1. Prediction of NBA games based on Machine Learning Methods
University Of Wisconsin Madison
Prediction of NBA games based on Machine Learning Methods
ECE/CS/ME 539 Introduction to Artificial Neural Networks and Fuzzy System
Renato Amorim Torres
December, 2013

2. Proposal The objectives of the project are:
Predict the winning team of a NBA game
The goal is to have the Prediction Rate higher than the rate of the Very Naive Majority Vote Classifier. This method looks at all previous games (in the season) of the two teams and picks the team with the fewest losses as the winner.

3. Preparation of the Data
Data was obtained on the website:

4. Preparation of the Data
Box scores were copied into a spreadsheet
Using macro all the team names were replaced by numbers, the unnecessary columns were deleted, and .txt files were generated with the data.
The .txt files were loaded in MatLab and used to generate the feature vectors and implement the methods.

5. Data Analysis
Blue Line >> 2012 to 2006 Regular Seasons Red Line >> Mean of Seasons

6. Feature Vectors Win-Loss percentage of both teams
Point differential per game of both teams
Win-Loss percentage in the previous 8 games for both teams
Visitor Team win-Loss percentage as visitor
Home Team win-Loss percentage at home
Total of eight features

7. Feature Vectors
Win-Loss percentage in the previous 8 games of both teams. Why eight games?
Blue Line >> 2012 to 2006 Regular Seasons Red Line >> Mean of Seasons

8. Naive Majority Vote Rate from 9th game
Prediction
Based on the graph shown before, it was found that each team should have played at least eight games prior to predicting games.
Considering this starting point, the Naive Majority Vote Rate was calculated in order to define the goal:
Naive Majority Vote Rate from 9th game
Data
2012
2011
2010
2009
2008
2007
2006
Mean
Rate(%)
61.83
65.94
64.75
66.30
63.86
65.81

9. Maximum Likelihood Classifier
Prediction
Maximum Likelihood Classifier
- Not all the feature vectors were used. In order to establish the best features, a code was implemented to verify the prediction rate for all combinations of features.
Features used:
Feature #2: Home Team Win-Loss percentage at home
Feature #4: Home Team total Win-Loss percentage
Feature #5: Visitor Team point differential per game in the actual season
Feature #7/8: Win-Loss percentage in the last 8 games for both teams

10. Prediction Maximum Likelihood Classifier
-The training data was a combination of the previous season's games and the testing data was random games of the “current” season.
The likelihood classifier achieved the following results:
Likelihood Classifier
Testing Data
2012
2011
2010
2009
2008
2007
2006
Mean
Rate(%)
67.31
64.37
67.88
67.43
69.43
65.05
67.42

11. Prediction Maximum Likelihood Classifier Results
Testing Data
2012
2011
2010
2009
2008
2007
2006
Mean
Rate(%)
67.31
64.37
67.88
67.43
69.43
65.05
67.42
Naive Majority Vote Rate from 8th game
Data
2012
2011
2010
2009
2008
2007
2006
Mean
Rate(%)
61.83
65.94
64.75
66.30
63.86
65.81
For comparison...
Table extracted from: Matthew Beckler, Hongfei Wang. and Michael Papamichael NBA Oracle

12. Linear Regression / LMS Algorithm
Prediction
Linear Regression / LMS Algorithm
- In order to find the weights, the LMS Algorithm was implemented: