Howdy!

If I have anything to add to the global conversation about the Red Sox, it's this: I am an inveterate pitch counter with a Bayesian / Predictive Modeling background. Hopefully that will provide some entertainment...

What is a Bayesian approach to pitch counting? Well it starts like this. Before the first pitch is thrown, I make two assumptions:

1) The average starter pitcher lobs 100 pitches over the plate over course of a game. The best pitchers will go over that -- even into the 120s. A pitcher having a bad day (even a star) will often fall short.

2) The average inning takes about 15 pitches. 105 pitches, on average, should take you through the 7th. Good innings go under 15 pitches. Bad innings can be interminable. If you only get 100 pitches (on average) to start with, throwing 30 pitches in the first inning means you've just lost the 7th inning.

The following observation is also important:

The middle relief staff is the weak underbelly of any pitching staff. The more innings they have to pitch the higher the probability of the other team winning.

So, with that in mind, every single pitch changes the probabilities of the outcome of the game. If the pitcher gets through the 1st on just 12 pitches, he has increased the probability of making it through the 7th (and dare I say 8th?). But it runs deeper than that. Throwing a ball on the first chance increases the expected total pitches in that at bat. It also increases the probability that future strikes will get hit into play, and maybe even fall for hits.

As I watch games, I take note of how the game hinges on these individual pitches, and that's what I intend to share.

Caveats

Before I begin, I should add two caveats. the first is that, I'm a busy guy. This means that I don't have time to test my theories quantitatively. If someone already has, I'm always happy to change my theories when faced with evidence to the contrary. This also means that I only watch Red Sox games. And "watch" should be in quotes -- I don't own a TV. I follow games on mlb.com. So I only learn from those pitchers I get to see, which can be limiting.

Second, I've toyed with blogs before. I offer no promises. Maybe this one will last as long as some of the last (2 of which survived more than a year). Maybe I won't make it through the season. If you like what you see, feel free to leave me a comment. Like every pitch, every comment influences the probability of future outcomes.

Cheers,
jg