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
225 29 28
226

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 = %

9. Predicted Week 15, 2013 SVM: MLP: Chargers Broncos Redskins Falcons
Buccs
Seahawks
Giants
Eagles
Vikings
Pats
Dolphins
Bills
Jags
Texans
Colts
Bears
Browns
Chiefs
Raiders
Jets
Panthers
Packers
Cowboys
Cards
Titans
Saints
Rams
Bengals
Steelers
Ravens
Lions
Chargers
Broncos
Redskins
Falcons
49ers
Buccs
Seahawks
Giants
Eagles
Vikings
Pats
Dolphins
Bills
Jags
Texans
Colts
Bears
Browns
Chiefs
Raiders
Jets
Panthers
Packers
Cowboys
Cards
Titans
Saints
Rams
Bengals
Steelers
Ravens
Lions

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