If game results were independent, yes. But lots of things in baseball aren't independent: teams play the same opponent repeatedly in short series, runs of home or away series occur, pitchers rotate, players get injured, teams may slack when overperforming or intensify their efforts to avoid extended losing streaks, etc.
Most of all: baseballers are fairly superstitious. They believe in streaks, lucky rituals, jinxes, and the gamblet's fallacy (being 'due' for a win or a loss). So some serial correlation could be a self-fulfilling prophesy.
Still, I'd expect tons of other available team stats to outperform the last N game results in predictive power.
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? ;) ).
Playing hard has very little to do with winning in baseball - oh, I didn't try hard so I didn't hit the ball? Or didn't take a walk? Or didn't watch the ball and it hit me in the head?
I pitched 5 mph less due to mental effort? Well, now you don't get to throw again for 5 days. Or you walked 15 batters in a row to try and get taken out of the game. It's not going to make your night easier, you sit there till the game is finally over. No clock to run out. Just outs.
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.
This is like saying I've accounted for the phase of the moon when predicting my productivity, but in the future I'd like to account for how much sleep I got and whether I've been eating right and exercising.
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).
Most of all: baseballers are fairly superstitious. They believe in streaks, lucky rituals, jinxes, and the gamblet's fallacy (being 'due' for a win or a loss). So some serial correlation could be a self-fulfilling prophesy.
Still, I'd expect tons of other available team stats to outperform the last N game results in predictive power.