Tagged: Curtis Granderson

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.

A smorgasbord of fantasy baseball advice

Need a Streamer has been slow lately, to say the least. I’ve missed discussing a lot of player news and opportunities to provide good streaming picks. So I’m going to try something new, and maybe it’ll stick. It should be fairly explanatory. I hope it holds readers over until the end of this week, which is probably the busiest week for me in a long time.

Player to add that isn’t Gregory PolancoA.J. Pollock, ARI OF
He’s on the DL, so you’ve got time to pull the trigger. His batting average isn’t for real, but the 6 homers and 8 steals are nice, and he will more than likely join the small number of players who achieve double-digits in each category in a given year. I would expect a batting average closer to .265, but if you can punt average for counting stats in a deeper league, I would go for it.

Hitter to drop: Jay Bruce, CIN OF
Honorable mention goes to Brandon Phillips, Bruce’s teammate, but it is more fitting that the suggested replacement player can actually replace someone. Bruce is striking out about 5 percentage points more often than last year and almost 8 percentage points more than his career rate. Meanwhile, he is hitting more ground balls than fly balls, whereas about two-thirds of all of Bruce’s batted balls over his career have been put in the air. The sample size is quite large now, and I think there may be something wrong with the slugger. His ratio of home runs to fly balls (HR/FB) is a little bit deflated, but even if it returns to his career average, I still wouldn’t expect him to hit much more than 20 home runs, and that’s a serious problem for a guy who’s value lies solely in his power. Bruce is shaping up to be the next Curtis Granderson, and I have legitimate concerns about his current and future value.

Pitcher to add: Marcus Stroman, TOR
Stroman could quickly rise to the top as Toronto’s ace come 2015 if he lives up to his minor league numbers. So far, he has. I liked Stroman a lot as a prospect, as he averaged 10.6 strikeouts and only 2.4 walks per nine innings. He began the year in the bullpen and suffered a couple of brutal appearances in a row, so his two recent (and excellent) starts have improved his numbers to a still-shaky 5.40 ERA and 1.53 WHIP. But I think he’s a starter by trade, and his 13 strikeouts and two walks over 12 innings as a starter support such a claim. Your window to claim Stroman may stay open for a while, especially if other owners simply look at his misleading ERA and WHIP or, on ESPN, his average points, which stands at an underwhelming 3.3 per appearance. However, if he keeps flashing this kind of quality, you’ll start to run out of time.

Wednesday streamer, other than Stroman: Rubby De La Rosa, BOS
I’ll be honest, I’m not thrilled about him, but everyone has caught on to Tyson Ross (although he’s still only 73-percent owned), so tomorrow’s options are slim. De La Rosa comes with K’s but also BB’s; however, he carries a 13-to-2 K/BB ratio into this start on the road, so perhaps he can continue to keep the command issues under control.

Prospect(s) to watch: Joc Pederson, LAD OF, and Mookie Betts, BOS 2B
Pederson and Betts will likely not be up any time soon, as they’re blocked by some pretty large figures at their respective positions. But given the hype surrounding a couple of 2014’s call-ups in George Springer and, most recently, Gregory Polanco, it’s good to know who the next impact players will be. Pederson is batting .327/.437/.615 with 16 home runs and 14 steals. Are you serious? I think he’s a bit too far to reach a 40/40 season, but 30/30 is probably at this point. It’s unfortunate the Dodgers are letting him rot in the minors beneath a pile of unmovable cash in their impacted outfield. Betts recently moved up to Triple-A Pawtucket; prior to this move, he stole 22 bases in 285 plate appearances while batting .346 with almost twice as many walks as strikeouts. He’s going to be really good, with astounding plate discipline, decent speed and a little bit of pop, too. If you hear Pederson’s and Bett’s names, or the names of their predecessors (Yasiel Puig, Carl Crawford, Matt Kemp, Andre Ethier, Dustin Pedroia…), in next month’s trade talks, get ready to prospectively add, add, add.

2014 Rankings: Outfielders

Rankings based on 10-team standard 5×5 rotisserie format.

Name – R / RBI / HR / SB / BA

  1. Mike Trout – 119 / 91 / 31 / 39 / .320
  2. Ryan Braun – 98 / 103 / 30 / 28 / .308
  3. Andrew McCutchen – 102 / 90 / 23 / 27 / .298
  4. Adam Jones – 97 / 91 / 32 / 15 / .283
  5. Jose Bautista – 101 / 96 / 37 / 6 / .276
  6. Carlos Gonzalez – 92 / 86 / 24 / 20 / .299
  7. Matt Holliday – 95 / 97 / 24 / 5 / .300
  8. Carlos Gomez – 95 / 69 / 24 / 39 / .268
  9. Alex Rios – 91 / 82 / 21 / 28 / .284
  10. Hunter Pence – 88 / 99 / 23 / 14 / .275
  11. Jay Bruce – 86 / 101 / 33 / 8 / .253
  12. Jacoby Ellsbury – 84 / 56 / 13 / 45 / .286
  13. Justin Upton – 95 / 77 / 24 / 15 / .270
  14. Josh Hamilton – 79 / 92 / 28 / 8 / .272
  15. Austin Jackson – 105 / 53 / 16 / 13 / .292
  16. Alex Gordon – 90 / 76 / 19 / 12 /.281
  17. Shane Victorino – 91 / 62 / 16 / 26 / .278
  18. Yoenis Cespedes – 78 / 87 / 26 / 12 / .265
  19. Michael Cuddyer – 86 / 84 / 21 / 10 / .271
  20. Giancarlo Stanton – 75 / 85 / 31 / 5 / .259
  21. Bryce Harper – 88 / 60 / 21 / 15 / .273
  22. Yasiel Puig – 91 / 73 / 19 / 16 / .256
  23. Carlos Beltran – 75 / 80 / 22 / 3 / .286
  24. Torii Hunter – 79 / 83 / 17 / 6 / .283
  25. Curtis Granderson – 81 / 63 / 32 / 15 / .250
  26. Jayson Werth – 68 / 62 / 23 / 13 / .298
  27. Starling Marte – 89 / 51 / 14 / 43 / .249
  28. Adam Eaton – 98 / 45 / 10 / 29 / .274
  29. Norichika Aoki – 87 / 47 / 11 / 25 / .289
  30. Matt Kemp – 70 / 68 / 20 / 13 / .294
  31. Jason Heyward – 82 / 65 / 25 / 11 / .263
  32. Melky Cabrera – 77 / 66 / 14 / 11 / .297
  33. Michael Bourn – 94 / 52 / 7 / 31 / .269
  34. Alfonso Soriano – 72 / 99 / 27 / 7 / .241
  35. Carl Crawford – 81 / 62 / 12 / 20 / .284
  36. Shin-Soo Choo – 77 / 66 / 17 / 19 / .272
  37. Nelson Cruz – 66 / 81 / 25 / 10 / .267
  38. Coco Crisp – 84 / 59 / 11 / 29 / .264
  39. Wil Myers – 82 / 86 / 17 / 8 / .258
  40. Nick Markakis – 83 / 75 / 13 / 1 / .281
  41. Khris Davis – 74 / 74 / 23 / 8 / .254
  42. Desmond Jennings – 87 / 51 / 14 / 26 / .255
  43. Rajai Davis – 68 / 44 / 8 / 47 / .267
  44. Billy Hamilton – 77 / 39 / 2 / 68 / .241
  45. Brett Gardner – 92 / 48 / 7 / 27 / .263
  46. Justin Ruggiano – 63 / 63 / 22 / 18 / .253
  47. Angel Pagan – 70 / 51 / 8 / 22 / .285
  48. Domonic Brown – 68 / 79 / 19 / 6 / .251
  49. Michael Brantley – 66 / 59 / 8 / 17 / .285
  50. B.J. Upton – 72 / 60 / 15 / 27 / .224
  51. Christian Yelich – 80 / 53 / 11 / 21 / .246
  52. Josh Reddick – 71 / 66 / 19 / 8 / .240
  53. Will Venable – 61 / 51 / 12 / 24 / .265
  54. Josh Willingham – 67 / 77 / 21 / 3 / .237
  55. Andre Ethier – 60 / 64 / 15 / 3 / .281
  56. Dayan Viciedo – 61 / 68 / 21 / 0 / .264
  57. Colby Rasmus – 75 / 63 / 19 / 4 / .244
  58. Corey Hart – 64 / 61 / 16 / 3 / .272
  59. Kole Calhoun – 61 / 65 / 16 / 5 / .269
  60. 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.

Nelson Cruz’s fantasy value, regardless of his team

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 110 games, but if he can play a full 162 (or close to it), he could threaten 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.

Tigers and Pirates continue to puzzle; Mets gearing up

Nothing looked unusual when the Detroit Tigers traded first baseman Prince Fielder to the Boston Red Sox for second baseman Dustin Pedroia, despite the trade being very high-profile. It appeared as if the Tigers were clearing up salary space to sign starting pitcher and 2013 Cy Young winner Max Scherzer to a long-term deal. Instead, they dealt pitcher Doug Fister and signed outfielder Rajai Davis and former Yankee reliever Joba Chamberlain (great last name, by the way) for depth. So… now what? The salary they freed up has been spent, and all the moves made have been lackluster. And, in a latest turn of events, Scherzer is on the market. What the heck is going on?

(Although, honestly, I think Scherzer’s value peaked in 2013. Dude had control issues his whole career until the 2012 All-Star Break, and he’s about to enter the latter half of his career. 0

The Bucs have been worse. The Tigers’ moves have been sensible; the Pirates moves have been indefensible. Charlie Morton for three years? Edinson Volquez for one year? These guys are rotation fillers who expect to not contend. These are not the moves a contending team makes. Unfortunately, it appears they’re sold on Morton’s illusory 2013, and unless Volquez is merely for depth (beyond a No. 5 starter), this is money wasted.

Meanwhile, the New York Mets may fancy themselves contenders.

NYM sign OF Curtis Granderson
I didn’t realize the Grandy Man was so divisive. I guess Yankees fans are bitter or something. Maybe I’m overexposed to a microcosm of the Yankee-Red Sox rivalry. Regardless, four years, $50 million for a proven power hitter and decent defensive outfielder ain’t bad. I like it a whole lot more than the Jacoby Ellsbury signing, based mostly on the length. The Mets think they’ll contend, and while I think they won’t realistically do it until 2015 or later, they plan to make a 2013-Kansas-City-Royals-type of splash next season. Either that, or it’ll be a Blue Jays-caliber flop, but without the hype, so it won’t be as bad.

Winner: Mets
Preseason rank: Top-50 OF

NYM sign SP Big Fat Bartolo Colon
BFB revived his career, got caught with steroids, then continued to impress afterward. I have no idea how he does it, because metrics all point to some sort of regression, but his excellent command of his fastball must keep him afloat. (Other than, well, all his fat. OK, that was mean. Sorry!) Two years isn’t bad, especially if the Mets think they’ll contend this year… But I really don’t. But 2015? Maybe. World Series team? Probably not. So I don’t know. And, again, I can’t imagine Colon will repeat his 2012 and 2013. But who knows? He could be even better. Baseball is a funny sport. As far as fantasy baseball implications go, he’s going to arguably a worse team, and his strikeout rate is, well, pretty miserable. He’s a three-category contributor at best, but if he regresses, it could be more like zero categories.

Winner: Colon
Preseason rank: 69th