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Posted
I think geocities is angry that you used their bandwidth (i.e. the site is blocked due to excess data transfer...I would imagine it's so highly limited it probably allowed a total of 10 views or so)
Posted

Question: Are you submitting this analysis for college credit, or was this just for fun? If it's the former, I just wanted to note that the graphs in the appendix come up very blurry for me, such that the axis labels are completely unreadable.

 

Also, I found it odd that the graphs with the least variance between models appeared to be Boston, LA and Cleveland. Curious if that's merely coincidence or if there was some correlation there between quality of team and consistency of the model.

Posted

Former. The graphs suck, I know. Luckily in the final version someone else is doing the graphs. I'm a LaTex n00b. I couldn't get it to work well. I would assume that the consistency is because that it's a lot harder, statistically, for a good pitching team to go on a prolonged losing streak that causes variability.

 

Think of a the probability of rolling a die and getting a six. What's the probability of getting a 6 on one roll? 1/6. Does that mean that you will get a 6 at least once on six roles? No. In fact there is still a 1/3 chance of you not getting a six at all. I think it's the same thing here because for these teams giving up runs is such a low probability event compared to the rest of the leagues.

Posted

Meph, I think you are missing a "not" in a sentence in your second to last paragraph.

 

In this method, all teams were assumed to have identical home field

advantages, but each park may have characteristics that amplify certain aspects of the game. For instance,

dense air and large park dimensions in San Diego is conducive to home runs.

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