# Find me on FanGraphs!

My dearest readership: I now write for FanGraphs, under the RotoGraphs banner, which is the fantasy baseball corner of the website. I have no plans to retire this site, but updates will come infrequently as I feel out my role and get comfortable at FG.

I’ve published two pieces there already, which can be accessed via the links below:

# Fairly early 2015 1B rankings

I posted my very early 2015 closer rankings a couple of weeks ago. In continuing with the trend, I present to you my preliminary, but mostly complete, rankings for first basemen. The prices are based on a standard 5×5 rotisserie league with a budget of $260 per team. In this instance, I assume 60 percent of all teams’ budgets are spent on hitters, as is done in mine.

In a later version of this, I will enable the spreadsheet to be dynamic and allow users to input their own budget amounts and percentages spent. In the meantime, here is the static version.

Let me try to be as clear as possible about how I determine prices: I do not discount or add premiums based on positional scarcity or relativity. I like to know exactly what a home run, a steal, a run, etc. is worth, no matter who it comes from. It gives me a better idea of the depth at each position and how urgently I need to overspend at the so-called shallower positions, such as catcher and third base, as y’all will see in future installments of these rankings.

Some thoughts:

- The statistics, to my eye, are all scaled down slightly (except for maybe home runs). However, this effect happens to every player, so the changes are relative and, thus, the prices are theoretically unaffected.
**Jose Abreu**is the #2 first baseman, and it’s not even that close of a call. I honestly thought**Paul Goldschmidt**‘s stock would be a bit higher — remember, my computer calls the shots here, not me — but the projections believe more in Goldy’s 2014 power (which paced out to 27 home runs in a full season) than his 2013 power, when he dropped 36 bombs. He’s also no lock to stay healthy. Which no one is, really. Still, I may take the over on all his stats, but not by a large margin.- I will, however, take the over on
**Edwin Encarnacion**‘s statistics, as he has bested all the projected numbers each of the past three seasons, and he does it all while battling injuries. I will take him at the price simply because of what I will call “health upside” — everyone assumes he will get hurt, but if he can play a full 162, he’s a monster — and because if his batting average on balls in play (BABIP) ever reaches a normal level, his batting average boost will send his stock through the roof. - No surprise to see
**Anthony Rizzo**at #5 after last season. I’m a believer, and he will be surrounded by a slew of talented youngsters next year. **Freddie Freeman**, hero of my hometown, is simply not where I expected him to be after his 2012 season. Granted, he’s an excellent player, but until he chooses to hit for power rather than spray line drives (again, not a problem in real, actual MLB baseball), and until the Braves stop sucking (which may not be any time soon), he may not be that great of a first base option.- The two Chrises —
**Chris Davis**and**Chris Carter**— round out the top 10 with almost identical profiles. Lots of power, lots of strikeouts, low batting averages. The shift may have suffocated Davis’ batting average, but it shouldn’t happen again, and I am considering investing in him if his stock has devalued enough after last year’s atrocity. **Joey Votto**,**Prince Fielder**and**Ryan Zimmerman**are shells of their former selves.**Lucas Duda**is for real, but his batting average is a liability, as is a lot of the Mets’ lineup.- The projections have what amounts to almost zero faith in
**Ryan Howard**,**Joe Mauer**and**Brandon Belt**. Mauer may be the saddest tale of them all. He’s still good for a cheap batting average boost, but single-digit homers? I just feel bad for the guy. And the owner who banks on the rebound. - Looking at
**Adam LaRoche**‘s projection, I’m starting to really like that move by the White Sox. Part of me feels like he’s going to be undervalued or maybe even not considered on draft day, and that’s appealing to me. **Steve Pearce**at #16 is an upside play, given his 2014 looks all sorts of legit.**Jon Singleton**: the poor man’s Chris Carter.- And just because
**Matt Adams**is beating the shift instead of hitting home runs doesn’t render him without value. He’s not my cup of tea, but 19 home runs could be conservative for him.

# Very early 2015 Closer rankings

Teams are dancing the Depth Chart Shuffle, but the closer landscape has remained relatively steadfast. Per MLB.com, 27 of 30 teams have denoted who will be their respective 9th-inning man on their depth charts (labeled “(CL)”, for reference). For reasons largely pertaining to simplicity, I have completed a preliminary round of projections for closers and have provided it for your viewing pleasure. Keep in mind that things will (likely) change as the offseason progresses into the preseason progresses into the real, authentic season.

The rankings are catered to classic 5-by-5 rotisserie leagues with $260 budgets. **Bonus feature:** You can manually input a budget amount as well as an expected share of total spending on closers. For example, the teams in my league historically spend about 10 percent of the aggregate wealth on closers. If your league values closers more highly, you can accordingly adjust for such.

The players on teams that have not solidified their closer situations are marked with asterisks. Note that the very elite **Dellin Betances** is one of these players. This will inevitably be sorted out by March.

**Some reflections:**

**Craig Kimbrel** will likely fall short of 49 saves — although, if the Braves can compete in the few games they are expected to win, he may have a lot of small-margin-of-victory save chances coming his way. Tough call, but there’s legitimate arguments to be made about him being maybe only a top-3 RP — which is really nothing about which to write home.

The aforementioned Betances is projected for the second-best ERA, second-most strikeouts and third-best WHIP among all closers. Betances threw a ton of innings last year, so it suffices to say I’m eager to see how his usage shakes out. Given how the Yankees have historically used closers, however, I think he’ll be closer to his projected 63 innings than his 90 last year.

**Sean Doolittle** isn’t an upside play, but I suspect he will be underrated on draft day. **Koji Uehara** *is* perhaps an upside play: his projection factors in his health concerns, so if he can stay healthy all year, he should bolster his return on investment.

**David Robertson**: he’s good, but his competition is great. Not a top-10 RP in my book. Likewise with **Trevor Rosenthal**, who has never really had a good grasp on where the strike zone is.

Will **Zach Britton** continue to induce an absurd number of ground balls? Yes, although perhaps not as extremely as he did last year.

No offense to **Brett Cecil**, but I think the Blue Jays will trade for someone in due time.

Dark horse candidates in **Mark Melancon** and **Jake McGee** as they round out the top 10. I think they may be a bit overrated, but I would take them over literally everyone below them except maybe Cishek, if we’re pulling hairs.

**Bobby Parnell** is competing, so to speak, with **Jenrry Mejia**; **Jonathan Broxton** is competing with who the heck knows. **Santiago Casilla** could likely cede the role back to **Sergio Romo**. Other pitchers in some sort of danger of losing their jobs during the seasons include **Fernando Rodney**, **Joaquin Benoit**, **Drew Storen**, **LaTroy Hawkins**, **Neftali Feliz** and **Chad Qualls**.

**Jonathan Papelbon**, **Joe Nathan** and **Addison Reed** seem to have some semblance of job security, but they also seem to have a semblance of not being very reliable anymore. Papelbon and Nathan will be the most interesting bullpen storylines, especially if Nathan struggles again and the Tigers are competing.

I haven’t contextualized these rankings for points leagues or a top-300 type of thing for roto formats, but hey, that’s why it’s preliminary.

# Predicting HR/FB rates for hitters using weighted pitch values

(If you care only about results and not about the process, scroll down to the section aptly titled **HERE YA GO**.)

**Victor Martinez** indirectly and semi-strangely inspired this post. I was browsing FanGraphs’ weighted pitch values for hitters — something I hadn’t done before, as I’ve really only used the metric for pitchers — for 2014 and my thought process went something like that:

*Jose Abreu feasted on fastballs; V-Mart feasted on sliders… Wow, V-Mart actually fared better against sliders and curveballs than fastballs and cutters. I wonder if that has any correlation with his plate discipline.*

In short: no. A hitter’s success versus pitches according to weighted pitch values (per 100 of that pitch) determines about 40 percent of his walk rate and barely 4 percent of his strikeout rate. (I’m ballparking it on the K% figure.)

But I got to thinking a little more: these weighted pitch values have to be good for *something* other than scouting hitters (which, moving forward, maybe we starting throwing Abreu some more offspeed stuff? I don’t know).

Alas, I took a crack at it: I tested the correlation between weighted pitch values and home runs per fly ball (HR/FB) rates. And I was very pleasantly surprised.

Let’s start with context. Each player, very obviously, records his own HR/FB rate each year. Players with more power will record higher HR/FB rates, and players with less power will record lower rates. Therefore, each player, in a sense, creates his own benchmark (which, arguably, is his career HR/FB rate: he hits this many home runs as a percentage of fly balls *on average*). However, we know that HR/FB fluctuates annually: a player with a 15% career-HR/FB does not hit exactly 15 of every 100 fly balls over outfield walls every season like clockwork. Still, there is an *expectation* that he will hit a certain number of them out — hence, the benchmark.

Using regression analysis, the idea of the benchmark can be captured by seeing how, say, 2014’s HR/FB rates correlate with 2013’s rate, as well as 2012’s, 2011’s and so on. I downloaded all available ball-in-play data for seasons by “qualified” hitter as separate seasons dating back to 2002, thereby representing an exhaustive list. The line of best fit looks as follows, where L1 represents the year prior, L2 two years prior, L3 three years prior:

**x(HR/FB) = .018 + .321*L1.(HR/FB) + .252*L2.(HR/FB) + .228*L3.(HR/FB)
Between R-squared: .74**

One might astutely observe that a player who hit exactly zero home runs the three previous years can still be expected to hit about 1.8 percent of his fly balls over the wall, and one might call to arms to force the intercept term to zero. It seems absurd, nay, impossible that a player who never hits home runs could be expected to suddenly hit one, but let us not forget we witnessed the impossible happen just last year. That’s what makes baseball a beautiful sport: anything can happen.

Anyway, the equation above is actually really helpful in predicting expected HR/FB; its R-squared indicates the line explains almost three-quarters of the model’s fit. It also bestows the greatest significance to the most recent year as measured by its coefficient, with declining significance associated as years become further removed, which makes sense. But… BUT.

It’s not helpful in predicting HR/FB for hitters who have only been in the league fewer than three years. Moreover, it seems especially difficult to predict future HR/FB rates for hitters with only one year of data, such as the monstrous Abreu. (Maybe Abreu *did* inspire this post after all.) Observe:

**x(HR/FB) = .032 + .694*L1(HR/FB)**

After a little bit of algebra, we can intuit that the equilibrium HR/FB rate is roughly 10.4 percent. I use the term “equilibrium” because it appears that no matter what HR/FB a hitter posted in his first career season, his next-year HR/FB will be expected to converge (aka regress) toward the magical number of 10.4 percent. Again, observe:

.032 + .694*(12%) = 11.5%

.032 + .694*(8%) = 8.7%

You can perform this exercise with any value, and the results will be the same: a 2014 HR/FB rate lower than ~10.4 percent will be expected to increase in 2015, and a rate higher than ~10.4 percent will be expected to decrease in 2015. Now this, *this*, is actually absurd. Granted, the equation is communicating what would happen *on average*, but hitters are not homogeneous.

This is all a very long-winded way of saying two things:

1) When the sample is incredibly small — namely, one observation — using history as a guide fails us.

2) I think I may have found an alternative that relies not on a single year’s worth of HR/FB data but on a single year’s worth of weighted pitch value data.

## HERE YA GO

Let me be clear, up front: I know there will be a lot of multicollinearity inherent in this analysis — that is, HR/FB and weighted pitch values are dependent on each other in some fashion. I don’t know how weighted pitch values are calculated exactly — it would behoove me to look it up, but I am lazy, a current self-descriptor of which I am not proud — but, intuitively, a hitter who hits home runs more frequently off of particular pitch types will likely record higher weighted values for those pitches. Essentially, the weighted values are calculated *using* home run frequency, and I am now trying to reverse-engineer it.

But I don’t see that as a bad thing. There is a profound correlative capability in the data, and using that information to glean whether or not a hitter was, perhaps, a bit lucky when it came to his HR/FB frequency is, I hope, less preposterous than pulling a number out of your rear-end.

## HERE YA GO, FOR REALSIES

I will use strictly weighted pitch values per 100 pitches (denoted wXX/C, where XX represents the pitch abbreviation). I omit knuckleballs because not all players saw them, and I omit splitfingers because they are statistically insignificant, probably because they aren’t thrown very often, rendering the weighted pitch values more volatile. I also add K% and BABIP presuming the following: strikeout rates are positively correlated with HR/FB rates, and BABIP, which positively correlates with hard-hit balls such as line drives, is likely to also positively correlate with similarly-hard-hit balls such as home runs. (A regression that includes only weighted pitch values and excludes K% and BABIP produces an adjusted R-squared of .45.) The line of best fit equation is as follows:

**x(HR/FB) = .2049 + .0352*(wFB/C) + .0081*(wSL/C) + .0014*(wCT/C) + .0041*(wCB/C) + .0063*(wCH/C) + .5244*K% — .6706*BABIP
Adjusted R-squared: .75**

Again, the model produces a great line of best fit per its R-squared — almost identical to its lagged-variable counterpart. As it should; if there’s multicollinearity, it should. (And there is.) But reverse-engineering the process should create accurate predictions of what should have been a hitter’s HR/FB rate in a given season *because* of the multicollinearity; in this instance, it’s not a bad thing.

Some trends emerge instantly, trends similar to those I saw in the xK% and xBB% studies I performed earlier: regardless of a player’s power potential, he will over-perform or under-perform his expected HR/FB rate, and he will do so with consistency. For example, Adam LaRoche, despite his apparent power stroke, consistently under-performs his xHR/FB:

HR/FB: actual minus expected

2010: -1.89%

2012: -1.87%

2013: -1.77%

2014: -0.75%

Meanwhile, **Albert Pujols** consistently out-performs his xHR/FB:

2010: +2.02%

2011: +5.67%

2012: +1.80%

2014: +2.13%

Each data set has its noise, but you can see based on these limited samples where each hitter experienced a bit of luck: LaRoche, in 2014, saw a minor spike, and Pujols saw a major spike in 2011.

Rather than going through each player individually, I will highlight a few extreme, fantasy-relevant outliers from 2014 and reflect accordingly. Without further adieu (and in alphabetical order by first name):

** Adam Eaton, -8.03%
**This is the largest negative differential in the 2014 data. Without another full season of data to compare, this huge difference is likely a sign of bad luck, although there

*is*a chance that he is a severe under-performer in the same vein as

**Matt Carpenter**(who has under-performed his xHR/FB by about 7 percent the past two years). I already liked the guy for his speed and control of the strike zone, and the prospect of a pending power spike is enticing.

** Coco Crisp, -5.78%
**Crisp is a great case study: he notched a career-high 12.4-percent HR/FB in 2013, then promptly slid back down to single digits in 2014. His 2014 xHR/FB, however, indicates his HR/FB should have been closer to 11.5 percent, almost 6 percent higher than his actual mark and only 1.2 percent less than 2013. Meanwhile, his 2012 and 2013 expected and actual HR/FB rates are almost identical. His power-speed combination was pretty valuable two years ago — when he wasn’t on the disabled list, at least.

** Curtis Granderson, -5.92%
**Granderson bottomed out in woeful aplomb last year, but his xHR/FB offers a glimmer of hope. I’ll be honest, though, I can’t remember the last time this guy was fantasy relevant. But if you’re looking for sneaky power at the expense of everything ever, he could be your guy.

** Giancarlo Stanton, +5.33%
**The Artist Formerly Known as Mike posted positive differentials in 2011 and 2013, but each was one-half and one-third the magnitude of last year’s differential. His 2013 and 2014 xHR/FBs are practically identical — 20.16% and 20.17% — so it looks like Stanton chose a good year to get a little bit lucky.

** Jason Heyward, -5.56%
**Speaking of bottoming out, Heyward’s power all but evaporated last year. Fear not, however, as his 2014 xHR/FB is only 4 percentage points less than 2013’s — which still sucks, but at least it’s not as bad as a whopping 10 percentage points. It’s probably too obvious to count on a comeback, but no matter.

** Jason Kipnis, -4.39%
**His year-by-year differentials: -0.01%, -2.61%, -4.39%. His year-by-year xHR/FB: 9.71%, 15.01%, 9.19%. I don’t know what to believe, really, because it’s hard to tell what’s real here and what’s not. But, again, here ye beholdeth another bounceback candidate.

** Jonathan Lucroy, -3.77%
**His 2014 xHR/FB was a percentage point better than 2013’s. The dude is too good.

** Jose Abreu, +8.52%
**Now this man, THIS MAN, is the real reason why we’re all here. What can we make of that? We know that prodigious power hitters such as Pujols and Stanton can exceed expectations. But this expectation is set pretty high. I think we’re all expecting regression, but it’s everyone’s best guess as to how much. I’m thinking a drop from 27-ish percent closer to a

**Chris Davis**-esque 22 percent.

** Lucas Duda, -3.38%
**I don’t have any other reliable full-season data for Duda to compare, but at least it wasn’t a positive differential. The negative implies that last year’s breakout was probably legit — and maybe there’s still room for improvement.

** Matt Adams, -3.49%
**Similarly to Duda, Adams’ only full season came last year. But the mammoth power we saw in 2013 didn’t disappear as much as it did suffer some bad luck. His 2014 xHR/FB of 12.19 percent still isn’t where any of us would like it to be, but again, maybe there’s still room for improvement.

** Matt Holliday, -3.09%
**Holliday, who perennially

*out-*performs his xHR/FB, appears to have gotten pretty unlucky last year. Of the last five years (dating back to 2010), 2014’s xHR/FB was right in the middle. I know he’s getting old, but man, he’s a monster, and I think there’s juice still in the tank.

** Nick Castellanos, -5.20%
**Might be a little more pop in that bat than we know.

** Nori Aoki, -6.08%
**His power simply vanished, but the xHR/FB is in line with past years. He could return to his 10-HR, 25-SB ways in short order.

** Robinson Cano, +2.33%
**This is my absolutely favorite result in the entire 2014 data set. Cano always out-performs his xHR/FB; that part does not concern me. It’s the xHR/FB itself: it dropped off almost 7 percent from 2013 to 2014. Seven percent! Say what you will about Safeco Field sapping power, but methinks a larger share of that 7 percent is a 32-year-old man in decline.

** Xander Bogaerts, -3.88%
**See Castellanos, Nick.

** Yasiel Puig, -4.58%
**Remember how Puig hit way fewer home runs last year and all that stuff? Hey, I traded him midseason (he will cost only $13 next year, but I won my league so it all works out) for

**Carlos Gomez**and a closer. In the moment, I think I made the right move: Puig’s home run rate never really improved. But his 2013 differential was +5.24%. Cutting the crap, his 2013 and 2014 xHR/FB rates were 16.56% and 15.68%, respectively — smack-dab in the middle of both years. Thus, taking the average of the two may not be such a bad method for projection after all.

OK, that’s everything. The players listed above were merely a sample and are by no means exhaustive when it comes to the peculiar splits I saw. More importantly, the implications are most interesting where they are hardest to draw: players such as Abreu and Eaton very clearly seem to have benefited (and suffered) at the hands of luck, and we can surely expect regression. But… how much? ‘Tis the question of the day, my friends.

*Edit (1/8/15, 11:42 am): FanGraphs’ Mike Podhorzer, who coincidentally posted a xHR/FB metric for pitchers today, developed a similar metric for hitters a while back, to the tune of a .65 adjusted R-squared. I feel pretty good about my work now.*

# 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.

# Predicting pitchers’ strikeouts using xK%

Expected strikeout rate, or what I will henceforth refer to as “xK%,” is exactly what it sounds like. I want to see if a pitcher’s strikeout rate actually reflects how he has pitched in terms of how often he’s in the zone, how often he causes batters to swing and miss, and so on. Ideally, it will help explain random fluctuations in a pitcher’s strikeout rate, because even strikeouts have some luck built into them, too.

An xK% metric is not a revolutionary idea. Mike Podhorzer over at FanGraphs created one last year, but he catered it to hitters. Still, it’s nothing too wild and crazy like WAR or SIERA or any other wacky acronym. (A *wackronym*, if you will.)

Courtesy of Baseball Reference, I constructed a set of pitching data spanning 2010 through 2014. I focused primarily on what I thought would correlate highly with strikeout rates: looking strikes, swinging strikes and foul-ball strikes, all as a percentage of total strikes thrown. I didn’t want the model specification to be too close to a definition, so it’s beneficial that these rates are on a per-strike, rather than per-pitch, basis.

The graph plots actual strikeout rates versus expected strikeout rates with the line of best fit running through it. I ran my regression using the specification above and produced the following equation:

**xK% = -.6284293 + 1.195018*lookstr + 1.517088*swingstr + .9505775*foulstr
R-squared = .9026**

The R-squared term can, for easy of understanding, be interpreted as how well the model fits the data, from 0 to 1. An R-squared, then, of .9026 represents approximately a 90-percent fit. In other words, these three variables are able to explain 90 percent of a strikeout rate. (The remaining 10 percent is, for now, a mystery!)

In order for the reader to use this equation to his or her own benefit, one would insert a pitcher’s looking strike, swinging strike and foul-ball strike percentages into the appropriate variables. Fortunately, I already took the initiative. I applied the results to the same data I used: all individual qualified seasons by starting pitchers from 2010 through 2014.

The results have interesting implications. Firstly, one can see how lucky or unlucky a pitcher was in a particular season. Secondly, and perhaps most importantly, one can easily identify which pitchers habitually over- and under-perform relative to their xK%. Lastly, you can see how each pitcher is trending over time. Every pitcher is different; although the formula will fit most ordinary pitchers, it goes without saying that the aces of your fantasy squad are far from ordinary, and they should be treated on an individual basis.

(Keep in mind that a lot of these players only have one or two years’ worth of data (as indicated by “# Years”), so the average difference between their xK% and K% as a representation of a pitcher’s true skill will be largely unreliable.)

It is immediately evident: the game’s best pitchers outperform their xK% by the largest margins. **Cliff Lee**, **Stephen Strasburg**, **Clayton Kershaw**, **Felix Hernandez** and **Adam Wainwright** are all top-10 (or at least top-15) fantasy starters. But let’s look at their numbers over the years, along with a few others at the top of the list.

Kershaw and King Felix have not only been consistent but also look like like they’re getting better with age. Wainwright’s difference between 2013 and 2014 is a bit of a concern; he’s getting older, and this could be a concrete indicator that perhaps the decline has officially begun. Darvish’s line is interesting, too: you may or may not remember that he had a massive spike in strikeouts in 2013 compared to his already-elite strikeout rate the prior year. As you can see, it was totally legit, at least according to xK%. But for some reason, even xK% can fluctuate wildly from year to year. I see it in the data, anecdotally: **Anibal Sanchez**‘s huge 6.7-percent spike in xK% from 2012 to 2013 was followed by a 5.5-percent drop from 2013 to 2014. Conversely, **David Price**‘s 5-percent decrease in xK% from 2012 to 2013 was followed by an almost perfectly-equal 5-percent increase from 2013 to 2014. So the phenomenon seems to work both ways. Thus, perhaps it shouldn’t have come as a surprise when Darvish couldn’t repeat his 2013 success. To the baseball world’s collective dismay, we simply didn’t have enough data yet to determine which Yu was the true Yu. I plan to do some research to see how often these severe spikes in xK% are mere aberrations versus how often they are sustained over time, indicating a legitimate skills improvement.

I have also done my best to compile a list of players with only one or two years’ worth of data who saw sizable spikes and drops in their K% minus xK% (“diff%”). The idea is to find players for whom we can’t really tell how much better (or worse) their actual K% is compared to their xK% because of conflicting data points. For example, will **Corey Kluber** be a guy who massively outperforms his xK% as he did in 2014, or does he only slightly outperform as he did in 2013? I present the list not to provide an answer but to posit: Which version of each of these players is more truthful? I guess we will know sometime in October.

**Name: [2013 diff%, 2014 diff%]**

**Andrew Cashner**: -0.45%, +0.71%**Chris Archer**: -1.60%, -0.43%- Corey Kluber: +0.62%, +3.30%
**Garrett Richards**: -0.80%, +0.35%**Hector Santiago**: -3.44%, -1.70%- Hyun-Jin Ryu: -0.10%, +1.11%
**Tyson Ross**: -0.17%, -1.46%

And here some fantasy-relevant guys with only data from 2014:

**Alex Wood**: +1.25%**Drew Hutchison**: +1.25%**Drew Smyly**: -2.32%**Jarred Cosart**: -1.84%**Marcus Stroman**: +0.95%**Masahiro Tanaka**: +1.81%**Matt Shoemaker**: +1.14%**Sonny Gray**: +1.23%**Zack Wheeler**: -1.45%

# A: No, Jon Lester is not an ace

ESPN’s David Schoenfield asked a timely question yesterday: Is Jon Lester really an ace?

Timely, because not yet reading Schoenfield’s piece (which was posted two hours prior), I wrote this in one of my fantasy league’s message boards:

Unfortunately, Schoenfield made a lot of points I would have liked to make, most of them concerning overall value. Some of it concerned innings pitched. These kinds of things matter in fantasy, but not as much. Sometimes, innings can be harmful, in the sense that a pitcher who eats up a lot of your innings with *bad starts*, given your league has an innings cap, could do you more harm than good.

But I want to take a step back and look at it through a simpler lens. Lester had the 46th-best WHIP, arguably the best indicator of probably success among the traditional metrics, among qualified starters in 2011 before MLB.com ranked him No. 61 overall in their pre-2012 rankings. And, as Schoenfield states, his ERA ranked 34th. I’m more or less trying to paint the picture of a player who is perennially overrated. In fact, for the duration Schoenfield describes (2008 through 2014), Lester ranks 58th in WHIP and 36th in ERA. Maybe I’m misunderstanding the definition of “ace”, but I think if 30 other guys could be another team’s number-one, you shouldn’t be considered an ace.

The important distinction to make is a lot of those guys who were once good now suck, and Lester continues to be relatively good. For example, Roy Halladay, Dan Haren and Josh Johnson, among many, many others, were once considered top-shelf fantasy goods. That doesn’t really help Lester’s case, though, given a new wave of talented young pitching has completely changed the fantasy pitching landscape, at least in the short-term. Lester’s 2014 season, absolutely his best season by leaps and bounds, culminated with a 2.46 ERA and 1.10 WHIP — good for only 8th- and 16-best, respectively, among qualified starters.

So: Lester’s best year saw him barely scraping the top-10 threshold — at age 30, no less.

So: His strikeout rate soared and his walk rate plummeted. Are these gains even sustainable?

In his defense, he posted career bests in the following metrics: strike percentage, first-pitch strike percentage, 0-2 count percentage, 3-0 count percentage, number of three-pitch strikeouts. These are all things I would expect to see from a pitcher who just notched his best season. In fact, three of these statistics — strikes, first-pitch strikes, 3-0 counts — have all been trending in the right direction for at least four years. That doesn’t necessarily mean Lester can improve upon, or even merely repeat, his success.

The reason I’m concerned in the first place is my projections rank Lester 35th overall, with a 3.66 ERA and 1.26 WHIP. That is, it expects some severe regression.

I don’t think it will be that severe. Lester *is* a good pitcher, and he obviously knows how to make adjustments. He always seems to post a good ERA no matter how many runners he lets on base, but he also benefited from MLB’s 4th-best defense from 2008 through 2014. Granted, he moves to a Chicago team that ranks 8th (aka marginally worse) in that same time span. Unfortunately, that same Cubs defense plummeted to 17th overall last year, notching a below-average mark. (You can Corey Kluber and the rest of the Indians’ rotation why defense is important.)

And even Lester’s strikeout rate outperformed his peripherals by about 1.8 percent — that is, there’s reason to believe his strikeout rate should have been almost 2 percent lower in the first place, especially given that his strikeout rate was about even with his expected rate the past two years (about five-hundredths of a percent lower than expected, equivalent to half a strikeout each year).

*Note: I will discuss expected strikeout percentage in an upcoming post.*

My projections expect him to maintain most of the gains in strikeouts (21.0 percent) but for the walk rate to fall back in line with career norms. That sets him up for perhaps an above-average year compared to Jon Lester but a way-below-average year in terms of talking about aces, whether in fantasy or reality.

If you’re in a keeper league, you probably got him relatively cheap — and if you didn’t get him cheap, you can now cover yourself by lying and saying you saw it coming — so keeping him with the hopes of a repeat may not come so steep. But I anticipate Lester being even more overvalued than he usually is, and I will be avoiding him like the plague.