The 2014 xK% Underachievers, AKA: The Upsiders
Last year, I shared my updated xK% equation, which blends a pitcher’s overall strike percentage with his called, swinging and foul strike rates to produce an expected strikeout rate. While its wonderfully high adjusted R-squared tells us how well it works, it’s even better used when dealing with a small number of innings since the metric uses pitches thrown, greatly alleviating sample size issues. It’s therefore a huge help when projecting young starting pitchers for my Pod Projections who were up in the Majors for just a grande sized cup of coffee.
What follows is a select group of starting pitchers whose xK% were well above their actual K% marks. This is not your leaderboard, but the more interesting names that you actually care about.
Michael Pineda — 24.2% xK% vs 20.3% K%
Pineda enjoyed remarkable success during his return from major shoulder surgery. Sure, he only lasted long enough to make 13 starts, but pinpoint control offset some strikeout rate erosion to result in an overall skills package identical to his exciting 2011 debut. His fastball velocity was down over two miles per hour post-surgery, which wasn’t surprising, but he still managed to generate swinging strikes at an above average clip. The good news is that perhaps that down K% should have been better…even with the decline in velocity. And what if his velocity improves as he continues to build shoulder strength? He’s already a strike-throwing machine.
He makes for an absolutely perfect shallower league rosteree. With replacement level high, it’s easy to plug in someone else if/when he lands back on the disabled list. He’s a bit riskier in deeper leagues, but his upside cannot be ignored.
Jeremy Hellickson — 22.9% xK% vs 19.2% K%
Hellickson has posted an xK% above his actual K% every season he’s been in the league, so perhaps we shouldn’t take this latest outperformance very seriously. But here are the positives: a) his actual K% has risen for three straight seasons, b) his xK% has risen for three straight seasons, c) his SIERA has declined for three straight seasons, d) he’s moving to the National League, which should boost his K% and reduce his ERA and WHIP.
His changeup remains fantastic and his curve ball has been pretty good, generating enough swinging strikes, but also lots of grounders. He’s still off the radar in shallow mixers with so many other options, but I think he makes a nice cheap gamble in NL-Only leagues.
Allen Webster — 17.2% xK% vs 13.9% K%
Another newly minted Diamondback! It should surprise no one that Webster is better than a measly 13.9% K%, but perhaps what is surprising is that his xK% isn’t higher. His changeup has been spectacular at inducing swings and misses, while his slider has been above average.
But for whatever reason, he has been unable to get a whole lot of both called and foul strikes. Both rates have been pretty tiny at well below the league average, which explains his uninspiring xK%. But I’d much prefer to see improvements needed in those two strike types than in swinging, which Webster has generated in spades. We know his control needs to improve, but he, too, is now in the National League and makes for an intriguing flyer in NL-Only and deep mixed leagues.
Drew Smyly — 23.8% xK% vs 21.2% K%
Smyly’s strikeout rate predictably fell as he moved back into the rotation, but xK% suggests the decline shouldn’t have been as dramatic. Both his swinging and foul strike rates were higher in 2014 than 2012, when his strikeout rate was higher, and he threw a significantly higher rate of strikes.
And perhaps he found the magic elixir in Tampa Bay, as he strikeout rate surged to 25.4%, while his swinging strike rate jumped from 15.7% in Detroit to 19.3% in TB. That’s an enormous jump. Given his SIERA outperformance, I’m not sure he has any further ratio upside, but more strikeouts could offset any ERA jump and keep his fantasy value high.
Rubby de la Rosa — 18.2% xK% vs 16.1% K%
Should I have just renamed this column “The Diamondbacks Intriguing Rotation and Other Starting Pitchers of Note”? The many they call Rubby is the third D-Back on this list, who should also enjoy the benefits of moving to the National League.
How does a pitcher who averages 94.0 mph with his fastball post just a 16.1% strikeout rate? Oh wait, hi Nathan Eovaldi! Like Eovaldi, de la Rosa has one strong secondary pitch, but his is the changeup. It has generated whiffs and grounders. As is the case with Webster above, control could be an issue, but this is an intriguing arm that needs to be remembered.
Brett Anderson — 18.0% xK% vs 16.1% K%
I couldn’t pass up an opportunity to include my favorite man of glass on a happy list. Anderson’s strikeout rate dipped to just above his career low, which may or may not have been fueled by a loss of fastball velocity. Under normal circumstances, it would seem obvious that the decline was the cause, but based on his strikes, his results maybe should have been better. His swinging strike rate was actually the second highest of his career, just below his 2013 mark, while both his four-seam and two-seam fastballs induced swinging strikes at clips just above his career averages. So was it really a slower fastball?
He’s now moving into a great situation, backed by what should be a strong offense and defense and will be aided by pitching half his games in a favorable home park. Of course, he has generally performed pretty well when he actually takes the mound, so as usual, it will come down to his health to determine how much fantasy value he ends up earning.
Mike Podhorzer is the founder of ProjectingX IQ, an advanced fantasy baseball analytics platform that transforms projection data and in-season performance signals into actionable intelligence. He is the 2015 Fantasy Sports Writers Association Baseball Writer of the Year and three-time Tout Wars champion. He is the author of the eBook Projecting X 2.0: How to Forecast Baseball Player Performance, which teaches you how to project players yourself. Follow Mike on X@MikePodhorzer and contact him via email.
Rubby also has a nice slider. Hasn’t been worth anything, but it looks good when he has command of it.
In shallow leagues it is always worth to gamble on high injury high reward pitcher. That is why I did not understand why people were down on Cueto last year. Even without his improvement last year, he has been a top 30 pitcher when healthy.
ESPN had him ranked as the 70th pitcher. Even if he missed half the year, all you would need to do is find a top 70 pitcher in a shallow league to make him worth the pick.
IN shallow leagues many people are too scared to leave a position uncertain so it leads to drafting proven mediocrities to fill out the lineup instead of the guys who can really make a difference with their upsides.
I especially like this with fragile types like Anderson. IF they stay healthy, you got a bargain, if not, given the cost you lose little and the shallow league ensures a viable alternative is out there to take his spot.
The fact that pitchers are:
1. violate
2. high injury risk
3. very stream-able
makes it worth it to trade risk for reward especially in the 2nd half of your rotation.
I think what happens owners will have one of those seasons where either the pitchers they pick up after injuries perform horribly or the upside pitchers implode. Then they become gun shy.
Why are you saying shallow leagues? Shouldn’t this be more true in deep leagues where you’re really drafting either shit that’s likely to stay healthy and eat innings up or upside that may not pitch?
Brett Anderson…what a waste of time.
I’m a little confused… If this is the list of upsiders, with xK% greater than K%, why do both Rubby de la Rosa and Brett Anderson have higher K% than xK%?
It’s a typo. Brett Anderson had a 16.1 K% last year, so I imagine his xK% was 18.0
Yeah whoops! Will fix
What are you setting as your minimum number of pitches, and what is your time window? I collected all of Baseball Reference’s pitch data dating back to 2010 (a semi-arbitrary five-year window) and set a minimum pitch limit of 900, which seems to be close to an optimal threshold, which yielded a 0.931 adjusted R-squared.
I’m hesitant to bet on positive regression for some of these guys: Webster under-performed his xK% in 2013 by an almost-identical margin to 2014; jury is still out on Smyly, who recorded a -2.5% differential in 2013 but a +1.0% in 2012, per my numbers; and you already noted that Hellickson consistently under-performs his xK% as well (although, in his defense, the margin was much larger in 2014 than in 2010-13).
My study included data going back to 2008, minimum of 50 innings pitched. I guess using a pitch minimum would have been better. About how many innings is 900 pitches? Your R-squared with slightly higher than mine.
Webster only pitched 30 innings in 2013, so K% itself is prone to wild fluctuations in a small sample. But yes, it does seem like some guys consistently under or overperform. With any formula, there are going to be outliers. Either it’s just randomness and these are the guys who just so happen to be that group, or there’s something else not being accounted for by the equation that these guys aren’t doing as well.
50 innings is probably about the same as 900 pitches if we loosely assume the average 100-pitch start gets a pitcher through 6 innings (so 6*9=54). Maybe 60-ish innings? Still, pretty similar.4
I think there are simply a handful of guys on both ends of the spectrum that will consistently produce outlier xK% rates. Cliff Lee is the obvious example on the positive end. Maybe a comprehensive list of consistent underperformers would reveal the missing link.
I think strike/ball sequencing might boost the R-squared, but I don’t know if that’s a skill. Am working on getting some research on this though.
“The many they call Rubby is the third D-Back on this list”
I didn’t know Rubby de la Rosa was Legion.
Pod you should have you own site and podcast sir. Will miss your Roundtable episodes. It was a good name and thought. You with 2 guests every week would be awesome. Well sure I’ll pay for it lol
Haha, thanks. If I had the time, perhaps.