if you seek an image of the most Wisconsin OL ever, enter here
Apologies to the Mathlete if this constitutes stepping on his turf.
Last year's Michigan roster had a very high percentage of first- and second-year players. I looked back at the past 16 seasons and compared under vs. upper classmen on the roster and mapped it with the team's winning percentage.
It's not airtight but I think there are some trends worth observing:
We've only had one really good year with an underclassmen-heavy roster, and that was the year with fifth-year senior Tom Brady under center.
Other other two "really good years" had an upperclassmen majority and followed a year where the opposite was true.
Noticeable year-to-year shifts in underclassmen:upperclassmen tend to go along with similar shifts in winning percentage. When the percentage of upperclassmen dives, so does win %, and generally vice versa.
It suggests that this year will be better than last year...the huge sophomore class jumps to upperclass status, we've got a small freshman class and only 1-2 of them will be counted on for production (Peppers, possibly Canteen and Cole), and a fifth-year senior QB.
Due to all of the debate regarding the Wisconsin game and the quality of the 2010 offense in general, I've been thinking about stats a fair bit. Thus, I went to find out some more regarding FEI calculation- I ended up not finding the information that I needed so I emailed Brian Fremeau to see if he can provide some illumination (although I believe the actual formula he uses is proprietary so I don't expect to learn too much).
The functional end result is that I've become curious about how people such as Brian Fremeau and others that create advanced stats based on play-by-play or drive-by-drive data are able to collect their data.
The NCAA team reports have game-by-game play-by-play data, but extracting the necessary information from them seems difficult since it's all text based. I'm guessing that it just looks complicated to me since I'm not a CS or CE person. But, I'm still interested in how the data is extracted.
So, if there a better site than the NCAA team reports to get play-by-play data to extract and distill down into the necessary components (pass, rush, yards, player(s), etc.) or is the NCAA site the best and it just takes some coding to make it work efficiently?
I wonder what kind of advanced stats the MGoCommunity could come up with access to years worth of distilled data from every team in the country...