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Neural Network Prediction of NFL Football Games

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.

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Neural Network Prediction of NFL Football Games

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  1. Neural Network Prediction of NFL Football Games Joshua Kahn ECE539 – Fall2003

  2. Overview • Introduction • Work Performed • Data Collection • Preliminary Study • Training and Prediction Set Creation • Data Preprocessing • Making Predictions • Results • Conclusion

  3. 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?

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. Results • Neural network classification correct 94% when actual (not predicted) statistics are used • NFL teams seem to be consistent over the long-term

  15. Results Week 14 Week 15

  16. Baseline Study • Neural network was more accurate on average • Previous neural networks predictors accurate for 63% of games

  17. Conclusions • Of eight misclassifications, each can be subjectively identified in one of 3 categories

  18. 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

  19. 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].

  20. Questions??? Thank you…

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