# 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.

# Six pitchers I’m not targeting in drafts

As much as it feels good to correctly bet on a bounceback, it sucks harder to be the guy who loses the coin flip. I looked at my 2012 standard 5×5 rotisserie auction draft and the list is, frankly, hilarious. The top 10 pitchers were:

1. Clayton Kershaw (\$32)
3. Justin Verlander (\$26)
4. Felix Hernandez (\$26)
5. Tim Lincecum (\$24)
6. Jered Weaver (\$24)
7. Cliff Lee (\$23)
8. Dan Haren (\$21)
9. Cole Hamels (\$19)
10. CC Sabathia (\$19)

Wow. That was only two years ago. Half those names have fallen from grace — more than half if you’re in the camp that think last year was not an anomaly for Verlander and that we’ve reached the beginning of the end with him. It’s truly hard to believe that anyone thought Halladay would be the second-best pitcher in the MLB in 2012 after the numbers he put up, but it just goes to show how suddenly a pitcher’s decline can sneak up on everyone.

Humorously enough, three of the pitchers in that top 10 make my forthcoming list of pitchers who I will not be targeting in drafts. This can also be viewed as a list of the largest differences between ESPN’s and my rankings.

Justin VerlanderESPN rank: 14, My rank: 25
I have more faith in his strikeout rate, but ESPN has more faith in his overall effectiveness. Truth is, he didn’t suffer an abnormally high BAbip or anything like that. He was simply more hittable and, honestly, ESPN’s projection doesn’t make a lot of sense when you consider that fewer strikeouts should lead to a higher probability he will give up a hit. Regardless of how you feel about him, it’s the offseason surgery that freaks me out. Does that not freak YOU out? It came out of nowhere, and there are rumors he may not even be ready for Opening Day. Toss in the fact that he has a pretty rigorous offseason routine that, for the first time, he won’t be able to stick to, and you have a guy that may not only start the season but also be out of shape, relative to his standards. Unless I get him as low as 30th, he’s not worth the risk.

Shelby Miller | ESPN rank: 26, My rank: 48
This is not a testament to Miller’s abilities — he’s a very good pitcher. This time, ESPN believes more in the strikeout rate; my research leads me to bet against it, although I’m sure he has the capability to improve. The most important aspect of his game this year will be how deeply he pitches into games. I’m not banking on 200 innings, let’s put it that way. I simply believe he will be overvalued on draft day, especially if ESPN thinks he will be better than Gerrit Cole or Alex Cobb. Even if Cole doesn’t ramp up the strikeouts, I still can’t get behind them on this one (Cole struck out 10 batters per nine innings over his handful of starts and was an absolute beast. He gasses 100 mph). Miller is o-ver-ra-ted. Case closed.

Hyun-jin Ryu | ESPN rank: 31, My rank: 50
I actually think he will perform better than ESPN thinks. I also think ESPN simply underrates a lot of players. They have an audience to please, and I think intuition prevails sometimes, even if it’s wrong. Ryu is good but not elite; he pitches more to contact but keeps the ball on the ground. With that said, the strikeout rate suffers, so he’s not really a guy I want on my team. However, he’ll get wins, and that’s great. But we all knows wins are unpredictable. Ask 2012 Cliff Lee and 2013 Cole Hamels. (Or maybe just don’t pitch for the Phillies next time.) Anyway, again, another case of overrating in my opinion.

Jon Lester | ESPN rank: 37, My rank: 56
With so much pitching depth, there’s no reason to tolerate a career 1.30 WHIP and a pedestrian K/9 rate since 2012 just to bank on wins. It only takes one bad year.

CC Sabathia | ESPN rank: 39, My rank: 41
At least ESPN and I are on the same page on this one. Still, what if it gets worse? I think 41st is a neutral projection, and with Hiroki Kuroda and Tony Cingrani following right behind, there are clearly other worthy commodities for which you can pass up Sabathia. Also, don’t forget that these rankings don’t tell you exactly how closely players are ranked together. Players within five slots or so of one another are practically interchangeable.

Dan Haren | ESPN rank: 44, My rank: 73
Let me make my official declaration: Dan Haren’s strikeout rate is NOT back — I repeat, NOT back! ESPN only sees a slight regression, but I dug deeper into PITCHf/x data and basically revealed Haren’s strikeout rate in 2013 was anomalous. I truly think he is more likely to record fewer than seven strikeouts per nine (aka 6.9 K/9) than 7.7 K/9 as expected by ESPN. Be warned, friends. The Dodgers will make his win column tolerable, but only if he pitches somewhat respectably — and I don’t know if he’s capable of doing that. As I’ve said a hundred times already, there’s simply too much volatility here.

Honorable Mentions:
Julio Teheran – He’s good, but I’d rather another owner jump the gun on him (which I can almost guarantee will happen) and pass up on better talent for him.
Jeff Samardzija – Serious question: has he ever won more than nine games? (Also, not coincidentally, a rhetorical question.)
Zack Wheeler – ESPN is really bullish on him. Maybe I’ll be the guy who misses the breakout year, but he finished 2013 with a 4.1 BB/9. He walked 5+ guys in four starts, and failed to strike out more batters than he walked in five. That’s simply unacceptable, and command does not shore up overnight.

# 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:

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

# Three new* Red Sox, three new* Yankees. Who fared better?

*Each team re-signed one player, so they’re technically not all “new.” Moving on.

Maybe I missed something, but have any AL East teams other than the Red Sox and Yankees made moves this offseason? Boston and New York has made three signings a piece. That sets up a pretty easy comparison for the question to which everyone wants an answer: Which team “wins” this postseason so far?

(I get really self-conscious when using questions in my writing.  My high school English teachers drilled into my brain that using rhetorical questions is a crutch in persuasive writing, as is asking yourself a question to simply answer it later. But really, am I trying to impress Advanced Placement test graders anymore? No. No I’m not.

If you are an AP test grader, I apologize.)

BOS signs C A.J. Pierzynski
AJP bounced back from down years in 2010 and 2011, and his 2013 was pretty much in line with how he has typically produced throughout his career. He’s 36 — that kind of tread on a catcher’s tires is always a red flag — but he’ll be hitting in the friendly confines of Fenway Park, so maybe another 15+-HR season and .275 average is not out of the question. Pierzynski is probably an upgrade over Jarrod Saltalamacchia behind the dish, despite Salty’s productive 2013.

Winner: Both parties
Pierzynski’s preseason rank: No. 2 catcher in deep leagues

NYY signs OF Carlos Beltran
Man, I love Beltran. Who doesn’t? (Answer: Mets fans.) (Shit, I used a rhetorical question again…) But where does he fit in the Yankees’ plans? That team is just flat-out old now. Their outfield is already bursting at the seams with Jacoby Ellsbury (spoiler alert), Ichiro Suzuki, Alfonso Soriano, Vernon Wells (negligible) and Brett Gardner. Meanwhile, the average age of their potential starting outfielders is 35, and that’s before Ellsbury joined the crew. I’m guessing Wells will be relegated to bench duty. But I have also heard Gardner figures to play into the Yankees’ everyday plans. Sounds like Ichiro is on the market, and I’ve read that the Giants are possible suitors. Anyway, Beltran is good, but he’s not a solution to the Yankees’ problems, which is zero preparedness for beyond 2015 when every single one of their players falls apart.

Winner: Beltran
Preseason rank: Probably a top-3o OF — full rankings pending

BOS signs RP Edward Mujica
Mujica made a name for himself as Jason Motte‘s replacement at the back end of the St. Louis Cardinals’ bullpen, and there’s no reason he can’t do it again. Some people argue he broke down at the end of the year, but manager Mike Matheny wore him down with consecutive (two, if not three) multi-inning outings in August, and even Mujica said it wore him out. It’s fair to worried about durability, but if you’re going to write off a solid closer for a good team because he might wear down in September, you have bigger things to worry about. HOWEVER… Koji Uehara is still there. And so is Junichi Tazawa. And given Uehara’s incredible success last year, Mujica would only see chances for saves on Uehara’s rest days at best. Unfortunately, I must politely ask everyone who rode the Mujica train last year, myself included, to disembark.

Winner: Red Sox
Preseason rank: Unranked/not draftworthy

NYY re-sign SP Hiroki Kuroda
Not much to see here — he’s old, but it’s a one-year deal and he has proven he’s still plenty effective. It still doesn’t solve the Yankees’ age problem, though.

Winner: Kuroda
Preseason rank: 40th

BOS re-signs 1B Mike Napoli
Feels weird to call Napoli a first baseman and only a first baseman. Again, nothing to see here, but Napoli’s lack of eligibility at catcher is kind of a deal breaker.

Winner: Both
Preseason rank: Low-tier 1B

And finally…

NYY signs OF Jacoby Ellsbury
Yes, I couldn’t help but spoil the surprise earlier (even though it’s not really a surprise). Ellsbury has joined the archenemy, and of course all Red Sox fans are really flustered. Meanwhile, the Yankees overpaid… Way overpaid. Something like \$20 million a year for seven years. Look, guys who rely on their wheels for productivity have been known to break down at about his age (see: Michael Bourn). Give it another couple of years and 50 stolen bases will only be 20, or maybe even 10. His power will likely decline, too, as will his defensive range. It’s just not a good situation. Seven years was way too long to begin with, and the price makes it worse — and I have yet to touch upon his abundant injury risk. Don’t fret too much, Red Sox nation. You’ll be grinning about this one in 2017 as the Yankees dynasty completes its collapse.

Winner: Ellsbury
Preseason rank: Top-1o OF, with downside

# 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.