Predicting the Winner of an NFL Football Game

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 • 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=535226