Tagged: Ervin Santana

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.

An impossibly hot stove and an embarrassingly long absence

The stove is hot, people. HOT! And as Every Time I Die once said: I been gone a long time. Sorry about that. I finished the first term of my last year of graduate school. It was probably the hardest one, and it should be smooth sailing from here on out.

I’m also pretty proud of a research paper I just completed regarding the probability of future success of minor leagues. The results are robust and I couldn’t be more pleased. It was a school project, so I didn’t have time to make it nearly as complex as I would have hoped, but it’s something I plan to further investigate in the coming days, weeks, months, what-have-you.

Anyway, there is plenty of news flying around as well as plenty of analysis. I’ll do my best to recap, but surely I’ll miss some things:

And I’m ignoring all the prospects involved as well. Marcus Semien, Austin Barnes, Jairo Diaz and others got shipped. I can only imagine a whole lot more action will be happening soon, as there still are teams with surpluses and deficits at all positions and some big-name free agents left on the market, including Max Scherzer and James Shields.

It is clear, however, that the Cubs  and Blue Jays intend to more than simply contend. I would say the Marlins intend to as well, but I don’t even think they know what they’re doing, let alone we do. The White Sox are looking like a trendy sleeper with some key pitching additions (LaRoche is also an addition, but far from what I would call a “key” one), but they are far from a championship team.

But with so much more yet to happen, maybe it’s best to wait and see. There are obviously some ballpark and team-skill implications that will affect all these players’ projections, but I’ll get around to those in 2015.

I’ve finished my preliminary set of pitcher projections. I’ll share them but they’ll see some refining by the time March rolls around.

I’m also looking at how my projections fared last year. That will come in the next couple of days.

Keep your ear to the ground, people. Or to the stove. Never mind. Terrible idea. You’ll burn yourself. Just keep it to the ground.

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!

Updated SP rankings

Click here for updated SP rankings.

I have updated the starting pitcher rankings to reflect offseason signings, rotation battles and spring training injuries — and holy cow, have there been a lot of spring training injuries.

I also truncated the list to the top 90 pitchers. I will write about my favorite pitchers outside the top 90 because a lot of them are really good; they simply won’t get enough get enough starts or pitch enough innings for them to crack the top 90 in value. In terms of stuff, though, there are plenty of diamonds to find in the rough.

Stock up: James Paxton, Justin Verlander, Ervin Santana

Stock down: Cole Hamels (shoulder), Hisashi Iwakuma (finger), Kris Medlen (elbow), Patrick Corbin (elbow), Jarrod Parker (elbow), A.J. Griffin (elbow), Brandon Beachy (elbow), Miguel Alfredo Gonzalez

Pitchers due for strikeout regression using PITCHf/x data

If FanGraphs were a home, or a hotel, or even a tent, I’d live there. I would swim in its oceans of data, lounge in its pools of metrics.

It houses a slew of PITCHf/x data — the numbers collected by the systems installed in all MLB ballparks that measure the frequency, velocity and movement of every pitch by every pitcher. It’s pretty astounding, but it’s also difficult for the untrainted eye to make something of the numbers aside from tracking the declining velocities of CC Sabathia‘s and Yovani Gallardo‘s fastballs.

I used linear regression to see how a pitcher’s contact, swinging strike and other measurable rates affect his strikeout percentage, and how that translates to strikeouts per inning (K/9). Ultimately, the model spits out a formula to generate an expected K/9 for a pitcher. I pulled data from FanGraphs comprised of all qualified pitchers from the last four years (2010 through 2013).

The idea is this: A pitcher who can miss more bats will strike out more batters. FanGraphs’ “Contact %” statistic illustrates this, where a lower contact rate is better. Similarly, a pitcher who can generate more swinging strikes (“SwStr %”) is more likely to strike out batters.

Using this theory coupled with the aforementioned data, I “corrected” the K/9 rates of all 2013 pitchers who notched at least 100 innings. Instead of detailing the full results, here are the largest differentials between expected and actual K/9 rates. (I will list only pitchers I deem fantasy relevant.)

Largest positive differential: Name — expected K/9 – actual K/9) = +/- change

  1. Martin Perez — 7.77 – 6.08 = +1.69
  2. Jarrod Parker — 7.74 – 6.12) = +1.62
  3. Dan Straily — 8.63 – 7.33 = +1.30
  4. Jered Weaver — 8.09 – 6.82 = +1.27
  5. Hiroki Kuroda — 7.93 – 6.71 = +1.22
  6. Kris Medlen — 8.38 –  7.17 = +1.21
  7. Francisco Liriano — 10.31 – 9.11 = +1.20
  8. Ervin Santana — 8.06 – 6.87 = +1.19
  9. Ricky Nolasco — 8.47 – 7.45 = +1.02
  10. Tim Hudson — 7.42 (6.51) | +0.91

Largest negative differential:

  1. Tony Cingrani — 8.15 – 10.32 = -2.17
  2. Ubaldo Jimenez — 7.68 – 9.56 = -1.88
  3. Cliff Lee — 7.11 – 8.97 = -1.86
  4. Jose Fernandez — 8.15 – 9.75 = -1.60
  5. Shelby Miller — 7.20 – 8.78 = -1.58
  6. Scott Kazmir — 7.71 – 9.23 = -1.52
  7. Yu Darvish — 10.41 – 11.89 = -1.48
  8. Lance Lynn — 7.58 – 8.84 = -1.26
  9. Justin Masterson — 7.84 (9.09) | -1.25
  10. Chris Tillman — 6.60 (7.81) | -1.21

There’s a lot to digest here, so I’ll break it down. It appears Perez was the unluckiest pitcher last year, of the ones who qualified for the study, notching almost 1.7 fewer strikeouts per nine innings than he would be expected to, given the rate of whiffs he induced. Conversely, rookie sensation Cingrani notched almost 2.2 more strikeouts per nine innings than expected.

There is a caveat. I was not able to account for facets of pitching such as a pitcher’s ability to hide the ball well, or his tendency to draw strikes-looking. With that said, a majority of the so-called lucky ones are pitchers who, in 2013, experienced a breakout (Cingrani, Fernandez, Miller, Darvish, Masterson, Tillman) or a renaissance (Jimenez, Kazmir, Masterson — woah, all Cleveland pitchers). Is it possible these pitchers can all repeat their performances — especially the ones who have disappointed us for years? Perhaps not.

(Update, Jan. 24: Cliff Lee’s mark of -1.86 is, amazingly, not unusual for him. Over the last four years, the average difference between his expected and actual K/9 rates is … drum roll … -1.88. Insane!)

Darvish and Liriano were in a league of their own in terms of inducing swings and misses, notching almost 30 percent each. (Anibal Sanchez was third-best with 27 percent. The average is about 21 percent.) However, Darvish recorded 2.78 more K/9 than Liriano. Is there any rhyme or reason to that? Darvish is, without much argument, the better pitcher — but is he that much better? I don’t think so. Darvish was expected to notch 10.41 K/9 given his contact rate. Any idea what his 2012 K/9 rate was? Incredibly: 10.40 K/9.

More big names produced equally interesting results. King Felix Hernandez recorded a career-best 9.51 K/9, but he was expected to produce something closer to 8.57 K/9. His rate the previous three years? 8.52 K/9.

Dan Haren didn’t produce much in the way of ERA in 2013, but he did see a much-needed spike in his strikeout rate, jumping above 8 K/9 for the first time since 2010. His expected 7.07 K/9 says otherwise, though, and it fits perfectly with how his K/9 rate was trending: 7.25 K/9 in 2011, 7.23 K/9 in 2012.

I think my models tend to exaggerate the more extreme results (most of which are noted in the lists above) because they could not account for intangibles in a player’s natural talent. However, they could prove to be excellent indicators of who’s due for regression.

Only time will tell. Maybe Jose Fernandez isn’t the elite pitcher we already think he is — not yet, at least.

————

Notes: The data almost replicates a normal distribution, with 98 of the 145 observations (67.6 percent) falling within one standard deviation (1.09 K/9) of the mean value (7.19 K/9), and 140 of 145 (96.6 percent) falling within two standard deviations. The median value is 7.27 K/9, indicating the distribution is very slightly skewed left.

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