Tagged: Drew Hutchison

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.

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:

Need some streamers? T. Ross, Hutchison, Colon, Wood

I’ve been slacking on my streamer picks, so let’s cut straight to the chase.

Today, 5/15:
Tyson Ross, SD @ CIN
Mr. Ross is the real deal, my friends. He’s 10th of all pitchers in batters’ contact on pitches in the zone, sandwiched between the unfamiliar names of Jose Fernandez and Zack Greinke (and the players who precede him include Michael Wacha, Yordano Ventura, Julio Teheran and Max Scherzer). He doesn’t make batters chase pitches at an overwhelming rate, but they make contact on such pitches only half the time, which ranks Ross fourth only to Ervin Santana, Garrett Richards and Masahiro Tanaka. At 7.99 K/9, his K-rate should actually improve. You can really only bash him for his walk rate, but it’s no worse than Gio Gonzalez or Justin Verlander. I don’t care if it’s a road game; Ross should be owned in all leagues at this point.

Friday, 5/16:
Drew Hutchison, TOR @ TEX
I’ll be honest with you: I’m not totally sold on this matchup. Hutchison hasn’t been very impressive, but there are simply not many matchups worth exploiting on Friday. I like Hutchison for his strikeouts, and before his last start (during which he walked four), he had only walked five guys across 32-1/3 innings. His control escaped him, but if it comes back, he should be able to control a miserable Texas offense that ranks 26th of 30 teams in extra-base hits.

Saturday, 5/17:
Bartolo Colon, NYM @ WAS
Again, not crazy about this one, either. But Colon has been incredibly unlucky. The dude is walking fewer than a batter per nine innings (0.9 BB/9), so all the baserunners (and, consequently, earned runs) he has allowed are a largely a function of an elevated batting average on balls in play (BABIP). It’s hard to trust a guy who’s mired in a slump, but the luck should eventually turn in his favor. Who’s to say it won’t be this weekend? I’d take a chance. The Nationals don’t score a ton of runs, either. It’s not the best play, but it’s safer than most.

Sunday, 5/18:
Travis Wood, CHC vs. MIL
After a hot start, albeit a brief one, Wood has since collapsed in spectacular fashion, sporting a 4.91 ERA and 1.43 WHIP. So why would I ever vouch for this guy? Check out his home-road splits:

Split W L W-L% ERA GS IP H ER HR BB SO WHIP SO9 SO/W
Home 2 1 .667 2.39 4 26.1 22 7 2 4 32 0.987 10.9 8.00
Away 1 3 .250 8.02 4 21.1 31 19 2 11 12 1.969 5.1 1.09
Provided by Baseball-Reference.com: View Original Table
Generated 5/15/2014.

The splits are ridiculous. They speak for themselves, although I’ll highlight the ones that are most impressive. With that said, he’s starting at home. Enough said.

Good luck and happy streaming!

Need a Streamer? Recommendations for April 26 to May 1

We’re too deep into the season for me to have a good excuse as to why I haven’t posted any streamer recommendations yet. Sorry! Considering this is the marquee feature after which this website is named, I think it’s high time I give some recommendations, especially now that we’ve got a feel for what guys are made of, veterans and no-namers alike.

The idea is that I only choose pitchers owned in 30 percent or fewer of ESPN leagues. The exercise is pointless if I tell you to stream guys who are 100-percent owned because you can’t pick them up on a whim to stream them into your lineup for the starts the next day.

I keep track of the ongoing stats of streamer starts here, and the streamer for the day will be listed in the right-hand module above the links.

Sat. 4/26: Vidal Nuno, NYY (v. LAA)
Nuno’s first start was ugly, but his second was much better, striking out six in five innings and allowing only three hits (zero runs). I’m not a huge fan of Nuno, but it’s all about the matchup, as the Angels’ Hector Santiago is homer-prone and headed into a very hitter-friendly ballpark. Get past the fact that the Angels spanked the Yankees tonight and you won’t feel so bad.

Sun. 4/27: Ian Kennedy, SD (@ WAS)
A road game in Washington is not as threatening as it may have once seemed. The Nationals are sluggish right now, and Kennedy has been a bright spot on an otherwise lackluster Padres team. He has posted a 3.60 ERA and 1.07 WHIP through five starts with 28 strikeouts in 30 innings. He’s also facing Taylor Jordan, who has been incredibly hittable through his first four starts. Whether or not you think Kennedy is legit, I’m riding the hot hand on this one.

Mon. 4/28: Tyson Ross, SD (@ SF)
I made a bold prediction about Ross before the season started, so it’s no surprise I’m on board for this matchup against a team that, aside from its decent run total, can’t hit a lick, batting only .234 as a team this season. Ross has shaken off his command problems from his first two starts and has struck out 28 in 31-1/3 innings.

Tue. 4/29: Corey Kluber, CLE (@ LAA)
This isn’t about me loving Kluber as much as it is there just aren’t many options today, with a lot of guys owned in a lot of leagues. It helps, however, that in his last start he allowed no earned runs on four hits with 11 strikeouts and no walks. That’s the Kluber I know and love.

Wed. 4/30: Nathan Eovaldi, MIA (v. ATL)
The Braves aren’t a miserable offense, although they certainly can be when they go cold. Eovaldi appears to have shored up his command problems by walking only four batters over his first five starts while striking out 30 in 31-1/3 innings, waltzing to a 2.87 ERA and 1.12 WHIP. Honestly, the Marlins aren’t terrible, especially with how Giancarlo Stanton is hitting and some dynamism from the young Christian Yelich. Take a chance! It’s not always about the win column; Eovaldi should be able to help in ratios and K’s, too (a win would be nice, however).

Update, 10:45 p.m.: I overlooked it when I wrote this piece, but Drew Hutchison of Toronto is pitching in Kansas City on Wednesday. He has struck out nine in each of his last two starts, and whatever problems he was having at the onset of the season have vanished, at least temporarily. If he has another monster game, I can assure you he will be a hot addition on May 1. He faces a flailing Royals team led by the eternally mediocre Bruce Chen; if the game were in Toronto instead of one the road, I would actually endorse him over Eovaldi.

Thu. 5/1: Josh Beckett, LAD (@ MIN)
He probably will be owned in more than 30 percent of leagues by next week, but whatever. He doesn’t look like vintage Beckett, but he looks good enough to roll out there on a slow day. Also, I didn’t want to have to pick Nuno again.

Man, I don’t like picking streamers this far in advance, especially if offenses start to get hot or go cold, but that’s just the way it goes. I’m going to have to suck it up and deal with the consequences. I can only hope they’re all good consequences.

Thanks for reading! Enjoy some streaming success!

Pitchers to sell high, buy low or cut bait

All right. It’s April. It’s horrifying, unless you’re doing well, and then it’s not. But, full disclosure, I’m not. Chicago White Sox staff ace Chris Sale just hit the 15-day disabled list yesterday, joining the Philadelphia Phillies’ Cole Hamels, Seattle Mariners’ James Paxton, Tampa Bay Rays’ Alex Cobb, Cincinnati Reds’ Mat Latos, New York Yankees’ David Robertson and the Detroit Tigers’ Doug Fister on my teams’ DLs. It’s killing me, really. It’s incredibly painful.

What I’m saying is I’ve spent more time than I’d care to admit frolicking in free agency, trying to figure out which early-season studs are legit or not. I’ve been pondering various buy-low situations as well. So I jumped into a pool of peripherals and PITCHf/x data to look for answers.

The list below is not remotely exhaustive. It’s mostly players I am watching or already using as replacements for my teams. Here they are, in no particular order.

Jake Peavy, BOS | 0-0, 3.33 ERA, 1.48 WHIP, 9.25 K/9
Peavy’s prime came and went about five years ago, so, full disclosure, I don’t know as much about him off the top of my head as I should. But I do know one thing: he doesn’t strike out a batter per inning anymore. In his defense, batters’ contact rate against him is the best it has been since 2009, his last truly good year. So maybe he will strike out a few more batters than last year, but I think it’ll be closer to 2012’s 7.97 K/9, not 2009’s 9.74 K/9. The WHIP is atrocious;  the walk rate is through the roof. If there’s a guy in your league who will pay for what will end up being the illusion of ERA and strikeouts, by all means, trade him. He’s owned in 100 percent of leagues but doesn’t deserve to be.
Verdict: Sell high

John Lackey, BOS | 2-2, 5.25 ERA, 1.46 WHIP, 8.63 K/9
Another Boston pitcher, another bad start to the season. I like Lackey a lot more, though, for a variety of reasons. One, last year’s renaissance was legitimate. Two, he’s not walking many batters right now, so his unspectacular ratios are more a result of an unlucky batting average on balls in play (.333 BABIP) than incompetence. Three, his swinging strike and contact rates are currently career bests. Again, we’re working with small sample sizes here, and this could easily regress. But considering his velocity is also at a career high, I don’t find it improbable that Lackey actually does better than he did last season. If an owner in your league has already dropped him, put in your waiver claim now.
Verdict: Buy low

Jesse Chavez, OAK | 1-0, 1.38 ERA, 0.92 WHIP, 9.69 K/9
Talk about unexpected. Chavez, who has been relevant about zero times, is making for an intriguing play in all leagues. It’s a given he will regress, especially considering the .242 BABIP, but his improved walk rate could be here to stay, as he is pounding the zone more than he ever has in his career. The strikeouts are somewhat of a mirage, but it looks like he can be a low-WHIP, moderate-strikeout guy, and that’s still valuable.
Verdict: Sell really high, or just ride the hot hand

Nathan Eovaldi, MIA | 1-1, 3.55 ERA, 1.14 WHIP, 8.17 K/9
I wouldn’t call Eovaldi a trendy sleeper, but he certainly was a sleeper coming into 2014. It was all about whether he could command his pitchers better — and, like magic, it appears he has, walking only 1.07 batters per nine innings as opposed to 3.39-per-nine last year. The swinging strike and contact rates are concerning, as they are the lowest of his career, so it’s hard to see his strikeout rate going anywhere but down. However, he’s throwing 65 percent of his pitches in the strike zone, highest of all qualified pitchers. So there are two ways to look at this. His control has probably legitimate improved. Unfortunately, even the masterful Cliff Lee only threw 53.3 percent of pitches in the zone last year, and I am hesitant to claim Eovaldi has better control than Lee. This could be a “breakout” year of sorts for Eovaldi, but I’m using that term liberally here. He’s only owned in 20.5 percent of leagues, so this makes him more of a ride-the-hot-hand type, like Mr. Chavez above.
Verdict: Eventually drop, ideally before he does damage to your team

Mark Buehrle, TOR | 4-0, 0.64 ERA, 0.93 WHIP, 6.11 K/9
Look, I have had a long-standing man crush on Buehrle, but this is ridiculous. You know better than I that these happy dreams will soon become nightmares, not because Buehrle is awful or anything, but because regression rears its head in occasionally very brutal ways.
Verdict: Sell high

Alfredo Simon, CIN | 0.86 ERA, 0.81 WHIP, 5.57 K/9
Something isn’t right here. A 0.81 WHIP and… fewer than six strikeouts per nine innings? As you become more familiar with sabermetrics, you quickly realize certain things don’t mesh. A low WHIP combined with the low strikeout rate is one of those things. I can tell you without looking that his BABIP is impossibly low — and, now looking, I see I’m right: it’s .197. Tristan H. Cockcroft of ESPN is all about Simon, and in his defense, Simon’s PITCHf/x data foreshadows some positive regression coming his way in the strikeout department. But it can only get worse from here for Simon. However, I think he has a bit of a Dan Straily look to him, and that’s certainly serviceable.
Verdict: Sell high, or just ride the hot hand

Yovani Gallardo, MIL | 1.46 ERA, 1.09 WHIP, 6.93 K/9
This is a disaster waiting to happen. Like Simon, his strikeout rate is low, but for Gallardo, it is deservedly so: his swinging strike and contact rates are, by far, career worsts. Meanwhile, his ratios are buoyed by a .264 BABIP and 89.8% LOB% (left-on-base percentage), despite his 74.7% career LOB%. The Brewers will fall with him. Sell high, and sell fast.
Verdict: Sell high

Shelby Miller, STL | 3.57 ERA, 1.50 WHIP, 8.34 K/9
Miller is the first pitcher on this list in whom owners actually invested a lot. Be patient. The 98.3-percent of owners who didn’t cut bait before his last start were surely rewarded. I imagine he’s leaving his pitches up in the zone, given his increased percentage of pitches thrown in the zone coupled with his home run rate. Speaking of which, he shouldn’t be walking five batters per nine innings when he’s throwing more than 50 percent of his pitches in the zone. He’ll be fine.
Verdict: Buy low

Homer Bailey, CIN | 5.75 ERA, 1.87 WHIP, 11.07 K/9
Two words: .421 BABIP. Yowza. Again, owners invested way too much in this guy. Perfect buy-low opportunity here if you know your fellow owner is impatient.
Verdict: Buy low

Drew Hutchison, TOR | 3.60 ERA, 1.45 WHIP, 10.80 K/9
I’ll be honest, I was surprised to see Hutchison’s xFIP stand at 3.43. It seems like he has been much worse — but has he really? The walks are problematic but not unmanageable (see: Matt Moore), and they’ve actually shored up a bit in his last couple of starts. Moreover, he is still striking out batters at an elite rate, and the PITCHf/x data supports his success, albeit probably not with quite as much success as he’s having now. As for the WHIP? A .365 BABIP sure doesn’t help. Hutchison was once a highly-touted prospect. Your window of opportunity to gamble on this live arm may be closing if he can keep his ERA down.
Verdict: Add via free agency, sooner rather than later

Time to panic? Pitcher edition, week 1

Should I panic? How can I even tackle this question right now? The breadth of pitchers who performed poorly so far is astonishing, so it’s understandable why you might want to not start the Philadelphia Phillies’ Cliff Lee in his next start or cut ties with Chicago White Sox closer Nate Jones all together. There are times you should panic, and there are times you should remain calm. I’m here to help you tell the difference.

Disclaimer: I get kind of annoyed when analysts waffle with guys, like, “well, I know he’s going to fall apart, but I’ll give him one more chance”. NO! You know he’s going to fall apart, but you’re giving yourself an out! I’m drawing a line in the sand, across this line YOU DO NOT — also, Dude, Chinaman is not the preferred nomenclature. … Wait, where was I? Anyway, I’m not letting myself off the hook. I am here to make the impulse decisions with (and maybe for) you, because sometimes, these impulse decisions make or break a season. Unfortunately, making them really early in the season is an absolutely horrifying experience.

Alex Cobb, SP (TB)
Dilemma: He was less than sharp, and although he gave up only five hits in five innings, he managed to walk more batters than he struck out (four to three). This is highly unlike Cobb, and that’s why I’m more inclined to think it was a case of first-start jitters rather than the beginning of a depressing trend.
Verdict: Don’t panic.

Homer Bailey, SP (CIN)
Dilemma: Lots of hits with as many walks as strikeouts. It was ugly, but he did face the Cardinals, which is no easy task. It’s hard to cut Bailey loose with how much you invested in him on draft day (outside of keeper leagues), but his breakout last year didn’t come out of nowhere, to which his second-half-of-2012 owners can attest. Unfortunately, he faces the Cardinals again in his next start. I’m not one to sit a guy early in the season, and I think it’s Bailey who will make adjustments the second time around, not the Cardinals.
Verdict: Don’t panic.

Stephen Strasburg, SP (WAS)
Dilemma: A 6.00 ERA?! Yeah, but 10 strikeouts in six innings and only a 1.167 WHIP. He got pretty unlucky, and that will happen from time to time. I would be more amped about the other batters he humiliated.
Verdict: Don’t panic.

CC Sabathia, SP (NYY)
Dilemma: Well, uh, he looked horrible. Against the Astros. It’s fine and dandy that he struck out a batter per innings and only walked one, but his fastball has become too hittable with that diminished velocity. I expect the trend to continue, and I think the solid strikeout total is the result of a free-swinging, hapless Astros offense. Remember, I said these are impulse decisions I’m making here. With a bevy of young pitching talent on waivers, I say…
Verdict: Panic.

C.J. Wilson, SP (LAA)
Dilemma: Kind of the same as Strasburg’s. High strikeouts and lots of hits sounds like an old wives’ tale about bad luck on balls in play that I’ve heard many a time. Wilson is not a second-tier starter anymore like he used to be, but he’s solid, and there’s no reason to fret.
Verdict: Don’t panic.

R.A. Dickey, SP (TOR)
Dilemma: Wow… Wow. Six walks. That hurts. I don’t know the first thing about throwing a knuckleball, and I’m sure if you have a bad day, it can be really be bad. But six walks? At least the strikeouts are there, but if your league is anything like any of mine, you probably got Dickey on the cheap. If I saw enticing performances by Seattle’s James Paxton or Toronto’s Drew Hutchison, I may cut ties, too. Surely no one else will touch him with a 10-foot pole until after his next start.
Verdict: Panic.

Corey Kluber, SP (CLE)
Dilemma: If you follow this website, you know how much I love Kluber, and how I preemptively purchased a five-year membership to the Society. Everything about the start is concerning, but I’m too proud to cut him loose. If you got him cheap, you can let him go and try your luck later. And I truly think he will break out; his peripherals were simply too good last year, and I don’t think you can fluke your way into talent like that. But perhaps I’m wrong…
Verdict: Don’t panic.

Cliff Lee, SP (PHI)
Dilemma: Wait, is this a serious question? Look, I know that sucked, but he’s freakin’ Cliff Lee. Calm down.
Verdict: Don’t panic.

Jonathan Papelbon, RP (PHI)
Dilemma: Dude, if you wanted to know what the end of the world would look like, this is it. Except in the form of a metaphor called Jonathan Papelbon.
Verdict: Panic.

Jim Johnson, RP (OAK)
Dilemma: I’ve expressed my distaste for Johnson before. He’s simply not good, and fantasy owners are blinded by two straight seasons of 50-plus saves. He would be lucky to save 35 this year without trouble; it looks like he may not get he chance to save 20 by the end of the week.
Verdict: Panic.

Nate Jones, RP (CHW)
Dilemma: The closer role was never a lock for him to keep. It looks like he agrees. Two hits, three walks and four earned runs without recording an out. Making Casper Wells look like a Cy Young candidate.
Verdict: Panic.

Ten bargain starters outside my top 60

The idea is simple: In a standard 10-team mixed league, an owner is allotted six spots to fill with starting pitchers. That relegates everyone else drafted No. 61 and higher to fantasy benches or free agency.

That doesn’t mean pitchers drafted outside the top 60 are worse than pitchers in the top 60. You can find good pitchers up until the 60th pick — heck, it’s the Brewers’ Marco Estrada, who has excellent control and solid strikeout numbers — but as many as a third of those 60 are risky are overvalued. Value bleeds into the late rounds  and it’s worth figuring out who’s worth reaching for, despite pitchers with better ADPs (average draft positions) still on the board, and who’s worth waiting for.

I’ll discuss a handful of pitchers I like outside my top 60, in order of ESPN ADP.

John Lackey | ADP: 63rd
Lackey had a renaissance 2013, coming back from a lost 2012 and miserable 2011. The strikeout and walk rates were second-best and best of his career, respectively, and there’s little reason to think he’ll crumble overnight. He’s less risky than Dan Haren (about whom I’ve been vocal about my distrust), who is being drafted 49th of starting pitchers, or Dan Straily, going 56th, who is honestly mediocre. He’s enough to fill the back of your rotation, let alone a bench spot.

Alex Wood | ADP: 66th
Wood is a control artist, and the Braves simply know how to develop pitchers. Scouts and experts are excited about him; I don’t know why he’s not getting more draft love. He’s guaranteed a rotation spot, due to the rash of injuries to Atlanta starters, and should be more than serviceable.

Corey Kluber | ADP: 79th
I love Kluber.

Josh Beckett | ADP: 91st
Sources say he’s recovering well from his surgery. If he makes the Dodgers’ rotation and remotely resembles the Beckett of old, he’s  a value.

Tyson Ross | ADP: 103rd
He absolutely dealt for the Padres last year. A reader mentioned he could be on an innings limit, but I would still ride him until he’s shuffled out of the rotation, and then simply find a replacement for him.

James Paxton | ADP: 105th
If the Royals’ Yordano Ventura is going 62nd on average, there’s no reason Paxton should be going outside the top 100 pitchers. Paxton doesn’t gas a 10o-mph heater like Ventura does but his strikeout and walk rates are very similar to Ventura’s.

Tyler Skaggs | ADP: 110th
Skaggs was a three-time top-100 prospect for Baseball America, peaking at No. 12 in 2013 (and No. 17 for Baseball Prospectus). It would be a mistake to write him off so soon after one bad season, especially with minor-league numbers better than those of Ventura or Paxton. His 2013 and current spring training numbers are an eyesore, though, so the repulsion is understandable. But, as I always say, he’s a name worth remembering.

Other notables: Drew Hutchison (114th), Erik Johnson (133rd), Jake Odorizzi (151st)