Tagged: Corey Kluber

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.

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%]

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

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.

What did and didn’t work this year

A part of me feels like I need to provide some credentials if I’m dishing out fantasy advice. I’ve been waiting all year just to see if following my own advice would pay off. I played in four leagues, and the results are in:

1st place – 10-team roto, auction (League of Women Voters)
1st place – 10-team H2H roto, snake
2nd place – 10-team H2H points, snake
3rd place – 10-team H2H points, snake

The most important victory to me is the first one, in the League of Women Voters, a league in which a bunch of my dad’s friends have been playing for decades. I want to look back and 1) try to remember my exact draft strategy; 2) see how well I adhered to it; and 3) see where I went wrong.

I went into the draft knowing I would target a very specific and short list of players. This did not allow a lot of room for flexibility, although I did leave a couple of outfield spots open that I would fill on the fly. I can tell you right away I wish I was stricter on those last two outfield spots. I also did not target any specific category, although I did punt saves for the most part. Although I simultaneously led every offensive category except for stolen bases for most of the summer, it became obvious to me that I accidentally loaded up on batting average and undervalued steals.

What I did right:

  • $1 for Yan Gomes. I guaranteed Gomes would be a top-10 catcher with the chance to break the top 5; he finished No. 4 on ESPN’s player rater. (I also drafted Victor Martinez, and once he gained catcher eligibility, I dropped Gomes. It happened early in the season — too early for me to know better — but I wish I hadn’t.)
  • $16 for Jose Abreu. There’s no way I knew he’d be this good, but after snatching up Yoenis Cespedes off of free agency in the first week of 2012 and drafting Yasiel Puig to my bench in 2013, I pledged to gamble as much as $20, maybe more, on the MLB’s most recent Cuban import.
  • $13 for Martinez. I think he’s perpetually underrated, but I can tell you that not a single person in the world knew V-Mart would hit 30 home runs, let alone 20. I won’t pat my back on this one. I normally wouldn’t keep him, but I may have to in the off-chance he’s pulling a late-career Marlon Byrd on us (in terms of power, that is).
  • $1 for Corey Kluber. My love for Kluber is well-documented. I tempered my expectations and slotted him as my No. 32 starting pitcher, but I vastly underestimated his innings total (45 more innings than I projected), his wins (17 to 10) and, of course, his strikeouts (10.3 K/9 to 8.4 K/9). But I’m glad I took a conservative approach; the most important takeaway is that Kluber clearly exhibited the talent to be at least a middle-tier fantasy starter with upside. And boy, did everyone underestimate that upside.
  • $11 for Cole Hamels. I liked this play at the time, and I still do: I waited maybe a month to get a potential top-10 starter at about half-price. He’s a possible keeper next year ($14 on a $260 budget), but the Phillies’ inability to help him reach double-digit wins is troubling.
  • $2 for LaTroy Hawkins. He’s terrible, but at least I wasn’t the idiot who overspent on the perpetually inept Jim Johnson. How he lucked into more than 100 wins in two seasons is beyond me.

What I did wrong:

  • $51 for Miguel Cabrera. It was the most a player had ever gone for in the league, at least since the Rickey Henderson days. It was hard to predict such a massive drop-off in power — maybe 30 home runs was understandable, but only 25? — and I didn’t leave myself any room for savings. That is, I paid full price instead of looking for bargains, the latter of which was my game plan from the start.
  • $37 for Ryan Braun. An even worse bid, in hindsight, and another instance of paying full price instead of finding the bargain.
  • $10 for Everth Cabrera. Cabrera was a keeper, and he may have gone for more at auction. But wow, what a bust. Again, tough to see something like that coming, especially such a steep decline in on-base percentage.
  • $10 for Brad Miller. I made a bold prediction about Miller before the season started. I think the only thing more amazing than his plate discipline completely vanishing is how much owners in my league were willing to spend on a largely unknown quantity. I really thought I was being sneaky on this one, especially so late in the draft. This was a case in which I was too sold on a guy to budge and take a different name — especially when Dee Gordon and Brian Dozier were still on the board.
  • $12 for Shane Victorino. Was 2013 a flash in the pan or what? I don’t know if this guy’s legs will ever be the same again.

I’m excited to start preparing my projections for next year. I have made some revisions, tweaked some formulas… I’m looking forward to how the projections turn out.

And now I have a concrete idea in my head of how I should approach my ideal draft.

Under-the-radar 2015 fantasy prospects

Another month without any published material has come and gone. Meanwhile, Matt Shoemaker earned himself American League Pitcher of the Month and Rookie of the Month honors for August. Good thing I wrote my glowing endorsement for him on July 25. It will be interest to see how he’s ranked next year. Julio TeheranMichael Wacha, Sonny Gray and Tony Cingrani were touted prospects and drafted Nos. 29, 32, 41 and 44 on average in ESPN live drafts this year after pretty amazing 2013s. So, what now for Shoemaker? He was never a touted prospect; most fans still probably don’t know who he is, similar to how I anticipate Corey Kluber will get robbed of Cy Young votes this year simply because he isn’t a name-brand ace.

Alas, there will be doubts about Shoemaker’s ability to repeat his performance — his swinging strike and contact rates have tailed off a bit since I wrote about him a month ago, and he doesn’t occupy the strike zone enough for me to think the walk rate is sustainable — which could make him a 2015 draft day bargain. Starting pitching is deeper than ever, so it would not surprise me whatsoever to see Shoemaker make a variety of “just missed” lists, right outside the Top 60 pitchers or so, with an average draft positions of maybe 45th for starting pitchers.

If I had to run a quick-‘n’-dirty projection for next year right now, it would look something like:

Bearish: 180 IP, 11 W, 3.15 ERA, 1.20 WHIP, 160 K
Bullish: 200 IP, 13 W, 2.94 ERA, 1.13 WHIP, 189 K

FYI, those are both pretty darn good projections, good for what will probably be Top-30 in my 2015 rankings.

Anyway. ANYWAY.

Let’s get to what I really wanted to discuss: 2015 fantasy prospect sleepers. Many notable prospect lists are published prior to the start of each season, and a handful are updated as the season unfolds. Case in point, ESPN’s Keith Law published his updated Top 50 list about a month ago. Obviously, the list accounts for the triumphs — and tribulations — of current and now-former top prospects in whichever league(s) the player performed this season as of July 17.

Today’s scouting report has two faces: the qualitative, through which we award players a grade of 20 through 80 for their five tools; and the quantitative, through which we assess the progress of a player based on what he has actually accomplished.

It’s all good and well that prospect lists exist — especially updated ones. But, frankly, there isn’t room on the list for everyone, and the lists often span more than just players who are Major League-ready.

Thus, I occasionally look at Minor League leaderboards and try to find less-trendy fantasy prospects to scoop in the late rounds of a draft or spend a dollar on in the twilight of an auction. I create a list and periodically update it, tracking the player’s progress or lack thereof.

In alphabetical order, here are some players who, given playing time, could be impact players in 2015:

Steven Moya, DET OF | 40 Hit, 60 Power, 50 Run (MLB.com)
It wouldn’t surprise me if 99 percent of baseball fans outside of Michigan knew Moya was called up when the Tigers’ roster expanded. Absent from all major prospect lists, Moya belted 35 home run, swiped 16 bases and batted .276 in 133 games at Double-A Erie. Those are numbers that could get anyone all hot and bothered. It’s not a huge surprise to me why he wasn’t so highly touted: he combined for only 42 home runs and 16 stolen bases combined in his first four years in the minors. What’s overlooked, though, is he debuted when he was 17, and he has obviously made great strides as he fills out at the ripe age of 22. All that glimmers is not gold, however; Moya struck out in almost 30 percent of plate appearances while walking only 4 percent of the time. There’s a lot of potential for bust simply because he may never catch up to Major League pitching.

Looking forward: Moya is currently buried on the depth chart, as he was called up more for depth and reps than impact contributions. Still, right fielder Torii Hunter‘s contract expires this year, leaving Moya to compete with Rajai Davis, J.D. Martinez and Ezequiel Carrera (whom I actually like as a speedy, Leonys Martin-type of outfielder). Davis is underrated and Martinez has reestablished himself as a credible starter, although it remains to be seen if he sustains it, but I would not be the least bit surprised to see Moya win a starting role over Carrera — or all of them, really. His plate discipline is problematic, though; even notorious free-swinger Pedro Alvarez had better discipline before his call-up. Still, not all prospects with poor hit tools are doomed to bust, but given his relatively unknown prospect status — he’s buried at No. 7 in the Tigers’ organizational depth, according to MLB.com — he could be a low-risk, high-reward (and also high-volatility) player in 2015.

Steven Souza, WAS OF | 40 Hit, 50 Power, 50 Run (MLB.com)
Souza is perhaps the most talented and enigmatic of the three players listed here, based strictly on 2014 performance and MLB.com’s scouting grades. Like Moya, Souza didn’t make any preseason or midseason top-prospect lists, despite hitting 18 home runs, stealing 26 bases and batting a whopping .350 across 407 Triple-A plate appearances. (In there defense, Souza was really, really bad prior to 2012, and was busted for PEDs in 2010.)

Again, if you live outside of New England and knew Souza was recently called up when rosters expanded: congratulations! All of Souza’s numbers — his speed, his power and especially his hit tool — correlate very poorly with how MLB.com evaluated him above. Even if the power and speed do somehow project to be average, his plate discipline is very evidently better than below-average: he struck out in 18.4 percent of plate appearances and walked in 12.8 percent of them. And he achieved this in Triple-A, not Double-A, where Moya flailed away. Future Cleveland Indians shortstop Francisco Lindor allegedly has a 70-hit tool, yet posted strikeout and walk rates of 19.5 and 5.2 percent — not at all elite. Trust the statistics.

Looking forward: The only things standing between Souza and a starting role in 2015 is Denard Span‘s 2015 team option (who has performed well enough to earn it and then some) and the next guy on this list. Thus, Souza may be doomed to a fourth-outfielder role next year until Bryce Harper inevitably injures himself, so Souza’s heyday may not truly come until 2016. If he somehow assumes the first baseman role, it would be hard to rely on a guy who hits 15 home runs, steals 10 bases, bats .275. But if he eventually moves to the outfield where he belongs, or gets traded, his potential /.280/.340/.380 would be serviceable in fantasy leagues.

Michael Taylor, WAS OF | 40 Hit, 50 Power, 60 Run (MLB.com)
Shoot. I kind of forgot that Taylor and Souza are on the same Triple-A team battling for the same potential center-field opening that will, realistically speaking, not be vacated by Span next year. Taylor got a brief look earlier in the year and promptly hit a home run — but also struck out eight times in 22 trips to the plate. It’s difficult to ignore his 22 home runs, 34 stolen bases and .313 average at Double-A Harrisburg, and the 51 steals at Single-A Potomac last year add a nice touch. Like Moya, the hit tool as graded by MLB.com is probably accurate: Taylor struck out 130 times in 441 plate appearances (25 percent), but at least he walked more than the league average.

Looking forward: Taylor and Souza are theoretically competing with each other, which could make either of them offseason trade bait. Taylor, however, spent the majority of this year in Double-A, only recently getting promoted, so he may have a year of development ahead of him, despite being ahead of Souza on MLB.com’s organizational depth for the Nationals (Nos. 5 and 7). It’s also worth noting  that Souza is listed as third on the depth chart at first base, and Adam LaRoche is in the final year of his contract, so it’s possible that Taylor earns the fourth-outfielder role and Souza earns first base outright (or becomes the backup to Kevin Frandsen… yuck). Ultimately, it’s hard to ignore any potential 20/20 players, and he looks like another guy who could get there, albeit with a low batting average.

If I had to guess which of these three players will make the biggest impact in 2015, I would say Moya, who I think has the highest bust potential but also the highest ceiling. Souza is the safest and will make for an adequate, and perhaps enticing, replacement given the event of an injury in the Nationals’ outfield. Taylor has the same kind of boom-or-bust potential as Moya, albeit with a little less power and a little more speed.


Matt Shoemaker, pending fantasy star

How does one apologize for not writing for more than a month and a half? It’s hard, man. Maybe one does not apologize to one’s readers. Maybe one’s readers accepts that it is what it is.

You know what else is what it simply is? Matthew David Shoemaker, the Los Angeles Angels of Anaheim’s right-handed reliever-turned-starter.

Every baseball season often produces more questions than answers. Namely: Who is Matt Shoemaker? Where did he come from? Why am I writing about him if he’s not that good?

Let us rewind to 2012. A mysterious figure emerged from the mist of the Seattle Mariners’ bullpen to dazzle us– or maybe just me, given he never really received the recognition he deserved. Maybe he had a right to be ignored: he posted a 4.75 ERA and 1.42 WHIP in 30-1/3 relief innings. If you don’t know how this fairy tale ends, it goes something like: goes largely unnoticed in 2012, is drafted outside the top 75 pitchers on average in ESPN drafts in 2013, and eventually emerges as a borderline fantasy ace by the name of Hisashi Iwakuma.

There’s a lesson to be learned. Iwakuma’s horrid statistics as a reliever muddied his season numbers. In hindsight, a 3.15 ERA for the year is solid, but a 2.65 ERA is better, and that’s what Iwakuma posted strictly as a starter. Yet fantasy owners who opted only to scratch the surface saw mostly unsightly ratios.

The same fairy tale manifested itself in a different form in 2013 that would make the Brothers Grimm proud. The Cleveland Indians’ Corey Kluber emerged from the bullpen in May, albeit after only half a dozen innings, many more than that in 2012. Kluber’s season, however, began with aplomb — and by aplomb, I mean “a handful of horrible starts.” Starts horrible enough to sully his numbers for the year (3.95 ERA). But the peripherals were there at season’s end: 8.28 strikeouts per nine innings, 2.09 walks per nine, 3.12 xFIP. In case you haven’t kept track, Kluber has more or less assumed the role as Cleveland’s staff ace this year, posting a 2.95 ERA with more strikeouts than innings.

I will now shortsightedly assume, without any kind of research, that this kind of thing happens every year. Every year, there’s at least one player who emerges from the bullpen and becomes an ace. Sure, you have the Chris Sales and Adam Wainwrights of the baseball world, who make a gigantic, whale-sized spash, but you also have the Iwakumas and Klubers, who basically don’t make a splash at all and probably sit on the side of the pool with their feet dangling in and shirts still on.

So I’m calling it: Mr. Shoemaker will be 2015’s reincarnation of this fairy tale.

In keeping the trend alive, a look at Shoemaker’s stats tell you… well, in the way of anything positive, not much. He has somehow notched seven wins despite a 4.54 ERA and 1.30 WHIP, so that rules. Worse, his WHIP was, like, 1.42 before his most recent start. So, bad season stat line? Check.

Meanwhile, he has struck out 9.68, and walked only 1.87, hitters per nine innings. It would behoove me to point out that these numbers dwarf those posted by Kluber in 2013, during which Kluber existed primarily in a gelatinous state of Emerging Star. It would also behoove me to point out that a reader with a discerning eye would notice that Shoemaker has a still-lackluster 4.37 ERA and 1.28 WHIP as a starter, fitting the mold of “maybe his season numbers are ruined.” It would further behoove me to point out that he is suffering the misfortune of a .350 batting average on balls in play (BABIP), which, if normalized to a more reasonable .320, would produce a 1.20 WHIP. A league-average .300 BABIP? A 1.14 WHIP. So, distorted stats as a starter? Check.

Perhaps the most important, and valid, question at this point is whether or not Shoemaker can sustain what he’s doing. Small sample size caveats abound here, but I think the results are still substantial, if not due for regression. For all pitchers who have thrown at least 60 innings, Shoemaker ranks 11th in swinging strike percentage (11.9), one spot behind Stephen Strasburg, the MLB strikeout leader, and three spots ahead of his teammate Garrett Richards, who has done all kinds of breaking out this year. Shoemaker also ranks 9th in hitter contact allowed (73.5 percent), sandwiched between Gio Gonzalez and, yes, Richards. Thus, even given small sample size caveats, Shoemaker is among excellent company. The walk rate may suffer; it’s hard to say, and even harder still given that I’m on an airplane over central California with no internet. But, given the browser tabs I still have open, I can tell you that Shoemaker’s percentage of pitches thrown in the strike zone, according to FanGraphs’ data, trails only Clayton Kershaw and Mets reliever Carlos Torres among the 10 names ahead of his on the swinging strike percentage list. That bodes well for projecting his control going forward. (PITCHf/x, however, portends another story, as his zone percentage trails six of the eight names ahead of his. But when the names you trail are Felix Hernandez, Masahiro Tanaka and Kershaw, I’d say you’re not doing so bad for yourself.) So, solid peripherals? Check.

It’s a makeshift and largely personal checklist, but so far, Shoemaker meets all my criteria for the gelantinous Emerging Star. Who knows how Shoemaker will fare during the season’s last two months, but I think he’s worth owning now despite his current stat line. As for 2015 and beyond, I like him — for now. I wouldn’t bother keeping him, as I think his value will be depressed heading into next year’s draft, so you can easily wait around for him in the late rounds, if not add him as free agency in the first couple of weeks of the season, just as many owners did with Iwakuma and Kluber the past two years.

I hesitate to say Shoemaker is a lock for success. If anything, this post is less about finding The Next Big Thing as it is finding a pitcher whose performance betrays his value. There are the Sonny Grays and Michael Wachas of the world, whose status as top prospects make them costly prospective adds. Then there are the Matt Shoemakers, whose obscurity and relative misfortune keep him out of the fantasy limelight — and, one would hope, on the clearance shelf, from which you can swipe him on the cheap.