1. Using Neural Networks to Determine NFL Game Outcomes
Presented by Alex Dixon

2. Motivation NFL games are enjoyed by 264 million people each week.
Millions of dollars are bet each week on the NFL.
Experts try to predict winners every week
Numerous programs already exist to predict the winner of a game

3. Accuracy of Current Prediction Methods
NFL Analysists
The very best analysts picked around 64% of games correct.
This year the average accuracy of the ten best annalists is 65.3%
Computer Models
Last year Microsoft’s “Cortana” finished with a  record (63 percent), but still a few games ahead of the Las Vegas oddsmakers.
This season, through week 13 [2]
Nate Silver’s “Elo” predictor is 63% accurate so far this season
Microsoft’s “Cortana” is 66% Accurate

4. Feature Vectors & Data Collection
9 Feature vectors extracted from
Home/Away
Differential Statistics
Points For
Points Allowed
Yards For
Yards Allowed
First Downs For
First Downs Allowed
Turnovers For
Turnovers Against

5. Neural Network Choice Multilayer Perceptron
Can process a large amount of data
Good choice for predicting nonlinear data

6. Multilayer Perceptron Configuration
9 Inputs
5 Hidden Nodes
1 output (0 or 1)
Sigmoidal Activation Function
Backpropagation

7. Original Multilayer Perceptron Cross Validation
Each cross validation is for 50 epochs
Data Set
Model MSE
Correct/Total
Percentage Correctly Predicted
Set 1
.09878
138/240
57.5%
Set 2
.1001
144/240
60.0%
Set 3
.1006
129/240
53.8%
Set 4
.0981
130/240
54.2%
TOTAL
.0994
541/960
56.35%

8. Feature Vector Elimination based upon Correlation Coefficients
Remain in Feature Vectors?
Home/Away
0.1211
Yes
Points For
0.1297
Points Allowed
Yards For
0.0722 No
Yards Allowed
First Downs For
0.0656
First Downs Allowed
Turnovers Committed
Turnovers Allowed
0.0845

9. Feature Vector Elimination Cross Validation
Each cross validation is for 50 epochs
Data Set
Model MSE
Correct/Total
% Correct
Set 1
.1103
143/240
59.6%
Set 2
.1142
152/240
63.3%
Set 3
.1114
131/240
54.6%
Set 4
.1111
140/240
58.3%
TOTAL
.1118
566/960
59%
LAST
.0994
541/960
56.35%
IMPROVEMENT
25/960
2.6%

10. The Final Model
The feature vector of “Winning Percentage” was calculated and extracted.
The Correlation coefficient of the feature vector is .2266, much higher than any other feature vector
This feature vector was then added to the 5 from the previous model
Final Multilayer Perceptron
6 Inputs
3 Hidden Nodes
Sigmoidal Activation Function

11. Final Model Cross Validation
Each cross validation is for 50 epochs
Data Set
Model MSE
Correct/Total
% Correct
Set 1
.1067
134/240
55.8%
Set 2
.1085
154/240
64.2%
Set 3
.1095
143/240
59.6%
Set 4
.1087
136/240
56.7%
TOTAL
.1084
567/960
59%

12. Results of Final Model on the 2017 NFL Season so Far
The Predictor was able to correctly determine the outcome of 110 of the games played so far in the NFL season (Week 2 – Week 13)
62.8% Correct
.2 % less than Nate Silver’s “Elo” Predictor
3.2 % less than Microsoft’s “Cortana”

13. Possible Reasons For Error
Takes into account past averaged statistics
Model does not take into account current state
Injury to key players
Win/Loss streaks
Teams that clinch a playoff spot often bench their starters
Averages for games at start of season are inconsistent

14. References
[1]
[2]
[3]