• Name
The University/School’s name, (EX: Ohio State, Oklahoma, Miami (FL), and Texas.)
• Strength of Schedule
Here is the first of 3 formulas in my Ranking. For this I will just average all of the rankings of each team’s opponents. Then I will divide this number by ten.
Ohio State’s Example:
Ohio State, over the course of the season plays teams ranked, 15, 5, 25, 30, 65, 45, 40, 70, 100, 50, and 40.
First average all of those numbers. It is 44.0909
Then divide that number by 10 and round to the third decimal place. Their SOS is 4.409.
Akron’s Example:
Akron, over the course of the season plays teams ranked, 85, 20, 45, 65, 75, 110, 115, 95, 85, 70, and 90.
First average all of those numbers. It is 77.7272
Then divide that number by 10 and round to the third decimal place. Their SOS is 7.773.
• Combined Ranks
This is the second formula included in my Ranking. This also is the easiest formula in my BCS. This is an average of the last five weeks ranking of a certain team. Over the years this has been an average of random rankings but this has been changed so as to bring the ranking to a higher degree.
Ohio State's Example:
For the week 10 ranking... Their rankings from the last five weeks; week5 #10, week6 #9, week7 #16, week8 #14 and week9 #13. Then average the five numbers together to get their Combined Ranking score. Their score is 12.4.
Akron's Example:
For the week 13 ranking... Their rankings from the last five weeks: week8 #68, week9 #76, week10 #72, week 11 #69 and week 12 #79. Then average the five numbers together to get their Combined Ranking score. Their score is 72.8.
• Average Game Score
The Average Game Score has been by far the most complex statistic in my ranking since its inception three years ago. The past two seasons, I have used random numbers as the game score for every ranking. This year I have taken this to a whole new level. Instead of the random numbers that I have used the past two seasons, I will now use a system that depends upon their ranking, their opponent’s ranking and the outcome of the game. Instead of the two different kinds of numbers (Wins and Loss, from last year) there will now be six different ways to get the Game Score. These six ways are; a win over a opponent that was ranked lower than you, a win over a opponent that was ranked higher than you, a win over a Division 1-AA or lower school, a loss to a team ranked higher than you, a loss to a team ranked lower than you and finally a loss to a team in Division 1-AA or lower. There are six separate equations for all of these situations. ROO = Rank Of Opponent. ROS = Rank of Self.
1. A win over a team ranked lower than you
a. The equation:
• {(120 – ROO) * (-5/119)} + {(ROO – ROS) * 0.001}
b. Real life story example (from the 2005 season):
• On September 17th the San Diego State Aztecs traveled to Ohio Stadium to play the Ohio State Buckeyes. The Aztecs looked overmatched but they came out strong with an 80-yard touchdown pass on the first play of the game. However Ohio State’s defense stepped it up a notch as they allowed the Aztecs only 99 more yards the rest of the game. Final score in this one, Ohio State 27 and San Diego State 6.
c. Real life story example’s equation:
• {(120 – 84) * (-5/119)} + {(84 – 14) * 0.001} = -1.4426
2. A win over a team ranked higher than you
a. The equation:
• {(120 - ROO) * (-5/119)} + {(ROO - ROS) * 0.04}
b. Real life story example (from the 2005 season):
• On September 24th the Northern Illinois Huskies traveled to Akron to take on the University of Akron Zips at the Rubber Bowl. Akron failed to keep a 21-point 4th quarter advantaged and the game headed to overtime. In the first overtime the Zips intercepted the Huskies pass in the end zone then in their overtime Brett Biggs ran it in from 1 yard out to give the Zips the win. Final score in this one, Akron 48 and Northern Illinois 42.
c. Real life story example’s equation:
• {(120 – 41) * (-5/119)} + {(41 - 92) * 0.04} = -5.3593
3. A win over a team in Division 1-AA or lower
a. The equation:
• {(1/ROO) * (-5/119)} + {(ROO - ROS) * 0.001}
b. Real life story example (from the 2005 season):
• On September 22nd, in a game moved up two days due to the threat of Hurricane Rita, The Texas State Bobcats traveled to play in-state rivals the Texas A&M Aggies. Even though barely anybody showed up in the crowd, the Aggies put on a good show led by quarterback Reggie McNeal. McNeal threw for 317 yards and two touchdowns in the Texas A&M victory. The Final score in this one, Texas A&M 44 and Texas State 31.
c. Real life story example’s equation:
d. {(1/154) * (-5/119) + {(154 – 48) * 0.001} = 0.1057
4. A loss to a team ranked higher than you.
a. The equation:
• {ROO * (10/119)} + {(ROS – ROO) * -0.001}
b. Real life story example (from the 2005 season):
• On September 10th the Akron Zips traveled to play the Purdue Boilermakers. The Zips were overmatched from the start and it showed. Purdue was able to put up 478 yards even though they are a very pass-oriented team and they ran the ball 50 times. A few trick plays including a 79-yard touchdown pass from the halfback helped put a few points up on the board for the Zips but to no avail. The final score in this one, Akron 24 and Purdue 49.
c. Real life story example’s equation:
• {36 * (10/119)} + {(92-36) * -0.001} = 2.9692
5. A loss to a team ranked lower than you.
a. The equation:
• {ROO * (10/119)} + {(ROO – ROS) * 0.04}
b. Real life story example (from the 2005 season):
• On September 9th the Pittsburgh Panthers traveled the Athens, Ohio to take on the Ohio Bobcats. The Panthers were highly favored in this game but were coming off of a 21 point loss to Notre Dame. This was a highly eventful game in which the stadium was sold out and new record crowd of 24 thousand plus filled Peden Stadium. Pittsburgh returned the opening kick for a touchdown but then later in the first quarter Ohio’s Dion Bynum returned an interception for a touchdown. With seven seconds remaining Pittsburgh tied the score at 10 points with a field goal. The game headed into overtime where Dion Bynum returned another interception for a touchdown and all of the students ran onto the field in victory. Final score in this thrilling upset, Pittsburgh 10 and Ohio 16.
c. Real life story example’s equation:
• {101 * (10/119)} + {(101-29) * 0.04} = 11.3674
6. A loss to a team in Division 1-AA or lower.
a. The equation
• {ROO * (10/119)} + {(ROO – ROS) * 0.04}
b. Real life story example (from the 2005 season):
• On September 17th the Aggies of California – Davis traveled to Stanford to take on the Cardinal. This seemed like the average Division 1 – Division 1-AA blowout as Stanford led by new coach Walt Harris jumped to an early 17-0 second quarter lead. However the Aggies (who had just made the transition to Division 1-AA from Division II. Then the Aggies mounted a comeback led by quarterback Jon Grant who made this speech to his teammates late in the fourth quarter, "What better stage could we be on. We're at Stanford Stadium. We're about to go on a game-winning drive.” Indeed they did, the Aggies got a game-winning touchdown pass with 8 seconds left to pull of the first victory of a Division 1-AA team over Stanford in university history. Final score in this one, Stanford 17 and UC Davis 20.
c. Real life story example’s equation:
• {151 * (10/119)} + {(151 – 36) * 0.04} = 17.2891
A team’s AGS is then gathered by averaging their Game Scores for the entire season and then subtracting their Average Margin of Victory divided by 100.
Ohio State’s Example:
Averaged Game Scores = -1.986
Average Margin of Victory = 7.2
-1.986 – (7.2/100)
-1.986 - 0.072 = -1.914
Akron’s Example:
Averaged Game Scores = 0.456
Average Margin of Victory = -3.4
0.456 – (-3.4/100)
0.456 + 0.034 = 0.49
• Losses
The total number of losses for this team. (EX1: Ohio State is 9-2; they get 2.0 points for this section.) (EX2: Akron is 6-5; they get 5.0 points for this section.)
• Total
Here is the total of my Ranking’s equation. All you do for this one is add them up. Add up Strength of Schedule plus Combined Ranks plus Average Game Score (which could be a negative number because good teams usually have many more good games than bad games) plus losses. For this total the team with the lowest amount is the best team in college football. Every week I will be doing a ranking of all 119 teams in Division 1-A. However from the beginning ranking through the ranking for week 6 I will have to average last season’s average game score and strength of schedule with this season’s average game score and strength of schedule.
Starting with the first week of November I will have another, more complex, weekly ranking. For this ranking I am using two brand new statistics that I have created. The first of which is Relative Performance. Relative Performance has two different calculations. The first one is for Wins and the second is for Losses.
Wins:
[(20 * GS) * (MOV/-20) + {(ROO – ROS) / 2.5}] / 75 = Relative Performance
Loss:
[(20 * GS) * (MOV/20) + {(ROO-ROS) / 2.5}] / 75 = Relative Performance
The other brand new statistic is called Team Performance and this has five steps. For this example you are finding the TP of X for these 10 teams.
1. First, find the average X.
2. Then subtract everyone’s X from the average X.
3. Then divide the difference (the answer from number 2) by the average.
4. Next find the remainder, to do so find the lowest difference / average (the answer from number 3) and add a number(the remainder) to make it equal to 1/the amount of teams.
5. Add the remainder to every team’s difference / average.
Name X AvgX AvgX-X Dif/Avg Rem Total
Team 1 7.7 7.0 -0.7 -0.1 0.743 0.643
Team 2 6.5 7.0 0.5 0.071 0.743 0.814
Team 3 10.6 7.0 -3.6 -0.514 0.743 0.229
Team 4 8.9 7.0 -1.9 -0.271 0.743 0.471
Team 5 11.5 7.0 -4.5 -0.643 0.743 0.1
Team 6 9.3 7.0 -2.3 -0.329 0.743 0.414
Team 7 5.1 7.0 1.9 0.271 0.743 1.014
Team 8 2.5 7.0 4.5 0.643 0.743 1.386
Team 9 3.2 7.0 3.8 0.543 0.743 1.286
Team 10 4.7 7.0 2.3 0.329 0.743 1.071
Multiply all of those totals by 2 to find TP of X. There may also be a time when I use the statistic NTP of X which is that total multiplied by -2. Usually during this ranking I will use the stat called TP of SA or NTP of SA. SA is equal to Strength Of Schedule plus Average Game Score.
This ranking is then calculated using these numbers for all 119 teams:
ARP
AGS
SOS
TP of SA
NTP of SA
Losses
There are four components to this ranking:
L = ARP + SOS + AGS + Losses + NTP of SA
M = SOS + AGS + Losses + (TP of SA * ARP)
N = ARP + SOS + Losses + (TP of SA * AGS)
Original Ranking = SOS + AGS + CR + Losses
Add the four components together to find this ranking:
{(ARP + SOS + AGS + Losses + NTP of SA) + (SOS + AGS + Losses + (TP of SA * ARP)) + (ARP + SOS + Losses + (TP of SA * AGS)) + (SOS + AGS + CR + Losses)}