College Football Win Totals 2014

Every May or June, a handful of bookmakers release the first odds. I’m not quite sure what their method is, but I’m happy to explain my own. In doing so it’s easy to identify lines that are just plain wrong. In my last article, I detailed the methods of generating win totals. Now I’m releasing the results from my simulations.

My favorite line released this year was Notre Dame 9.5 wins, -150 under,  in a schedule with games at Florida State, Arizona State, and USC, in addition to facing Stanford, Michigan, and a very underrated North Carolina team). The line has since moved dramatically.

Below are all the teams ranked by average win totals.

Team Avg Win
Florida State 11.30
Alabama 10.87
Utah State ** 10.67
Marshall 10.64
Wisconsin 10.61
Ohio State 10.34
Baylor 10.33
Michigan State 10.17
Washington ** 10.04
Oregon 9.99
Missouri 9.83
Bowling Green 9.80
Northern Illinois 9.64
Oklahoma 9.50
North Texas 9.45
UCF 9.39
Louisville 9.28
South Carolina 9.26
Navy 9.25
Buffalo 9.24
Houston 9.16
Clemson 9.15
Brigham Young 9.15
Stanford 9.04
Auburn 9.00
Oklahoma State 8.87
Boise State 8.83
Iowa 8.81
LSU 8.79
UCLA 8.63
Ball State 8.52
Louisiana-Lafayette 8.37
Duke 8.32
Arizona State 8.19
Georgia Tech 8.15
Fresno State 8.13
USC 8.12
Kansas State 8.11
Georgia 8.06
Colorado State 7.93
Mississippi State 7.83
Arizona 7.79
Cincinnati 7.74
Rice 7.73
Texas Tech 7.62
East Carolina 7.60
North Carolina 7.56
Nebraska 7.47
Texas A&M 7.45
Vanderbilt 7.41
Arkansas State 7.40
Pittsburgh 7.33
Virginia Tech 7.33
Toledo 7.22
Michigan 7.21
Oregon State 7.07
Ole Miss 7.02
South Alabama 6.98
Penn State 6.94
Western Kentucky 6.94
Middle Tennessee 6.93
San Diego State 6.72
Troy 6.64
Notre Dame 6.60
UTSA 6.52
Florida Atlantic 6.50
TCU 6.38
Texas 6.15
Old Dominion 6.12
Miami (FL) 5.96
Northwestern 5.77
Florida 5.72
Minnesota 5.56
San Jose State 5.56
Boston College 5.55
Washington State 5.55
Utah 5.49
Memphis 5.46
Indiana 5.46
Ohio 5.28
Texas State 5.21
Kent State 5.21
Syracuse 5.17
Central Michigan 5.15
Tulane 5.11
UNLV 5.06
Nevada 4.99
Illinois 4.94
Akron 4.84
Maryland 4.62
North Carolina State 4.43
Louisiana-Monroe 4.39
Georgia Southern 4.38
Wake Forest 4.31
Tennessee 4.31
Temple 4.14
Connecticut 4.08
SMU 3.75
UAB 3.72
New Mexico 3.49
Colorado 3.47
Army 3.47
Kentucky 3.36
South Florida 3.30
Hawaii ** 3.24
West Virginia 3.18
Arkansas 3.14
Iowa State 3.08
Louisiana Tech 3.08
Air Force 3.02
Wyoming 3.00
New Mexico State 2.98
Georgia State 2.96
Rutgers 2.94
Tulsa 2.92
Western Michigan 2.88
Idaho 2.70
Appalachian State 2.67
Virginia 2.45
Kansas 2.41
UTEP 2.32
Southern Miss 2.23
Purdue 2.16
California 2.08
Massachusetts 1.65
Miami (OH) 1.44
Florida International 1.29
Eastern Michigan 0.97


** Utah State, Washington, and Hawaii play 13 games.



How to Make Your Own College Football Win Totals

How does one go about setting the win total for every college football team? A simple Monte Carlo simulation is able to do the trick! In this article I will detail exactly how you can generate your own win totals for the college football season.

Step 1: Generate power ratings of each team using a nonlinear regression (or use someone else’s). 

You can generate your own power ratings using the Solver tool in Excel for all FBS and FCS teams. In an upcoming post I will detail the exact process for doing this.

Step 2: Build a list in Excel for every college football game that is being played.

This data can be found on any site such as ESPN and formatted into columns. You must then use the VLOOKUP function in excel to look up the power ratings for each team. (You’ll need to make adjustments to those power ratings based on returning starters, but that’s a discussion for another day). Add 3.5 points to each home team’s power rating to account for home field advantage, then find the difference between the two. This difference is the point spread that you would expect for the game.

Step 3: Convert Spreads into Win Probabilities

I did a regression of wins and losses of all games based on the closing point spreads, and in doing so created the following formula to estimate win probability based on a point spread:

Win Probability = 55.63% – 1.71% * (Point Spread)

For example, if a team is a 14 point favorite, or ” -14 ” as we will say, then their odds of winning are 55.63% – 1.71% * -14 = 79.57%.

Step 4: Randomly Generate Game Outcomes

In a column next to each game, generate a random number using the =RAND() function in excel. Next to it, use an =IF function to determine the winner. In the example above, let’s say Alabama is a 14 point favorite over Oklahoma. We would write a formula as such:


This means that if the randomly generated number is anywhere from 0% to 79.57%, Alabama will be deemed the winner, but if it is any higher then Oklahoma is the winner. By inserting these formulas for every game in an entire season, we can simulate every game. By pressing F9, a new batch of random numbers is generated and another full season simulated.

Step 5: Create a Win Totals List

In a separate column, create a list of every college football team playing. In one column, do =COUNTIF(*columns listing teams playing*,”Alabama”,”W”) and in another do =COUNTIF(*columns listing teams playing*,”Alabama”,”L”).

Step 6: Run Monte Carlo Simulation

Don’t freak out if you’ve never done this before. All you have to do is record a macro that copies and pastes each team’s win record into a column A on a new sheet, and then moves over to column B. Title tha Macro as “Macro 1”. Then go into Developer > Record Macro and create a new macro called “Loop.” The screen will open up with a development tab for you to write VBA. Type in:

i = 0 

Do While i < 10000

Call Macro 1

i = i + 1


This simple code will repeat the simulation 10,000 times (or however many you choose). Once the simulation is completed, you can find the average wins (or the mode wins) and use it as your win total.