Using a MultiLayer Perceptron Model to Predict MLB

Using a Multi-Layer Perceptron Model to Predict MLB Post. Season Outcomes ALEYSHA BECKER ECE 539, FALL 2018

PROBLEM Tasks: Goal : predict outcome of MLB Postseason based on regular season statistics • PCA on feature vectors • Train MLP (2008 – 2016) • Test MLP (2017 - 2018) Success: Greater than 50%[1] • Vegas[2], other Machine Learning Applications[3] can get just under 60% accuracy

METHODS: DATA Statistics from Baseball. Reference. com[4] Manual Excel manipulation: Team statistics after PCA for each matchup were subtracted (Team 1 – Team 2) to get a differential input vector 1: Team 1 won; -1: Team 1 lost Order was chosen so that ~1/2 the input vectors had output 1 and ~1/2 had output -1 [5]

METHODS: PROGRAM PCA[6] and Back Propagation[7] programs from Professor Hu were used with slight modifications Heuristic experimentation: Number of hidden layers : [1, 3, 5] Neurons per hidden layer : [5, 10, 20] Learning Rate : [0. 05, 0. 1, 0. 2] Momentum Constant : [0. 7, 0. 8, 0. 9] Epoch Size : [24, 41, 64] [8]

MLB Post. Season Outcome Heuristic Experimentation Results RESULTS = 76% Best Classification was with: 1 or 3 Hidden Layers 5 Neurons per Layer Alpha = 0. 1 Momentum = 0. 8 Epoch size of 64 Testing Classification Rate Averaged Over 3 Trials Maximum Testing Classification Rate 80. 00% 70. 00% 60. 00% 50. 00% 40. 00% 30. 00% 20. 00% 10. 00% 0. 01 0. 1 1 10 Number of Hidden Layers Neurons per Layer Epoch Size Learning Rate Momentum Constant 100

DISCUSSION 76% classification rate was much higher than expected Not very repeatable – may be due to random chance and small testing set 10 trials with “ideal” classifier averaged 58. 23% classification rate More testing data ! This model doesn’t take into account game-by-game variance

REFERENCES [1] Tim Elfrink, “Predicting the outcomes of MLB games with a machine learning approach, ” 18 -Jun-2018. [Online]. Available: https: //beta. vu. nl/nl/Images/werkstuk-elfrink_tcm 235 -888205. pdf. [Accessed: 01 -Oct-2018]. [2] Jia, R. Wong, C, et al. “Predicting the Major League Baseball Season, ” 2013. [Online]. Available: http: //cs 229. stanford. edu/proj 2013/Jia. Wong. Zeng-Predicting. The. Major. League. Baseball. Season. pdf. [Accessed: 05 -Dec-2018]. [3] Soto-Valero, C. “Predicint Win-Loss Outcomes in MLB Regular Season Games – a Comparative Study Using Data Mining Methods, ” Dec-2016. [Online]. Available: https: //www. researchgate. net/publication/311862823_Predicting_Win. Loss_outcomes_in_MLB_regular_season_games_-_A_comparative_study_using_data_mining_methods. [Accessed 05 -Dec-2018]. [4] “ 2018 Major League Baseball Season Summary, ” Baseball Reference, 08 -Oct-2018. [Online]. Available: https: //www. baseballreference. com/leagues/MLB/2018. shtml. [Accessed: 08 -Oct-2018]. [5] image from : https: //thehillnews. org/sports/marin-murphy/new-york-houston-la-chicago-analysis-mlb-postseason [6] Yu Hen Hu, my. PCA, 2 -February-2016. [Online]. Available: http: //homepages. cae. wisc. edu/~ece 539/matlab/mypca. m. [Accessed: 10 Nov-2018]. [6] Yu Hen Hu, bpconfig, 15 -October-2003. [Online]. Available: http: //homepages. cae. wisc. edu/~ece 539/matlab/bpconfig. m. [Accessed: 10 Nov-2018]. [7] Image from : http: //kindsonthegenius. blogspot. com/2018/01/basics-of-multilayer-perceptron-simple. html