Tagged: Garrett Richards

Predicting pitchers’ walks using xBB%

The other day, I discussed predicting pitchers’ strikeout rates using xK%. I will conduct the same exercise today in regard to predicting walks. Using my best intuition, I want to see how well a pitcher’s walk rate (BB%) actually correlates with what his walk rate should be (expected BB%, henceforth “xBB%”). Similarly to xK%, I used my intuition to best identify reliable indicators of a pitcher’s true walk rate using readily available data.

An xBB% metric, like xK%, would not only if a pitcher perennially over-performs (or under-performs) his walk rate but also if he happened to do so on a given year. This article will conclude by looking at how the difference in actual and expected walk rates (BB – xBB%) varied between 2014 and career numbers, lending some insight into the (un)luckiness of each pitcher.

Courtesy of FanGraphs, I constructed another set of pitching data spanning 2010 through 2014. This time, I focused primarily on what I thought would correlate with walk rate: inability to pitch in the zone and inability to incur swings on pitches out of the zone. I also throw in first-pitch strike rate: I predict that counts that start with a ball are more likely to end in a walk than those that start with a strike. Because FanGraphs’ data measures ability rather than inability — “Zone%” measures how often a pitcher hits the zone; “O-Swing%” measures how often batters swing at pitches out of the zone; “F-Strike%” measures the rate of first-pitch strikes — each variable should have a negative coefficient attached to it.

I specify a handful of variations before deciding on a final version. Instead of using split-season data (that is, each pitcher’s individual seasons from 2010 to 2014) for qualified pitchers, I use aggregated statistics because the results better fit the data by a sizable margin. This surprised me because there were about half as many observations, but it’s also not surprising because each observation is, itself, a larger sample size than before.

At one point, I tried creating my own variable: looks (non-swings) at pitches out of the zone. I created a variable by finding the percentage of pitches out of the zone (1 – Zone%) and multiplied it by how often a batter refused to swing at them (1 – O-Swing%). This version of the model predicted a nice fit, but it was slightly worse than leaving the variables separated. Also, I ran separate-but-equal regressions for PITCHf/x data and FanGraphs’ own data. The PITCHf/x data appeared to be slightly more accurate, so I proceeded using them.

The graph plots actual walk rates versus expected walk rates. The regression yielded the following equation:

xBB% = .3766176 – .2103522*O-Swing%(pfx) – .1105723*Zone%(pfx) – .3062822*F-Strike%
R-squared = .6433

Again, R-squared indicates how well the model fits the data. An R-squared of .64 is not as exciting as the R-squared I got for xK%; it means the model predicts about 64 percent of the fit, and 36 percent is explained by things I haven’t included in the model. Certainly, more variables could help explain xBB%. I am already considering combining FanGraphs’ PITCHf/x data with some of Baseball Reference‘s data, which does a great job of keeping track of the number of 3-0 counts, four-pitch walks and so on.

And again, for the reader to use the equation above to his or her benefit, one would plug in the appropriate values for a player in a given season or time frame and determine his xBB%. Then one could compare the xBB% to single-season or career BB% to derive some kind of meaningful results. And (one more) again, I have already taken the liberty of doing this for you.

Instead of including every pitcher from the sample, I narrowed it down to only pitchers with at least three years’ worth of data in order to yield some kind of statistically significant results. (Note: a three-year sample is a small sample, but three individual samples of 160+ innings is large enough to produce some arguably robust results.) “Avg BB% – xBB%” (or “diff%”) takes the average of a pitcher’s difference between actual and expected walk rates from 2010 to 2014. It indicates how well (or poorly) he performs compared to his xBB%: the lower a number, the better. This time, I included “t-score”, which measures how reliable diff% is. The key value here is 1.96; anything greater than that means his diff% is reliable. (1.00 to 1.96 is somewhat reliable; anything less than 1.00 is very unreliable.) Again, this is slightly problematic because there are five observations (years) at most, but it’s the best and simplest usable indicator of simplicity.

Thus, Mark Buehrle, Mike Leake, Hiroki Kuroda, Doug Fister, Tim Hudson, Zack Greinke, Dan Haren and Bartolo Colon can all reasonably be expected to consistently out-perform their xBB% in any given year. Likewise, Aaron Harang, Colby Lewis, Ervin Santana and Mat Latos can all reasonably be expected to under-perform their xBB%. For everyone else, their diff% values don’t mean a whole lot. For example, R.A. Dickey‘s diff% of +0.03% doesn’t mean he’s more likely than someone else to pitch exactly as good as his xBB% predicts him to; in fact, his standard deviation (StdDev) of 0.93% indicates he’s less likely than just about anyone to do so. (What it really means is there is only a two-thirds chance his diff% will be between -0.90% and +0.96%.)

As with xK%, I compiled a list of fantasy-relevant starters with only two years’ worth of data that see sizable fluctuations between 2013 and 2014. Their data, at this point, is impossible (nay, ill-advised) to interpret now, but it is worth monitoring.

Name: [2013 diff%, 2014 diff%]

Miller is an interesting case: he was atrociously bad about gifting free passes in 2014, but his diff% was only marginally worse than it was in 2013. It’s possible that he was a smart buy-low for the braves — but it’s also possible that Miller not only perennially under-performs his xBB% but is also trending in the wrong direction.

Here are fantasy-relevant players with a) only 2014 data, and b) outlier diff% values:

I’m not gonna lie, I have no idea why Cobb, Corey Kluber and others show up as only having one year of data when they have two in the xK% dataset. This is something I noticed now. Their exclusion doesn’t fundamentally change the model’s fit whatsoever because it did not rely on split-season data; I’m just curious why it didn’t show up in FanGraphs’ leaderboards. Oh well.

Implications: Richards and Roark perhaps over-performed. Meanwhile, it’s possible that Odorizzi, Ross  and Ventura will improve (or regress) compared to last year. I’m excited about all of that. Richards will probably be pretty over-valued on draft day.

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Predicting pitchers’ strikeouts using xK%

Expected strikeout rate, or what I will henceforth refer to as “xK%,” is exactly what it sounds like. I want to see if a pitcher’s strikeout rate actually reflects how he has pitched in terms of how often he’s in the zone, how often he causes batters to swing and miss, and so on. Ideally, it will help explain random fluctuations in a pitcher’s strikeout rate, because even strikeouts have some luck built into them, too.

An xK% metric is not a revolutionary idea. Mike Podhorzer over at FanGraphs created one last year, but he catered it to hitters. Still, it’s nothing too wild and crazy like WAR or SIERA or any other wacky acronym. (A wackronym, if you will.)

Courtesy of Baseball Reference, I constructed a set of pitching data spanning 2010 through 2014. I focused primarily on what I thought would correlate highly with strikeout rates: looking strikes, swinging strikes and foul-ball strikes, all as a percentage of total strikes thrown. I didn’t want the model specification to be too close to a definition, so it’s beneficial that these rates are on a per-strike, rather than per-pitch, basis.

The graph plots actual strikeout rates versus expected strikeout rates with the line of best fit running through it. I ran my regression using the specification above and produced the following equation:

xK% = -.6284293 + 1.195018*lookstr + 1.517088*swingstr + .9505775*foulstr
R-squared = .9026

The R-squared term can, for easy of understanding, be interpreted as how well the model fits the data, from 0 to 1. An R-squared, then, of .9026 represents approximately a 90-percent fit. In other words, these three variables are able to explain 90 percent of a strikeout rate. (The remaining 10 percent is, for now, a mystery!)

In order for the reader to use this equation to his or her own benefit, one would insert a pitcher’s looking strike, swinging strike and foul-ball strike percentages into the appropriate variables. Fortunately, I already took the initiative. I applied the results to the same data I used: all individual qualified seasons by starting pitchers from 2010 through 2014.

The results have interesting implications. Firstly, one can see how lucky or unlucky a pitcher was in a particular season. Secondly, and perhaps most importantly, one can easily identify which pitchers habitually over- and under-perform relative to their xK%. Lastly, you can see how each pitcher is trending over time. Every pitcher is different; although the formula will fit most ordinary pitchers, it goes without saying that the aces of your fantasy squad are far from ordinary, and they should be treated on an individual basis.

(Keep in mind that a lot of these players only have one or two years’ worth of data (as indicated by “# Years”), so the average difference between their xK% and K% as a representation of a pitcher’s true skill will be largely unreliable.)

It is immediately evident: the game’s best pitchers outperform their xK% by the largest margins. Cliff Lee, Stephen Strasburg, Clayton Kershaw, Felix Hernandez and Adam Wainwright are all top-10 (or at least top-15) fantasy starters. But let’s look at their numbers over the years, along with a few others at the top of the list.

Kershaw and King Felix have not only been consistent but also look like like they’re getting better with age. Wainwright’s difference between 2013 and 2014 is a bit of a concern; he’s getting older, and this could be a concrete indicator that perhaps the decline has officially begun. Darvish’s line is interesting, too: you may or may not remember that he had a massive spike in strikeouts in 2013 compared to his already-elite strikeout rate the prior year. As you can see, it was totally legit, at least according to xK%. But for some reason, even xK% can fluctuate wildly from year to year. I see it in the data, anecdotally: Anibal Sanchez‘s huge 6.7-percent spike in xK% from 2012 to 2013 was followed by a 5.5-percent drop from 2013 to 2014. Conversely, David Price‘s 5-percent decrease in xK% from 2012 to 2013 was followed by an almost perfectly-equal 5-percent increase from 2013 to 2014. So the phenomenon seems to work both ways. Thus, perhaps it shouldn’t have come as a surprise when Darvish couldn’t repeat his 2013 success. To the baseball world’s collective dismay, we simply didn’t have enough data yet to determine which Yu was the true Yu. I plan to do some research to see how often these severe spikes in xK% are mere aberrations versus how often they are sustained over time, indicating a legitimate skills improvement.

I have also done my best to compile a list of players with only one or two years’ worth of data who saw sizable spikes and drops in their K% minus xK% (“diff%”). The idea is to find players for whom we can’t really tell how much better (or worse) their actual K% is compared to their xK% because of conflicting data points. For example, will Corey Kluber be a guy who massively outperforms his xK% as he did in 2014, or does he only slightly outperform as he did in 2013? I present the list not to provide an answer but to posit: Which version of each of these players is more truthful? I guess we will know sometime in October.

Name: [2013 diff%, 2014 diff%]

And here some fantasy-relevant guys with only data from 2014:

Matt Shoemaker, pending fantasy star

How does one apologize for not writing for more than a month and a half? It’s hard, man. Maybe one does not apologize to one’s readers. Maybe one’s readers accepts that it is what it is.

You know what else is what it simply is? Matthew David Shoemaker, the Los Angeles Angels of Anaheim’s right-handed reliever-turned-starter.

Every baseball season often produces more questions than answers. Namely: Who is Matt Shoemaker? Where did he come from? Why am I writing about him if he’s not that good?

Let us rewind to 2012. A mysterious figure emerged from the mist of the Seattle Mariners’ bullpen to dazzle us– or maybe just me, given he never really received the recognition he deserved. Maybe he had a right to be ignored: he posted a 4.75 ERA and 1.42 WHIP in 30-1/3 relief innings. If you don’t know how this fairy tale ends, it goes something like: goes largely unnoticed in 2012, is drafted outside the top 75 pitchers on average in ESPN drafts in 2013, and eventually emerges as a borderline fantasy ace by the name of Hisashi Iwakuma.

There’s a lesson to be learned. Iwakuma’s horrid statistics as a reliever muddied his season numbers. In hindsight, a 3.15 ERA for the year is solid, but a 2.65 ERA is better, and that’s what Iwakuma posted strictly as a starter. Yet fantasy owners who opted only to scratch the surface saw mostly unsightly ratios.

The same fairy tale manifested itself in a different form in 2013 that would make the Brothers Grimm proud. The Cleveland Indians’ Corey Kluber emerged from the bullpen in May, albeit after only half a dozen innings, many more than that in 2012. Kluber’s season, however, began with aplomb — and by aplomb, I mean “a handful of horrible starts.” Starts horrible enough to sully his numbers for the year (3.95 ERA). But the peripherals were there at season’s end: 8.28 strikeouts per nine innings, 2.09 walks per nine, 3.12 xFIP. In case you haven’t kept track, Kluber has more or less assumed the role as Cleveland’s staff ace this year, posting a 2.95 ERA with more strikeouts than innings.

I will now shortsightedly assume, without any kind of research, that this kind of thing happens every year. Every year, there’s at least one player who emerges from the bullpen and becomes an ace. Sure, you have the Chris Sales and Adam Wainwrights of the baseball world, who make a gigantic, whale-sized spash, but you also have the Iwakumas and Klubers, who basically don’t make a splash at all and probably sit on the side of the pool with their feet dangling in and shirts still on.

So I’m calling it: Mr. Shoemaker will be 2015’s reincarnation of this fairy tale.

In keeping the trend alive, a look at Shoemaker’s stats tell you… well, in the way of anything positive, not much. He has somehow notched seven wins despite a 4.54 ERA and 1.30 WHIP, so that rules. Worse, his WHIP was, like, 1.42 before his most recent start. So, bad season stat line? Check.

Meanwhile, he has struck out 9.68, and walked only 1.87, hitters per nine innings. It would behoove me to point out that these numbers dwarf those posted by Kluber in 2013, during which Kluber existed primarily in a gelatinous state of Emerging Star. It would also behoove me to point out that a reader with a discerning eye would notice that Shoemaker has a still-lackluster 4.37 ERA and 1.28 WHIP as a starter, fitting the mold of “maybe his season numbers are ruined.” It would further behoove me to point out that he is suffering the misfortune of a .350 batting average on balls in play (BABIP), which, if normalized to a more reasonable .320, would produce a 1.20 WHIP. A league-average .300 BABIP? A 1.14 WHIP. So, distorted stats as a starter? Check.

Perhaps the most important, and valid, question at this point is whether or not Shoemaker can sustain what he’s doing. Small sample size caveats abound here, but I think the results are still substantial, if not due for regression. For all pitchers who have thrown at least 60 innings, Shoemaker ranks 11th in swinging strike percentage (11.9), one spot behind Stephen Strasburg, the MLB strikeout leader, and three spots ahead of his teammate Garrett Richards, who has done all kinds of breaking out this year. Shoemaker also ranks 9th in hitter contact allowed (73.5 percent), sandwiched between Gio Gonzalez and, yes, Richards. Thus, even given small sample size caveats, Shoemaker is among excellent company. The walk rate may suffer; it’s hard to say, and even harder still given that I’m on an airplane over central California with no internet. But, given the browser tabs I still have open, I can tell you that Shoemaker’s percentage of pitches thrown in the strike zone, according to FanGraphs’ data, trails only Clayton Kershaw and Mets reliever Carlos Torres among the 10 names ahead of his on the swinging strike percentage list. That bodes well for projecting his control going forward. (PITCHf/x, however, portends another story, as his zone percentage trails six of the eight names ahead of his. But when the names you trail are Felix Hernandez, Masahiro Tanaka and Kershaw, I’d say you’re not doing so bad for yourself.) So, solid peripherals? Check.

It’s a makeshift and largely personal checklist, but so far, Shoemaker meets all my criteria for the gelantinous Emerging Star. Who knows how Shoemaker will fare during the season’s last two months, but I think he’s worth owning now despite his current stat line. As for 2015 and beyond, I like him — for now. I wouldn’t bother keeping him, as I think his value will be depressed heading into next year’s draft, so you can easily wait around for him in the late rounds, if not add him as free agency in the first couple of weeks of the season, just as many owners did with Iwakuma and Kluber the past two years.

I hesitate to say Shoemaker is a lock for success. If anything, this post is less about finding The Next Big Thing as it is finding a pitcher whose performance betrays his value. There are the Sonny Grays and Michael Wachas of the world, whose status as top prospects make them costly prospective adds. Then there are the Matt Shoemakers, whose obscurity and relative misfortune keep him out of the fantasy limelight — and, one would hope, on the clearance shelf, from which you can swipe him on the cheap.

Need some streamers? T. Ross, Hutchison, Colon, Wood

I’ve been slacking on my streamer picks, so let’s cut straight to the chase.

Today, 5/15:
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.

Friday, 5/16:
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.

Saturday, 5/17:
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.

Sunday, 5/18:
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:

Split W L W-L% ERA GS IP H ER HR BB SO WHIP SO9 SO/W
Home 2 1 .667 2.39 4 26.1 22 7 2 4 32 0.987 10.9 8.00
Away 1 3 .250 8.02 4 21.1 31 19 2 11 12 1.969 5.1 1.09
Provided by Baseball-Reference.com: View Original Table
Generated 5/15/2014.

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!

The role of luck in fantasy baseball

I apologize for being that guy that ruins that ooey gooey feeling you get when think about the fantasy league you won last year. As much as you want to think you are a fantasy master — perhaps even a fantasy god — you should acknowledge that you probably benefited from a good deal of luck. Sure, for your sake, I will admit you made a great pick with Max Scherzer in the fifth round. But did you, in all your mastery, predict he would win 21 games?

Don’t say yes. You didn’t. And frankly, you would be crazy to say he’ll do it again.

I focus primarily on pitching in this blog, and let it be known that pitchers are not exempt from luck in the realm of fantasy baseball. If you’re playing in a standard rotisserie league, you probably have a wins category. In a points league, you likely award points for wins.

Wins. Arguably the most arbitrary statistic in baseball. Let’s not have that discussion, though, and instead simply accept the win as it is. The win has the most drastic uncontrollable effect on a fantasy pitcher’s value. (ERA and WHIP experiences similar statistical fluctuations, but at least they aren’t arbitrary.)

I had an idea, but before I proceed, let me interject: if you’re drafting for wins, you’re doing it wrong. But, as I said, you can’t ignore wins.

But let’s say you did, and drafted strictly on talent, or “stuff” (which, here, factors in a pitcher’s durability). How would the top 30 pitchers change? Here’s my “stuff” list, which you can compare with the base projections:

  1. Clayton Kershaw
  2. Adam Wainwright
  3. Felix Hernandez
  4. Max Scherzer
  5. Cliff Lee
  6. Yu Darvish
  7. Chris Sale
  8. Cole Hamels
  9. Jose Fernandez
  10. Madison Bumgarner
  11. Stephen Strasburg
  12. David Price
  13. Justin Verlander
  14. Alex Cobb
  15. Homer Bailey
  16. Mat Latos
  17. Gerrit Cole
  18. Michael Wacha
  19. Anibal Sanchez
  20. James Shields
  21. Danny Salazar
  22. Marco Estrada
  23. A.J. Burnett
  24. Corey Kluber
  25. Brandon Beachy
  26. Zack Greinke
  27. Matt Cain
  28. Sonny Gray
  29. Hisashi Iwakuma
  30. Gio Gonzalez

Here are the five players with the biggest positive change and a breakdown of each:

  1. Brandon Beachy, up 23 spots
    His injury history has weakened his wins column projection. Consequently, the number of innings Beachy is expected to throw is significantly less than a full season. But if he managed to stay healthy for the full year (say, 200 innings)? He’s a top-1o pick based on pure stuff. If you draft with the philosophy that you can always find a viable replacement on waivers, Beachy could be your big sleeper.
  2. Marco Estrada, up 22 spots
    Estrada’s diminished expected wins is more a function of his terrible team than ability. Estrada has underperformed the past two years, Ricky Nolasco style, but if he can pull it together, he’s a top-30 pitcher based on “stuff.” And hey, maybe he can luck into some extra wins. However, if he can’t pull it together — Ricky Nolasco style — he’ll be relegated to fringe starter.
  3. Danny Salazar, up 9 spots
    Salazar has immense potential. His injury history led the Indians to cap his per-game pitch count last year, and that has been factored into his projection. But if he’s a full-time, 200-inning starter? He’s a top-25 starter with top-15 upside. Again, this is in terms of “stuff”. But is Ivan Nova better than Felix Hernandez because he can magically win more games? Of course not. Among a slew of young studs, including Jose Fernandez, Shelby Miller, Michael Wacha and so on, Salazar is a diamond in the rough.
  4. A.J. Burnett, up 8 spots
    His projection is already plenty good. But you saw how many games he won in 2013. Anything can happen.
  5. Corey Kluber, up 8 spots
    Most people were probably scratching their heads when they saw Kluber’s name listed above. Frankly, I’m in love with him, and it’s because he’s a stud with a great K/BB ratio. I understand why someone may be inclined to dismiss it as an aberration, but his swinging strike and contact rates are truly excellent. Even if they regress, he should be a draft-day target.

Here are the three starting pitchers with the biggest negative change.

  1. Anibal Sanchez, down 10 spots
    He’s great, but he also plays for a great team. Call it Max Scherzer syndrome. He carries as big a risk as any other player to pitch great but only win five or six games, as do the next two players.
  2. Hisashi Iwakuma, down 6 spots
  3. Zack Greinke, down 4 spots

Let me be clear that although I created a hypothetical scenario where wins didn’t exist, I don’t advocate for blindly drafting based on “stuff.” It’s important to acknowledge that certain players have a much better chance to win than others. Chris Sale of the Chicago White Sox could win 17 games just as easily as he could win seven. It’s about playing the odds — and unless a pitcher truly pitches terribly, don’t blame the so-called experts for your bad luck. He probably put his money where his mouth is, too, and is suffering along with you.

Here is a more comprehensive list of pitchers ranked by “stuff,” if that’s the way you sculpt your strategy:

  1. Clayton Kershaw
  2. Adam Wainwright
  3. Felix Hernandez
  4. Max Scherzer
  5. Cliff Lee
  6. Yu Darvish
  7. Chris Sale
  8. Cole Hamels
  9. Jose Fernandez
  10. Madison Bumgarner
  11. Stephen Strasburg
  12. David Price
  13. Justin Verlander
  14. Alex Cobb
  15. Homer Bailey
  16. Mat Latos
  17. Gerrit Cole
  18. Michael Wacha
  19. Anibal Sanchez
  20. James Shields
  21. Danny Salazar
  22. Marco Estrada
  23. A.J. Burnett
  24. Corey Kluber
  25. Brandon Beachy
  26. Zack Greinke
  27. Matt Cain
  28. Sonny Gray
  29. Hisashi Iwakuma
  30. Gio Gonzalez
  31. Doug Fister
  32. Jordan Zimmermann
  33. Alex Wood
  34. Kris Medlen
  35. Jeff Samardzija
  36. Mike Minor
  37. Jake Peavy
  38. Kevin Gausman
  39. Tyson Ross
  40. Patrick Corbin
  41. Lance Lynn
  42. Francisco Liriano
  43. Andrew Cashner
  44. Ricky Nolasco
  45. CC Sabathia
  46. Hiroki Kuroda
  47. Tim Lincecum
  48. Tim Hudson
  49. Jered Weaver
  50. Shelby Miller
  51. Clay Buchholz
  52. Tony Cingrani
  53. Matt Garza
  54. John Lackey
  55. Ubaldo Jimenez
  56. Justin Masterson
  57. Julio Teheran
  58. R.A. Dickey
  59. A.J. Griffin
  60. Hyun-Jin Ryu
  61. Dan Haren
  62. Johnny Cueto
  63. C.J. Wilson
  64. Ian Kennedy
  65. Chris Archer
  66. Kyle Lohse
  67. Scott Kazmir
  68. Carlos Martinez
  69. Jon Lester
  70. Ervin Santana
  71. Jose Quintana
  72. Derek Holland
  73. Garrett Richards
  74. Dan Straily
  75. Tyler Skaggs

Early SP rankings for 2014

I wouldn’t say pitching is deep, but I’m surprised by the pitchers who didn’t make my top 60.

Note: I have deemed players highlighted in pink undervalued and worthy of re-rank. Do not be alarmed just yet by what you may perceive to be a low ranking.

2014 STARTING PITCHERS

  1. Clayton Kershaw
  2. Adam Wainwright
  3. Max Scherzer
  4. Yu Darvish
  5. Felix Hernandez
  6. Cliff Lee
  7. Stephen Strasburg
  8. Jose Fernandez
  9. Cole Hamels
  10. Justin Verlander
  11. Anibal Sanchez
  12. Chris Sale
  13. Mat Latos
  14. Madison Bumgarner
  15. Alex Cobb
  16. Homer Bailey
  17. Gerrit Cole
  18. Zack Greinke
  19. David Price
  20. James Shields
  21. Jordan Zimmermann
  22. Michael Wacha
  23. Danny Salazar
  24. Jered Weaver
  25. A.J. Burnett *contingent on if he retires
  26. Kris Medlen
  27. Mike Minor
  28. Jake Peavy
  29. Corey Kluber
  30. Lance Lynn
  31. Matt Cain
  32. Hisashi Iwakuma
  33. CC Sabathia
  34. Gio Gonzalez
  35. Doug Fister
  36. Patrick Corbin
  37. Francisco Liriano
  38. Sonny Gray
  39. Ricky Nolasco
  40. Hiroki Kuroda
  41. Tim Hudson
  42. Marco Estrada
  43. Shelby Miller
  44. Trevor Rosenthal
  45. Tony Cingrani
  46. A.J. Griffin
  47. Brandon Beachy
  48. Tim Lincecum
  49. Clay Buchholz
  50. Ubaldo Jimenez
  51. Alex Wood
  52. Julio Teheran
  53. Tyson Ross
  54. Hyun-jin Ryu
  55. Matt Garza
  56. Andrew Cashner
  57. Johnny Cueto
  58. C.J. Wilson
  59. John Lackey
  60. Justin Masterson
  61. R.A. Dickey
  62. Kevin Gausman
  63. Jon Lester
  64. Dan Haren
  65. Ervin Santana
  66. Derek Holland
  67. Chris Archer
  68. Jeff Samardzija
  69. Bartolo Colon
  70. Ivan Nova
  71. Matt Moore
  72. Ian Kennedy
  73. Dan Straily
  74. Rick Porcello
  75. Jarrod Parker
  76. Carlos Martinez
  77. Jeremy Hellickson
  78. Kyle Lohse
  79. Scott Kazmir
  80. Jason Vargas
  81. Tommy Milone
  82. Wade Miley
  83. Dillon Gee
  84. Brandon Workman
  85. Chris Tillman
  86. Zack Wheeler
  87. Yovani Gallardo
  88. Miguel Gonzalez
  89. Jose Quintana
  90. Garrett Richards
  91. Robbie Erlin
  92. Felix Doubront
  93. Jhoulys Chacin
  94. Jonathon Niese
  95. Chris Capuano
  96. Nick Tepesch
  97. Alexi Ogando
  98. Bronson Arroyo
  99. Travis Wood
  100. Trevor Cahill
  101. Tyler Skaggs
  102. Randall Delgado
  103. Martin Perez
  104. Mike Leake
  105. Carlos Villanueva
  106. Todd Redmond
  107. Brandon Maurer
  108. Tyler Lyons
  109. Ryan Vogelsong
  110. Zach McAllister
  111. Wily Peralta
  112. Brett Oberholtzer
  113. Erik Johnson
  114. Jorge De La Rosa
  115. Paul Maholm
  116. Hector Santiago
  117. Burch Smith
  118. Jeff Locke
  119. Joe Kelly
  120. Jason Hammel
  121. Jake Odorizzi
  122. Danny Hultzen
  123. Anthony Ranaudo
  124. Archie Bradley
  125. Rafael Montero
  126. James Paxton
  127. Taijuan Walker
  128. Yordano Ventura