Neural Network Prediction of NFL Football Games

Joshua Kahn

ECE539 – Fall2003

Overview

Introduction Work Performed

Data Collection Preliminary Study Training and Prediction Set Creation Data Preprocessing Making Predictions

Results Conclusion

Introduction

The National Football League (NFL) is a multi-billion dollar business

Many web sites claim to be able to predict the outcome of NFL games

Some of these sites are trustworthy, others are downright seedy

Why are actually correct?

Project Goal

Most prognostications are based on human opinion Invariably, some degree of bias enters in

This project aims to create a completely objective, statistics based system for predicting the outcome of NFL games The trouble lies in the “intangible” aspects of the

game It seems plausible to do create a statistical system

Why a Neural Network?

Teams can win in a variety of ways No linear mapping exists to determine the

outcome This problem essentially boils down to a

pattern classification problem Neural networks are very good at solving these

problems Neural network provides a non-linear mapping

Data Collection

Data was to be available from a typical NFL box score

A large data set was required to represent the large number of ways to win

Collected from NFL.com Used Excel’s web query feature to acquire tabular

data, such as box scores and team averages

Data Collection

Data was extracted from the box scores using a Perl script Perl provides an Excel interface

Statistics could be selected from the box scores as desired Perl also allowed additional data processing

Needed to determine which statistics to use

Preliminary Study

Data was analyzed using Matlab to look for dependency, redundant data, etc.

No hyperplane exists to separate wins and losses based on statistical analysis

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Total Yardage Differential Time of Possession Differential

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Preliminary Study Results

Determined the following statistics were most predictive: Total yardage differential Rushing yardage differential Time of possession differential (in seconds) Turnover differential Home or away

Differential statistics provide insight into offensive and defensive performance

Scoring data was excluded as it would bias the network’s output toward a single feature

Training and Prediction Sets

Training sets include the statistics for both teams for each game

Each training vector also includes the outcome of the game Outcome marked for both teams 1 = win, -1 = loss

Two prediction sets were created: One based on team season averages Other based on average of prior 3 weeks Both sets were applied to determine effectiveness

Neural Network Selection

Back-propagation multi-layer perceptron provides a great deal of flexibility Good pattern classifier Supervised learning

Network parameters and structure were determined based on testing

Data Preprocessing

Processed all data using singular value decomposition Gives additional weight to the most pertinent

features prior to network input Makes training more effective

Performed using Matlab’s svd function

Making Predictions

Trained network using training data Applied prediction data three times

Used both season and three week average to determine effectiveness of the two

Found the average of the three trials Classified winner/loser of game

Winner had higher network output

Results

Neural network classification correct 94% when actual (not predicted) statistics are used

NFL teams seem to be consistent over the long-term

Prediction Rate

Week Season Average Data

Three Week Average Data

Week 14 75% 62.5%

Week 15 75% 37.5%

Results

Indianapolis def. Atlanta

Tennessee def. Buffalo

Kansas City def. Detroit

Tampa Bay def. Houston

New England def. Jacksonville

Minnesota def. Chicago

New York Jets def. Pittsburgh

St. Louis def. Seattle

Cincinnati def. San Francisco

Oakland def. Baltimore

Denver def. Cleveland

Carolina def. Arizona

Dallas def. Washington

Green Bay def. San Diego

New Orleans def. NY Giants

Philadelphia def. Miami

Green Bay def. Chicago

Baltimore def. Cincinnati

Philadelphia def. Dallas

Jacksonville def. Houston

Indianapolis def. Tennessee

Pittsburgh def. Oakland

San Diego def. Detroit

Minnesota def. Seattle

Tampa Bay def. New Orleans

New York Giants def. Washington

San Francisco def. Arizona

Denver def. Kansas City

New England def. Miami

Buffalo def. New York Jets

Atlanta def. Carolina

St. Louis def. Cleveland

Week 14 Week 15

Baseline Study

Prediction Rate

Week Neural Network ESPN.com

Week 14 75% 57%

Week 15 75% 87%

Neural network was more accurate on average Previous neural networks predictors accurate for

63% of games

Conclusions

Of eight misclassifications, each can be subjectively identified in one of 3 categories

Game Misclassification Reasoning

Philadelphia def. Dallas Misclassification

San Diego def. Detroit Too close to call

Atlanta def. Carolina Upset

Minnesota def. Seattle Too close to call

New England def. Jacksonville

Misclassification

New York Jets def. Pittsburgh

Too close to call

Cincinnati def. San Francisco

Too close to call

Oakland def. Baltimore Upset

Conclusions

Prediction rate could be improved by adding the “human element” Take immeasurable into consideration Las Vegas betting lines Subjective team rankings

Training set could be based on previous season data Ways in which teams win presumably does not change

over time Proves that a statistically based system can be

developed to predict outcome of NFL games

References

Haykin, S. (1999). Neural Networks: A Comprehensive Foundation. Upper Saddle River, New Jersey: Prentice-Hall, Inc.

ESPN.com, http://www.espn.com [Retrieved Dec 2003].Purucker, M.C. (1996) Neural Network Quarterbacking.

Potentials, IEEE, vol. 15:3, pp. 9-15.NFL.com, http://www.nfl.com [Retrieved Dec 2003].

Questions???

Thank you…