# 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

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):

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.

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

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

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

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

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

Xander Bogaerts, -3.88%
See Castellanos, Nick.

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

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

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

# What did and didn’t work this year

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

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

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

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

What I did right:

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

What I did wrong:

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

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

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

# Blind résumé: thoughts on perceived value

Fantasy analysts say things like, “I know, I know, blind résumés are cliché,” and then proceed to do them anyway. So.

I know, I know, blind résumés are cliché. But this is important, I swear. This is an exercise in perceived versus actual value, and exploiting market inefficiencies.

Note: I wish I had written this a month and a half ago (June 21, to be exact), when I talked about it with my good friend/league enemy Rob. You’ll see why.

OK, here are the stat lines, as of Sept. 5:

Player A: 81 R, 16 HR, 73 RBI, 6 SB, .301/.351/.458/.808
Player B: 73 R, 13 HR, 60 RBI, 8 SB, .295/.382/.481/.864

If we are talking about players’ offensive skills from a traditional standpoint, you can argue that Player B is perhaps more valuable, given the comparable batting average, better on-base percentage and better isolated power (ISO). However, this is a fantasy baseball blog, and Player A is clearly the more valuable one as he leads all categories except stolen bases.

The screenshot is from a pre-trade deadline conversation I was having with Rob. And if you haven’t caught on by now, Player A is Melky Cabrera, and Player B is Yasiel Puig. I set the blind résumé deadline at Sept. 5, marking Cabrera’s last game prior to missing the rest of the season with a finger injury.

Referring back to the side note at the beginning: On June 21, ESPN’s Tristan H. Cockcroft ranked Puig in his Top 10 and Cabrera outside his Top 50. At the time, the blind résumés would have looked more like this:

Cabrera: 47 R, 11 HR, 38 RBI, 4 SB, .300/.345/.476/.821
Puig: 39 R, 11 HR, 44 RBI, 7 SB, .321/.411/.538/.949

Honestly, their stat lines were less similar than they are now. Anyway, the important part to note is Cockcroft’s updated ranking are always going-forward rankings — that is, Puig will be a Top-10 player going forward. And on June 21, Cockcroft thought Puig was the 8th-best fantasy option available (Cabrera, meanwhile, 65th or so) despite him going almost a month without clearing an outfield fence.

Things have changed, obviously. Cabrera was the better player in all categories since June 21 — in fact, his triple-slash rates are almost identical more than two months later, serving as a testament to his consistency — and his fantasy contributions have dwarfed those of Puig. Still, Cockcroft ranks Puig the No. 14 outfielder (40th overall) and Cabrera No. 20 outfielder (62nd overall). Elsewhere, CBS Sports’ Al Melchior still lists Puig as his No. 4 outfielder. (He has omitted Cabrera from his list given the news of his injury.) Michael Hurcomb, also of CBS Sports, lists Puig as his No. 4 outfielder and Cabrera at No. 15. (His list was last updated Aug. 12, but it’s not like Puig wasn’t already slumping.) Only one of the three CBS Sports analysts, Scott White, seems to have some sense, ranking Cabrera above Puig (Nos. 11 and 15, respectively).

Perhaps Puig is due to bounce back from a prolonged slump, which would justify his high ranking. But he had sported an abnormal BAbip (batting average on balls in play) all year; and while his 2013 BAbip was a monstrous .383, it would be wildly impressive for him to possess the hitting prowess to sustain one of the highest BAbips in MLB history. So, now, Puig owns a .353 BAbip, still well above the league average.

The question now: Is Puig a premium hitter, or has he been the beneficiary of a lot of good luck for a long time? ESPN’s hard-hit average data would be very beneficial right now, but alas, I don’t have access to it. Line drive rates can be used as a theoretical comparison, however, albeit not a pure substitute: line drives epitomize hard contact. And Puig has hit line drives only 14.3 percent of the time this year, as opposed to 19.1 percent of the time last year. (Mike Trout, who has seen his BAbip fall a comparable 31 points in BAbip from 2013, has seen his line drive rate drop an equally-comparable 4 percent.)

It will take a larger sample size — namely, the addition of the 2015 season — to determine whether Puig is closer to his 2013 line drive rate or his 2014 rate — and, thus, whether he is closer to his 2013 BAbip or his 2014 BAbip. For now, I would err on the side of caution and bank on the floor rather than the ceiling.

All of this slowly gets me back to my point: People have different perceptions of players’ values, and they often let other factors inhibit their judgments, even subconsciously. For example, people may still attribute Cabrera’s success to PEDs, a worry which seemed to be validated by his sub-optimal 2013 (during which he played through a herniated vertebrae, or something like that). Meanwhile, Puig is the next big thing, and people expect such from him. When you strip them of their names — from which their discrepancies in value stems, honestly — you uncover the market inefficiency lying within.

Not that I can say that I would have had the foresight to trade Puig for Cabrera on June 21 (mostly because I owned both), but if I was offered Puig for, say, Corey Dickerson, straight up, I would have pulled the trigger. Because at that point, it was all about name value and recognizing the true performance of each player without bias. Dickerson, having arguably a better season than Cabrera or Puig, is ranked Nos. 17, 25, 32 and 40 among all outfielders on the four expert lists I mentioned.

Moral of the story: Try not to let the name bias your projections. Exploit other owners’ misguided perceptions of value.

# 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.

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

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

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

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

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

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

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

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

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

# Cuban defector Jose Abreu signed by White Sox

ESPN just reported that Jose Abreu, who recently defected from Cuban, has signed to the Chicago White Sox for six years, \$68 million.

Everyone knew he would sign, and ultimately, where he signed matters only minimally in the context of fantasy baseball — playing for the Sox will hurt his value, but not a whole lot. What’s most important is the salary.

Let’s compare him to other Cuban players who have already made big splashes or recently defected:

Yoenis Cespedes – 4 years, \$36 million (\$9 mil/yr)
Yasiel Puig – 7 years, \$42 million (\$6 mil/yr)
Miguel Alfredo Gonzalez – 3 years, \$12 million (\$4 mil/yr) **almost signed for 6 years, \$60 million
Abreu – 6 years, \$68 million (\$11.33 mil/yr)

With Cespedes making headlines in 2012 and Puig in 2013, teams are turning their attention to Cuba and elsewhere. Abreu’s price tag may be a bit inflated because of demand (and, perhaps, White Sox desperation in part), but his salary ought to reflect his ability.

And it will if the legends of his prowess with the bat are even remotely true. His relative obscurity could make him a fantasy steal, no matter which round you take him, considering he probably won’t go before the top 10, but could very well end the season there.

Remember the name.