# 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.

# Panning for gold using spring stats, pitcher edition

Here’s the second installment of my breakdown of spring training stats. You can view the first one by scrolling down like four inches to the previous post. Here is a look at a variety of pitchers in no particular order.

James Shields, KC
Important stats: 14.2 IP, 18 K, 0 BB (0.61 ERA, 0.48 WHIP)
Why they’re important: Shields is firmly entrenched as a solid No. 2 fantasy starter, but he is off to as a hot a start as anyone right now, striking out 11.05 batters per nine innings and walking nobody. Not saying he’s worth bumping up in your rankings, but perhaps he’ll give you a little more than what you expected this year.

Max Scherzer, DET
Important stats: 14.1 IP, 16 K, 2 BB
Why they’re important: It would be unjust to exclude him. He’s having an excellent start, but he’s an excellent pitcher, so this is nothing extraordinary at this point.

Chris Tillman, BAL
Important stats: 12.2 IP, 14 K, 2 BB
Justin Masterson, CLE
Important stats: 13.0 IP, 14 K, 2 BB
Why they’re important: What’s the difference between them? Tillman has a 4.97 ERA and 1.26 WHIP while Masterson is sporting a 0.00 ERA and 0.62 WHIP. Meanwhile, their underlying stats are almost identical. This is where small sample sizes can really warp perspectives. Each guy is the victim and beneficiary of batting average on balls in play (BAbip), respectively. Only difference is Masterson is giving up fewer fly balls, making him less prone to home runs and hits.

Corey Kluber, CLE
Important stats: 14.1 IP, 15 K, 2 BB
Why they’re important: Maybe you’ve caught on to the trend again: I’m focusing on guys with excellent strikeout rates as well as strikeout-to-walk ratios (K/BB). Ignore the 5.02 ERA and 1.33 WHIP; Kluber’s BAbip is a sky-high .395 over this small sample size. He’s steal dealing. Also, he has the fifth-best ground ball rate of qualified spring training pitchers. I’ve read concerns about his home runs allowed last year. Can’t hit a home run on the ground, son. (Well, technically you can, but… shhhhhhh.)

Josh Johnson, SD
Important stats: 13.1 IP, 1.05 WHIP, 13 K, 4 BB
Why they’re important: For people hoping for a comeback, these ratios (8.78 K/9, 2.70 BB/9) are the makings of a solid starter. He’s not on my radar, but I acknowledge reasons why he could be on it (aside from the fact that he used to be one of the most dominant pitchers in all of baseball).

Alex Wood, ATL
Important stats: 14 IP, 0.00 ERA, 0.93 WHIP, 12 K, 2 BB
Why they’re important: He had a 1.73 ERA and 0.99 WHIP in the minors with a 3.78 K/BB. He followed it up with an 8.9 K/9 in the majors, nearly identical to his minor-league rate. The Braves develop great pitchers (and they know when to deal them… looking at you, Tommy Hanson). Wood is the next in line.

There are pitchers having bad springs, too. Guess which statistic I’m primarily using to evaluate them?

Tony Cingrani, CIN
Important stats: 12.2 IP, 6.39 ERA, 1.42 WHIP, 13 K, 6 BB
Why they’re important: I’m not as concerned with the ratios as I am the walks, which he’s handing out at a 5.68 walks-per-nine-innings (BB/9) clip. Strikeouts are still there, which is good, and, of course, it’s worth acknowledging the small sample size. Maybe he’s working off the offseason slumber. But I’m keeping my eye on his control.

Tim Hudson, SF
Important stats:
13.1 IP, 1.58 WHIP, 9 BB
Why they’re important: Nothing matters here except for the lack of control. Cingrani’s walks are a bit disconcerting; Hudson’s walks (6.08 BB/9) is really worrisome, especially for an older pitcher coming back from a gruesome foot/ankle/leg injury. Perhaps it’s a bit early to predict the beginning of the end, but I’ll say it anyway: this could be the beginning of the end of Tim Hudson. It’s a shame, but it ultimately happens to everyone.

Matt Moore, TB
Important stats: 10.1 IP, 2.32 WHIP, 10 K, 11 BB
Why they’re important: He’ll always be loved for his strikeout propensity but his walk rate (9.58 BB/9) is most horrifying of all. I understand if you like him, but I will never draft him because of how he damages my WHIP — and a player with bad command is one bad-luck-BAbip away from having an absolutely miserable year.

Jose Quintana, CHW
Important stats: 6 IP, 30.00 ERA, 4.00 WHIP
Why they’re important: And the Worst/Most Humiliating Spring Training award goes to… Jose Quintana! Just look at it. It’s almost impossible how bad he’s been. But, in his defense, there’s a .586 BAbip at work here. And that, my friends, is why sample sizes this small should not be trusted. Some statistical anomalies are worth noting, but this one is simply outrageous. I am not changing my ranking of him based on this.

Other notable pitchers having bad springs, in terms of control: Zach McAllister, Dan Straily

Rookies/prospects having good springs: Yordano Ventura, KC (1.76 ERA, 0.72 WHIP, 15-to-1 K/BB ratio in 15.1 IP)… Drew Hutchison, TOR (2.79 ERA, 0.83 WHIP, 16-to-1 K/BB ratio in 9.2 IP)…

Rookies/prospects having bad springs: Allen Webster, BOS, continually plagued by command issues (5.25 ERA, 1.42 WHIP, 5.25 BB/9)… Archie Bradley, ARI, baseball’s No. 1 pitching prospect, also plagued by command issues, a problem he has had his entire professional career (4.32 ERA, 1.68 WHIP, 6.48 BB/9)… Trevor Bauer, CLE, allegedly on the comeback trail, but starting to doubt it (10.29 ERA, 2.43 WHIP, 6.43 BB/9)…

I said this verbatim in my last post: “Do your own research, form your own opinions.” It’s important to remember that these are incredibly small smaple sizes, meaning there’s a lot of volatility involved here. Still, some metrics can be very telling, and strikeout and walk rates can be much more indicative of future performance than ERA (or even WHIP, which can be jerked around by fluctuations in BAbip). Again, don’t put your eggs into one basket (where spring training stats is the basket in this analogy), but it’s worth remembering a name or two.

# 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

# Breaking down recent trades and signings. Who won?

I been gone a long time. Sorry, folks. Let’s break down some recent trades now that the stove hath been declared “hot.”

DET 1B Prince Fielder for TEX 2B Ian Kinsler
With the emergence of second baseman Jurickson Profar, the Rangers had a logjam in the middle infield, especially after extending Elvis Andrus‘ contract. Trading Kinsler was the solution, and any chatter about trading Profar to St. Louis for outfield prospect Oscar Taveras was promptly silenced. The Rangers will take on about \$10 million more in salary per year, not to mention all the additional years at the tail end of Fielder’s contract, but will be able to replace the floundering Mitch Moreland at first base. Some analysts (and Detroit fans) have sworn off Fielder and declared his power decline already in motion. I’ll get to that.

The Tigers had more needs to fill. Infielders Jhonny Peralta and Omar Infante are free agents, and both are coming off solid years and will likely test the market. Trading for Kinsler fills one of these needs, and quite soundly, too. Kinsler will bring veteran presence and skills to an already highly-talented team. Moreover, moving Fielder away from Detroit, where his (alleged) declining production, poor postseason performance and lukewarm-at-best fan relations have alienated him, frees up salary space to offer Cy Young pitcher Max Scherzer a long-term contract. The one thing I haven’t seen discussed much: two-time AL MVP Miguel Cabrera was plagued by nagging injuries the last month of the season. Moving him to first base will alleviate defensive problems, yes, but it will also give him a chance to heal at a less intensive defensive position. I don’t know who will play third base, but rookie Nick Castellanos played third base before the Tigers moved him to left field.

As for Fielder’s power and production, let’s do a simplistic blind resume.

Player A – .313, 30 HR, 82 R, 108 RBI
Player B – .279, 25 HR, 83 R, 106 RBI

Player B is Fielder in 2013; Player A is Fielder in 2012. The big difference? He hit fewer than 30 home runs for the first time in forever, and his on-base-plus-slugging (OPS) is way down. You can complain about the batting average, too, but the 2012 average is the anomaly here, not the 2013 average. Looking more deeply into his peripherals, though, Fielder his a boatload of line drives — 26 percent of all balls in play, in fact. Fielder’s average line drive percentage is 21 percent; the MLB average is 19 percent. He also put the most balls into play in his career with the lowest ratio of home runs to fly balls (HR/FB). Unless Fielder was trying to hit line drives all last year, which he likely wasn’t, I expect 32-or-so home runs from Fielder in 2014. My one concern is his depleted walk rate (although his on-base percentage is still very solid), but the dude also dealt with a divorce all year, too. I can’t say I’ve ever been divorced before, so I don’t know what it’s like, but I can’t imagine it’s always pretty.

I understand both sides of this trade, though, and hesitate to declare one team the winner over the other. Kinsler and Fielder are getting old, so declines in production should be expected. I think the winner of this trade will be decided in if Profar pans out and Scherzer lives up to his ace potential and reputation.

Winners: Detroit Tigers, Texas Rangers

SF Giants sign SP Tim Hudson
Even as someone who doesn’t identify as an Atlanta Braves fan, it’s sad to see Hudson go. However, I don’t know how much good this does the Giants. Their farm system is weak and their rotation pitiful. Adding Hudson for a couple of years for back-end rotation help and veteran presence is not going to produce another championship. The team needs to focus on rebuilding, and shedding salary may be a good first step in doing so. Also, why didn’t the Braves re-sign him? Their rotation is very young with zero veteran presence. At least Hudson could fill up a back-end spot that would surely be better than what Paul Maholm could muster. Then again, they could probably turn me into a quality starting pitcher with the magic they evidently possess.

Winner: Tim Hudson
Loser: San Francisco Giants, Atlanta Braves

KC Royals sign SP Jason Vargas
Who considers this a major baseball-related announcement? Jokes aside, Vargas was probably the Angels’ most reliable pitcher and has been better than decent the past two or three years for the Los Angeles Angels of Anaheim and Seattle Mariners. For a team that’s working toward a postseason berth, this isn’t a bad play. Besides, who could be worse than Bruce Chen?

Winners: Kansas City Royals, Jason Vargas

STL 3B David Freese for LAA OFs Peter Bourjos, Randal Grichuk (AAA)
David Freese is an average third baseman who is widely (and incorrectly) perceived as an above-average player because of his postseason heroics. Freese is simply average, though, and shipping him to Anaheim makes it clear that the Angels are going to shop Mark Trumbo. It’s their best chance at getting some prospects, of which their depleted farm system has none.

Trading Freese allows the once-utility second baseman Matt Carpenter to move to third base in order to free up space for rookie Kolten Wong. With outfielder Carlos Beltran, a free agent, likely on the move, I expect to see über-prospect Oscar Taveras man center field while Matt Holliday and Allen Craig play left and right field, with Matt Adams at first base. Another scenario could see Taveras getting the call sometime in May or June and Bourjos manning center field until then. Yet another possibility — the least optimal of them — would see an outfield of Jon Jay, Bourjos and Holliday, with Craig at first base and Adams relegated to the bench.

Either way, the Cardinals are even more stacked than they were before the trade. Ridding of Freese was probably difficult, but it was necessary for progress. The Angels made a decent move in ridding of extra outfield pieces, but sending Randal Grichuk, the Angels No.-2 prospect, to a loaded St. Louis farm system (where Grichuk will likely rank no better than 10th) further guts the Angels minor league system. Rancho Cucamonga is a barren wasteland at this point. (Thank you, Cliff Clinton, for enlightening me as to who Grichuk is. Even as an Angels fan I sure as hell didn’t know.)

We’ll have to wait and see who the Angels get in return for Trumbo, but it won’t change the fact that they lost this trade.

Winner: St. Louis Cardinals
Loser: Los Angeles Angels of Anaheim

Fan report: Dan Uggla has put his Atlanta home up for sale. (Thanks, Charles Henninger, for this tidbit.)
Let’s get his ass out of there. I’ll never forgive you, Dan, for singlehandedly losing me the 2012 fantasy baseball title.

# 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.