The Importance of Swinging Strike Rate
A statistical recommendation
With the advent of the statistical revolution, it seems the baseball community is flooded with complex statistics for measuring a player’s performance. Newcomers and seasoned vets alike can find themselves flustered by the sheer amount of information available.
With that in mind, I want to look at one of my favorite stats for analyzing pitchers, Swinging Strike Rate (SwStr%).
SwStr% very simply tracks the percentage of pitches thrown by a player that result in the batter swinging and missing.
Keep in mind that no one statistic is perfect on its own. A true picture of a player’s value can only be created by cross-checking multiple bits of information against each other, as well as applying the always vital eye test.
That said, SwStr% is the purest statistical indicator of pitch quality, what the scouts call “stuff”, available. All hitter’s whiff on occasion, but if a pitcher is making a higher than average rate of hitters miss, over a long period, it’s likely they are doing something to cause it.
There is an important distinction to be made between called strikes and swinging strikes. While there is a certain amount of skill involved in getting called strikes, much of the process is out of the pitcher’s hands. Both the umpire and the catcher exercise a great deal of influence on whether a pitch, that the batter does not offer at, is called a strike. This means that it is difficult to use called strikes as an indicator for pitchers. Much like reverse platoon splits, they should be taken with a grain of salt and mistrusted until a substantial sample size is produced.
With swinging strike rate, on the other hand, there is very little gray area. The pitcher threw the ball, the hitter swung, and the ball missed the bat — simple as that. Since the only other factor involved is the batter, and any single batter does make up a significant portion of a pitcher’s sample, it is a far more stable statistic.
Zooming in and examining the individual pitch instead of the at bat is beneficial when attempting to make predictions of future success. For example, a pitcher may produce a swing and a miss on the first two pitches in an at bat, then give up a home run on the third. Two of three pitches in that at bat were quality, and if the pitcher can continue to throw 67% good pitches they will experience more success in the future. If one only examines the at bat, they will see just the home run.
Consider the top ten qualified pitchers in this statistical category from last season.
1. Max Scherzer: 15.3%
2. Noah Syndergaard: 14.2%
3. Jose Fernandez: 14.2%
4. Michael Pineda: 14.1%
5. Danny Duffy: 12.9%
6. Corey Kluber: 12.6%
7. Cole Hamels: 12.2%
8. Chris Archer: 12.2%
9. Jon Gray: 12.1%
10. Justin Verlander: 12.0%
All ten produced above 2.0 WAR seasons, making each an above-average player. In fact, to find a pitcher that did not reach the 2.0 WAR, you must scroll all the way down to R.A. Dickey — in 24th place. Of the 34 pitchers that produced a 10% rate, only three failed to reach the 2.0 mark. You get the idea.
SwStr% is a nice, simple statistic with a strong correlation to future production. If you are looking for a good way to evaluate pitchers, I could not recommend it enough.