Dr. Green and White Sports Authority

Last week, I made a post looking back at last year's football season, specifically at my picks last summer and how those projections played out.  What I found surprised me a bit. As it turns out, my probability-based predictions, specifically what I refer to as the "win distribution matrix" for each team were mathematically pretty accurate.  Essentially, if I predicted a certain team had only a 10% chance of winning "x" number of games, that wound up being true for about one out of 10 teams.  In other words, the math checked out.  Furthermore, in my conference-by-conference analysis, I did a fairly good job of predicting what would happen, and when my predictions failed, I could usually trace it back to a single game here or there or a team or two that was either way worse or way better than expected.  Finally, I only got 6 of the 12 NY6 participants correct, but again the reasons why were clear.  And so, with this all in mind, it is time to once again look for

It is once again that time of year.  Your neighbors finally used up all of their discount fireworks, the mercury has risen into the 90s, the talking heads on the TV are desperately trying to convince you that NBA free agency is important, and (most importantly) the preseason college football magazines have finally started to hit the shelves.  In continuing what is now my annual tradition, I have inputted all of this preseason football schedule and ranking data into a spreadsheet, and I have run several simulations on the upcoming season.  I would like to share with you want I have found. I can (and have in the past) spend several paragraphs explaining my methodology.  If you are curious about the details, more information is found here .  But, the basic idea is that over the years I have developed power rankings and algorithms that allow me to predict point spreads and victory probabilities for each game as well as simulate the final standings in each conference based on a reasonable