Tagged: Tyson Ross

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:

A smorgasbord of fantasy baseball advice

Need a Streamer has been slow lately, to say the least. I’ve missed discussing a lot of player news and opportunities to provide good streaming picks. So I’m going to try something new, and maybe it’ll stick. It should be fairly explanatory. I hope it holds readers over until the end of this week, which is probably the busiest week for me in a long time.

Player to add that isn’t Gregory PolancoA.J. Pollock, ARI OF
He’s on the DL, so you’ve got time to pull the trigger. His batting average isn’t for real, but the 6 homers and 8 steals are nice, and he will more than likely join the small number of players who achieve double-digits in each category in a given year. I would expect a batting average closer to .265, but if you can punt average for counting stats in a deeper league, I would go for it.

Hitter to drop: Jay Bruce, CIN OF
Honorable mention goes to Brandon Phillips, Bruce’s teammate, but it is more fitting that the suggested replacement player can actually replace someone. Bruce is striking out about 5 percentage points more often than last year and almost 8 percentage points more than his career rate. Meanwhile, he is hitting more ground balls than fly balls, whereas about two-thirds of all of Bruce’s batted balls over his career have been put in the air. The sample size is quite large now, and I think there may be something wrong with the slugger. His ratio of home runs to fly balls (HR/FB) is a little bit deflated, but even if it returns to his career average, I still wouldn’t expect him to hit much more than 20 home runs, and that’s a serious problem for a guy who’s value lies solely in his power. Bruce is shaping up to be the next Curtis Granderson, and I have legitimate concerns about his current and future value.

Pitcher to add: Marcus Stroman, TOR
Stroman could quickly rise to the top as Toronto’s ace come 2015 if he lives up to his minor league numbers. So far, he has. I liked Stroman a lot as a prospect, as he averaged 10.6 strikeouts and only 2.4 walks per nine innings. He began the year in the bullpen and suffered a couple of brutal appearances in a row, so his two recent (and excellent) starts have improved his numbers to a still-shaky 5.40 ERA and 1.53 WHIP. But I think he’s a starter by trade, and his 13 strikeouts and two walks over 12 innings as a starter support such a claim. Your window to claim Stroman may stay open for a while, especially if other owners simply look at his misleading ERA and WHIP or, on ESPN, his average points, which stands at an underwhelming 3.3 per appearance. However, if he keeps flashing this kind of quality, you’ll start to run out of time.

Wednesday streamer, other than Stroman: Rubby De La Rosa, BOS
I’ll be honest, I’m not thrilled about him, but everyone has caught on to Tyson Ross (although he’s still only 73-percent owned), so tomorrow’s options are slim. De La Rosa comes with K’s but also BB’s; however, he carries a 13-to-2 K/BB ratio into this start on the road, so perhaps he can continue to keep the command issues under control.

Prospect(s) to watch: Joc Pederson, LAD OF, and Mookie Betts, BOS 2B
Pederson and Betts will likely not be up any time soon, as they’re blocked by some pretty large figures at their respective positions. But given the hype surrounding a couple of 2014’s call-ups in George Springer and, most recently, Gregory Polanco, it’s good to know who the next impact players will be. Pederson is batting .327/.437/.615 with 16 home runs and 14 steals. Are you serious? I think he’s a bit too far to reach a 40/40 season, but 30/30 is probably at this point. It’s unfortunate the Dodgers are letting him rot in the minors beneath a pile of unmovable cash in their impacted outfield. Betts recently moved up to Triple-A Pawtucket; prior to this move, he stole 22 bases in 285 plate appearances while batting .346 with almost twice as many walks as strikeouts. He’s going to be really good, with astounding plate discipline, decent speed and a little bit of pop, too. If you hear Pederson’s and Bett’s names, or the names of their predecessors (Yasiel Puig, Carl Crawford, Matt Kemp, Andre Ethier, Dustin Pedroia…), in next month’s trade talks, get ready to prospectively add, add, add.

Revisiting my bold preseason predictions

This just in, folks: Corey Kluber leads all MLB pitchers in wins above replacement (WAR). The great thing about running your own website is you have full discretion to toot your own horn when you please. As much as I find it tacky to do so, I made bold predictions for a reason: to see if my projections are actually worth a damn. I just wish I had time to make more; I should have started early in the offseason as I ran out of time the longer the academic year has worn on. (I’m a graduate student, so publishing to this website is not always the most optimal use of my time. According to societal expectations, at least — I think it’s a great use of my time!)

Anyway, let’s revisit my bold predictions to discuss a) their accuracy thus far, and b) why they have (or have not) been accurate. Here they are, in chronological order:

Tyson Ross will be a top-45 starting pitcher

Ross is ranked 31st of all starters, according to ESPN’s Player Rater. Instead of rehashing details, you can read the linked article to see why I glowed about Ross this offseason and have chosen him as a streamer several times already this year (before he gained more recognition and, consequently, more ownership). That he qualifies as a reliever in ESPN leagues is a huge plus as well. I readily admit it’s not insane for a random names to rank highly in the player rater; just check out the names around Ross’, including Alfredo Simon, Josh Beckett, Aaron Harang and Collin McHugh. Unlike the names I mentioned, though, I think Ross has the natural ability to stay there, given his strikeout propensity that limit the damage done by walks (which, by the way, is a problem nowhere near as bad as Shelby Miller‘s — I guess six wins will mask his atrociously bad WHIP that will blow up in his face sooner rather than later.) Ross is still available in 21 percent of ESPN leagues, so if he’s out there, you should grab him. Just don’t expect him to keep winning as often in front of that terrible San Diego offense.

Brad Miller will be a top-5-to-7 shortstop

As terribly as this prediction has turned out — Miller is batting .151/.230/.247 with 3 HR and 3 SB — I do not regret making it. Miller has struck out in 28 percent of his plate appearances, which is way, way worse than he ever was in Triple-A or even last year, when he struck out 17 percent of the time. It pains me deeply that The Triple Machine hasn’t hit a triple. Have I given up on him this year? Honestly, yes. His batting average on balls in play is grossly unlucky right now, but even regression to the mean won’t fix what his strikeout tendency has broken. But I still like him as a sleeper for next year, or even as a late bloomer this year. If he can demonstrate an improvement in his plate discipline as the year wears on, I will give him another chance. It upsets me, though, that he had such a hot spring. It fuels the fire of analysts who criticize spring training stats as unreliable. I agree, to an extent, but Miller’s spring stats were an extension of his 2013 season — albeit an extension inflated by some good luck. It’s worth emphasizing here that strikeouts really aren’t luck-based, so to say the his spring training was lucky is an ignorant dismissal.

Corey Kluber is this year’s Hisashi Iwakuma (aka big breakout candidate)

There’s one thing I, at least, can privately appreciate about my bold predictions: I abided by all of them in every single I’m in, unless someone happened to grab a pitcher before me. Ultimately, in four leagues, I grabbed Ross and Miller in four of them, and Kluber in three — and in the fourth one, I promptly traded Jayson Werth and Tyson Ross (who I drafted in the last round) for Norichika Aoki and Corey Kluber (this is a points league, so Aoki carries some value for his lack of K’s and contact approach). Did I win the trade? Who knows — I traded one guy I liked for another I liked more. Point is, I actually rolled with my bold predictions. Might as well eat my words, right? (Is that how that saying goes?)

I got Kluber in the equivalent of the last round in every draft and for $1 in my primary keeper auction league. Yes, I’m bragging. But, more importantly, this isn’t a revelation to me. I knew Kluber would be good based on last year’s peripherals, as did a host of other people on FanGraphs (namely, Carson Cistulli and the Corey Kluber Society). But a lot of people didn’t see it coming, which is crazy to me, and it makes me question what it really takes to become a paid professional “fantasy expert.” Tristan H. Cockcroft ranked Kluber 58th of starting pitchers this preseason, which is better than I expected, but look at some of the names above him: Matt Garza? Justin Masterson? Zack Wheeler? For a guy who invests so much in seeing an improvement in skills, Wheeler has been, for his entire career, buying up billboards to plaster them with slogans such as I HAVE CONTROL ISSUES. Kluber is essentially the antithesis of Wheeler. And, yet, who has the smaller track record? Ridiculous… (In Eric Karabell’s defense, he said pitching is so deep this year that owners may not be able to draft Kluber, which was a roundabout way of indicating he liked him, at least somewhat, heading into draft day.)

Anyway, I’m clearly on a rant, and I need to get this train back on the rails. Kluber is somehow not 100-percent owned at this point — he’s 99.9-percent owned, but hey, at least I’m not lying — yet he’s striking out everyone and their mothers. I don’t know if he continues to strike out 10 per nine innings (10.28 K/9), but the percentage of swinging strikes he has produced has jumped 1.4 percent, placing in the top 1o in the category, behind Max Scherzer and ahead of Madison Bumgarner. This is all a long-winded way of saying he could, and perhaps should, be a 200-K guy this year. In that sense, maybe he’s not a buy-low guy, but his lack of name recognition and his .350 BABIP makes him a prime candidate to be exactly that. A handful of rankings have him in the 35-to-40 range; even then, I can give you a case to trade perhaps a dozen names ahead of him for Kluber, including Gio Gonzalez, Matt Cain and, yes, maybe even Justin Verlander (who, at this point, is still owned in most leagues simply because of name recognition and past performance; and while I understand the importance of past performance, do not let yourself be blinded by nostalgia).

Dan Haren will strike out fewer than 7 batters per nine innings

This one is random, but hey, it’s legit: Haren has only a 6.89 K/9 right now. You can read the linked post to find out way. I may rip him a little too hard — his control still makes him a fairly solid starter — but he’s more of a Kyle Lohse these days than, well, a Corey Kluber. Lohse is serviceable, but he’s not elite, and Haren should be able to net you an extra win or two along the way in front of a lethal Dodgers offense.

OK, that’s it. I’m 3-for-4 in my bold predictions so far this year, which is a pretty good day at the plate, so I’ll take it.

Also, the academic year is winding down, and once it winds down completely, Need a Streamer will ramp up with more content. Stay tuned, and thanks for reading.

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:

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? Roll out Tyson Ross on Sunday

I’m sitting in an airport so I’ll make this brief. The San Diego Padres’ Tyson Ross is pitching at home against the absolutely miserable Arizona Diamondbacks. You really couldn’t ask for more. He’s only owned in about 15 percent of ESPN leagues.

Another streamer, who doesn’t count as a “streamer” based on my criteria I’ve laid out, is the man pitching the night before (aka tomorrow, aka Saturday, May 3): Ian Kennedy. He has been excellent this season, with a 3.16 ERA and 0.95 WHIP with 37 strikeouts in 37 innings. Maybe he’s due for some regression — he’ll start allowing a few more hits eventually — but I would bet the house it won’t come against the lowly Diamondbacks. It’s worth noting that Kennedy has only walked eight batters so far this season, making for a 1.9 BB/9 and 4.6 K/BB — both career bests. It’s these adjustments that make Kennedy look like he’s shaping up for an encore of his 2011 performance, a year during which he finished 4th in the National League Cy Young voting.

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!