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Predicting NFL Game Outcomes: Back-Propagating MLP

Predicting NFL Game Outcomes: Back-Propagating MLP. By Paul McBride. Project Goal. To predict the outcome of NFL games. Remove human bias Create a completely objective and statistics based prediction method. Why a back-propagating MLP?.

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Predicting NFL Game Outcomes: Back-Propagating MLP

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  1. Predicting NFL Game Outcomes: Back-Propagating MLP By Paul McBride

  2. Project Goal • To predict the outcome of NFL games. • Remove human bias • Create a completely objective and statistics based prediction method

  3. Why a back-propagating MLP? • Since there are many ways a team can win, no linear mapping exists to conclude the outcome of the game • This is a pattern classification problem

  4. Data Collection • I collected my data from NFL.com • I chose to look at the entire 2012 season • Since the NFL is an incredibly offense dominated league, I decided to compare offenses

  5. Statistics • 15 stats: • Homefield, Firstdowns Totals, Totals yards, PassYards, etc. • Extracted a feature vector for each game played by taking the differential statistics of offensive performance.

  6. Example Feature Vector Team 1 vs. Team2: Each feature = Team 1 stat – team 2 stat Outcome of 1 = Team 1 won. Outcom of -1 = Team 2 won.

  7. Support Vector Machine • 4 – Way Cross validation. • Linear kernel function with C = 1 proved to be a good result • Confusion Matrix • Classification rate of 0.887795276

  8. MLP • Preprocessed the data with SVD • 4-Way cross validation to decide best classification rate • 3 layers • hidden layer neurons = 5 • mu = .2, alpha = .007 - Classification rate = 88.1234%

  9. Predicted Week 15, 2013 • MLP: • SVM:

  10. Future • I would like to trim down some of the less performance indicative stats • I would like to add defense

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