Exploring Pitching Tendencies

When discussing why a certain pitcher is effective, baseball people will most often cite stuff and command. Since these two attributes are easily detectable from simply watching the game, it’s understandable why they are so heavily cited. One thing that is often overlooked in understanding the successes or failures of pitching is how pitcher’s attack hitters. Turn on any game and you’ll undoubtedly hear an announcer say something like, “I’ll tell you what Jim, that high and tight fastball is setting up a slider down and away,” or “that changeup slowed his bat down, let’s see him try and zip a fastball by him.” It always struck me as odd that if the guidelines for selecting what pitch to throw were that standardized, then I can hardly see how it would be a wise choice to use those guidelines. Outside of facing Mariano Rivera, if a hitter knows what pitch is coming, it is a distinct advantage.

One can approach this subject from dozens of different angles. The first question I set out to answer is: Do pitchers exhibit any obvious tendencies to use a certain pitch based on what they previously threw?

The most basic way to answer this question is to look at the distribution of pitches that immediately follows each individual pitch type. If “guidelines” for pitch selection did not exist, then we might expect that the distribution following each pitch type would match the distribution of total pitches thrown. For example, Justin Verlander threw 57% fastballs, 18% curveballs, 16% changeups and 8% sliders this year. If Verlander did not show tendencies in how he selected a pitch based on his previous pitch, then the distribution of pitches thrown after a fastball should match the 57%/18%/16%/8% distribution that Verlander exhibited over the full season. The same would hold true for pitches after curveballs, changeups, and sliders.

I looked at every pitch thrown by a pitcher with at least 100 innings in 2011 (excluding Tim Wakefield and R.A. Dickey) and tallied the individual percentages of this two-pitch window within each at bat. To be clear, this means that the first pitch of a new at bat was not attributed to the last pitch of the previous at bat. This is meaningful because we don’t want to introduce bias from facing two different batters. Since Pitch f/x is not perfect in its pitch recognition, I grouped all pitches into four categories to ease the amount of error. Four-seamers, two-seamers and sinkers were classified as fastballs. Screwballs and knucklecurves were grouped with regular curveballs. Splitters and forkballs were combined into changeups. Finally, cutters were grouped with sliders. I also corrected for pitch outs and other intentional balls.

The chart above shows these four groups of pitch types clustered together and the percentages at which they were thrown. The color of the bar inside of each cluster indicates when that pitch type was thrown. For example, the dark blue bars show the percentages of the associated pitch type being thrown over the entire season and the red bars show the percentages of that type being thrown after fastballs. If pitchers did not show tendencies based on the pitch they had just thrown, what we’d see is that each bar in each cluster would be of comparable height. What we do see is that if pitchers threw a slider, curveball, or changeup, they were about twice as likely to throw another slider, curveball or changeup, respectively, than under any other circumstance. To put it another way, pitchers really like to double-up on their breaking ball and off-speed offerings.

My best explanation of this would be that hitters are generally told that pitchers will mix up their pitch selection. Since pitchers know this, they try to exploit it by repeating the same off-speed offering that hitters have been trained to believe is rare. How would you interpret these results?

In following posts, I’ll take a look at the most interesting individual cases of this group of pitchers studied and see how different approaches relate to performance.


The data above can be conceptualized as if every pitch in the study was thrown by one pitcher. This means that each pitcher represents just a section of the conglomerate pitcher’s season. Perhaps a better way to represent the data is shown below.










These plots were generated by taking each individual pitcher and finding the ratio between the percentage of a certain pitch he threw after the designated pitch and the percentage at which that pitch was thrown over his entire season. Referencing the example above, if Verlander’s distribution of pitches after fastballs matched the 57%/18%/16%/8% distribution he showed over the entire season, then the equivalent ratios for pitches after fastballs would be one for fastballs, one for sliders, one for curveballs and one for changeups. Therefore, the higher the ratio, the more frequently that pitch is thrown after the designated pitch. Each pitcher’s respective ratios were clustered by pitch and plotted in the same pitcher order.

If every pitcher showed identical distributions regardless of what he previously threw, all of the bars on each plot would be one. Again, we see that the majority of pitchers favor doubling-up on their off-speed offerings and show few tendencies to favor a pitch when working off a fastball.

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From this chart we can clearly see that throwing batters consecutive sliders is equivalent to flipping them the bird.