Pitch run values have been around for a while. When you assign the run value to a pitch, two factors contribute to the final number: the outcome of the pitch and the count on the batter before the pitch was thrown.
As John Walsh showed when he first introduced pitch run values, the difference between a strike and a ball is much higher on a full count (-.349 vs .271) than on the first pitch (-.044 vs .038). So the pitch count is already factored in the equation and we can forget about it, right?
Not so fast.
You probably remember that many times our (now Tampa Bay Rays’) Josh Kalk, when presenting the most effective pitches or the most lethal pitch combinations, specified that he had adjusted run values for the pitch count. And surely, you have read, in the comment section of an article, MGL criticizing the author for not having adjusted for pitch count.
What’s happening? Haven’t we already accounted for pitch count? A strike on 1-0 has a value of -.053 runs, while it’s -.062 on 0-1. Why does the need for an adjustment resurface?
Let’s go graphical.
A slider is thrown by a right-handed pitcher at the location shown above to a right-handed batter. What’s the expected run value of such a pitch? Here we are oversimplifying, pretending that only the location influences the effectiveness of the pitch, while Jeremy Greenhouse at Baseball Analyst has proposed a more advanced model that makes run value dependent on location, movement and speed.
Here’s the average run value of a slider (from a righty to a righty) according to its location (data MLB 08/09).
The hypothesized pitch, still visible on the chart, will produce on average -0.008 runs.
What happens if we calculate the expected value for the same pitch on a 1-0 count versus a 0-1 count?
Something is counterintuitive when comparing the pair of charts above: Batters fare better on sliders down the middle when they are behind 0-1. The possible explanation is that when they’re ahead 1-0, hitters aren’t sitting on the slider (or they are simply waiting for an easier pitch to hit), thus the outcome is usually a strike (-0.053 runs). On the contrary, on a 0-1 count, they can’t afford to fall behind 0-2, thus they swing at sliders clearly in the zone with moderate success.
However, our pitch is expected to produce 0.017 runs if delivered on 1-0, -0.016 runs on 0-1.
You surely don’t need next chart to know what’s going on, but let me show it just to confirm what everyone is expecting.
Hitters expand their zone when they fall behind (the 50 percent swing zone on 0-1 has an area two and a half times greater than on 1-0), thus swinging at pitches that are harder to reach or to make good contact with.
Add to the mix that a pitcher who is ahead tries to exploit the expanded strike zone of the batter (look below), and that a batter who is sitting on a favorable count can afford to let go a pitch he doesn’t like, and you are back to the run value charts shown above.
Let’s now make up an extreme example. Suppose two identical pitchers exist. They both have an average fastball and a very peculiar slider: that slider always nails the location we have used insofar.
Now, Pitcher A throws the slider only on 1-0 counts, while Pitcher B delivers it only when on 0-1. Using run values unadjusted by pitch count would show that Pitcher B’s slider is better than Pitcher A’s, while the only difference is in the pitch selection.
Thus, while pitch count is already factored in the calculation of pitch run values, we can’t let it out of our analyses, especially when evaluating effectiveness of pitches/pitch combinations.