measuring and predicting uw badgers’s performance by quarterback and running back stats

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MEASURING AND PREDICTING UW BADGERS’S PERFORMANCE BY QUARTERBACK AND RUNNING BACK STATS By: Tyler Chu ECE 539 Fall 2013

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By: Tyler Chu ECE 539 Fall 2013. Measuring and Predicting UW Badgers’s performance by quarterback and running back stats. Reasons to Predict. Millions of Badgers Fans who want to know how their team is going to do Immense amounts of money go into the NCAA football programs. - PowerPoint PPT Presentation

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Page 1: Measuring and Predicting UW  Badgers’s  performance by quarterback and running back stats

MEASURING AND PREDICTING UW BADGERS’S PERFORMANCE BY QUARTERBACK AND RUNNING BACK STATS

By: Tyler Chu

ECE 539

Fall 2013

Page 2: Measuring and Predicting UW  Badgers’s  performance by quarterback and running back stats

Reasons to PredictReasons to Predict• Millions of Badgers Fans who want to know

how their team is going to do

• Immense amounts of money go into the NCAA football programs

Page 3: Measuring and Predicting UW  Badgers’s  performance by quarterback and running back stats

Main Problem & GoalMain Problem & Goal• Problem:

• Most predictions available have a human bias in it which stems from personal opinions that could result in errors with the predictions.

• Goal:• Eliminate the human error by having a Multi-layer

Perceptron to perform the prediction

Page 4: Measuring and Predicting UW  Badgers’s  performance by quarterback and running back stats

Why MLPWhy MLP• Teams can win in a variety of ways

• No linear mapping exists to determine the outcome

• No one piece of the data always correlates to a win or loss as there are many ways in which a team can win or lose.

Page 5: Measuring and Predicting UW  Badgers’s  performance by quarterback and running back stats

Why MLPWhy MLP• MLPs

• Multi-Layer Perpceptrons are capable of predicting outcomes of non-linear data.

• Multi-Layer Perceptrons reduce the problem to a Neural Network prediction problem and remove the human personal bias of a teams performance from the prediction.

Page 6: Measuring and Predicting UW  Badgers’s  performance by quarterback and running back stats

Data CollectionData Collection• Data was to be available the web’s many

different sport statistic sites.

• A large data set was required to represent the large number of ways to win

• Used Sports References’s website• Used Excel’s web query feature to acquire tabular

data

Page 7: Measuring and Predicting UW  Badgers’s  performance by quarterback and running back stats

Data CollectionData Collection• Many feature vectors were collected

• Passing Completions, Attempts

• Yards per attempt

• Touchdowns

• Interceptions

• Passer Ratings

• Rushing equivalents for RB’s

Page 8: Measuring and Predicting UW  Badgers’s  performance by quarterback and running back stats

Preliminary ResultsPreliminary Results• Data was formatted in Matlab and then fed

into a modified MLP Matlab program provided from the class website.

• Multiple tests run using the same variables for alpha and momentum set to default values of 0.1 and 0.8 respectively

• Average of initial results on the data with one hidden layer and neuron was a 73.6842 classification rate

Page 9: Measuring and Predicting UW  Badgers’s  performance by quarterback and running back stats

Initial TestInitial Test

0 5 10 15 20 25 30 35 40 45 500.44

0.45

0.46

0.47

0.48

0.49training error (epoch size = 19)

epoch

erro

r

Page 10: Measuring and Predicting UW  Badgers’s  performance by quarterback and running back stats

Secondary TestSecondary Test

0 20 40 60 80 100 120 140 160 180 200

0.35

0.4

0.45

0.5

0.55training error (epoch size = 19)

epoch

erro

r

Page 11: Measuring and Predicting UW  Badgers’s  performance by quarterback and running back stats

ResultsResults• Additional hidden layers and neurons

eventually converged to a 95% classification rate

• Decided to predict future seasons based upon if the current quarterback and running back stay – generally large difference if they do not

Page 12: Measuring and Predicting UW  Badgers’s  performance by quarterback and running back stats

ResultsResults• Use a linear formula between each

consecutive season

• Found that UW would improve to a 9 win season if Stave and Ball both stayed

• Currently at 9 wins with one game to go

Page 13: Measuring and Predicting UW  Badgers’s  performance by quarterback and running back stats

ReferencesReferences

• Newman, M. E. J., and Park, Juyong; A network-based ranking system for US college Football. Department of Physics and Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109. arXiv:physics/0505169 v4 31 Oct 2013

• ESPN, ESPN College Football. 8 Dec. 2013 http://espn.go.com/college-football/team/_/id/275/

• Sports References. SR College Football. 8 Dec. 2013 http://www.statfox.com/nfl/nfllogs.htm