Well, it all started back in my early college days in 1979 while I was earning my Bachelor's Degree in Applied Math at Georgia Tech, when I first really noticed that the college football national champion was chosen by a very arbitrary method. I knew there had to be a better way, a way that would be more objective and less subjective. As luck would have it, it just so happened that at that time I played tournament chess. The United States Chess Federation uses a rating system developed by chemist/statistician/computer consultant/musician Arpad Elo that bases its ratings on the difference between the two opponents previous ratings and the possible outcome of the game (win, lose, draw). I decided that the same could be done for college football since the results are the same, so I analyzed the Elo rating system and found that if you plotted the amount a player's rating changed vs the rating of his opponent, those plots for each result very much fit an inverse tangent curve (with the proper scaling and constants added). That knowledge in hand, I designed my first system. That first system, and in actuality even the system today that it evolved into, was designed first and foremost for one goal: to give each team an objective rating at the end of the season. That rating was based on an equation that used an inverse tangent function along with the rating difference between the two teams going into a game to produce new ratings for each team based on the result of the game. Since then I've been tinkering with the formula quite a bit, so much so that it doesn't resemble the original all that much anymore. In fact, I even went so far as to replace that inverse tangent equation a few years ago with a more linear one when I decided to simplify the system a bit and make it an iterative process instead of a straight plug-and-chug equation. I also have included some obvious factors in computing a rating that chess doesn't. For example, the Elo system doesn't distinguish between a player losing by getting crushed or a player barely losing, but in college football the score is somewhat (repeat, somewhat) indicative of that distinction, so I added in a graduated factor for the score difference, making sure to limit it in case of blowouts of course, so that teams that barely lose get more credit than teams that get blown out. Also, the Elo system doesn't distinguish between who has the white pieces (the first move advantage), whereas in college football the team that is at home (the home field advantage is somewhat akin to the first move advantage) has a significant advantage, so I incorporated that into my system. There are some other factors, but I don't want to bore you with too many details. But basically that's the how and where my system got started. Over time, people asked me if I could produce intermediate results during the season, so that they could see what my system's ratings were after each week. While the system wasn't designed to do that, I modified it a bit so that it would be able to take the limited amount of data available during the season and produce a reasonable rating for each team. Hence the move to the iterative process ... otherwise, having only a few games for each team wouldn't allow enough movement to be really indicative of a team that had gotten much better or much worse since the previous season. The 1996 Northwestern team was a prime example. Without the iterative process, it would have taken all season for them to rise from their starting point (their very low final rating from the previous season) to where they truly belonged in 1996. With the iterative process, it took only a few weeks. Also, people asked if my system could predict point spreads for each game. Now that definitely wasn't something I even dreamed of trying to do with this system, but after comparing the difference between the ratings of several sets of two opposing teams to the point spreads out of Vegas, I started to notice that, with proper scaling and adjustment for such factors as home/away, turf/grass, etc, I could come up with an equation based on two teams' ratings that would give something reasonably close to the Vegas line for most matchups, and occasionally a surprise disagreement with Vegas when my rating for a team differs from what most other experts think. And that's the predictions you see posted today on my Web page. Remember there are no guarantees as to the accuracy of the predictions, so you are at your own risk if you use them for anything beyond just looking at them for your own enjoyment and amusement. I don't keep track of how correct those predictions have been, either against the spread or straight up, so don't ask ... I only post them for fun. There are others on the Internet that do track the results though, so look for them if you're interested. It's interesting to watch the numbers change over the course of a season. As more games are played, more data is accumulated, and the numbers get more accurate as to the true strength of each team. By the same token, sometimes early in the season you may see a team's rating be different by quite a large margin from what most people would think it would be or from what it ends up at the end of the season. Such is the way with computer ratings ... they are data-dependent, so trying to come up with a valid number early in the season when there isn't much data sometimes produces humorous results. I've found that by about the 5th week of the season, there is enough data in place to have fairly reasonable results. Finally, there's always the question of what rating does a team start with. I gave up years ago trying to guess how much a team's rating should be adjusted at the start of a season based on what players left or graduated from the team, what players returned or joined, or what coaches came and went. My guesses were as often wrong as they were right. So now I just start with my final ratings from the previous season, adjusted for the addition of new teams to or deletion of old teams from the I-A level, and let the teams results during the season move their ratings up and down accordingly. If a team is much better or worse than the previous season, the rating's iterative process will reflect that soon enough once a few games makes it apparent. Anyway, that's the scoop. Enjoy the numbers.