1. Scott Wiese ECE 539 Professor Hu
Artificial Neural Network Prediction of Major League Baseball Teams Winning Percentages
Scott Wiese
ECE 539
Professor Hu

2. Motivation
Current trends in managing player personnel focuses heavily on statistics to weigh future production against potential salaries.
Used to determine whether or not to sign specific players
Determine if current players are overpaid

3. Motivation
Claimed that statistics can be a valid predictor of both a player’s and team’s production
Claimed that one season, 162 games, is a long enough trial period that statistics can predict a team’s winning percentage

4. Goals
Can I develop an artificial neural network that when given a team’s statistics for a year that will accurately predict a team’s winning percentage?

5. Data Collection
Collected 3 years of data for all 30 Major League Baseball teams
Gathered from statistical database available on
74 statistics besides winning percentage gathered

6. Neural Network Selection
Back Trained Multi Layer Perceptron
Excellent at analyzing large feature sets
Supervised Training
Good at classification problems

7. Preprocessing Normalized each feature vector
Used singular value decomposition to emphasize most important features

8. Testing
Wanted to determine which MLP configuration would best predict winning percentage
Baseline MLP: 1 hidden layer, 1 hidden neuron
Tested MLPs: 1 through 5 hidden layers, 1, 3, or 5 hidden neurons in all layers

9. Testing Results

10. Testing Results

11. Testing
Now that we know the 4 hidden layers, 1 hidden neuron network performed the best, test it again against the baseline with new data
Success when predicted winning percentage within +/- 0.15

12. Testing Results
Best MLP’s performance almost twice as good as baseline’s performance.

13. Preliminary Conclusions
Advanced MLP structure is better at predicting a team’s winning percentage.
Unfortunately, still under 50% given a .15 error bound
Can classification work better

14. Classification Testing
Classify teams into 3 groups
Division winners (> .590)
Winning teams (.500<x<.589)
Losing teams (<.500)
Same process as above

15. Classification Results
3 hidden layers with 5 hidden neurons is best

16. Classification Results

17. Classification Results
Again, now that we know the best advanced network, test it against the baseline with more data.

18. Classification Results
Negligible difference between the two networks even though there was nearly a 50% improvement in the original trial.

19. Conclusions Advanced network better at pure prediction than baseline
Still a very moderate success rate given the error bounds
Classification results very promising
Shows that statistics are important in separating teams’ results