Tagged: Yovani Gallardo

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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Pitchers to sell high, buy low or cut bait

All right. It’s April. It’s horrifying, unless you’re doing well, and then it’s not. But, full disclosure, I’m not. Chicago White Sox staff ace Chris Sale just hit the 15-day disabled list yesterday, joining the Philadelphia Phillies’ Cole Hamels, Seattle Mariners’ James Paxton, Tampa Bay Rays’ Alex Cobb, Cincinnati Reds’ Mat Latos, New York Yankees’ David Robertson and the Detroit Tigers’ Doug Fister on my teams’ DLs. It’s killing me, really. It’s incredibly painful.

What I’m saying is I’ve spent more time than I’d care to admit frolicking in free agency, trying to figure out which early-season studs are legit or not. I’ve been pondering various buy-low situations as well. So I jumped into a pool of peripherals and PITCHf/x data to look for answers.

The list below is not remotely exhaustive. It’s mostly players I am watching or already using as replacements for my teams. Here they are, in no particular order.

Jake Peavy, BOS | 0-0, 3.33 ERA, 1.48 WHIP, 9.25 K/9
Peavy’s prime came and went about five years ago, so, full disclosure, I don’t know as much about him off the top of my head as I should. But I do know one thing: he doesn’t strike out a batter per inning anymore. In his defense, batters’ contact rate against him is the best it has been since 2009, his last truly good year. So maybe he will strike out a few more batters than last year, but I think it’ll be closer to 2012’s 7.97 K/9, not 2009’s 9.74 K/9. The WHIP is atrocious;  the walk rate is through the roof. If there’s a guy in your league who will pay for what will end up being the illusion of ERA and strikeouts, by all means, trade him. He’s owned in 100 percent of leagues but doesn’t deserve to be.
Verdict: Sell high

John Lackey, BOS | 2-2, 5.25 ERA, 1.46 WHIP, 8.63 K/9
Another Boston pitcher, another bad start to the season. I like Lackey a lot more, though, for a variety of reasons. One, last year’s renaissance was legitimate. Two, he’s not walking many batters right now, so his unspectacular ratios are more a result of an unlucky batting average on balls in play (.333 BABIP) than incompetence. Three, his swinging strike and contact rates are currently career bests. Again, we’re working with small sample sizes here, and this could easily regress. But considering his velocity is also at a career high, I don’t find it improbable that Lackey actually does better than he did last season. If an owner in your league has already dropped him, put in your waiver claim now.
Verdict: Buy low

Jesse Chavez, OAK | 1-0, 1.38 ERA, 0.92 WHIP, 9.69 K/9
Talk about unexpected. Chavez, who has been relevant about zero times, is making for an intriguing play in all leagues. It’s a given he will regress, especially considering the .242 BABIP, but his improved walk rate could be here to stay, as he is pounding the zone more than he ever has in his career. The strikeouts are somewhat of a mirage, but it looks like he can be a low-WHIP, moderate-strikeout guy, and that’s still valuable.
Verdict: Sell really high, or just ride the hot hand

Nathan Eovaldi, MIA | 1-1, 3.55 ERA, 1.14 WHIP, 8.17 K/9
I wouldn’t call Eovaldi a trendy sleeper, but he certainly was a sleeper coming into 2014. It was all about whether he could command his pitchers better — and, like magic, it appears he has, walking only 1.07 batters per nine innings as opposed to 3.39-per-nine last year. The swinging strike and contact rates are concerning, as they are the lowest of his career, so it’s hard to see his strikeout rate going anywhere but down. However, he’s throwing 65 percent of his pitches in the strike zone, highest of all qualified pitchers. So there are two ways to look at this. His control has probably legitimate improved. Unfortunately, even the masterful Cliff Lee only threw 53.3 percent of pitches in the zone last year, and I am hesitant to claim Eovaldi has better control than Lee. This could be a “breakout” year of sorts for Eovaldi, but I’m using that term liberally here. He’s only owned in 20.5 percent of leagues, so this makes him more of a ride-the-hot-hand type, like Mr. Chavez above.
Verdict: Eventually drop, ideally before he does damage to your team

Mark Buehrle, TOR | 4-0, 0.64 ERA, 0.93 WHIP, 6.11 K/9
Look, I have had a long-standing man crush on Buehrle, but this is ridiculous. You know better than I that these happy dreams will soon become nightmares, not because Buehrle is awful or anything, but because regression rears its head in occasionally very brutal ways.
Verdict: Sell high

Alfredo Simon, CIN | 0.86 ERA, 0.81 WHIP, 5.57 K/9
Something isn’t right here. A 0.81 WHIP and… fewer than six strikeouts per nine innings? As you become more familiar with sabermetrics, you quickly realize certain things don’t mesh. A low WHIP combined with the low strikeout rate is one of those things. I can tell you without looking that his BABIP is impossibly low — and, now looking, I see I’m right: it’s .197. Tristan H. Cockcroft of ESPN is all about Simon, and in his defense, Simon’s PITCHf/x data foreshadows some positive regression coming his way in the strikeout department. But it can only get worse from here for Simon. However, I think he has a bit of a Dan Straily look to him, and that’s certainly serviceable.
Verdict: Sell high, or just ride the hot hand

Yovani Gallardo, MIL | 1.46 ERA, 1.09 WHIP, 6.93 K/9
This is a disaster waiting to happen. Like Simon, his strikeout rate is low, but for Gallardo, it is deservedly so: his swinging strike and contact rates are, by far, career worsts. Meanwhile, his ratios are buoyed by a .264 BABIP and 89.8% LOB% (left-on-base percentage), despite his 74.7% career LOB%. The Brewers will fall with him. Sell high, and sell fast.
Verdict: Sell high

Shelby Miller, STL | 3.57 ERA, 1.50 WHIP, 8.34 K/9
Miller is the first pitcher on this list in whom owners actually invested a lot. Be patient. The 98.3-percent of owners who didn’t cut bait before his last start were surely rewarded. I imagine he’s leaving his pitches up in the zone, given his increased percentage of pitches thrown in the zone coupled with his home run rate. Speaking of which, he shouldn’t be walking five batters per nine innings when he’s throwing more than 50 percent of his pitches in the zone. He’ll be fine.
Verdict: Buy low

Homer Bailey, CIN | 5.75 ERA, 1.87 WHIP, 11.07 K/9
Two words: .421 BABIP. Yowza. Again, owners invested way too much in this guy. Perfect buy-low opportunity here if you know your fellow owner is impatient.
Verdict: Buy low

Drew Hutchison, TOR | 3.60 ERA, 1.45 WHIP, 10.80 K/9
I’ll be honest, I was surprised to see Hutchison’s xFIP stand at 3.43. It seems like he has been much worse — but has he really? The walks are problematic but not unmanageable (see: Matt Moore), and they’ve actually shored up a bit in his last couple of starts. Moreover, he is still striking out batters at an elite rate, and the PITCHf/x data supports his success, albeit probably not with quite as much success as he’s having now. As for the WHIP? A .365 BABIP sure doesn’t help. Hutchison was once a highly-touted prospect. Your window of opportunity to gamble on this live arm may be closing if he can keep his ERA down.
Verdict: Add via free agency, sooner rather than later

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.

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

A look at how run support affects a pitcher’s value

Some pitchers get better run support than others. It separates the fantasy studs from the fantasy duds, turns nobodies into somebodies and sometimes silences ace pitchers. Remember Cliff Lee‘s dismal 6-9 record last year despite his 3.05 ERA?

I won’t call them luckiest, for all these pitchers are plenty talented. So let’s say… run supportiest. Take a look at the run supportiest pitchers this year, followed by their average run support per game:

  1. Max Scherzer, 7.64
  2. Jeremy Hellickson, 6.70
  3. Justin Verlander, 6.64
  4. Anibal Sanchez, 6.57
  5. Ryan Dempster, 6.38
  6. Bartolo Colon, 6.22
  7. Chris Tillman, 6.18
  8. Matt Moore, 6.16
  9. Lance Lynn, 6.00
  10. Mike Minor, 6.00

Well, look at that. Mr. 15-game winner Max Scherzer is at the top of the list, and by no small margin. Without digging further, it’s important to make some distinctions. The average team scores approximately 4.20 runs per game, but no team is the average team. Although the Boston Red Sox lead the majors in scoring, it’s Scherzer’s own Detroit Tigers who lead in runs scored per game at 5.18 runs. It probably comes as no surprise that the Miami Marlins are last in runs scored at 3.19 per game, almost a full two runs fewer than the Tigers.

Part of the strategy in fantasy baseball is finding not necessarily the best pitchers but the above-average pitchers on good teams who will naturally get a lot of run support. Ryan Dempster isn’t having a great season by measure of his 4.54 ERA, but playing for the Red Sox certain bolsters his chances of collecting wins without having lights-out stuff. (Unfortunately, it hasn’t worked out that way for Dempster, notching only six wins.)

Instead of looking at the top 10 run supportiest pitchers in nominal terms, we ought to normalize the list by taking the difference between the pitchers’ run support and the average runs scored by their teams. The new list looks like this:

  1. Max Scherzer, 2.46
  2. Jeremy Hellickson, 2.09
  3. Bartolo Colon, 1.77
  4. Yovani Gallardo, 1.61
  5. Matt Moore, 1.51
  6. Hyun-Jin Ryu, 1.54
  7. Chris Tillman, 1.50
  8. Mike Minor, 1.47
  9. Yu Darvish, 1.46
  10. Justin Verlander, 1.46

The number following each name is the difference between the pitcher’s run support and his team’s average runs scored per game. Scherzer and Tampa Bay Rays pitcher Jeremy Hellickson lead the list again, but some new names popped up: Yovani GallardoHyun-Jin Ryu and Yu Darvish. The 10 pitchers above have combined for 115 wins, or 11.5 wins on average. Even Gallardo has eight wins despite having the eighth worst ERA of all qualified starters.

This list serves two purposes, although both aren’t immediately valuable: 1) although most of these pitchers are pitching well, don’t be surprised if they win less often as their run support regresses toward the mean; 2) if you’re in a dynasty league. don’t bank on a potential 20-game winner to do it again next year, especially if he’s the beneficiary of randomly elevated run support.

In contrast, here are the 10 least run-supportiest pitchers (relative to average team run support like the previous list):

  1. Chris Sale, -1.22
  2. Homer Bailey, -1.19
  3. Kris Medlen, -1.00
  4. Eric Stults, -0.99
  5. A.J. Burnett, -0.88
  6. Joe Blanton, -0.82
  7. Roberto Hernandez, -0.78
  8. Julio Teheran, -0.75
  9. John Lackey, -0.75
  10. Travis Wood, -0.74

The above pitchers have combined for only 61 wins, or 6.1 wins on average, a far cry from 115 wins (11.5 average) posted by the top 10 run supportiest pitchers. These pitchers don’t throw for terrible teams, either — six of them play for contenders, or call it seven if you’re a hopeless Angels fan.

(Interjecting some notes: Red Sox starter John Lackey is having a renaissance season, and it looks like he has nobody but himself to thank for his seven wins; Chicago Cubs starter Travis Wood is having a breakout year despite a lack of run support; I just want a reason to say “the artist formerly known as Fausto Carmona”; if I’m in a dynasty league, I’m gunning for Cincinnati Reds starter Homer Bailey, who would be having a breakout season ,piggybacking on his very solid second half of 2012, if it were not for his miserable run support… he ought to have better stats to go with his 1.14 WHIP.)

My takeaway from all of this, again, is as much predictive as it is descriptive. If I had to offer bits of advice based on what I’ve presented some of it would be the following:

  • Buy low on A.J. Burnett, who is 4-7 with a sub-3.00 ERA playing for the NL Central-leading Pittsburgh Pirates…
  • Do the same for Lackey, who shows no signs of slowing down…
  • Sell high on Tampa Bay Rays pitcher Jeremy Hellickson, who is sporting a career-worst ERA and is being buoyed by his win total…
  • I’d even venture to say sell high on Los Angeles Dodgers pitcher Hyun-Jin Ryu and Baltimore Orioles pitcher Chris Tillman, who are both benefiting from high strand rates even amid seasons I would classify as underwhelming…
  • And I’d even sell high on Big Fat Bartolo Colon, who simply won’t keep winning every game and has a lackluster strikeout rate…
  • Remember these names during your draft next year! Run support can fluctuate randomly and wildly year to year. Just ask Cliff Lee.

A look at how strand rates affect ERA

Scott Spratt of ESPN wrote a nice, albeit brief piece about pitchers who have their defenses to thank for their deflated ERAs, which for some of them have contributed to their breakout seasons.

Many advanced metrics in baseball don’t have inherent benchmarks that differentiate good from bad or lucky from unlucky.  The metrics, however, self-define their benchmarks given time and larger samples. BAbip, the metric Spratt uses, can not be measured not only for one pitcher during several seasons but also for several pitchers during one season. The MLB average BAbip is .299. The number itself doesn’t mean anything when it stands alone, but when it is paired with numbers such as .246 (for Patrick Corbin) or .350 (for Barry Zito), you can gauge who is getting lucky or unlucky.

Let’s take a look at left-on-base percentage (aka LOB% or strand rate), another advanced metric fantasy owners can access on websites such as Baseball Reference and FanGraphs. Fortunately, the casual observer needs not fully understand how LOB% works to identify its trends. (It is good to know, however, that the number of runners a pitcher leaves on base is not entirely out of his hands, so one should not attribute a skewed LOB% entirely to good or bad luck.)

For now, just know that the median LOB% for qualified starters in 2013 is 73.9 percent — that is, about three of every four baserunners are stranded on the basepaths. Now, let’s take a look at three poor seasons from otherwise very good pitchers.

Yovani Gallardo, MIL
2013 LOB%: 67.2
2012 LOB%: 78.4
Career LOB%: 74.2
Difference: -11.2 from last year, -7.0 from career

Gallardo had defined pitching consistently the past two years, registering the second-most quality starts between 2011 and 2012 (behind only Clayton Kershaw — who would’ve guessed?), but he has been mostly unreliable for all of 2013. His ERA may be inflated from his poor strand rate — his xFIP is 3.80, well below his actual ERA of 4.58 — but a deeper dig into his peripherals reveals declining velocity and a major drop-off in strikeout rate. Gallardo is a case where you can’t take the low LOB% and chalk it all up to bad luck.

Cole Hamels, PHI
2013 LOB%: 69.9
2012 LOB%: 78.1
Career LOB%: 76.3
Difference: -8.2 from last year, -6.4 from career

Hamels strikeout (K/9) and walk (BB/9) rates have trended in the wrong direction since last year. However, those rates are not the worst we’ve seen from him during his career — his 8.26 K/9 and 2.42 BB/9 surely beat out his worst rates of 7.76 and 3.26 — or even the worst we’ve seen from him in the past three years. Hamels’ K/9 has fluctuated pretty wildly throughout his career with no discernible pattern to it — 9.10 in 2009, 8.08 in 2010, 9.03 in 2011, 8.26 in 2012 — so that would be the least of my concerns as a Hamels owner. And with an inflated BAbip relative to his career mark, the LOB% looks more and more like an anomaly. The real question, however, is: can he shore up the walks? Because if not, the 1.25 WHIP may become reality, and a sub-3.50 ERA a thing of the past.

Matt Cain, SF
2013 LOB%: 64.1
2012 LOB%: 79.0
Career LOB%: 74.3
Difference: -14.9 from last year, -10.2 from career

Cain suffers from the biggest differences in LOB% of the trio. His 1.20 WHIP and .258 BAbip are very similar to his career marks of 1.17 and 2.64, yet his ERA is more than a run and a half higher. He’s even striking out pitchers at a rate unseen from him since 2006, his first full year in the majors. Then two numbers leap off the page: his LOB%, and a HR/FB% that’s more than five percent higher than usual. The ratio of home runs to fly balls has been proven to largely be out of the hands of the pitcher, and if Cain is giving up more home runs especially while more men are on base, it helps explain his 5.00 ERA (although it doesn’t explain why he has been so unlucky). Cain’s WHIP is a far better indicator of the type of pitcher he has been this year. Look forward to him returning to form next year.

Know that the metrics I presented are not the be all, end all of a pitcher’s performance. But understanding advanced metrics and learning to recognize trends (and abrupt distortions) are beneficial skills for any fantasy owner.