I have been toying around with an idea for pitcher-hitter match-ups based not on prior head-to-head performance or platoon splits, but rather pitch type linear weights.
For those that are unfamiliar, pitch type linear weights basically takes a batter or pitcher’s performance on each type of pitch they throw or face during the year (e.g. four-seam fastball, slider, etc.) and converts that performance into runs created or runs saved relative to average. At FanGraphs, we show both the total runs created or saved for each pitch (e.g. wFB) and a normalized version for the value per 100 pitches thrown (e.g. wFB/C).
I thought it would be interesting to compare the starting pitcher’s pitch type linear weight performance against the lineup he is facing. To do this, I calculated the difference in run value between each pitch type for each starting pitcher and the hitters they might face. The difference is shown in the tables below. Green coding denotes an advantage to the pitcher, while red indicates an advantage for the hitter. I used the normalized version of each pitch type (i.e. run value per 100 pitches thrown/faced) to control for playing time, pitches seen, etc.
A few caveats. First, I only show data for hitters that accumulated >= 300 plate appearances over the past three years and currently appear on our active roster leader boards. Second, the pitch data is from BIS, so the classifications will vary. Often you will see a slider classified as a cutter, and vice versa. Ideally, I wanted to use our Pitch FX data here, but we do not list out the run values by pitch type for hitters.
The top of each table lists how often the pitcher has thrown each pitch type over the past three years. In the bottom table I will try to list where that hitter is slotted in the batting order, if I get my hands on the line up in time. For this post, I did not have a chance to incorporate batting order position, so I’ve just left it blank.
Looking at the match ups, Josh Johnson has a pretty significant advantage over the Phillies’ hitters. Jim Thome and Shane Victorino are pretty good fastball hitters, but outside of those two Johnson shows a clear advantage in terms of his two predominant pitches (fastball and slider).
Roy Halladay, on the other hand, is facing a more potent line up. Halladay’s cutter is his more dominant type of fastball, and you can see how much of a run expectancy advantage he has over most Marlins hitters; Jose Reyes, Hanley Ramirez, and Mike Stanton are the exceptions in this case. Halladay should generally dominate this lineup with his curveball. Halladay sports one of the best curves in the league, and outside of Chris Coghlan, no Marlins hitters has an advantage against the curveball when it comes to run values.
One big caution about these numbers; they are averages. More specifically, the values for each pitch type are stripped of just about all context. So when we see that some Marlins hitters have higher run values against the cutter than Roy Halladay, we have to remember that Halladay has one of the most dominant cutters in all of baseball. Hitters like Reyes and Stanton accumulated those averages against all sorts of different pitchers, not all elite hurlers like Halladay. It also doesn’t account for the unique combinations that different pitchers utilize in a game. So while a hitter might appear to have an advantage over a pitcher when it comes to curveballs, one can imagine that things may be different when facing someone like Justin Verlander, whose fastball-curveball combination makes both pitches more difficult to handle.
Another caution is that, generally speaking, pitch type linear weights are more retrospective than prospective. More simply, they tell you what has happened, not what is going to happen in the future. The weights are not adjusted for park or defense, so there are many factors that could affect how hitters and pitchers will fare in the future that the previous values do not accurately represent. Did the hitter or pitcher switch parks? Has the defense behind a pitcher changed? What catcher is the pitcher throwing to? These are things we must think about when interpreting the values.
Even if the predictive value is less than with other stats, I think the picture it paints is interesting and adds a bit more information to consider when analyzing a match up. As I mentioned at the outset, this is brand new and definitely in “beta” form, so feedback is welcome.
Bill leads Predictive Modeling and Data Science consulting at Gallup. In his free time, he writes for The Hardball Times, speaks about baseball research and analytics, has consulted for a Major League Baseball team, and has appeared on MLB Network's Clubhouse Confidential as well as several MLB-produced documentaries. He is also the creator of the baseballr package for the R programming language. Along with Jeff Zimmerman, he won the 2013 SABR Analytics Research Award for Contemporary Analysis. Follow him on Twitter @BillPetti.