A question that every baseball fan has asked at one time or another.
How valuable is a baseball pitch in context of a baseball game?
How does Clayton Kershaw’s curveball relate to Andrew Miller’s slider?
If you could only have one which one, would you prefer?
So many questions, but thankfully through the power of mathematics, we can answer many of these questions.
As with the other pieces in the Sabermetrics Glossary section of the website. We will first define what pitch value is, then break down how to calculate it.
The piece will then serve as a backbone for other articles, such as who has the most valuable curveball? Stay tuned for those pieces down the road.
What is Pitch Value?
Each situation in a ball game has something called run-expectancy.
Or how many runs on average are expected to occur during a certain situation.
That sounds like a lot, but we will break it down for you.
Back in the mid 90’s David Nichols analyzed nearly 10 years of data to help determine how many runs are expected to be scored per situation.
Imagine nobody on, nobody out, and a 0-0 count.
Nichols concluded that on average (based on data from 1984 to 1994), we would expect 0.49 runs per inning.
If the batter was to get on, the run expectancy would increase to 0.88 runs per innings.
These values were researched extensively by many baseball and mathematical minds, far greater than mine.
However, more recently these studious individuals expanded the run expectancy (per situation) calculations to per pitch (pitch value).
They figured out how to break it down down per count, per situation, and per batter in the lineup.
We will not bore you with the specifics of how run expectancy is calculated, as luckily, we have computers to run these analytics.
However, we will provide you a contextual example.
How to Calculate Pitch Value
Like we noted, pitch value utilizes run expectancy values and expands upon them contextually.
Let’s pull an example explained by FanGraphs to further illustrate the point.
Imagine a 1-1 count.
The run expectancy value sits at -0.02.
If the batter singles, the run expectancy increases to 0.45.
This means the cumulative run expectancy for the batter is calculated by taking the magnitude between the two without signs.
0.02 plus 0.45, which equals 0.47.
The batter, since the hit is a positive outcome, receives a +0.47.
The pitcher, allowing the hit, will get a corresponding value with opposite sign, or -0.47 run expectancy.
More easily understood for pitchers a -0.47 pitch value.
This process is then repeated for each time a particular pitch is thrown in a particular situation.
So if the 1-1 pitch was a fastball and the batter singled, the pitcher would receive a negative 0.47 for that pitch.
Simple in one situation, but repeated across every ball thrown across every game?
Good things computers exist.
Now I know this article is shorter than what you have grown to expect from us here at Innings Pitched, but we promise that there are more detailed analyses to come.
This article just serves as a stepping stone for the future. Stay tuned!
As a side note the past week has been hectic with a little one on the way in a few short months!