Dynamics of the system
My rankings are in effect, a “power rating” and it is possible to derive a projected point spread from them by subtracting ratings, dividing by three and adding three points to the home team, however, I’m not as concerned about predicting future outcomes as I am honoring what transpired most recently on the field of play. Let me give you a general example. If #35 Texas Tech beats #10 Texas (regardless of the score as margin of victory is not a consideration), and both teams have an identical record of 5-1, then my philosophy dictates the Red Raiders should be ranked ahead of Texas in my next poll, regardless of whether the odds are the Red Raiders would win again if they played the next week. The results may not hold true for more than one week, but that’s OK because if a team EARNED that position, they deserve the ranking, regardless of what happens in the next week of play. Ranking winning teams above losing teams is not always possible. It’s not logical to rank 1-5-0 # 110 Temple over 5-1-0 #21 Virginia Tech (as in the case of the Owls 28-24 win in 1998). I guess in a sense, my rankings are a combination of the “best team” and “most deserving” team.
Let’s start from the very beginning and move through the system using the data included, in order of its inclusion in the formula, and then detail each of the components.
#2- Accumulating points
#3- Strength of opponent
#4- Instituting deductions for losses
#5- Site of the game
#6- Instituting head to head rules
Starting Position- This is one of the most hotly debated subjects in rankings. Starting position DOES have an impact in rankings, but what I’ve done is create a system where the pre-season ranking is corrected early on (through strength of schedule and head to head results) so there is no undue advantage from the pre-season poll that remains past about the third week of September. I respect many different points of view here, ranging from creating a pre-season poll based on returning starters and media hype (as in the AP/Coaches), starting everyone equal (as in some computer polls), or having a starting position based on an average of 3-5 previous seasons (also in some computer models). I believe having a starting position is best, but starting everyone equal is not logical to me. We know through observation of past seasons that some teams are stronger than others. No disrespect to the Vandals, but in 2007 Idaho was not as strong a team as Texas. If we know this in advance, to a high degree of accuracy, then ranking Texas and Idaho equal is not only illogical, it is unfair to Texas and completely (in my mind) skews any hope of an accurate strength of schedule. In the past I kept teams in their earned rank positions from the end of one season to the beginning of the next. If a team finished #10 in 2007, they started #10 in 2008. For decades I felt it was the fairest way to establish a starting position. However, beginning in 2014 I began creating a pre-season ranking based on returning starters and coaching changes. It’s not so much that I changed my mind as it is the fact that enormous amounts of information are now available to me now through the internet, and being semi-retired, I have the time to devote to that process. I still adjust a team’s RATING to a standard point value that brings teams closer together, preventing an unfair advantage in points from one season to the next. Each year the #1 team starts at 270 points; #2, 269.250 points; #3, 268.500 points and so forth all the way to #128 starting at 174.150 points.
Accumulating Points- My system is the only one I am aware of that uses an “accumulating” value system. It was designed this way to emphasize a team’s most recent game as the AP and Coaches do. As a result, a team only gets credit for playing an opponent ONE TIME. Whatever happens to that opponent from that point forward is “water under the bridge.” Some would argue that not going back and “re-calculating” the opponent strength as the season progresses, and therefore not allowing for a more up to date opponent rank, is a mistake. I disagree. In order to balance out this potential issue a lower rate of value is given to teams as opponents in September. To give you an example, playing a #1 team as an opponent in September may be worth 7 points, whereas playing a #1 team in November may be worth 17 points. Using my method also prevents teams from being penalized later in the season if an opponent loses a star player to injury and falters greatly after being highly ranked. The greatest example I could ever use to defend this philosophy came in 2007 in a scenario involving Oregon. After beating #4 USC and #5 Arizona State on successive weekends the Ducks rose to #3 in the Billingsley Report (#3 AP, #3 Coaches), and USC, even in a loss received a high value for playing Oregon. But, after losing QB Dennis Dixon to injury, Oregon fell to Arizona, UCLA and Oregon State. Every time Oregon lost, USC suffered in some computer rankings because Oregon’s won loss record became mediocre. I don’t agree with that methodology. My contention is that the Trojans played an Oregon team that was playing some of the best football in the nation during those games and was truly a #3 team. Southern Cal should not have to suffer because of an injury that happened to Oregon after the fact. In my rankings it did not matter. USC went on to finish #3 in the Billingsley Report, #3 in the AP, and #3 in the Coaches Poll. Each week a team accumulates or “earns points” based on the current week’s opponent and nothing else. If a team has a bye week, their rating does not change, with two exceptions. A special rule is in place (in the head to head section) that allows an undefeated team to ALWAYS be ranked ahead of every opponent they have beaten, and allows any team experiencing a bye week to remain ahead of a team they had just beaten before their bye week.
Strength of opponent- This is another great topic of discussion. The value placed on the strength of an opponent is (as it should be) the core of most computer rankings. My system is unique in it’s calculation of strength of schedule as most models use wins and losses and I do not. I use an opponent’s RANK and RATING instead. Let me give you an example. In the 8th week of 2007 Washington posted a record of 2-4 while playing one of the most the nations most difficult schedules. Army recorded 3-4 while playing a milder schedule. By counting wins and losses (as the NCAA and some computers do) as a method of determining strength, Army would be given equal or slightly more value as an opponent. In my system Washington was ranked #44 and Army at # 109, therefore a team playing the Huskies would receive more than 3 times as much credit. I believe strongly this is a more accurate method of determining opponent strength. Wins and losses do not always tell the whole story.
Instituting deductions for losses- Remaining undefeated is paramount in my system. A team with no losses has, in effect, a “ticket to the top ten” as long as they are playing a reasonable schedule. With no losses a team receives “full earnings” of their “available opponent value”, but each loss creates a percentage of deduction. For instance, if Maryland is 5-0-0 playing a #35 opponent, and North Carolina is 4-1-0 playing a #35 opponent, the Terps will still receive more points that week than the Tar Heels because Maryland is undefeated. North Carolina will experience a penalty because of their one loss. However, if Maryland is playing a significantly lower opponent, say #70 Colorado State, then North Carolina, even with one loss, will receive more credit that week than the Terps because the sheer strength of schedule outweighs the loss on the Tar Heels record. Two losses create a larger handicap and so on. The only way for a team to overcome a loss is to beat higher ranked opposition.
Site of the game- I realize some computers do not take the site of the game into consideration, but I believe it is important. The reward, once again is slight, but it is still a consideration. I believe that playing at Tennessee in front of 106, 000 fans screaming Rocky Top is more difficult than playing in front of 15,000 at Rice stadium. There are some who say any form of measuring the value of the site of a game is biased, but I disagree. My scale is based on information available to the general public through the NCAA and is evaluated by stadium size and average attendance over a 5 year period. Rice plays in a 50,000 seat stadium, but only fills a portion of that to capacity, so playing at Rice is not as valuable as playing at some MAC teams who fill their smaller stadiums to capacity.
Instituting head to head rules- The most powerful part of the program states that if certain criteria is met in regards to wins, losses, ratings and rankings that the winner of a game will be ranked ahead of the loser in the next poll. These rules set me apart from most computer analyst. I realize that by instituting these rules the program basically creates a situation where it is not the best “power rating” system it could be. Winning teams will not always be able to maintain their most recent level of play, but again, I feel if they earned it, they deserve to be ranked higher over an opponent they have beaten. In 2008 East Carolina was a perfect example of that. The Pirates, as an underdog, beat Virginia Tech and West Virginia. They earned the right to be ranked ahead of both of those teams at the time. Rising to #8 in the Billingsley report and losing to North Carolina State the next week makes the system look like a failure but I defend East Carolina’s right to be ranked in the Top 10, even though it was temporary, based on what they accomplished on the field. In spite of any issues in the power rating, the system still holds an average 73% of higher ranked teams beating lower ranked opponents over its 45 year history. I also publish a system that uses margin of victory in its calculation. You will find it listed under Billingsley+ in the ranking section. It is more accurate as a predictive tool (76%), but not nearly as fair in head to head competition, which I feel is more important.
One final thought before I close. College Football enters a new dimension in 2014 with the new four team playoff format. As a long time devoted college fan I’m optimistic and hopeful that it is the right direction to go, but only time will tell. There are many concerns, particularly with the selection committee process which will choose four teams by secret ballot behind closed doors. I find it ironic that the BCS Commissioners pledged “total transparency” but instead we are left with a familiar stench of 1960’s back room deals. It’s no secret that I was a big fan of the BCS. I suppose the majority of fans who read this will feel it’s because I was part of the process. That is not the case. I would have been in favor of that format even if I had not been a participant because I believe strongly in what the BCS accomplished in college football. There would be no playoff today if the BCS had not come to pass. It was a necessary transition. The mission of the BCS was clear; create a set of rules and match the #1 and #2 teams in a championship game. I’ve been a fan of college football for 50 years. I lived through the days of bowl game participants being determined sometimes weeks before the regular season was complete. I’ve lived through seasons where the top teams could not be matched in a game because of conference ties to specific bowl games. What a tragedy it was that we could not witness Ohio State and Penn State in 1968, Texas/ Penn State in 1969, Georgia Tech/Colorado in 1990; Miami/Washington in 1991 or Nebraska/ Michigan in 1997 just to mention a few. Thanks to the 16 years of the BCS we no longer had to deal with “mythical” national championships. This is an evolving sport. At some point we will see an 8 team playoff and the controversy will be just as great as the four team format will be, and the BCS was before that.