Whiff+: A Look at Which Pitchers Dominate by Generating Swings and Misses
Metric Explainers

Whiff+: A Look at Which Pitchers Dominate by Generating Swings and Misses

Whiff rate doesn’t paint an accurate picture when it comes to measuring a pitcher’s stuff. Whiff+ accounts for what whiff rate does not.


While command+ provides a more complete and accurate examination of a pitcher’s command, whiff+ can give a closer look at a pitcher’s stuff by determining the rate at which he generates swings and misses.

Whiff+ enables analysts to compare players of different years on the same scale, which is something other swing and miss rate metrics aren’t able to accomplish.

How is this done? Whiff+ is based on the league average pitch type for that season. Understandably, the whiff rate for hitters has been steadily trending upwards as the percentage of all plate appearances resulting in a strikeout has risen in 15 consecutive seasons. Whiff+ adjusts for that.

Let’s say the average fastball has a swing and miss rate of 8% and a pitcher coaxes a whiff on 10% of his fastballs. By this, we can assess that he has a whiff+ of 125 – 25% above the league average of 100.

The analysis is done across a pitcher’s entire repertoire to determine a weighted average because whiff rate increases dramatically for pitch types like sliders and splitters more than they do for sinkers, changeups or curveballs. The goal of whiff+ is to discover how good a pitcher is at generating whiffs based on his pitch types.

It’s not surprising that two-time Cy Young winner Jacob deGrom and three-time winner Max Scherzer ranked in the top 10 among starting pitchers for the second year in a row in 2020. After leading the majors in whiff+ and going 20-5 with a 2.50 ERA and an MLB-best 326 strikeouts in 2019, Gerrit Cole ranked third while going 7-3 with a 2.84 ERA and finished sixth in baseball with 94 strikeouts in that 60-game season.

For a look at which players are currently the best in the majors in this category, check out our metrics leaderboard.

We have always known that not all balls and strikes are equally effective (or ineffective), but now we have more tools to measure the difference in effectiveness for every pitch.


Data modeling provided by Lucas Haupt.