**Predicting Baseball Game Outcomes**"It's tough to make predictions, especially about the future."

— Yogi Bera, Apocryphal

If you're following me on twitter (h/t to you,@moneySpamBot2123!) or have been glancing at my periodic predictions here on G+, you'll notice that most of the time I'm so darn

*wrong.*But occasionally, I'm on the money. What's up with that?

Well, after much examination of the matter, it appears that the problem boils down to

**not accounting for the pitcher's abilities.** Overall, in a batter-pitcher interaction, roughly 40% of the outcome is determined by the pitcher. (Alright, well, 39.78%, but who's counting?)

So if we have a bad pitcher (e.g., Ervin Sanatana for the Minnesota Twins returning after being banned for 80 games), then the outcome will not resemble the prediction at all (FWIW, I predicted it would be Twins 5 vs Cincinnati 4 --- it turned out to be 4-17...I was close!).

I'm still thinking about how to adequately represent this, because a naive Markov chain won't do anymore. The random variation in the pitcher's ability is a

*real* effect (as I study in this post). I'm going to have to sit down, and think carefully about how to model these interactions.

The other low-hanging fruit was determining the lineup. But this turns out to be fairly easy to predict, since it doesn't change too much game-to-game.

http://pqnelson.github.io/2015/08/17/lineups-and-pitchers.html