Baseball fan in me says that is correct but statistician in me would like to see more models like yours to quantify it.
How I'm Predicting Baseball Outcomes
11–20 of 45 posts
Re: How I'm Predicting Baseball Outcomes
#12What, this is just streak-based? Do streaks even exist in baseball?
Re: How I'm Predicting Baseball Outcomes
#13You might also be interested in checking out the book The Signal and The Noise by Nate Silver (He runs the FiveThirtyEight political/data blog that notably predicted last year's election results with great accuracy.)
Re: How I'm Predicting Baseball Outcomes
#14Isn't this an example of the gambler's fallacy, that previous outcomes impact future outcomes?
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.
Re: How I'm Predicting Baseball Outcomes
#15Rather than waiting for data from future games, you should backtest on old data and see how it performs ( http://en.wikipedia.org/wiki/Backtesting ). You might also be interested in checking out the book The Signal and The Noise by Nate Silver (He runs the FiveThirtyEight political/data blog that notably predicted last year's election results with great accuracy.)
I actually just started that book and so far so excellent.
Re: How I'm Predicting Baseball Outcomes
#16What, this is just streak-based? Do streaks even exist in baseball?
yeah for now... They're most definitely a thing, though I have a documents worth of baseball elements I hope to incorporate
jerf@jerfhom:~$ python
Python 2.7.3 (default, Sep 26 2012, 21:51:14)
[GCC 4.7.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import random
>>> 94.0/(94+68)
0.5802469135802469
>>> winp = 94.0/(94+68)
>>> games = []
>>> for x in range(50):
... games.append('w' if random.random() >> ''.join(games)
'wLwLwwwwwLwLwLwwwLLLwLwwLwLLLwwLwwwwwLwwwLwwLLwLLw'
In my full simulation of 162 games, the longest streak was a 7 game losing streak, despite the higher win percentage. Of course you'll get different results each run; my next run produced a 9 game winning streak, which some quick Googling suggests is in line with what happened in 2010.Combine this with the fact that real play is not drawn uniformly (you may play a much worse team against which you have a much better win percentage for several games in a row) and I don't see much need for some sort of meaningful, statistically-predictive "streak" to explain game results.
Re: How I'm Predicting Baseball Outcomes
#17Earlier quoted context omitted.
yeah for now... They're most definitely a thing, though I have a documents worth of baseball elements I hope to incorporate
Are they? jerf@jerfhom:~$ python Python 2.7.3 (default, Sep 26 2012, 21:51:14) [GCC 4.7.2] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import random >>> 94.0/(94+68) 0.5802469135802469 >>> winp = 94.0/(94+68) >>> games = [] >>> for x in range(50): ... games.append('w' if random.random() >> ''.join(games) 'wLwLwwwwwLwLwLwwwLLLwLwwLwLLLwwLwwwwwLwwwLwwLLwLLw' In my full simulatio…
And I understand exactly where you're coming from. This is very preliminary, and if anything it was good coding practice for me. Though I very much intend to incorporate more significant factors like the lineup, the opposing team, and their history.
Re: How I'm Predicting Baseball Outcomes
#18Rather than waiting for data from future games, you should backtest on old data and see how it performs ( http://en.wikipedia.org/wiki/Backtesting ). You might also be interested in checking out the book The Signal and The Noise by Nate Silver (He runs the FiveThirtyEight political/data blog that notably predicted last year's election results with great accuracy.)