1. Alberto Trindade Tavares ECE/CS/ME 539 - Introduction to Artificial Neural Network and Fuzzy Systems

2. Since 2003, the Brazilian Soccer League has the following format:  20 participating clubs  Each club faces every other club twice in the season, once at their home stadium, and once at that of their opponents  380 matches divided into two parts:  First half: May-August  Second half: September-December A match has three possible results:  Win of the home team  Draw  Loss of the home team Brazilian Soccer League

3. Predict the outcome (win of home team, draw, or loss of home team) of every game of the second half for the current season (2013) Using as training data the game results of the first half of 2013 season Develop two classifiers, using MATLAB, for performing these predictions :  Maximum Likelihood Classifier  Multi-Layer Perceptron Compare their accuracy between themselves and to other works Goal of this Work

4. The feature vector for representing a match instance has six features, the first three for the first team (home), and last three for the second team (visiting): Results from 2003 season to the last match of current season Feature Vector # wins as home team # draws as home team # losses as home team # wins as visiting team # draws as visiting team # losses as visiting team First Team Second Team

5. Extraction of results of every match since 2003 Two different sources:  2003-2004 seasons: http://www.bolanaarea.com/gal_brasileirao.htm  2005-2013 seasons: http://www.campeoesdofutebol.com.br Python program for parsing the HTML pages, and storing the results into text files, which can be read via MATLAB function load Data Extraction

6. Gaussian Distribution Maximum Likelihood Classifier x P(x) Win Draw Loss

7. Classification rate per round: Maximum Likelihood Classifier (Results) Average Classification Rate = 53.1579%

8. Total Confusion Matrix: Maximum Likelihood Classifier (Results) Predicted Wins 71818 25915 19421 Predicted Draws Predicted Losses Actual Wins Actual Draws Actual Losses

9. # Hidden Layers = 3 # Neurons in First Hidden Layer = 3 # Neurons in First Hidden Layer = 20 # Neurons in First Hidden Layer = 3 Learning rate (α) = 0.1 Momentum = 0 Hidden layers use hyperbolic tangent activation function, and output layer uses sigmoid activation function Multi-Layer Perceptron

10. Classification rate per round (10 runs): Multi-Layer Perceptron(Results) Average Classification Rate = 55.7895%

11. Total Confusion Matrix: Multi-Layer Perceptron(Results) Predicted Wins 78712 2711 23417 Predicted Draws Predicted Losses Actual Wins Actual Draws Actual Losses

12. A. Joseph, N.E. Fenton, M. Neil. Predicting football results using Bayesian nets and other machine learning techniques (2006) Published in the Journal Knowledge-Based Systems Their results:  Naïve BN: 47.86%  kNN: 50.58%  Expert BN: 59.21% Comparison with other work My Results:  Maximum Likelihood: 53.1579%  Multi-Layer Perceptron: 55.7895%

13. Questions?