How I'm Predicting Baseball Outcomes

see also: http://www.baseball-reference.com/about/wpa.shtml

My bet is that your model is no better than (weighted) chance to predict the next game. Just run the model on the years before 2012 and you'll see what I mean.

Sort of but not exactly. In this case a prior outcome is generally caused by the same factors that would influence the next outcome. There are also factors in baseball that make putting together a long streak harder the deeper you get into it.

That said, there are a gazillion other data points that are more granular and would provide more predictive value than simply the binary result of prior games (especially if those games took place 12+ months ago).

I understand where you're coming from. My friend's statistics teacher said that if a flipped coin results in 5 heads, it doesn't owe you a tails – this seems to support the gambler's fallacy. Though looking at something like running: say I run the first mile in a race in 7 minutes. Odds are the next mile will be a bit slower given I'm bad at pacing myself and I'm now tired. This is an extreme example, but I've been trying to look at baseball with this approach, that prior games influence the outcome of the next game. And I know that I am simplifying my prediction by just accounting for streaks. I would love to lengthen the script to look at factors like how much the team has won/lost by, who's on the lineup, where the game is (home versus away), etc.

But at its least this was good coding practice.

If I remember correctly, the gambler's fallacy is for fully independent events. I think the assumption here is that the team's performance in a game will impact their next result. After a win, they get on a roll. After a loss, the coach gives them a stern talking to and they come out playing a bit harder the next night. There's some correlation between games, but I'd say it's mostly independent...

I'm not a statistician, so I can't really speak to how 'valid' the analysis is, but I'd be curious to see how it does in different tests--unless I'm misinterpreting, the biggest check so far was done on 2012, which is exactly what was used to train it. It would be interesting to see what happens if you train with half of 2012 and test the second half. Or check 2011 (do you predict an end-of-the-year collapse, allowing my Cardinals to sneak in again? ;) ).