# 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.

# 2014’s SP projections: the best and the worst

Maybe this is absurd, but I’ve never honestly checked the accuracy of my projections. It’s partly because I have placed a lot of trust in a computer that runs regressions with reliable data I have supplied, but it’s mostly because I originally started doing this for my own sake. I used to rely on ESPN’s projections, but the former journalist in me started to realize: it has a customer to please, and the customer may not be pleased, for example, if he sees Corey Kluber ranked in the Top 60 starting pitchers for 2014. (At this point, I am giving ESPN an out, given that everyone at FanGraphs and elsewhere knew the kind of upside he possessed.) Kluber is not the issue, however; the issue is that although ESPN (probably) wants to do its best, it also does not want to alienate its readers who, given its enormous audience, are more likely to be less statistically-inclined than FanGraphs’ faction of die-hards.

In sum: I started doing this because I no longer trusted projections put forth by popular media outlets.

So I didn’t really care how every single projection turned out. I wanted to find the players I thought were undervalued. For three years, it has largely worked in my rotisserie league. (Honestly, I am a complete mess when I enter a snake draft.)

Anyway. All of that is no longer. I quickly sampled 2014’s qualified pitchers — 88 in all — to investigate who panned out and who didn’t. I will ignore wins because they are pretty difficult to project with accuracy; I’m more concerned about ERA, WHIP and K’s.

Here is a nifty table that quickly summarizes what would have been tedious to transcribe. You will see a lot of repeat offenders, which should come as no surprise. At least there is some semblance of a pattern for the misses: I underestimated unknown quantities (and aces, who all decided to set the world ablaze in 2014) and overestimated guys in their decline. There isn’t much of a pattern to the guys I got right. Just thank mathematics and intuition for that.

Here would be a shortlist of my most accurate projections from last year, measured by me using the eye test:

Name: 2014 projected stats (actual stats)

Nathan Eovaldi: 5 W, 3.82 ERA, 1.32 WHIP, 6.4 K/9 (6 W, 4.37 ERA, 1.33 WHIP, 6/4 K/9)
R.A. Dickey: 11 W, 3.84 ERA, 1.24 WHIP, 7.2 K/9 (14 W, 3.71 ERA, 1.23 WHIP, 7.2 K/9)
Alex Cobb: 12 W, 3.49 ERA, 1.17 WHIP, 8.1 K/9 (10 W, 2.87 ERA, 1.14 WHIP, 8.1 K/9)
Hiroki Kuroda: 11 W, 3.60 ERA, 1.18 WHIP, 6.7 K/9 (11 W, 3.71 ERA, 1.14 WHIP, 6.6 K/9)
John Lackey: 10 W, 3.67 ERA, 1.25 WHIP, 7.5 K/9 (14 W, 3.82 ERA, 1.28 WHIP, 7.5 K/9)
Kyle Lohse: 9 W, 3.60 ERA, 1.17 WHIP, 6.1 K/9 (13 W, 3.54 ERA, 1.15 WHIP, 6.4 K/9)

If it brings consolation to the reader, I have since tightened the part of the projection system that predicts win totals. I’m not gonna lie, it was pretty primitive last year because I thought it’s already a crapshoot to begin with. Obviously, it shows, even in the small sample above. It’s still difficult given the volatility inherent in the category, but the formulas are now precise.

# Revisiting my bold preseason predictions

This just in, folks: Corey Kluber leads all MLB pitchers in wins above replacement (WAR). The great thing about running your own website is you have full discretion to toot your own horn when you please. As much as I find it tacky to do so, I made bold predictions for a reason: to see if my projections are actually worth a damn. I just wish I had time to make more; I should have started early in the offseason as I ran out of time the longer the academic year has worn on. (I’m a graduate student, so publishing to this website is not always the most optimal use of my time. According to societal expectations, at least — I think it’s a great use of my time!)

Anyway, let’s revisit my bold predictions to discuss a) their accuracy thus far, and b) why they have (or have not) been accurate. Here they are, in chronological order:

Tyson Ross will be a top-45 starting pitcher

Ross is ranked 31st of all starters, according to ESPN’s Player Rater. Instead of rehashing details, you can read the linked article to see why I glowed about Ross this offseason and have chosen him as a streamer several times already this year (before he gained more recognition and, consequently, more ownership). That he qualifies as a reliever in ESPN leagues is a huge plus as well. I readily admit it’s not insane for a random names to rank highly in the player rater; just check out the names around Ross’, including Alfredo Simon, Josh Beckett, Aaron Harang and Collin McHugh. Unlike the names I mentioned, though, I think Ross has the natural ability to stay there, given his strikeout propensity that limit the damage done by walks (which, by the way, is a problem nowhere near as bad as Shelby Miller‘s — I guess six wins will mask his atrociously bad WHIP that will blow up in his face sooner rather than later.) Ross is still available in 21 percent of ESPN leagues, so if he’s out there, you should grab him. Just don’t expect him to keep winning as often in front of that terrible San Diego offense.

Brad Miller will be a top-5-to-7 shortstop

As terribly as this prediction has turned out — Miller is batting .151/.230/.247 with 3 HR and 3 SB — I do not regret making it. Miller has struck out in 28 percent of his plate appearances, which is way, way worse than he ever was in Triple-A or even last year, when he struck out 17 percent of the time. It pains me deeply that The Triple Machine hasn’t hit a triple. Have I given up on him this year? Honestly, yes. His batting average on balls in play is grossly unlucky right now, but even regression to the mean won’t fix what his strikeout tendency has broken. But I still like him as a sleeper for next year, or even as a late bloomer this year. If he can demonstrate an improvement in his plate discipline as the year wears on, I will give him another chance. It upsets me, though, that he had such a hot spring. It fuels the fire of analysts who criticize spring training stats as unreliable. I agree, to an extent, but Miller’s spring stats were an extension of his 2013 season — albeit an extension inflated by some good luck. It’s worth emphasizing here that strikeouts really aren’t luck-based, so to say the his spring training was lucky is an ignorant dismissal.

Corey Kluber is this year’s Hisashi Iwakuma (aka big breakout candidate)

There’s one thing I, at least, can privately appreciate about my bold predictions: I abided by all of them in every single I’m in, unless someone happened to grab a pitcher before me. Ultimately, in four leagues, I grabbed Ross and Miller in four of them, and Kluber in three — and in the fourth one, I promptly traded Jayson Werth and Tyson Ross (who I drafted in the last round) for Norichika Aoki and Corey Kluber (this is a points league, so Aoki carries some value for his lack of K’s and contact approach). Did I win the trade? Who knows — I traded one guy I liked for another I liked more. Point is, I actually rolled with my bold predictions. Might as well eat my words, right? (Is that how that saying goes?)

I got Kluber in the equivalent of the last round in every draft and for \$1 in my primary keeper auction league. Yes, I’m bragging. But, more importantly, this isn’t a revelation to me. I knew Kluber would be good based on last year’s peripherals, as did a host of other people on FanGraphs (namely, Carson Cistulli and the Corey Kluber Society). But a lot of people didn’t see it coming, which is crazy to me, and it makes me question what it really takes to become a paid professional “fantasy expert.” Tristan H. Cockcroft ranked Kluber 58th of starting pitchers this preseason, which is better than I expected, but look at some of the names above him: Matt Garza? Justin Masterson? Zack Wheeler? For a guy who invests so much in seeing an improvement in skills, Wheeler has been, for his entire career, buying up billboards to plaster them with slogans such as I HAVE CONTROL ISSUES. Kluber is essentially the antithesis of Wheeler. And, yet, who has the smaller track record? Ridiculous… (In Eric Karabell’s defense, he said pitching is so deep this year that owners may not be able to draft Kluber, which was a roundabout way of indicating he liked him, at least somewhat, heading into draft day.)

Anyway, I’m clearly on a rant, and I need to get this train back on the rails. Kluber is somehow not 100-percent owned at this point — he’s 99.9-percent owned, but hey, at least I’m not lying — yet he’s striking out everyone and their mothers. I don’t know if he continues to strike out 10 per nine innings (10.28 K/9), but the percentage of swinging strikes he has produced has jumped 1.4 percent, placing in the top 1o in the category, behind Max Scherzer and ahead of Madison Bumgarner. This is all a long-winded way of saying he could, and perhaps should, be a 200-K guy this year. In that sense, maybe he’s not a buy-low guy, but his lack of name recognition and his .350 BABIP makes him a prime candidate to be exactly that. A handful of rankings have him in the 35-to-40 range; even then, I can give you a case to trade perhaps a dozen names ahead of him for Kluber, including Gio Gonzalez, Matt Cain and, yes, maybe even Justin Verlander (who, at this point, is still owned in most leagues simply because of name recognition and past performance; and while I understand the importance of past performance, do not let yourself be blinded by nostalgia).

Dan Haren will strike out fewer than 7 batters per nine innings

This one is random, but hey, it’s legit: Haren has only a 6.89 K/9 right now. You can read the linked post to find out way. I may rip him a little too hard — his control still makes him a fairly solid starter — but he’s more of a Kyle Lohse these days than, well, a Corey Kluber. Lohse is serviceable, but he’s not elite, and Haren should be able to net you an extra win or two along the way in front of a lethal Dodgers offense.

OK, that’s it. I’m 3-for-4 in my bold predictions so far this year, which is a pretty good day at the plate, so I’ll take it.

Also, the academic year is winding down, and once it winds down completely, Need a Streamer will ramp up with more content. Stay tuned, and thanks for reading.

# 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:

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
3. Felix Hernandez
4. Max Scherzer
5. Cliff Lee
6. Yu Darvish
7. Chris Sale
8. Cole Hamels
9. Jose Fernandez
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
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.

# Steamers for Sept. 24 and 25

SEPT. 24 MARQUEE STREAM: Tyson Ross (SD) vs. ARI
Ross, owned in only 10.2 percent of ESPN leagues after his dismal outing in Philadelphia (2/3 IP, 6 ER), returned to form in his next start, allowing only three hits and striking out seven across seven innings. I get why he’s not owned in more leagues — he’s 3-8 in 14 starts, which will turn away just about any fantasy owner — but let that bias help you. Ross has a 3.42 ERA, 1.17 WHIP and 8.5 K/9. Know who that trumps in all three categories? Derek Holland, Jorge De La Rosa, Kyle Lohse, Ricky Nolasco, Justin Verlander, Chris Tillman… and that’s just the beginning. Look, if you chase wins, then you’re going to live and die by that sword. But wins are hard to predict, and it’s safe to say Ross has gotten unlucky based on how well he has pitched and how the team for which he plays isn’t that bad at 72-83, good for third in the NL West (ahead of the Giants, no less). So, whatever. If you ignore him in this last week, you ignore him. But remember the name Tyson Ross as the final rounds of next year’s draft approach.

If I don’t go Ross, I may gamble on the St. Louis Cardinals’ Michael Wacha at home versus the Nationals. If you’re looking for strikeouts, Wacha (22.9 percent ESPN ownership) has ’em. People have been on and off the Wacha bandwagon after alternating good and bad starts, the most recent of which ended after 4-2/3 innings and 12 hits at Colorado. But he still struck out seven, and he still has a 3.21 ERA and 1.21 WHIP, and he still has massive upside. He’s not today’s best option, but I’d take him over Jason Vargas, Dan Straily or Doug Fister.

SEPT. 25 MARQUEE STREAM: Danny Salazar (CLE) vs. CHW
He’s 1-3 through nine starts, but it’s only by design. Salazar (14.2 percent ESPN ownership) had his pitch count lifted and he’s ready to humiliate more White Sox — last time he faced them, he struck out nine of them. Through 3-2/3 innings. THAT’S NINE STRIKEOUTS IN 11 TOTAL OUTS, PEOPLE. By the way, his line for the season stands at 3.09 ERA, 1.11 WHIP and 11.0 K/9 (with a 4.0 K/BB). If you need more proof as to why he’s so great, you can search for the love poems I’ve written him in the archives. (Disclaimer: There aren’t any Danny Salazar love poems in the archives. Yet.)

Otherwise, I honestly wouldn’t touch anyone else. The fringe streamers (i.e. Jake Arrieta, even Ervin Santana in some leagues) face stiff competition.