Two Simple Metrics To Use To Find Pitching Regression Candidates
It’s a term we hear over the course of a 162-game season many, many times. Players like Aaron Judge and Rhys Hoskins were hitting home runs at an absurd, unsustainable pace, which meant they were due to regress. It’s the same way that Jason Vargas wasn’t going to carry over his 2.62 ERA from the first half of the season throughout the year, as his peripherals weren’t extremely different from his career.
But regression isn’t just a negative thing, though, even if it’s typically taken that way. Players can have positive regression, too, in which they rebound from a start that is out of the norm for them. Manny Machado last season is the perfect example of this.
As we turn our sights toward the 2018 season, let’s identify some pitchers that are due for regression – in one form or another – and we’ll tackle hitters next week.
ERA and FIP Differentials
One of the first signs I look for in regression for a pitcher is the difference between his ERA and his FIP. If his ERA is higher than his FIP, it tends to mean that his numbers should have been better than they were. If his ERA is lower than his FIP, it means that the pitcher had a lot of things go right for him throughout the year, and that negative regression should be expected.
Here are the qualified pitchers from the 2017 season, sorted by ERA, with their respective FIP and differential in the next columns:
If we sort, we can see those pitchers who were the luckiest in 2017, which really, the first five come as no surprise:
Any Fantasy advice you read last year told you to sell high or avoid using the top names on the above chart. Should they have finished the year – especially Lance Lynn and Gio Gonzalez – with the numbers they had? No, absolutely not. However, pitchers can outperform their peripherals and keep that up over not just a year, but a career. The most obvious example of this is Johnny Cueto, who has a 3.33 lifetime ERA and a 3.77 FIP.
It just so happens that the Top 5 names on this list had five of the seven lowest BABIPs against last year.
Coincidence? I think not.
The smart move, though, when you see differentials this high is to shop the pitcher because the likely outcome is that they regress in a bad way.
On the other side of the coin, let’s look at pitchers that weren’t as fortunate and experienced bad luck on the mound:
What’s important about these types of exercises isn’t just the differential, it’s where the differential takes you. While we said Gonzalez was a sell-high player, and he had one of the Top 5 luckiest years in terms of ERA and FIP differential, if he pitched to his FIP, he would have been at a 3.93 ERA, which way below the 4.36 league average in 2017.
Keep that in mind when you see names like Matt Moore and Jason Hammel at the top of the “unlucky” pitchers. If they would have pitched to what they should have pitched to, they still would have been worse than league average.
But the pitcher that stands out to me the most is Jeff Samardzija. His price is more than affordable (132 and 156 in two mocks that I’ve done so far with Fantasy experts), as his ERA will stand out. There’s something to say for a guy that consistently throws 200-plus innings. I’m buying him where I can this year.
We love the strikeout in Fantasy. It’s the most valuable out, as you don’t have to depend on a fielder to make a play on the ball. It’s just the pitcher, the catcher and the batter … and of course, we can’t forget the #umpshow. Taking strikeouts further, we like pitchers who have a high swinging strike rate (SwStr%), that just dominates hitters and take it out of the hands of the umps.
Alex Chamberlain wrote a good piece over at FanGraphs taking a closer look at Luke Weaver. Chamberlain tells Fantasy owners to have some pause with Weaver, as he explores his below-average SwStr% and his above-average K%. They do, as Chamberlain explains, correlate with one another.
Similar to the above exercise, I wanted to see which pitchers with at least 100 innings pitched were on the leaderboards for top SwStr% and K%, and to see which ones may be misleading.
I broke it down by looking at pitchers with a SwStrk% of 10 percent or higher, and then pitchers with a K% of 21 percent or higher.
That’s … a lot of names. First, let’s take a look at the pitchers who appear just once on the spreadsheet.
- Sean Manaea
- Jaime Garcia
- Ricky Nolasco
- Ariel Miranda
- Tim Adleman
- John Lackey
- James Shields
- Erasmo Ramirez
- Ervin Santana
- Kyle Gibson
- Marcus Stroman
- Matt Boyd
- Jose Quintana
- Trevor Bauer
- Jon Gray
- Nick Pivetta
- Drew Pomeranz
- Gerrit Cole
- Jake Arrieta
- Gio Gonzalez
- J.A. Happ
- Jose Berrios
- Michael Wacha
- Mike Fiers
- Kyle Hendricks
- Anibal Sanchez
- Ubaldo Jimenez
- Taijuan Walker
- Jameson Taillon
- German Marquez
So, what does this mean? That means that those 18 names that appear in K% only have some question marks with their strikeouts last year, as they fail to appear in the Top 63 pitchers with at least 100 innings pitched in SwStr%. While those that appear just on the SwStrk% but not on the K% list should have had more strikeouts than they ended up with for the year.
Here are some notables from those that fit both criteria and the differences that caught my eye.
Difference in spot where K% is > SwStr%
- Chase Anderson 16 spots
- Carlos Martinez 21 spots
- Charlie Morton 21 spots
- Rich Hill 22 spots
- Justin Verlander 23 spots
- Aaron Nola 25 spots
Difference in spot where SwStr% is > K%
- Sonny Gray 21 spots
- Masahiro Tanaka 22 spots
- Dylan Bundy 22 spots
- Dan Straily 28 spots
- Jordan Montgomery 28 spots
- Danny Duffy 30 spots
- Jake Odorizzi 32 spots
- Joe Musgrove 37 spots
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