1. Predicting the Winner of an NFL Football Game Matt Gray CS/ECE 539

2. Reasons to Predict NFL Football is watched by millions of people every weekend during the season. Vast amounts of money invested in NFL Football Prediction Polls such as Weekly Football Polls, etc.

3. Main Problem & Goal Problem: –Most predictions available have a human bias in it which stems from personal opinions that could result in errors with the predictions. Goal: –Eliminate the human error by having a Multi- layer Perceptron to perform the prediction.

4. Why MLP Non-linear: –Data from NFL football games covers a vast amount of information, such as home versus away, yards gained, yards allowed, etc. –No one piece of the data always correlates to a win or loss as there are many ways in which a team can win or lose.

5. Why MLP MLPs –Multi-Layer Perpceptrons are capable of predicting outcomes of non-linear data. –Multi-Layer Perceptrons reduce the problem to a Neural Network prediction problem and removes the human personal bias of a teams performance from the prediction.

6. Data Collection I collected the data from http://www.statfox.com/nfl/nfllogs.htm http://www.statfox.com/nfl/nfllogs.htm Regular Season and Post Season Data per team was put into an Excel file. To keep data even per team, Post Season data was removed. Averages and Standard Deviations for both Yards Gained and Yards Allowed per team were calculated from the team’s games up through the current game.

7. Data Collection To make the data easier to manage and handle, separate values of Rushing and Passing Yards gained and allowed and removed, keeping Total Yards gained and allowed. Teams were given unique team IDs for purposes of arranging the data. The Excel File was then saved as a text document that could be read in by Matlab. Team IDs were removed by Matlab so that they do not affect the data.

8. Preliminary Results Data was formatted in Matlab and then fed into a modified MLP Matlab program provided from the class website. Order of data was randomized, and then a separated into a training and data set. Without scaling data, division by zero occurs, so data was scaled.

9. Preliminary Results Using a MLP with a 2-5-2 structure as an initial setup. Multiple tests run using the same variables for alpha and momentum set to default values of 0.1 and 0.8 respectively. Average of initial results on the training data was a 57.71% classification rate as to whether or not a team won its game.

10. Preliminary Results Training Error from one test run of the initial setup

11. Preliminary Results Training Error from a second test run of the initial setup

12. Further Plans My next steps in obtaining better prediction results include the following. –Perform feature reduction of data that may not be necessary or that causes confuses –Perform multiple loops of varying the variables of alpha, momentum, number of layers, and number of hidden neurons. –For each game, combine the data from the two opposing teams to hopefully form a stronger correlation of the data.

13. References http://www.statfox.com/nfl/nfllogs.htm http://www.nfl.com Newman, M. E. J., and Park, Juyong; A network-based ranking system for US college Football. Department of Physics and Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109. arXiv:physics/0505169 v4 31 Oct 2005 Purucker, Michael C; Neural network quarterbacking: Who different training methods perform in calling the games. IEEE Potentials, August/September 1996. http://ieeexplore.ieee.org/iel1/45/11252/00535226.pdf?arnumber=53 5226