So, I'm a scientist/engineer by trade, and I'd like to start mining all data that's generated from the game to do some analysis.  Particularly, I'd like to compare ratings to actual statistics to see what stats are driven the most by which ratings. 

Now, you could do this by simply copying and pasting into spreadsheets a whole bunch, but that's very tedious.  Is anybody familiar enough with web programming to know how to do this more efficiently?  I'm playing around with the Web Import Data functions in Excel, and I'm making some progress.  Has anyone else tried to do anything like this? Hopefully I'm not re-inventing the wheel and someone can help me. I'd be happy to help in development, but web programming isn't my strong suit.  I do more C++ and Matlab stuff for work.

There's someone that does this for GID, I think his site name is Bhazlewood. He  runs this site:

http://www.gdreports.com/

Posted by nauds3000 on 2/4/2012 1:40:00 AM (view original):
There's someone that does this for GID, I think his site name is Bhazlewood. He  runs this site:

http://www.gdreports.com/

this is correct. Send yatzr a site mail too. They both are very good at this.

I recently started doing a lot of analysis using sabermetric formula's and theories. I went to www.fangraphs.com to learn more about wOBA, WAR, and FIP. I then matched up a lot of this information with real players from www.baseballreference.com. It's taught me quite a bit. Once you start to see certain patterns show up, you can start to find value and make better decisions on signing, trading, and promoting players. I used this in my new league (Kinsella) and in my established league (Minors) and now I can see why my offense always sucked so bad in Minors and how I can be successful in Kinsella. The league has been making very positive comments on the trades I've been able to pull off.