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Artificial Neural Network Prediction of Major League Baseball Teams Winning Percentages

Artificial Neural Network Prediction of Major League Baseball Teams Winning Percentages. Scott Wiese ECE 539 Professor Hu. Motivation. Current trends in managing player personnel focuses heavily on statistics to weigh future production against potential salaries.

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Artificial Neural Network Prediction of Major League Baseball Teams Winning Percentages

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  1. 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 www.MLB.com • 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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