater, though. That’s what injuries will do to a great player.
I’ve been slacking on my streamer picks, so let’s cut straight to the chase.
Tyson Ross, SD @ CIN
Mr. Ross is the real deal, my friends. He’s 10th of all pitchers in batters’ contact on pitches in the zone, sandwiched between the unfamiliar names of Jose Fernandez and Zack Greinke (and the players who precede him include Michael Wacha, Yordano Ventura, Julio Teheran and Max Scherzer). He doesn’t make batters chase pitches at an overwhelming rate, but they make contact on such pitches only half the time, which ranks Ross fourth only to Ervin Santana, Garrett Richards and Masahiro Tanaka. At 7.99 K/9, his K-rate should actually improve. You can really only bash him for his walk rate, but it’s no worse than Gio Gonzalez or Justin Verlander. I don’t care if it’s a road game; Ross should be owned in all leagues at this point.
Drew Hutchison, TOR @ TEX
I’ll be honest with you: I’m not totally sold on this matchup. Hutchison hasn’t been very impressive, but there are simply not many matchups worth exploiting on Friday. I like Hutchison for his strikeouts, and before his last start (during which he walked four), he had only walked five guys across 32-1/3 innings. His control escaped him, but if it comes back, he should be able to control a miserable Texas offense that ranks 26th of 30 teams in extra-base hits.
Bartolo Colon, NYM @ WAS
Again, not crazy about this one, either. But Colon has been incredibly unlucky. The dude is walking fewer than a batter per nine innings (0.9 BB/9), so all the baserunners (and, consequently, earned runs) he has allowed are a largely a function of an elevated batting average on balls in play (BABIP). It’s hard to trust a guy who’s mired in a slump, but the luck should eventually turn in his favor. Who’s to say it won’t be this weekend? I’d take a chance. The Nationals don’t score a ton of runs, either. It’s not the best play, but it’s safer than most.
Travis Wood, CHC vs. MIL
After a hot start, albeit a brief one, Wood has since collapsed in spectacular fashion, sporting a 4.91 ERA and 1.43 WHIP. So why would I ever vouch for this guy? Check out his home-road splits:
The splits are ridiculous. They speak for themselves, although I’ll highlight the ones that are most impressive. With that said, he’s starting at home. Enough said.
Good luck and happy streaming!
Should I panic? How can I even tackle this question right now? The breadth of pitchers who performed poorly so far is astonishing, so it’s understandable why you might want to not start the Philadelphia Phillies’ Cliff Lee in his next start or cut ties with Chicago White Sox closer Nate Jones all together. There are times you should panic, and there are times you should remain calm. I’m here to help you tell the difference.
Disclaimer: I get kind of annoyed when analysts waffle with guys, like, “well, I know he’s going to fall apart, but I’ll give him one more chance”. NO! You know he’s going to fall apart, but you’re giving yourself an out! I’m drawing a line in the sand, across this line YOU DO NOT — also, Dude, Chinaman is not the preferred nomenclature. … Wait, where was I? Anyway, I’m not letting myself off the hook. I am here to make the impulse decisions with (and maybe for) you, because sometimes, these impulse decisions make or break a season. Unfortunately, making them really early in the season is an absolutely horrifying experience.
Alex Cobb, SP (TB)
Dilemma: He was less than sharp, and although he gave up only five hits in five innings, he managed to walk more batters than he struck out (four to three). This is highly unlike Cobb, and that’s why I’m more inclined to think it was a case of first-start jitters rather than the beginning of a depressing trend.
Verdict: Don’t panic.
Homer Bailey, SP (CIN)
Dilemma: Lots of hits with as many walks as strikeouts. It was ugly, but he did face the Cardinals, which is no easy task. It’s hard to cut Bailey loose with how much you invested in him on draft day (outside of keeper leagues), but his breakout last year didn’t come out of nowhere, to which his second-half-of-2012 owners can attest. Unfortunately, he faces the Cardinals again in his next start. I’m not one to sit a guy early in the season, and I think it’s Bailey who will make adjustments the second time around, not the Cardinals.
Verdict: Don’t panic.
Stephen Strasburg, SP (WAS)
Dilemma: A 6.00 ERA?! Yeah, but 10 strikeouts in six innings and only a 1.167 WHIP. He got pretty unlucky, and that will happen from time to time. I would be more amped about the other batters he humiliated.
Verdict: Don’t panic.
CC Sabathia, SP (NYY)
Dilemma: Well, uh, he looked horrible. Against the Astros. It’s fine and dandy that he struck out a batter per innings and only walked one, but his fastball has become too hittable with that diminished velocity. I expect the trend to continue, and I think the solid strikeout total is the result of a free-swinging, hapless Astros offense. Remember, I said these are impulse decisions I’m making here. With a bevy of young pitching talent on waivers, I say…
C.J. Wilson, SP (LAA)
Dilemma: Kind of the same as Strasburg’s. High strikeouts and lots of hits sounds like an old wives’ tale about bad luck on balls in play that I’ve heard many a time. Wilson is not a second-tier starter anymore like he used to be, but he’s solid, and there’s no reason to fret.
Verdict: Don’t panic.
R.A. Dickey, SP (TOR)
Dilemma: Wow… Wow. Six walks. That hurts. I don’t know the first thing about throwing a knuckleball, and I’m sure if you have a bad day, it can be really be bad. But six walks? At least the strikeouts are there, but if your league is anything like any of mine, you probably got Dickey on the cheap. If I saw enticing performances by Seattle’s James Paxton or Toronto’s Drew Hutchison, I may cut ties, too. Surely no one else will touch him with a 10-foot pole until after his next start.
Corey Kluber, SP (CLE)
Dilemma: If you follow this website, you know how much I love Kluber, and how I preemptively purchased a five-year membership to the Society. Everything about the start is concerning, but I’m too proud to cut him loose. If you got him cheap, you can let him go and try your luck later. And I truly think he will break out; his peripherals were simply too good last year, and I don’t think you can fluke your way into talent like that. But perhaps I’m wrong…
Verdict: Don’t panic.
Cliff Lee, SP (PHI)
Dilemma: Wait, is this a serious question? Look, I know that sucked, but he’s freakin’ Cliff Lee. Calm down.
Verdict: Don’t panic.
Jonathan Papelbon, RP (PHI)
Dilemma: Dude, if you wanted to know what the end of the world would look like, this is it. Except in the form of a metaphor called Jonathan Papelbon.
Jim Johnson, RP (OAK)
Dilemma: I’ve expressed my distaste for Johnson before. He’s simply not good, and fantasy owners are blinded by two straight seasons of 50-plus saves. He would be lucky to save 35 this year without trouble; it looks like he may not get he chance to save 20 by the end of the week.
Nate Jones, RP (CHW)
Dilemma: The closer role was never a lock for him to keep. It looks like he agrees. Two hits, three walks and four earned runs without recording an out. Making Casper Wells look like a Cy Young candidate.
Rarely do you hear the term “upside” used for players entering their age-33 seasons, but hear me out: Nelson Cruz has upside.
Reason #1: He’ll likely come at a PEDs discount. Is this reasonable? Sure. Rational? Not entirely. It’s hard to say how much performance enhancing drugs has affected Cruz’s performance as a hitter, but there’s no denying he’s a monster. I think people run for the hills when they hear “PEDs,” but it’s simply too difficult to prove how much PEDs affects players. Feel free to disagree; the point of the matter is Cruz could fall in drafts because of Biogenesis.
Reason #2: If he makes it through a full season, he is capable of hitting 35 to 40 home runs. Of course, there’s a lot riding on the “if” portion of that claim, and detractors will be quick to note that Cruz hit only 24 home runs in his only full, healthy season as a Ranger. In that season, however, he posted his lowest HR/FB of his career. In every other almost-full season, he has hit home runs at full-season paces of 33, 37, 40 and 42. That’s an average of 38 home runs per year. So: imagine if he stays healthy.
ESPN projects Cruz to hit 26 home runs (they have him ranked No. 41) — and that’s reasonable, because the dude always get injured (or suspended). Still, injury woes can’t hold back the love for Carlos Gonzalez. Gonzalez and Cruz are in completely different leagues in regard to talent, but the point still stands: If CarGo can make it through a full season uninjured, he is the second-best player in baseball behind Mike Trout. The same goes for Cruz. People only expect him to play 120 games, but if he can play a full 162 (or close to it), he could threaten to hit 40.
I think that’s worthy of a bit of a premium, especially if guys like Alfonso Soriano (ESPN No. 38 OF) and Curtis Granderson (ESPN No. 40 OF) are expected to go off the board before Cruz but essentially put up the same stats as him in a full season.
Not everyone needs a player like Cruz, but if you’re looking for consistent power with upside at a possible discount, Cruz is your man. I hadn’t considered targeting him until now, but after signing a very disappointing contract with the Baltimore Orioles, he may have something to prove this year. I’ll gladly be the owner to benefit from that.