January 24, 2017
Let's Dig Into These Tunnels
With the release of BP’s new data on pitcher tunnels, the idea is that one way a pitcher can fool the hitter is to make sure that his pitches all look the same as they fly through the air toward the batter, until they get to the point where the batter has to make his decision of whether he will swing, and if so, where he will aim his bat. If a pitcher can keep all the pitches going through the same “tunnel” and have the break (or lack thereof) be a surprise that reveals itself only after the batter has started his swing, then he’s going to get more swings and misses and weakly hit balls.
With a little bit of math and physics, we can reverse engineer the flight path of every pitch thrown in the major leagues over the past nine years. And by doing that we can see some interesting things about pitchers. But before we freak out about these numbers, we need to ask some more basic questions about them, starting with “what do they really tell us about a pitcher?”
We need to establish them as a reasonably reliable reflection of a pitcher and his talent, and not just some numbers that end up being as random as ice cream bones. In other words, we need to do some #GoryMath.
Warning! Gory Mathematical Details Ahead!
The first thing that we need to do is establish reliability, which is the idea that a measure of something is stable over time. For example, we are comfortable labeling certain hitters as “power hitters” because hitters tend to hit home runs at roughly the same rate in one year as the next. It’s not exact, of course, but the leaderboards from one year to the next tend to have the same names on them. So, we assume that a player’s home run total is actually a reflection of some talent that he has, and not simply the result of randomness.
There are two types of numbers that we have released so far. One looks at all pitches that a pitcher threw during a given year and another looks at specific pitch sequences (i.e., fastball, then changeup) for a pitcher. We have calculated several indicators from this data, which are explained elsewhere, but to recap: