# Re-Contexualizing SwStr% for Efficiency

At the beginning of last season, I contextualized the swinging strike rate (SwStr%) (and refreshed those numbers after the season concluded). I had seen other analysts call certain pitches “above-average,” “below-average,” “elite,” etc. using the league-average whiff rate as a baseline. This is neither a criticism nor a judgment, as I absolutely did this before I had my statistically-driven epiphany. But understanding the average four-seamer’s or slider’s or cutter’s whiff rate lends additional context to any assertion one might make about the “elite-ness” of a pitch.

More recently, I wanted to convert discrete outcomes by pitch type into fielding independent pitching (FIP) statistics — namely, FIP and xFIP (expected FIP, which substitutes a pitcher’s rate of home runs per fly ball for the league-average rate). Let me warn you now: the results are very imperfect. It took some brute force on my part to get there, but I got there. I would wager that the the extreme (lowest and highest) values are probably a bit exaggerated. Regardless, it’s an interesting table to ingest:

2018 Pitch-Type FIPs
Pitch FIP xFIP
Change 3.98 3.99
Curve 2.11 1.93
Cutter 4.29 4.15
Fourseam 5.19 5.15
Sinker 5.03 4.91
Slider 2.33 2.32
Splitter 2.62 2.28
League 4.15 4.15
SOURCE: PITCHf/x
Starting pitchers only

Again, exaggerated values and all that, but you can see just how bad fastballs (four-seamers and sinkers) are relative to breaking pitches (curves, sliders, etc.). What caught me by surprise — maybe not on first glance for you, someone who doesn’t spend way too much time with this data — is that change-ups graded out so poorly. Slightly above-average, sure, but consider: change-ups sported the 3rd-best whiff rate in 2018 (behind sliders and splitters) alongside a coin-flip ground ball rate (GB%). A league-average change-up has 16% SwStr and 50% GB rates, which sounds like an awesome pitch to me. What gives?

Turns out, certain pitch types are more efficacious than others in converting swinging strikes into strikeouts. The league whiff rate (10.7%) is almost exactly half that of the league strikeout rate (K%) of 22.3%, which makes “SwStr% times two” an easy rule of thumb. (I outline that rule of thumb here, in which I compare the efficacy of swinging strikes inside and outside the zone and find that, generally speaking, a pitcher’s strikeout rate should be roughly double his whiff rate.) That rule of thumb, however, does not apply uniformly to each pitch:

2018 Strikeout Efficiency
Pitch K/SwStr*
Change 36%
Curve 58%
Cutter 48%
Fourseam 53%
Sinker 57%
Slider 51%
Splitter 51%
Average 49%
SOURCE: PITCHf/x
*Strikeouts per swinging strike

(Edit, 7:18 pm ET: K/SwStr is calculated using the raw numbers of each — that is, the number of strikeouts divided by the number of swinging strikes. In effect, it’s like saying, “out of 100 swinging strikes incurred on change-ups, 36 resulted in strikeouts on average.” So if a pitcher had a 10% swinging strike rate on his change-up, and he threw 500 change-ups, you could expect, on average, 18 to 19 strikeouts as a result [500 pitches * 10% SwStr * 36% K/SwStr]. It’s a little more abstract, since we’re so used to discussing plate appearance, not number of pitches thrown, as the lowest common denominator. More information, as elucidated by a tangible example of using this calculation, can be seen in the comments.)

Change-ups are actually incredibly inefficient at converting swinging strikes to strikeouts. It must be a function of usage/sequencing, as change-ups lag consistently in this regard. This list…

…feels less impressive knowing those change-ups aren’t converting strikeouts as frequently as I might’ve once expected them to. (Author’s note: the pitch usage numbers up there are a little off, as corrected here. Sanchez is pretty far off. My bad.) It’s not bad at all to have an excellent change-up — a league-average change-up is still significantly better than an elite sinker on the basis of strikeouts — but one should mentally downgrade a pitcher’s seemingly elite change-up, especially when comparing it to breaking balls or off-speed pitches of seemingly similar caliber (i.e., comparable peripherals).

Strikeouts-per-swinging strike is an odd quirk, one I’ll monitor when considering a pitcher’s strikeout rate on a pitch-specific basis. The typical fastball might over-perform the “SwStr% times two” rule of thumb, whereas it’s highly likely the typical change-up significantly underperforms it.

That’s it. No greater takeaway here. Just something to bear in mind while you analyze pitchers ahead of the 2019 season.

RotoGraphs is teaming up with the NFBC to provide unique content throughout the season. To find out about the various NFBC events like the Online Championship go over to the NFBC website to see their various league options.

We hoped you liked reading Re-Contexualizing SwStr% for Efficiency by Alex Chamberlain!

Please support FanGraphs by becoming a member. We publish thousands of articles a year, host multiple podcasts, and have an ever growing database of baseball stats.

FanGraphs does not have a paywall. With your membership, we can continue to offer the content you've come to rely on and add to our unique baseball coverage.

Currently investigating the relationship between pitcher effectiveness and beard density. Biased toward a nicely rolled baseball pant. Reigning FSWA Baseball Writer of the Year and 5-time award finalist. Featured in Lindy's Sports' Fantasy Baseball magazine (2018, 2019). Now a Tout Wars competitor.

Member

I’m not surprised by this finding about changeups. The phrase “pitching backwards” almost exclusively refers to throwing changeups in fastball counts. Fastball counts are mostly not two-strike counts. So the sample of changeups thrown is heavily infected by hitters trying to ambush 2-0 fastballs. Hence, lots of whiffs but few Ks.