The sophisticated game of college football we know today scarcely resembles that first game played in 1869. Those games were brutal by some accounts, and one dimensional by today's standards. In the early years teams just lined up and used brute strength to move the ball forward. Today we have complex offensive and defensive schemes that make the mental part of the game just as important as the physical. But the simplicity of the game in the pioneer era of the sport was not without controversy. Like determining a national champion for instance. In 1869 there were two games played, Rutgers beat Princeton 6-4 and in a rematch Princeton beat Rutgers 8-0. So, who do you think should have been crowned as the inaugural national champion? As you can see, things are not always as simple as they seem.

The popularity of college football spread widely in the early 1900’s. What began in 1869 with two teams grew to 98 major teams by 1920. The NCAA was founded in 1906 to regulate the sport and points for scores, size of field, and penalties etc were all standardized by 1912. But the NCAA failed to address the one issue that burned in the hearts and minds of players, alumni, and fans of all ages…the question of “who is #1”. Perhaps if they had addressed it 140 plus years ago we would not have the controversy we have today. Instead we have a plethora of polls and ranking systems which don’t always agree.

The History of Polls

The first widely recognized college football poll did not originate until 1926. It was a mathematical rating system developed by Frank Dickinson, a professor of economics at the University of Illinois. Later, an onslaught of pollsters came onto the scene, all prepared to crown college football’s best. The list was staggering; 1927 Deke Houlgate; 1929, Dick Dunkel; 1930, William Boand; 1932 Paul Williamson; 1934 Edward Litkenhouse; and in 1935, Richard Poling. Mathematical systems were considered to be the “norm” for determining national championships in those days. But all of that changed in 1936 when the Associated Press (AP) began publishing a poll voted on by a national board of sportswriters and broadcasters. Because of their national distribution, the AP poll instantly became gospel. The United Press International (UPI) joined the hoopla in 1950. Their theory I suppose was that coaches know more about football than writers and broadcasters.

It was bound to happen sooner or later, but it wasn’t until 1954 that the AP and UPI disagreed on who the national champion should be. The AP chose Ohio State and the UPI favored UCLA. Both teams were undefeated as was Oklahoma. Ever since that fateful day when the two “biggies” couldn’t agree, the controversy of “Who’s Number One” has raged on from the Golden Dome to the Tiger Den, from the Coliseum to the Swamp, from Happy Valley to Death Valley, and everywhere in between. Eventually everyone got in on the action, from the New York Times, Football News, Sports Illustrated, Sporting News, Sears, and McDonalds. Heck fire, there are more polls than bowls and God knows we’ve got more than we need of both. Over the years there have been many fine rating systems developed, and with the advent of the internet you may easily access all of them simply by clicking a button. Check out David Wilson’s Library of College Football Polls.

In the summer of 2008 college football lost a great pollster, historian and wonderful human being. Herman Matthews died on August 22, 2008 at his home in Middlesboro, Kentucky. Herman was part of the BCS from 1999-2001, but politely resigned as the BCS moved towards a no margin of victory status. The Matthews Grid Ratings were a staple in college football from 1966-2007. He appeared regularly in the Football News and later provided rankings for the Scripps-Howard news service. He was a good friend and will be greatly missed.

The Birth of the Billingsley Report

The first thing I want to say is that my ranking system is not “better” than any other computer system formerly used in the BCS. It is certainly unique in its design, and I’m proud to say that it is very widely accepted and praised. ESPN used my computer rankings in their “College Football Encyclopedia” listing the results right next to the AP and Coaches Polls as “official” National Champions, but that doesn’t make the system itself any better than many you will find. I have tons of respect for my former BCS counterparts; Jeff Sagarin, Jeff Anderson, Chris Hester, Wes Colley, Kenneth Massey, and Peter Wolfe who are all light years ahead of my mathematical skills. But my system is not about mathematical algorithms. It’s about rules created to compliment a common sense human response to a football game. The BCS computer pollsters come from different perspectives, and we all believe strongly in our positions, but we all have a healthy respect for one another. I would argue their right to stand up for their position as much as I would my own. I’m proud to call them my friends. We all have stories to tell, this just happens to be mine.

I became an avid poll watcher in 1966 and very quickly became disillusioned in the AP and UPI polls. At times I wondered if the coaches and sportswriters were even voting in response to the same games I watched, seemingly paying no attention to the strength of an opponent. In 1967 I embarked on the path of creating a mathematical formula. I was 16 years old. My goal was to create a ranking that was unbiased, logical, focused on strength of schedule and was fair to teams in regards to head to head competition. Two years later I had a blueprint in hand, the core of which I still use today. My first ranking was released in 1970. I created what I often refer to as “an improved AP” in the sense my rankings react like human voters, moving teams up and down based on the most recent week’s performance, but does it without the inherent human biases. My rankings are unlike any other you will encounter because the formula consists of a series of checks and balances in regards to wins, losses, power ratings, and team rankings. I know that sounds like a bunch of mumbo jumbo so along the way I’ll provide some examples to help explain what that means. There is one thing for sure, you’ll either agree with my methodology and love the Billingsley Report, or you’ll hate it, there seems to be little room in between.

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.

1. Starting position
2. Accumulating points
3. Strength of opponent
4. Instituting deductions for losses
5. Site of the game
6. Instituting head to head rules

1. 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.

2. Accumulating 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.

3. 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.

4. 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.

5. 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.

6. 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.