Ten 2018 Pitcher Strikeout Rate Decliners
On Tuesday, I hopped over to the pitcher side of the ledger to discuss nine fantasy relevant starting pitchers with strikeout rate upside this season. I used my xK% equation and compared what the formula spit out to what the pitcher’s actual strikeout was. Today, I’m going to share the ten pitchers who most outperformed their xK% marks.
Name | Str% | L/Str | S/Str | F/Str | K% | xK% | K%-xK% |
---|---|---|---|---|---|---|---|
Jose Quintana | 61.9% | 29.3% | 15.1% | 29.6% | 26.2% | 22.7% | 3.5% |
Ivan Nova | 66.3% | 24.0% | 13.6% | 28.0% | 16.7% | 13.4% | 3.3% |
Chris Sale | 68.3% | 26.7% | 23.3% | 29.0% | 36.2% | 33.3% | 2.9% |
Noah Syndergaard | 66.7% | 25.2% | 21.9% | 24.8% | 27.4% | 24.7% | 2.7% |
Corey Kluber | 67.9% | 27.9% | 24.2% | 24.4% | 34.1% | 31.6% | 2.5% |
Madison Bumgarner | 66.5% | 23.6% | 16.4% | 30.8% | 22.4% | 20.0% | 2.4% |
Clayton Kershaw | 68.6% | 24.6% | 22.5% | 27.1% | 29.8% | 27.6% | 2.2% |
Jake Arrieta | 62.5% | 29.6% | 15.0% | 27.5% | 23.1% | 21.1% | 2.0% |
Carlos Rodon | 60.7% | 27.5% | 18.4% | 28.0% | 25.6% | 23.6% | 2.0% |
Carlos Martinez | 65.2% | 28.4% | 17.6% | 26.5% | 25.3% | 23.3% | 2.0% |
League Avg | 63.2% | 26.4% | 17.9% | 27.9% |
One of just two starters who outperformed his xK% by more than 3% is Jose Quintana. Though he also outperformed significantly in 2016, he never showed any such ability before that. Did he suddenly discover something not captured in the equation heading into 2016? I doubt it. That said, his xK% did hit a career high, which is interesting considering he also threw the lowest percentage of strikes in his career. The good news is that some of the strikeout rate regression is going to be offset by pitching a full season in the National League.
It’s sad when even a 16.7% strikeout rate could be deemed fortunate. That’s the situation Ivan Nova finds himself in, as he enjoyed no bump despite pitching his first full season in the NL. However, in my historical xK% spreadsheet going back to 2011, Nova has actually outperformed his xK% every single season. And he’s done so by an unweighted average of 1.9%, which is huge. I don’t know what he’s doing not being captured, but at least NL-Only leaguers could hope that outperformance continues, because any further drop will make him worthless in even deep formats.
I’m going to group Chris Sale, Noah Syndergaard, Corey Kluber, and Clayton Kershaw together. Aside from Syndegaard, all of them were among the league leaders in strikeout rate. Regression equations by design are notoriously poor at matching the extremes, and these pitchers represent it.
Syndergaard’s strikeout rate was high, but not extreme enough to totally ignore his much weaker xK%. Of course, this came over a small sample size because he missed most of the season with a lat tear. Since there’s seemingly no discount in early drafts, I don’t think there’s really a reason to pay market price. There’s more downside than upside.
Madison Bumgarner is yet another consistent xK% beater, doing so every season since 2011. However, this was the first season he beat his xK% by more than 1.8%, so this is clearly worrisome. Then again, it’s likely that the shoulder injury he suffered in a dirt bike accident affected his performance, so perhaps this is nothing to worry about.
In three of the last four seasons, Jake Arrieta has outperformed his xK% by at least 2%. What are these guys doing that xK% is missing?! I’m concerned that his swinging strike rate tumbled to its lowest mark since 2013. We’re still waiting for him to sign, so that will obviously affect his fantasy value this year.
Carlos Rodon is dealing with a shoulder problem and is going to get a late start to the season. That alone is enough to avoid him, but he also lands on this list! I wouldn’t touch him.
Carlos Martinez has alternated matching his xK% and outperforming it by at least 1%. This was his best outperformance yet. I continue to believe he’s dramatically overvalued and worry about that ERA-SIERA discrepancy. Since he’s clearly being paid based on his ERA and the assumption of drastic improvement this season, I would not want to be paying the going rate.
Mike Podhorzer is the 2015 Fantasy Sports Writers Association Baseball Writer of the Year. He produces player projections using his own forecasting system and is the author of the eBook Projecting X 2.0: How to Forecast Baseball Player Performance, which teaches you how to project players yourself. His projections helped him win the inaugural 2013 Tout Wars mixed draft league. Follow Mike on Twitter @MikePodhorzer and contact him via email.
Has this ever been back-tested, and if so, what were the results?
I may have just published one review, of the 2017 articles. The results were terrible, but the sample was tiny, obviously. Also, Steamer performed no better, so it was just a weird collection of pitchers.
https://www.fangraphs.com/fantasy/reviewing-the-2017-starting-pitcher-strikeout-rate-upsiders/
https://www.fangraphs.com/fantasy/reviewing-the-2017-starting-pitcher-strikeout-rate-downsiders/
I’ve never done a historical study, but the R-squared is 0.93, which is insanely high. There are some consistent outperformers and underperformers, but trying to account for them a year ago with a new equation failed.
A high R-squared does not mean you have a predictive model. As I commented in your first article, has your model been tested for multicollinearity, for example? Are your independent variables correlated? I would say so. Your model is like saying someone who is 1) very tall and 2) has big hands likely has big feet. 1 and 2 are highly correlated and will give a false sense of what the R-squared result is telling you.
My bad, the equation isn’t meant for predictive purposes, it’s backwards looking. I use historical xK% marks to guide my K% projection, since it’s much better than actual K% when dealing with small sample sizes.
The variables are the different strike types. A pitcher good at inducing swinging strikes has no bearing on whether he’s also good at inducing called strikes. They are separate skills.
Forgot that my original article linked to in the intro has all the predictive metrics, xK% in Y1 to X% in Y2 compared to K% in Y1 to K% in Y2. My metric was ever so slightly better overall (even though I didn’t develop it to be predictive), but the gap widened for pitchers with less than 50 IP, which is where the real value lies.
https://www.fangraphs.com/fantasy/introducing-the-new-pitcher-xk-updated-for-2017/