Which Hitters Perform Best Against Better Stuff?
Evaluating hitters using pitch characteristics and pitch quality metrics.
Over the past couple seasons, the introduction of pitch quality metrics to the public (such as Stuff+) has added another layer to pitching analysis, allowing for analysts to evaluate pitcher’s arsenals based on their characteristics as opposed to their results. Utilizing pitch quality metrics has been both an effective predictive metric and an impactful player development tool for improving individual pitcher’s arsenals. One element of analysis I have not seen in the public space yet is how well hitters perform against varying levels of “stuff”, which I hypothesize is a valuable element of game-planning for many teams at the professional level. For example, if a given hitter is very good at hitting Sliders with a high Stuff+, then maybe a pitcher with a plus Slider should utilize a different offering as a putaway pitch against this hitter. In this article, I am going to look at pitches with a Stuff+ over 110 for six different pitch classifications (Four Seamers, Sliders, Curveballs, Sinkers, Changeups, and Cutters) to evaluate which hitters are the best/worst at producing against each pitch’s top quality “stuff”.
Since my own pitch quality metric is still in the works, I will be utilizing the Stuff+ model found on FanGraphs (championed by Eno Sarris) for this analysis. The following Stuff+ data was collected before the latest update to the model which now includes adjustments for platoon splits. Given the fact that this model does not provide Stuff+ on a pitch-by-pitch basis, I had to get a little creative in order to conduct this analysis properly. First off, this analysis will consist of only pitches from the 2023 Major League Baseball season. For each pitcher, I took the Stuff+ for each of their pitches during each month of the season and assigned each pitch’s respective Stuff+ to each pitch thrown in a given month. As an example, every Four Seamer that Spencer Strider threw in the month of April was assigned his Fastball Stuff+ value for the month of April.
Is this method perfect? No. It doesn’t capture changes a pitcher made to their arsenal within a given month, doesn’t separate between Sliders and Sweepers (when a pitcher has both in their arsenal), and doesn’t capture “bad” pitches on a given day. However, given the data, computational, and time constraints that I encountered, I believe that this the best method to compile the data for this analysis.
For Fastballs and Sliders, a hitter had to face at least 150 pitches in order to qualify for the following leaderboards. For all other pitches, a hitter had to face at least 50 pitches in order to qualify. Each hitter’s wOBAcon, xwOBAcon, Whiff%, Swing%, and O-Swing% were calculated for each pitch, and the leaderboards below are ranked by each hitter’s Run Value per 100 pitches against each pitch type. Onto the leaderboards!
Leaderboards:
As shown by the tables above, analyzing how well hitters perform against specific types of pitches can be an important element of game planning for opposing teams. For example, Spencer Strider should have a lot of success utilizing his 141 Stuff+ Fastball against Trea Turner, who has struggled against plus Four Seamers, while he may encounter some difficulties utilizing the Fastball against Mookie Betts, who is elite against plus Four Seamers. On a more granular level, analyzing hitters by how well they perform against certain levels of Stuff+ is also probably a factor in making decisions regarding how well relief pitchers line up against opposing hitters.
As mentioned earlier, there are difficulties with assigning a Stuff+ value to each individual pitch and I acknowledge that these leaderboards probably experience quite a bit of noise in their results. In addition, each Stuff+ value is not “constructed the same” (ex. A “dead zone” Four Seamer at 98 mph might have a similar Stuff+ value as a Fastball with plus iVB at 94 mph, however these are two distinct pitch shapes), which might also limit the effectiveness of this type of analysis.
For these reasons, I decided to also evaluate hitters based on the movement characteristics of the pitches they faced. I evaluated hitters based off of three movement profiles: Four Seam Fastballs with at least 16” of induced vertical break, Sliders and Sweepers with at least 10” of horizontal movement, and Four Seam Fastballs with at least 16” of induced vertical break while located in the upper part of the strike zone or above the zone. Onto the leaderboards once again!
Concluding Thoughts:
By delving into the intricacies of how well hitters perform against pitches with different Stuff+ and movement characteristics, we’ve uncovered patterns that could redefine pitching strategies against hitters, potentially leading to further innovation in game planning and preparation. For instance, if a given hitter is very good at hitting Sliders with a high Stuff+, then maybe a pitcher with a plus Slider should utilize a different offering as a putaway pitch against this hitter. Conversely, if a hitter struggles heavily against a specific pitch type, such as up-in-the-zone Fastballs with plus ride, then a pitcher that has that offering in their pitch arsenal should not be afraid to frequently utilize the pitch against this hitter.
This analysis is simply the beginning of utilizing pitch quality metrics to better understand the strengths and weaknesses of hitters. Perhaps analyzing how well a hitter produces against the best “Stuff” could be a predictor of offensive performance moving forward, and I believe that this type of analysis could be of particular value when it comes to evaluating whether or not Minor League players are ready for promotion to the Major Leagues. Either way, evaluating how well hitters perform against specific types of pitches is a new endeavor in the public sphere of baseball analysis, and hopefully the continuation of this research will lead to new insights in game planning and player evaluation.
Follow @MLBDailyStats_ on X (Twitter) and Adam Salorio on Substack for more in-depth MLB analysis. Data from FanGraphs and Statcast.
Photo credits to Getty Images.