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Scott Wiese ECE 539 Professor Hu

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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

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