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

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

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

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.

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.

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

Data CollectionData Collection• Many feature vectors were collected

• Passing Completions, Attempts

• Yards per attempt

• Touchdowns

• Interceptions

• Passer Ratings

• Rushing equivalents for RB’s

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

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

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

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

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

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