Presentation on theme: "Scott Wiese ECE 539 Professor Hu"— Presentation transcript:
1Scott Wiese ECE 539 Professor Hu Artificial Neural Network Prediction of Major League Baseball Teams Winning PercentagesScott WieseECE 539Professor Hu
2MotivationCurrent 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 playersDetermine if current players are overpaid
3MotivationClaimed that statistics can be a valid predictor of both a player’s and team’s productionClaimed that one season, 162 games, is a long enough trial period that statistics can predict a team’s winning percentage
4GoalsCan 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?
5Data CollectionCollected 3 years of data for all 30 Major League Baseball teamsGathered from statistical database available on74 statistics besides winning percentage gathered
6Neural Network Selection Back Trained Multi Layer PerceptronExcellent at analyzing large feature setsSupervised TrainingGood at classification problems
7Preprocessing Normalized each feature vector Used singular value decomposition to emphasize most important features
8TestingWanted to determine which MLP configuration would best predict winning percentageBaseline MLP: 1 hidden layer, 1 hidden neuronTested MLPs: 1 through 5 hidden layers, 1, 3, or 5 hidden neurons in all layers
17Classification Results Again, now that we know the best advanced network, test it against the baseline with more data.
18Classification Results Negligible difference between the two networks even though there was nearly a 50% improvement in the original trial.
19Conclusions Advanced network better at pure prediction than baseline Still a very moderate success rate given the error boundsClassification results very promisingShows that statistics are important in separating teams’ results