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